LLMsForHepth/cs_primary_200k · Datasets at Hugging Face

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2305.06185	Chidi Agbo	Chidi Agbo, Hoda Mehrpouyan	Conflict Analysis and Resolution of Safety and Security Boundary Conditions for Industrial Control Systems	12 pages, 10 figures, 2022 6th International Conference on System Reliability and Safety (ICSRS)\|978-1-6654-7092-6 @IEEE	null	10.1109/ICSRS56243.2022.10067393	null	cs.CR	http://creativecommons.org/licenses/by-nc-sa/4.0/	Safety and security are the two most important properties of industrial control systems (ICS), and their integration is necessary to ensure that safety goals do not undermine security goals and vice versa. Sometimes, safety and security co-engineering leads to conflicting requirements or violations capable of impacting the normal behavior of the system. Identification, analysis, and resolution of conflicts arising from safety and security co-engineering is a major challenge, an under-researched area in safety-critical systems(ICS). This paper presents an STPA-SafeSec-CDCL approach that addresses the challenge. Our proposed methodology combines the STPA-SafeSec approach for safety and security analysis and the Conflict-Driven Clause Learning (CDCL) approach for the identification, analysis, and resolution of conflicts where conflicting constraints are encoded in satisfiability (SAT) problems. We apply our framework to the Tennessee Eastman Plant process model, a chemical process model developed specifically for the study of industrial control processes, to demonstrate how to use the proposed method. Our methodology goes beyond the requirement analysis phase and can be applied to the early stages of system design and development to increase system reliability, robustness, and resilience.	[ { "created": "Wed, 10 May 2023 14:16:49 GMT", "version": "v1" } ]	2023-05-11	[ [ "Agbo", "Chidi", "" ], [ "Mehrpouyan", "Hoda", "" ] ]	Safety and security are the two most important properties of industrial control systems (ICS), and their integration is necessary to ensure that safety goals do not undermine security goals and vice versa. Sometimes, safety and security co-engineering leads to conflicting requirements or violations capable of impacting the normal behavior of the system. Identification, analysis, and resolution of conflicts arising from safety and security co-engineering is a major challenge, an under-researched area in safety-critical systems(ICS). This paper presents an STPA-SafeSec-CDCL approach that addresses the challenge. Our proposed methodology combines the STPA-SafeSec approach for safety and security analysis and the Conflict-Driven Clause Learning (CDCL) approach for the identification, analysis, and resolution of conflicts where conflicting constraints are encoded in satisfiability (SAT) problems. We apply our framework to the Tennessee Eastman Plant process model, a chemical process model developed specifically for the study of industrial control processes, to demonstrate how to use the proposed method. Our methodology goes beyond the requirement analysis phase and can be applied to the early stages of system design and development to increase system reliability, robustness, and resilience.
cs/0504044	Richard McClatchey	Michael Thomas, Conrad Steenberg, Frank van Lingen, Harvey Newman, Julian Bunn, Arshad Ali, Richard McClatchey, Ashiq Anjum, Tahir Azim, Waqas ur Rehman, Faisal Khan, Jang Uk In	JClarens: A Java Framework for Developing and Deploying Web Services for Grid Computing	8 pages, 4 figures. Paper at the 3rd IEEE International Conference on Web Services (ICWS05). Florida, USA. July 2005	null	null	null	cs.DC	null	High Energy Physics (HEP) and other scientific communities have adopted Service Oriented Architectures (SOA) as part of a larger Grid computing effort. This effort involves the integration of many legacy applications and programming libraries into a SOA framework. The Grid Analysis Environment (GAE) is such a service oriented architecture based on the Clarens Grid Services Framework and is being developed as part of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at European Laboratory for Particle Physics (CERN). Clarens provides a set of authorization, access control, and discovery services, as well as XMLRPC and SOAP access to all deployed services. Two implementations of the Clarens Web Services Framework (Python and Java) offer integration possibilities for a wide range of programming languages. This paper describes the Java implementation of the Clarens Web Services Framework called JClarens. and several web services of interest to the scientific and Grid community that have been deployed using JClarens.	[ { "created": "Mon, 11 Apr 2005 21:45:07 GMT", "version": "v1" } ]	2007-05-23	[ [ "Thomas", "Michael", "" ], [ "Steenberg", "Conrad", "" ], [ "van Lingen", "Frank", "" ], [ "Newman", "Harvey", "" ], [ "Bunn", "Julian", "" ], [ "Ali", "Arshad", "" ], [ "McClatchey", "Richard", "" ], [ "Anjum", "Ashiq", "" ], [ "Azim", "Tahir", "" ], [ "Rehman", "Waqas ur", "" ], [ "Khan", "Faisal", "" ], [ "In", "Jang Uk", "" ] ]	High Energy Physics (HEP) and other scientific communities have adopted Service Oriented Architectures (SOA) as part of a larger Grid computing effort. This effort involves the integration of many legacy applications and programming libraries into a SOA framework. The Grid Analysis Environment (GAE) is such a service oriented architecture based on the Clarens Grid Services Framework and is being developed as part of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at European Laboratory for Particle Physics (CERN). Clarens provides a set of authorization, access control, and discovery services, as well as XMLRPC and SOAP access to all deployed services. Two implementations of the Clarens Web Services Framework (Python and Java) offer integration possibilities for a wide range of programming languages. This paper describes the Java implementation of the Clarens Web Services Framework called JClarens. and several web services of interest to the scientific and Grid community that have been deployed using JClarens.
1409.5872	Peter Schrammel	Peter Schrammel, Daniel Kroening, Martin Brain, Ruben Martins, Tino Teige, Tom Bienm\"uller	Incremental Bounded Model Checking for Embedded Software (extended version)	extended version of paper submitted to EMSOFT'14	null	null	null	cs.SE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Program analysis is on the brink of mainstream in embedded systems development. Formal verification of behavioural requirements, finding runtime errors and automated test case generation are some of the most common applications of automated verification tools based on Bounded Model Checking. Existing industrial tools for embedded software use an off-the-shelf Bounded Model Checker and apply it iteratively to verify the program with an increasing number of unwindings. This approach unnecessarily wastes time repeating work that has already been done and fails to exploit the power of incremental SAT solving. This paper reports on the extension of the software model checker CBMC to support incremental Bounded Model Checking and its successful integration with the industrial embedded software verification tool BTC EmbeddedTester. We present an extensive evaluation over large industrial embedded programs, which shows that incremental Bounded Model Checking cuts runtimes by one order of magnitude in comparison to the standard non-incremental approach, enabling the application of formal verification to large and complex embedded software.	[ { "created": "Sat, 20 Sep 2014 09:14:04 GMT", "version": "v1" } ]	2014-09-23	[ [ "Schrammel", "Peter", "" ], [ "Kroening", "Daniel", "" ], [ "Brain", "Martin", "" ], [ "Martins", "Ruben", "" ], [ "Teige", "Tino", "" ], [ "Bienmüller", "Tom", "" ] ]	Program analysis is on the brink of mainstream in embedded systems development. Formal verification of behavioural requirements, finding runtime errors and automated test case generation are some of the most common applications of automated verification tools based on Bounded Model Checking. Existing industrial tools for embedded software use an off-the-shelf Bounded Model Checker and apply it iteratively to verify the program with an increasing number of unwindings. This approach unnecessarily wastes time repeating work that has already been done and fails to exploit the power of incremental SAT solving. This paper reports on the extension of the software model checker CBMC to support incremental Bounded Model Checking and its successful integration with the industrial embedded software verification tool BTC EmbeddedTester. We present an extensive evaluation over large industrial embedded programs, which shows that incremental Bounded Model Checking cuts runtimes by one order of magnitude in comparison to the standard non-incremental approach, enabling the application of formal verification to large and complex embedded software.
2110.03522	Jules Leguy	Jules Leguy, Thomas Cauchy, Beatrice Duval, Benoit Da Mota	Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties	Submitted to ICTAI 2021	null	null	null	cs.LG cs.AI	http://creativecommons.org/licenses/by-nc-nd/4.0/	AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials. An important difficulty is that the properties to be optimized rely on costly evaluations. Machine learning methods are investigated with success to predict these properties, but show generalization issues on less known areas of the chemical space. We propose here a surrogate-based black box optimization method, to tackle jointly the optimization and machine learning problems. It consists in optimizing the expected improvement of the surrogate of a molecular property using an evolutionary algorithm. The surrogate is defined as a Gaussian Process Regression (GPR) model, learned on a relevant area of the search space with respect to the property to be optimized. We show that our approach can successfully optimize a costly property of interest much faster than a purely metaheuristic approach.	[ { "created": "Fri, 1 Oct 2021 15:28:15 GMT", "version": "v1" } ]	2021-10-08	[ [ "Leguy", "Jules", "" ], [ "Cauchy", "Thomas", "" ], [ "Duval", "Beatrice", "" ], [ "Da Mota", "Benoit", "" ] ]	AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials. An important difficulty is that the properties to be optimized rely on costly evaluations. Machine learning methods are investigated with success to predict these properties, but show generalization issues on less known areas of the chemical space. We propose here a surrogate-based black box optimization method, to tackle jointly the optimization and machine learning problems. It consists in optimizing the expected improvement of the surrogate of a molecular property using an evolutionary algorithm. The surrogate is defined as a Gaussian Process Regression (GPR) model, learned on a relevant area of the search space with respect to the property to be optimized. We show that our approach can successfully optimize a costly property of interest much faster than a purely metaheuristic approach.
1610.08597	Sanjaya Wijeratne	Sanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth	Word Embeddings to Enhance Twitter Gang Member Profile Identification	7 pages, 1 figure, 2 tables, Published at IJCAI Workshop on Semantic Machine Learning (SML 2016)	IJCAI Workshop on Semantic Machine Learning (SML 2016). pp. 18-24. CEUR-WS, New York City, NY (07 2016)	null	null	cs.SI cs.CL cs.CY cs.IR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members social media posts.	[ { "created": "Thu, 27 Oct 2016 03:21:49 GMT", "version": "v1" } ]	2016-10-28	[ [ "Wijeratne", "Sanjaya", "" ], [ "Balasuriya", "Lakshika", "" ], [ "Doran", "Derek", "" ], [ "Sheth", "Amit", "" ] ]	Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members social media posts.
1206.6030	Sundararajan Sellamanickam	Sundararajan Sellamanickam, Shirish Shevade	An Additive Model View to Sparse Gaussian Process Classifier Design	14 pages, 3 figures	null	null	null	cs.LG stat.ML	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new methods for two key components viz., site parameter estimation and basis vector selection in any SGPC design. The proposed adaptive sampling based basis vector selection method aids in achieving improved generalization performance at a reduced computational cost. This method can also be used in conjunction with any other site parameter estimation methods. It has similar computational and storage complexities as the well-known information vector machine and is suitable for large datasets. The hyperparameters can be determined by optimizing a predictive loss function. The experimental results show better generalization performance of the proposed basis vector selection method on several benchmark datasets, particularly for relatively smaller basis vector set sizes or on difficult datasets.	[ { "created": "Tue, 26 Jun 2012 15:58:21 GMT", "version": "v1" } ]	2012-06-27	[ [ "Sellamanickam", "Sundararajan", "" ], [ "Shevade", "Shirish", "" ] ]	We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new methods for two key components viz., site parameter estimation and basis vector selection in any SGPC design. The proposed adaptive sampling based basis vector selection method aids in achieving improved generalization performance at a reduced computational cost. This method can also be used in conjunction with any other site parameter estimation methods. It has similar computational and storage complexities as the well-known information vector machine and is suitable for large datasets. The hyperparameters can be determined by optimizing a predictive loss function. The experimental results show better generalization performance of the proposed basis vector selection method on several benchmark datasets, particularly for relatively smaller basis vector set sizes or on difficult datasets.
2405.04095	Yiling He	Yiling He, Junchi Lei, Zhan Qin and Kui Ren	DREAM: Combating Concept Drift with Explanatory Detection and Adaptation in Malware Classification	null	null	null	null	cs.CR cs.AI	http://creativecommons.org/licenses/by-sa/4.0/	Deep learning-based malware classifiers face significant challenges due to concept drift. The rapid evolution of malware, especially with new families, can depress classification accuracy to near-random levels. Previous research has primarily focused on detecting drift samples, relying on expert-led analysis and labeling for model retraining. However, these methods often lack a comprehensive understanding of malware concepts and provide limited guidance for effective drift adaptation, leading to unstable detection performance and high human labeling costs. To address these limitations, we introduce DREAM, a novel system designed to surpass the capabilities of existing drift detectors and to establish an explanatory drift adaptation process. DREAM enhances drift detection through model sensitivity and data autonomy. The detector, trained in a semi-supervised approach, proactively captures malware behavior concepts through classifier feedback. During testing, it utilizes samples generated by the detector itself, eliminating reliance on extensive training data. For drift adaptation, DREAM enlarges human intervention, enabling revisions of malware labels and concept explanations embedded within the detector's latent space. To ensure a comprehensive response to concept drift, it facilitates a coordinated update process for both the classifier and the detector. Our evaluation shows that DREAM can effectively improve the drift detection accuracy and reduce the expert analysis effort in adaptation across different malware datasets and classifiers.	[ { "created": "Tue, 7 May 2024 07:55:45 GMT", "version": "v1" }, { "created": "Thu, 8 Aug 2024 05:45:56 GMT", "version": "v2" } ]	2024-08-09	[ [ "He", "Yiling", "" ], [ "Lei", "Junchi", "" ], [ "Qin", "Zhan", "" ], [ "Ren", "Kui", "" ] ]	Deep learning-based malware classifiers face significant challenges due to concept drift. The rapid evolution of malware, especially with new families, can depress classification accuracy to near-random levels. Previous research has primarily focused on detecting drift samples, relying on expert-led analysis and labeling for model retraining. However, these methods often lack a comprehensive understanding of malware concepts and provide limited guidance for effective drift adaptation, leading to unstable detection performance and high human labeling costs. To address these limitations, we introduce DREAM, a novel system designed to surpass the capabilities of existing drift detectors and to establish an explanatory drift adaptation process. DREAM enhances drift detection through model sensitivity and data autonomy. The detector, trained in a semi-supervised approach, proactively captures malware behavior concepts through classifier feedback. During testing, it utilizes samples generated by the detector itself, eliminating reliance on extensive training data. For drift adaptation, DREAM enlarges human intervention, enabling revisions of malware labels and concept explanations embedded within the detector's latent space. To ensure a comprehensive response to concept drift, it facilitates a coordinated update process for both the classifier and the detector. Our evaluation shows that DREAM can effectively improve the drift detection accuracy and reduce the expert analysis effort in adaptation across different malware datasets and classifiers.
2012.02516	Patrick Esser	Patrick Esser and Robin Rombach and Bj\"orn Ommer	A Note on Data Biases in Generative Models	Extended Abstract for the NeurIPS 2020 Workshop on Machine Learning for Creativity and Design	null	null	null	cs.CV cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net .	[ { "created": "Fri, 4 Dec 2020 10:46:37 GMT", "version": "v1" } ]	2020-12-07	[ [ "Esser", "Patrick", "" ], [ "Rombach", "Robin", "" ], [ "Ommer", "Björn", "" ] ]	It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net .
2001.08014	Hongyu Jin	Hongyu Jin and Mohammad Khodaei and Panos Papadimitratos	Security and Privacy in Vehicular Social Networks	A chapter for the book "Vehicular Social Networks"	A. M. Vegni, V. Loscr\`i, and A. V. Vasilakos, Eds. CRC Press, Taylor & Francis Group, March 2017	null	null	cs.CR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We surveyed and presented the state-of-the-art VC systems, security and privacy architectures and technologies, emphasizing on security and privacy challenges and their solutions for P2P interactions in VSNs towards standardization and deployment. We note that beyond safety applications that have drawn a lot of attention in VC systems, there is significant and rising interest in vehicle-to-vehicle interaction for a range of transportation efficiency and infotainment applications, notably LBS as well as a gamut of services by mobile providers. While this enriches the VC systems and the user experience, security and privacy concerns are also intensified. This is especially so, considering (i) the privacy risk from the exposure of the users to the service providers, and (ii) the security risk from the interaction with malicious or selfish and thus misbehaving users or infrastructure. We showed existing solutions can in fact evolve and address the VSN-specific challenges, and improve or even accelerate the adoption of VSN applications.	[ { "created": "Wed, 22 Jan 2020 13:49:23 GMT", "version": "v1" } ]	2020-01-23	[ [ "Jin", "Hongyu", "" ], [ "Khodaei", "Mohammad", "" ], [ "Papadimitratos", "Panos", "" ] ]	We surveyed and presented the state-of-the-art VC systems, security and privacy architectures and technologies, emphasizing on security and privacy challenges and their solutions for P2P interactions in VSNs towards standardization and deployment. We note that beyond safety applications that have drawn a lot of attention in VC systems, there is significant and rising interest in vehicle-to-vehicle interaction for a range of transportation efficiency and infotainment applications, notably LBS as well as a gamut of services by mobile providers. While this enriches the VC systems and the user experience, security and privacy concerns are also intensified. This is especially so, considering (i) the privacy risk from the exposure of the users to the service providers, and (ii) the security risk from the interaction with malicious or selfish and thus misbehaving users or infrastructure. We showed existing solutions can in fact evolve and address the VSN-specific challenges, and improve or even accelerate the adoption of VSN applications.
2312.04590	Alexander Ziller	Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis	Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging	null	null	null	null	cs.CR cs.AI cs.CV cs.LG	http://creativecommons.org/licenses/by/4.0/	Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing Technologies (PETs), such as Differential Privacy (DP), aim to circumvent these susceptibilities. DP is the strongest possible protection for training models while bounding the risks of inferring the inclusion of training samples or reconstructing the original data. DP achieves this by setting a quantifiable privacy budget. Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models. This imposes a trade-off between robust performance and stringent privacy. Additionally, the interpretation of a privacy budget remains abstract and challenging to contextualize. In this study, we contrast the performance of AI models at various privacy budgets against both, theoretical risk bounds and empirical success of reconstruction attacks. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible. We thus conclude that not using DP -- at all -- is negligent when applying AI models to sensitive data. We deem those results to lie a foundation for further debates on striking a balance between privacy risks and model performance.	[ { "created": "Tue, 5 Dec 2023 12:21:30 GMT", "version": "v1" } ]	2023-12-11	[ [ "Ziller", "Alexander", "" ], [ "Mueller", "Tamara T.", "" ], [ "Stieger", "Simon", "" ], [ "Feiner", "Leonhard", "" ], [ "Brandt", "Johannes", "" ], [ "Braren", "Rickmer", "" ], [ "Rueckert", "Daniel", "" ], [ "Kaissis", "Georgios", "" ] ]	Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing Technologies (PETs), such as Differential Privacy (DP), aim to circumvent these susceptibilities. DP is the strongest possible protection for training models while bounding the risks of inferring the inclusion of training samples or reconstructing the original data. DP achieves this by setting a quantifiable privacy budget. Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models. This imposes a trade-off between robust performance and stringent privacy. Additionally, the interpretation of a privacy budget remains abstract and challenging to contextualize. In this study, we contrast the performance of AI models at various privacy budgets against both, theoretical risk bounds and empirical success of reconstruction attacks. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible. We thus conclude that not using DP -- at all -- is negligent when applying AI models to sensitive data. We deem those results to lie a foundation for further debates on striking a balance between privacy risks and model performance.
2210.13024	Martin Lentschat	Puthineath Lay, Martin Lentschat and Cyril Labb\'e	Investigating the detection of Tortured Phrases in Scientific Literature	null	null	null	null	cs.CL cs.IR	http://creativecommons.org/licenses/by-nc-sa/4.0/	With the help of online tools, unscrupulous authors can today generate a pseudo-scientific article and attempt to publish it. Some of these tools work by replacing or paraphrasing existing texts to produce new content, but they have a tendency to generate nonsensical expressions. A recent study introduced the concept of 'tortured phrase', an unexpected odd phrase that appears instead of the fixed expression. E.g. counterfeit consciousness instead of artificial intelligence. The present study aims at investigating how tortured phrases, that are not yet listed, can be detected automatically. We conducted several experiments, including non-neural binary classification, neural binary classification and cosine similarity comparison of the phrase tokens, yielding noticeable results.	[ { "created": "Mon, 24 Oct 2022 08:15:22 GMT", "version": "v1" } ]	2022-10-25	[ [ "Lay", "Puthineath", "" ], [ "Lentschat", "Martin", "" ], [ "Labbé", "Cyril", "" ] ]	With the help of online tools, unscrupulous authors can today generate a pseudo-scientific article and attempt to publish it. Some of these tools work by replacing or paraphrasing existing texts to produce new content, but they have a tendency to generate nonsensical expressions. A recent study introduced the concept of 'tortured phrase', an unexpected odd phrase that appears instead of the fixed expression. E.g. counterfeit consciousness instead of artificial intelligence. The present study aims at investigating how tortured phrases, that are not yet listed, can be detected automatically. We conducted several experiments, including non-neural binary classification, neural binary classification and cosine similarity comparison of the phrase tokens, yielding noticeable results.
1308.3136	Richard Preen	Richard J. Preen and Larry Bull	Toward the Coevolution of Novel Vertical-Axis Wind Turbines	appears in IEEE Transactions on Evolutionary Computation (2014). arXiv admin note: substantial text overlap with arXiv:1212.5271, arXiv:1204.4107	IEEE Transactions on Evolutionary Computation (2015), 19(2):284-294	10.1109/TEVC.2014.2316199	null	cs.NE cs.AI cs.CE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under fan generated wind conditions. Initially a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.	[ { "created": "Tue, 13 Aug 2013 14:02:34 GMT", "version": "v1" }, { "created": "Thu, 15 Jan 2015 16:25:33 GMT", "version": "v2" } ]	2015-07-02	[ [ "Preen", "Richard J.", "" ], [ "Bull", "Larry", "" ] ]	The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under fan generated wind conditions. Initially a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.
2208.11885	Melissa Swift	Melissa E. Swift (1 and 2), Wyatt Ayers (1), Sophie Pallanck (1), Scott Wehrwein (1) ((1) Western Washington University, (2) Pacific Northwest National Laboratory)	Visualizing the Passage of Time with Video Temporal Pyramids	11 pages, 9 figures, accepted for presentation at IEEE VIS 2022, will be published in conference proceedings, supplementary material and more can be found on our project page at https://fw.cs.wwu.edu/~wehrwes/TemporalPyramids	null	10.1109/TVCG.2022.3209454	null	cs.CV eess.IV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	What can we learn about a scene by watching it for months or years? A video recorded over a long timespan will depict interesting phenomena at multiple timescales, but identifying and viewing them presents a challenge. The video is too long to watch in full, and some occurrences are too slow to experience in real-time, such as glacial retreat. Timelapse videography is a common approach to summarizing long videos and visualizing slow timescales. However, a timelapse is limited to a single chosen temporal frequency, and often appears flickery due to aliasing and temporal discontinuities between frames. In this paper, we propose Video Temporal Pyramids, a technique that addresses these limitations and expands the possibilities for visualizing the passage of time. Inspired by spatial image pyramids from computer vision, we developed an algorithm that builds video pyramids in the temporal domain. Each level of a Video Temporal Pyramid visualizes a different timescale; for instance, videos from the monthly timescale are usually good for visualizing seasonal changes, while videos from the one-minute timescale are best for visualizing sunrise or the movement of clouds across the sky. To help explore the different pyramid levels, we also propose a Video Spectrogram to visualize the amount of activity across the entire pyramid, providing a holistic overview of the scene dynamics and the ability to explore and discover phenomena across time and timescales. To demonstrate our approach, we have built Video Temporal Pyramids from ten outdoor scenes, each containing months or years of data. We compare Video Temporal Pyramid layers to naive timelapse and find that our pyramids enable alias-free viewing of longer-term changes. We also demonstrate that the Video Spectrogram facilitates exploration and discovery of phenomena across pyramid levels, by enabling both overview and detail-focused perspectives.	[ { "created": "Thu, 25 Aug 2022 06:19:02 GMT", "version": "v1" } ]	2023-05-02	[ [ "Swift", "Melissa E.", "", "1 and 2" ], [ "Ayers", "Wyatt", "" ], [ "Pallanck", "Sophie", "" ], [ "Wehrwein", "Scott", "" ] ]	What can we learn about a scene by watching it for months or years? A video recorded over a long timespan will depict interesting phenomena at multiple timescales, but identifying and viewing them presents a challenge. The video is too long to watch in full, and some occurrences are too slow to experience in real-time, such as glacial retreat. Timelapse videography is a common approach to summarizing long videos and visualizing slow timescales. However, a timelapse is limited to a single chosen temporal frequency, and often appears flickery due to aliasing and temporal discontinuities between frames. In this paper, we propose Video Temporal Pyramids, a technique that addresses these limitations and expands the possibilities for visualizing the passage of time. Inspired by spatial image pyramids from computer vision, we developed an algorithm that builds video pyramids in the temporal domain. Each level of a Video Temporal Pyramid visualizes a different timescale; for instance, videos from the monthly timescale are usually good for visualizing seasonal changes, while videos from the one-minute timescale are best for visualizing sunrise or the movement of clouds across the sky. To help explore the different pyramid levels, we also propose a Video Spectrogram to visualize the amount of activity across the entire pyramid, providing a holistic overview of the scene dynamics and the ability to explore and discover phenomena across time and timescales. To demonstrate our approach, we have built Video Temporal Pyramids from ten outdoor scenes, each containing months or years of data. We compare Video Temporal Pyramid layers to naive timelapse and find that our pyramids enable alias-free viewing of longer-term changes. We also demonstrate that the Video Spectrogram facilitates exploration and discovery of phenomena across pyramid levels, by enabling both overview and detail-focused perspectives.
1810.05716	Yanting Pei	Yanting Pei, Yaping Huang, Qi Zou, Yuhang Lu and Song Wang	Does Haze Removal Help CNN-based Image Classification?	null	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance.	[ { "created": "Fri, 12 Oct 2018 20:46:29 GMT", "version": "v1" } ]	2018-10-16	[ [ "Pei", "Yanting", "" ], [ "Huang", "Yaping", "" ], [ "Zou", "Qi", "" ], [ "Lu", "Yuhang", "" ], [ "Wang", "Song", "" ] ]	Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance.
1905.02025	Grigorios Kalliatakis	Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Klaus McDonald-Maier	DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level	To be published in CVPR Workshop on Computer Vision for Global Challenges (CV4GC). arXiv admin note: substantial text overlap with arXiv:1902.03817	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at https://github.com/GKalliatakis/DisplaceNet.	[ { "created": "Fri, 3 May 2019 11:07:27 GMT", "version": "v1" } ]	2019-05-07	[ [ "Kalliatakis", "Grigorios", "" ], [ "Ehsan", "Shoaib", "" ], [ "Fasli", "Maria", "" ], [ "McDonald-Maier", "Klaus", "" ] ]	Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at https://github.com/GKalliatakis/DisplaceNet.
2208.02885	Zilin Si	Zilin Si, Zirui Zhu, Arpit Agarwal, Stuart Anderson and Wenzhen Yuan	Grasp Stability Prediction with Sim-to-Real Transfer from Tactile Sensing	null	null	null	null	cs.RO	http://creativecommons.org/licenses/by-nc-sa/4.0/	Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the sim-to-real transfer for tactile-based manipulation tasks still challenging. In this work, we integrate simulation of robot dynamics and vision-based tactile sensors by modeling the physics of contact. This contact model uses simulated contact forces at the robot's end-effector to inform the generation of realistic tactile outputs. To eliminate the sim-to-real transfer gap, we calibrate our physics simulator of robot dynamics, contact model, and tactile optical simulator with real-world data, and then we demonstrate the effectiveness of our system on a zero-shot sim-to-real grasp stability prediction task where we achieve an average accuracy of 90.7% on various objects. Experiments reveal the potential of applying our simulation framework to more complicated manipulation tasks. We open-source our simulation framework at https://github.com/CMURoboTouch/Taxim/tree/taxim-robot.	[ { "created": "Thu, 4 Aug 2022 20:55:09 GMT", "version": "v1" } ]	2022-08-08	[ [ "Si", "Zilin", "" ], [ "Zhu", "Zirui", "" ], [ "Agarwal", "Arpit", "" ], [ "Anderson", "Stuart", "" ], [ "Yuan", "Wenzhen", "" ] ]	Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the sim-to-real transfer for tactile-based manipulation tasks still challenging. In this work, we integrate simulation of robot dynamics and vision-based tactile sensors by modeling the physics of contact. This contact model uses simulated contact forces at the robot's end-effector to inform the generation of realistic tactile outputs. To eliminate the sim-to-real transfer gap, we calibrate our physics simulator of robot dynamics, contact model, and tactile optical simulator with real-world data, and then we demonstrate the effectiveness of our system on a zero-shot sim-to-real grasp stability prediction task where we achieve an average accuracy of 90.7% on various objects. Experiments reveal the potential of applying our simulation framework to more complicated manipulation tasks. We open-source our simulation framework at https://github.com/CMURoboTouch/Taxim/tree/taxim-robot.
1907.02703	Yong Min	Yong Min, Tingjun Jiang, Cheng Jin, Qu Li and Xiaogang Jin	Intelligent social bots uncover the link between user preference and diversity of news consumption	The refined manuscript is under review in Royal Society Open Science	Roy. Soc. Open Sci. 6 (2019) 190868	10.1098/rsos.190868	null	cs.SI physics.soc-ph	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The boom of online social media and microblogging platforms has rapidly alter the way we consume news and exchange opinions. Even though considerable efforts try to recommend various contents to users, loss of information diversity and the polarization of interest groups are still an enormous challenge for industry and academia. Here, we take advantage of benign social bots to design a controlled experiment on Weibo (the largest microblogging platform in China). These software bots can exhibit human-like behavior (e.g., preferring particular content) and simulate the formation of personal social networks and news consumption under two well-accepted sociological hypotheses (i.e., homophily and triadic closure). We deployed 68 bots to Weibo, and each bot ran for at least 2 months and followed 100 to 120 accounts. In total, we observed 5,318 users and recorded about 630,000 messages exposed to these bots. Our results show, even with the same selection behaviors, bots preferring entertainment content are more likely to form polarized communities with their peers, in which about 80\% of the information they consume is of the same type, which is a significant difference for bots preferring sci-tech content. The result suggests that users preference played a more crucial role in limiting themselves access to diverse content by compared with the two well-known drivers (self-selection and pre-selection). Furthermore, our results reveal an ingenious connection between specific content and its propagating sub-structures in the same social network. In the Weibo network, entertainment news favors a unidirectional star-like sub-structure, while sci-tech news spreads on a bidirectional clustering sub-structure. This connection can amplify the diversity effect of user preference. The discovery may have important implications for diffusion dynamics study and recommendation system design.	[ { "created": "Fri, 5 Jul 2019 07:20:48 GMT", "version": "v1" } ]	2020-03-05	[ [ "Min", "Yong", "" ], [ "Jiang", "Tingjun", "" ], [ "Jin", "Cheng", "" ], [ "Li", "Qu", "" ], [ "Jin", "Xiaogang", "" ] ]	The boom of online social media and microblogging platforms has rapidly alter the way we consume news and exchange opinions. Even though considerable efforts try to recommend various contents to users, loss of information diversity and the polarization of interest groups are still an enormous challenge for industry and academia. Here, we take advantage of benign social bots to design a controlled experiment on Weibo (the largest microblogging platform in China). These software bots can exhibit human-like behavior (e.g., preferring particular content) and simulate the formation of personal social networks and news consumption under two well-accepted sociological hypotheses (i.e., homophily and triadic closure). We deployed 68 bots to Weibo, and each bot ran for at least 2 months and followed 100 to 120 accounts. In total, we observed 5,318 users and recorded about 630,000 messages exposed to these bots. Our results show, even with the same selection behaviors, bots preferring entertainment content are more likely to form polarized communities with their peers, in which about 80\% of the information they consume is of the same type, which is a significant difference for bots preferring sci-tech content. The result suggests that users preference played a more crucial role in limiting themselves access to diverse content by compared with the two well-known drivers (self-selection and pre-selection). Furthermore, our results reveal an ingenious connection between specific content and its propagating sub-structures in the same social network. In the Weibo network, entertainment news favors a unidirectional star-like sub-structure, while sci-tech news spreads on a bidirectional clustering sub-structure. This connection can amplify the diversity effect of user preference. The discovery may have important implications for diffusion dynamics study and recommendation system design.
1906.00284	Ehsan Aryafar	Ehsan Aryafar, Alireza Keshavarz-Haddad, Carlee Joe-Wong	Proportional Fair RAT Aggregation in HetNets	Extended version of the 31st International Teletraffic Congress (ITC 2019) conference paper	null	null	null	cs.NI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Heterogeneity in wireless network architectures (i.e., the coexistence of 3G, LTE, 5G, WiFi, etc.) has become a key component of current and future generation cellular networks. Simultaneous aggregation of each client's traffic across multiple such radio access technologies (RATs) / base stations (BSs) can significantly increase the system throughput, and has become an important feature of cellular standards on multi-RAT integration. Distributed algorithms that can realize the full potential of this aggregation are thus of great importance to operators. In this paper, we study the problem of resource allocation for multi-RAT traffic aggregation in HetNets (heterogeneous networks). Our goal is to ensure that the resources at each BS are allocated so that the aggregate throughput achieved by each client across its RATs satisfies a proportional fairness (PF) criterion. In particular, we provide a simple distributed algorithm for resource allocation at each BS that extends the PF allocation algorithm for a single BS. Despite its simplicity and lack of coordination across the BSs, we show that our algorithm converges to the desired PF solution and provide (tight) bounds on its convergence speed. We also study the characteristics of the optimal solution and use its properties to prove the optimality of our algorithm's outcomes.	[ { "created": "Sat, 1 Jun 2019 20:22:14 GMT", "version": "v1" } ]	2019-06-04	[ [ "Aryafar", "Ehsan", "" ], [ "Keshavarz-Haddad", "Alireza", "" ], [ "Joe-Wong", "Carlee", "" ] ]	Heterogeneity in wireless network architectures (i.e., the coexistence of 3G, LTE, 5G, WiFi, etc.) has become a key component of current and future generation cellular networks. Simultaneous aggregation of each client's traffic across multiple such radio access technologies (RATs) / base stations (BSs) can significantly increase the system throughput, and has become an important feature of cellular standards on multi-RAT integration. Distributed algorithms that can realize the full potential of this aggregation are thus of great importance to operators. In this paper, we study the problem of resource allocation for multi-RAT traffic aggregation in HetNets (heterogeneous networks). Our goal is to ensure that the resources at each BS are allocated so that the aggregate throughput achieved by each client across its RATs satisfies a proportional fairness (PF) criterion. In particular, we provide a simple distributed algorithm for resource allocation at each BS that extends the PF allocation algorithm for a single BS. Despite its simplicity and lack of coordination across the BSs, we show that our algorithm converges to the desired PF solution and provide (tight) bounds on its convergence speed. We also study the characteristics of the optimal solution and use its properties to prove the optimality of our algorithm's outcomes.
2101.06409	Ashish Kumar	Ashish Kumar, L. Behera	Shape Back-Projection In 3D Scenes	7 pages, 7 figures, 3 tables	null	null	null	cs.CV cs.RO	http://creativecommons.org/licenses/by-sa/4.0/	In this work, we propose a novel framework shape back-projection for computationally efficient point cloud processing in a probabilistic manner. The primary component of the technique is shape histogram and a back-projection procedure. The technique measures similarity between 3D surfaces, by analyzing their geometrical properties. It is analogous to color back-projection which measures similarity between images, simply by looking at their color distributions. In the overall process, first, shape histogram of a sample surface (e.g. planar) is computed, which captures the profile of surface normals around a point in form of a probability distribution. Later, the histogram is back-projected onto a test surface and a likelihood score is obtained. The score depicts that how likely a point in the test surface behaves similar to the sample surface, geometrically. Shape back-projection finds its application in binary surface classification, high curvature edge detection in unorganized point cloud, automated point cloud labeling for 3D-CNNs (convolutional neural network) etc. The algorithm can also be used for real-time robotic operations such as autonomous object picking in warehouse automation, ground plane extraction for autonomous vehicles and can be deployed easily on computationally limited platforms (UAVs).	[ { "created": "Sat, 16 Jan 2021 09:00:34 GMT", "version": "v1" } ]	2021-01-19	[ [ "Kumar", "Ashish", "" ], [ "Behera", "L.", "" ] ]	In this work, we propose a novel framework shape back-projection for computationally efficient point cloud processing in a probabilistic manner. The primary component of the technique is shape histogram and a back-projection procedure. The technique measures similarity between 3D surfaces, by analyzing their geometrical properties. It is analogous to color back-projection which measures similarity between images, simply by looking at their color distributions. In the overall process, first, shape histogram of a sample surface (e.g. planar) is computed, which captures the profile of surface normals around a point in form of a probability distribution. Later, the histogram is back-projected onto a test surface and a likelihood score is obtained. The score depicts that how likely a point in the test surface behaves similar to the sample surface, geometrically. Shape back-projection finds its application in binary surface classification, high curvature edge detection in unorganized point cloud, automated point cloud labeling for 3D-CNNs (convolutional neural network) etc. The algorithm can also be used for real-time robotic operations such as autonomous object picking in warehouse automation, ground plane extraction for autonomous vehicles and can be deployed easily on computationally limited platforms (UAVs).
2205.14280	Li Niu	Li Niu, Qingyang Liu, Zhenchen Liu, Jiangtong Li	Fast Object Placement Assessment	null	null	null	null	cs.CV	http://creativecommons.org/publicdomain/zero/1.0/	Object placement assessment (OPA) aims to predict the rationality score of a composite image in terms of the placement (e.g., scale, location) of inserted foreground object. However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background and pass the obtained composite image through the model one at a time, which is very time-consuming. In this work, we investigate a new task named as fast OPA. Specifically, provided with a scaled foreground and a background, we only pass them through the model once and predict the rationality scores for all locations. To accomplish this task, we propose a pioneering fast OPA model with several innovations (i.e., foreground dynamic filter, background prior transfer, and composite feature mimicking) to bridge the performance gap between slow OPA model and fast OPA model. Extensive experiments on OPA dataset show that our proposed fast OPA model performs on par with slow OPA model but runs significantly faster.	[ { "created": "Sat, 28 May 2022 00:28:32 GMT", "version": "v1" } ]	2022-05-31	[ [ "Niu", "Li", "" ], [ "Liu", "Qingyang", "" ], [ "Liu", "Zhenchen", "" ], [ "Li", "Jiangtong", "" ] ]	Object placement assessment (OPA) aims to predict the rationality score of a composite image in terms of the placement (e.g., scale, location) of inserted foreground object. However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background and pass the obtained composite image through the model one at a time, which is very time-consuming. In this work, we investigate a new task named as fast OPA. Specifically, provided with a scaled foreground and a background, we only pass them through the model once and predict the rationality scores for all locations. To accomplish this task, we propose a pioneering fast OPA model with several innovations (i.e., foreground dynamic filter, background prior transfer, and composite feature mimicking) to bridge the performance gap between slow OPA model and fast OPA model. Extensive experiments on OPA dataset show that our proposed fast OPA model performs on par with slow OPA model but runs significantly faster.
2405.06652	Yuhongmo Mo	Yuhong Mo, Hao Qin, Yushan Dong, Ziyi Zhu, Zhenglin Li	Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm	6 pages	null	null	null	cs.CL	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	In this paper, a tool for detecting LLM AI text generation is developed based on the Transformer model, aiming to improve the accuracy of AI text generation detection and provide reference for subsequent research. Firstly the text is Unicode normalised, converted to lowercase form, characters other than non-alphabetic characters and punctuation marks are removed by regular expressions, spaces are added around punctuation marks, first and last spaces are removed, consecutive ellipses are replaced with single spaces and the text is connected using the specified delimiter. Next remove non-alphabetic characters and extra whitespace characters, replace multiple consecutive whitespace characters with a single space and again convert to lowercase form. The deep learning model combines layers such as LSTM, Transformer and CNN for text classification or sequence labelling tasks. The training and validation sets show that the model loss decreases from 0.127 to 0.005 and accuracy increases from 94.96 to 99.8, indicating that the model has good detection and classification ability for AI generated text. The test set confusion matrix and accuracy show that the model has 99% prediction accuracy for AI-generated text, with a precision of 0.99, a recall of 1, and an f1 score of 0.99, achieving a very high classification accuracy. Looking forward, it has the prospect of wide application in the field of AI text detection.	[ { "created": "Sat, 6 Apr 2024 06:22:45 GMT", "version": "v1" } ]	2024-05-14	[ [ "Mo", "Yuhong", "" ], [ "Qin", "Hao", "" ], [ "Dong", "Yushan", "" ], [ "Zhu", "Ziyi", "" ], [ "Li", "Zhenglin", "" ] ]	In this paper, a tool for detecting LLM AI text generation is developed based on the Transformer model, aiming to improve the accuracy of AI text generation detection and provide reference for subsequent research. Firstly the text is Unicode normalised, converted to lowercase form, characters other than non-alphabetic characters and punctuation marks are removed by regular expressions, spaces are added around punctuation marks, first and last spaces are removed, consecutive ellipses are replaced with single spaces and the text is connected using the specified delimiter. Next remove non-alphabetic characters and extra whitespace characters, replace multiple consecutive whitespace characters with a single space and again convert to lowercase form. The deep learning model combines layers such as LSTM, Transformer and CNN for text classification or sequence labelling tasks. The training and validation sets show that the model loss decreases from 0.127 to 0.005 and accuracy increases from 94.96 to 99.8, indicating that the model has good detection and classification ability for AI generated text. The test set confusion matrix and accuracy show that the model has 99% prediction accuracy for AI-generated text, with a precision of 0.99, a recall of 1, and an f1 score of 0.99, achieving a very high classification accuracy. Looking forward, it has the prospect of wide application in the field of AI text detection.
2112.01330	Moein Sorkhei	Moein Sorkhei, Yue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Athanasios Zouzos, Fredrik Strand, Kevin Smith	CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer	35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks	null	null	null	cs.CV cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers -- without being explicitly trained for these tasks -- than its breast density counterparts.	[ { "created": "Thu, 2 Dec 2021 15:31:51 GMT", "version": "v1" } ]	2021-12-03	[ [ "Sorkhei", "Moein", "" ], [ "Liu", "Yue", "" ], [ "Azizpour", "Hossein", "" ], [ "Azavedo", "Edward", "" ], [ "Dembrower", "Karin", "" ], [ "Ntoula", "Dimitra", "" ], [ "Zouzos", "Athanasios", "" ], [ "Strand", "Fredrik", "" ], [ "Smith", "Kevin", "" ] ]	Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers -- without being explicitly trained for these tasks -- than its breast density counterparts.
2311.07761	Maximilian Luz	Maximilian Luz, Rohit Mohan, Ahmed Rida Sekkat, Oliver Sawade, Elmar Matthes, Thomas Brox, Abhinav Valada	Amodal Optical Flow	null	null	null	null	cs.CV cs.AI cs.RO	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Optical flow estimation is very challenging in situations with transparent or occluded objects. In this work, we address these challenges at the task level by introducing Amodal Optical Flow, which integrates optical flow with amodal perception. Instead of only representing the visible regions, we define amodal optical flow as a multi-layered pixel-level motion field that encompasses both visible and occluded regions of the scene. To facilitate research on this new task, we extend the AmodalSynthDrive dataset to include pixel-level labels for amodal optical flow estimation. We present several strong baselines, along with the Amodal Flow Quality metric to quantify the performance in an interpretable manner. Furthermore, we propose the novel AmodalFlowNet as an initial step toward addressing this task. AmodalFlowNet consists of a transformer-based cost-volume encoder paired with a recurrent transformer decoder which facilitates recurrent hierarchical feature propagation and amodal semantic grounding. We demonstrate the tractability of amodal optical flow in extensive experiments and show its utility for downstream tasks such as panoptic tracking. We make the dataset, code, and trained models publicly available at http://amodal-flow.cs.uni-freiburg.de.	[ { "created": "Mon, 13 Nov 2023 21:21:43 GMT", "version": "v1" }, { "created": "Tue, 7 May 2024 17:36:29 GMT", "version": "v2" } ]	2024-05-08	[ [ "Luz", "Maximilian", "" ], [ "Mohan", "Rohit", "" ], [ "Sekkat", "Ahmed Rida", "" ], [ "Sawade", "Oliver", "" ], [ "Matthes", "Elmar", "" ], [ "Brox", "Thomas", "" ], [ "Valada", "Abhinav", "" ] ]	Optical flow estimation is very challenging in situations with transparent or occluded objects. In this work, we address these challenges at the task level by introducing Amodal Optical Flow, which integrates optical flow with amodal perception. Instead of only representing the visible regions, we define amodal optical flow as a multi-layered pixel-level motion field that encompasses both visible and occluded regions of the scene. To facilitate research on this new task, we extend the AmodalSynthDrive dataset to include pixel-level labels for amodal optical flow estimation. We present several strong baselines, along with the Amodal Flow Quality metric to quantify the performance in an interpretable manner. Furthermore, we propose the novel AmodalFlowNet as an initial step toward addressing this task. AmodalFlowNet consists of a transformer-based cost-volume encoder paired with a recurrent transformer decoder which facilitates recurrent hierarchical feature propagation and amodal semantic grounding. We demonstrate the tractability of amodal optical flow in extensive experiments and show its utility for downstream tasks such as panoptic tracking. We make the dataset, code, and trained models publicly available at http://amodal-flow.cs.uni-freiburg.de.
2102.11057	Guillaume Jaume	Pushpak Pati and Guillaume Jaume and Antonio Foncubierta and Florinda Feroce and Anna Maria Anniciello and Giosu\`e Scognamiglio and Nadia Brancati and Maryse Fiche and Estelle Dubruc and Daniel Riccio and Maurizio Di Bonito and Giuseppe De Pietro and Gerardo Botti and Jean-Philippe Thiran and Maria Frucci and Orcun Goksel and Maria Gabrani	Hierarchical Graph Representations in Digital Pathology	null	null	null	null	cs.CV	http://creativecommons.org/publicdomain/zero/1.0/	Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs that encode cell morphology and organization to denote the tissue information. These allow for utilizing machine learning to map tissue representations to tissue functionality to help quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a graph neural network is proposed to operate on the hierarchical entity-graph representation to map the tissue structure to tissue functionality. Specifically, for input histology images we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a graph neural network, to classify such HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of H&E stained breast tumor images, to evaluate our proposed methodology against pathologists and state-of-the-art approaches. Through comparative assessment and ablation studies, our method is demonstrated to yield superior classification results compared to alternative methods as well as pathologists.	[ { "created": "Mon, 22 Feb 2021 14:30:57 GMT", "version": "v1" }, { "created": "Wed, 17 Mar 2021 09:09:02 GMT", "version": "v2" } ]	2021-03-18	[ [ "Pati", "Pushpak", "" ], [ "Jaume", "Guillaume", "" ], [ "Foncubierta", "Antonio", "" ], [ "Feroce", "Florinda", "" ], [ "Anniciello", "Anna Maria", "" ], [ "Scognamiglio", "Giosuè", "" ], [ "Brancati", "Nadia", "" ], [ "Fiche", "Maryse", "" ], [ "Dubruc", "Estelle", "" ], [ "Riccio", "Daniel", "" ], [ "Di Bonito", "Maurizio", "" ], [ "De Pietro", "Giuseppe", "" ], [ "Botti", "Gerardo", "" ], [ "Thiran", "Jean-Philippe", "" ], [ "Frucci", "Maria", "" ], [ "Goksel", "Orcun", "" ], [ "Gabrani", "Maria", "" ] ]	Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs that encode cell morphology and organization to denote the tissue information. These allow for utilizing machine learning to map tissue representations to tissue functionality to help quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a graph neural network is proposed to operate on the hierarchical entity-graph representation to map the tissue structure to tissue functionality. Specifically, for input histology images we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a graph neural network, to classify such HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of H&E stained breast tumor images, to evaluate our proposed methodology against pathologists and state-of-the-art approaches. Through comparative assessment and ablation studies, our method is demonstrated to yield superior classification results compared to alternative methods as well as pathologists.
cs/0507029	Samuel Landau	Samuel Landau (INRIA Futurs), Olivier Sigaud (LIP6), Marc Schoenauer (INRIA Futurs)	ATNoSFERES revisited	null	Dans Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2005 [OAI: oai:hal.inria.fr:inria-00000158_v1] - http://hal.inria.fr/inria-00000158	null	null	cs.AI	null	ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which the rules are represented as edges of an Augmented Transition Network. Genotypes are strings of tokens of a stack-based language, whose execution builds the labeled graph. The original ATNoSFERES, using a bitstring to represent the language tokens, has been favorably compared in previous work to several Michigan style LCSs architectures in the context of Non Markov problems. Several modifications of ATNoSFERES are proposed here: the most important one conceptually being a representational change: each token is now represented by an integer, hence the genotype is a string of integers; several other modifications of the underlying grammar language are also proposed. The resulting ATNoSFERES-II is validated on several standard animat Non Markov problems, on which it outperforms all previously published results in the LCS literature. The reasons for these improvement are carefully analyzed, and some assumptions are proposed on the underlying mechanisms in order to explain these good results.	[ { "created": "Mon, 11 Jul 2005 13:11:25 GMT", "version": "v1" } ]	2019-05-01	[ [ "Landau", "Samuel", "", "INRIA Futurs" ], [ "Sigaud", "Olivier", "", "LIP6" ], [ "Schoenauer", "Marc", "", "INRIA Futurs" ] ]	ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which the rules are represented as edges of an Augmented Transition Network. Genotypes are strings of tokens of a stack-based language, whose execution builds the labeled graph. The original ATNoSFERES, using a bitstring to represent the language tokens, has been favorably compared in previous work to several Michigan style LCSs architectures in the context of Non Markov problems. Several modifications of ATNoSFERES are proposed here: the most important one conceptually being a representational change: each token is now represented by an integer, hence the genotype is a string of integers; several other modifications of the underlying grammar language are also proposed. The resulting ATNoSFERES-II is validated on several standard animat Non Markov problems, on which it outperforms all previously published results in the LCS literature. The reasons for these improvement are carefully analyzed, and some assumptions are proposed on the underlying mechanisms in order to explain these good results.
2407.13594	Nils Palumbo	Nils Palumbo, Ravi Mangal, Zifan Wang, Saranya Vijayakumar, Corina S. Pasareanu, Somesh Jha	Mechanistically Interpreting a Transformer-based 2-SAT Solver: An Axiomatic Approach	null	null	null	null	cs.LG	http://creativecommons.org/licenses/by/4.0/	Mechanistic interpretability aims to reverse engineer the computation performed by a neural network in terms of its internal components. Although there is a growing body of research on mechanistic interpretation of neural networks, the notion of a mechanistic interpretation itself is often ad-hoc. Inspired by the notion of abstract interpretation from the program analysis literature that aims to develop approximate semantics for programs, we give a set of axioms that formally characterize a mechanistic interpretation as a description that approximately captures the semantics of the neural network under analysis in a compositional manner. We use these axioms to guide the mechanistic interpretability analysis of a Transformer-based model trained to solve the well-known 2-SAT problem. We are able to reverse engineer the algorithm learned by the model -- the model first parses the input formulas and then evaluates their satisfiability via enumeration of different possible valuations of the Boolean input variables. We also present evidence to support that the mechanistic interpretation of the analyzed model indeed satisfies the stated axioms.	[ { "created": "Thu, 18 Jul 2024 15:32:44 GMT", "version": "v1" } ]	2024-07-19	[ [ "Palumbo", "Nils", "" ], [ "Mangal", "Ravi", "" ], [ "Wang", "Zifan", "" ], [ "Vijayakumar", "Saranya", "" ], [ "Pasareanu", "Corina S.", "" ], [ "Jha", "Somesh", "" ] ]	Mechanistic interpretability aims to reverse engineer the computation performed by a neural network in terms of its internal components. Although there is a growing body of research on mechanistic interpretation of neural networks, the notion of a mechanistic interpretation itself is often ad-hoc. Inspired by the notion of abstract interpretation from the program analysis literature that aims to develop approximate semantics for programs, we give a set of axioms that formally characterize a mechanistic interpretation as a description that approximately captures the semantics of the neural network under analysis in a compositional manner. We use these axioms to guide the mechanistic interpretability analysis of a Transformer-based model trained to solve the well-known 2-SAT problem. We are able to reverse engineer the algorithm learned by the model -- the model first parses the input formulas and then evaluates their satisfiability via enumeration of different possible valuations of the Boolean input variables. We also present evidence to support that the mechanistic interpretation of the analyzed model indeed satisfies the stated axioms.
1905.09958	Ren\'ee Burton	Ren\'ee Burton	Characterizing Certain DNS DDoS Attacks	25 pages, 21 figures	null	null	null	cs.CR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	This paper details data science research in the area of Cyber Threat Intelligence applied to a specific type of Distributed Denial of Service (DDoS) attack. We study a DDoS technique prevalent in the Domain Name System (DNS) for which little malware have been recovered. Using data from a globally distributed set of a passive collectors (pDNS), we create a statistical classifier to identify these attacks and then use unsupervised learning to investigate the attack events and the malware that generates them. The first known major study of this technique, we discovered that current attacks have little resemblance to published descriptions and identify several previously unpublished features of the attacks. Through a combination of text and time series features, we are able to characterize the dominant malware and demonstrate that the number of global-scale attack systems is relatively small.	[ { "created": "Thu, 23 May 2019 22:38:11 GMT", "version": "v1" }, { "created": "Sat, 20 Jul 2019 16:21:23 GMT", "version": "v2" } ]	2019-07-23	[ [ "Burton", "Renée", "" ] ]	This paper details data science research in the area of Cyber Threat Intelligence applied to a specific type of Distributed Denial of Service (DDoS) attack. We study a DDoS technique prevalent in the Domain Name System (DNS) for which little malware have been recovered. Using data from a globally distributed set of a passive collectors (pDNS), we create a statistical classifier to identify these attacks and then use unsupervised learning to investigate the attack events and the malware that generates them. The first known major study of this technique, we discovered that current attacks have little resemblance to published descriptions and identify several previously unpublished features of the attacks. Through a combination of text and time series features, we are able to characterize the dominant malware and demonstrate that the number of global-scale attack systems is relatively small.
2309.15418	Hengchang Hu	Hengchang Hu, Yiming Cao, Zhankui He, Samson Tan, Min-Yen Kan	Automatic Feature Fairness in Recommendation via Adversaries	SIGIR-AP'23	null	10.1145/3624918.3625318	null	cs.IR cs.LG	http://creativecommons.org/licenses/by/4.0/	Fairness is a widely discussed topic in recommender systems, but its practical implementation faces challenges in defining sensitive features while maintaining recommendation accuracy. We propose feature fairness as the foundation to achieve equitable treatment across diverse groups defined by various feature combinations. This improves overall accuracy through balanced feature generalizability. We introduce unbiased feature learning through adversarial training, using adversarial perturbation to enhance feature representation. The adversaries improve model generalization for under-represented features. We adapt adversaries automatically based on two forms of feature biases: frequency and combination variety of feature values. This allows us to dynamically adjust perturbation strengths and adversarial training weights. Stronger perturbations are applied to feature values with fewer combination varieties to improve generalization, while higher weights for low-frequency features address training imbalances. We leverage the Adaptive Adversarial perturbation based on the widely-applied Factorization Machine (AAFM) as our backbone model. In experiments, AAFM surpasses strong baselines in both fairness and accuracy measures. AAFM excels in providing item- and user-fairness for single- and multi-feature tasks, showcasing their versatility and scalability. To maintain good accuracy, we find that adversarial perturbation must be well-managed: during training, perturbations should not overly persist and their strengths should decay.	[ { "created": "Wed, 27 Sep 2023 05:48:05 GMT", "version": "v1" } ]	2023-09-28	[ [ "Hu", "Hengchang", "" ], [ "Cao", "Yiming", "" ], [ "He", "Zhankui", "" ], [ "Tan", "Samson", "" ], [ "Kan", "Min-Yen", "" ] ]	Fairness is a widely discussed topic in recommender systems, but its practical implementation faces challenges in defining sensitive features while maintaining recommendation accuracy. We propose feature fairness as the foundation to achieve equitable treatment across diverse groups defined by various feature combinations. This improves overall accuracy through balanced feature generalizability. We introduce unbiased feature learning through adversarial training, using adversarial perturbation to enhance feature representation. The adversaries improve model generalization for under-represented features. We adapt adversaries automatically based on two forms of feature biases: frequency and combination variety of feature values. This allows us to dynamically adjust perturbation strengths and adversarial training weights. Stronger perturbations are applied to feature values with fewer combination varieties to improve generalization, while higher weights for low-frequency features address training imbalances. We leverage the Adaptive Adversarial perturbation based on the widely-applied Factorization Machine (AAFM) as our backbone model. In experiments, AAFM surpasses strong baselines in both fairness and accuracy measures. AAFM excels in providing item- and user-fairness for single- and multi-feature tasks, showcasing their versatility and scalability. To maintain good accuracy, we find that adversarial perturbation must be well-managed: during training, perturbations should not overly persist and their strengths should decay.
1906.07601	Yannick Est\`eve	Antoine Caubri\`ere, Natalia Tomashenko, Antoine Laurent, Emmanuel Morin, Nathalie Camelin, Yannick Est\`eve	Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability	Accepted to the INTERSPEECH 2019 conference. Submitted on March 29, 2019 (Paper submission deadline)	null	null	null	cs.CL cs.SD eess.AS	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We present an end-to-end approach to extract semantic concepts directly from the speech audio signal. To overcome the lack of data available for this spoken language understanding approach, we investigate the use of a transfer learning strategy based on the principles of curriculum learning. This approach allows us to exploit out-of-domain data that can help to prepare a fully neural architecture. Experiments are carried out on the French MEDIA and PORTMEDIA corpora and show that this end-to-end SLU approach reaches the best results ever published on this task. We compare our approach to a classical pipeline approach that uses ASR, POS tagging, lemmatizer, chunker... and other NLP tools that aim to enrich ASR outputs that feed an SLU text to concepts system. Last, we explore the promising capacity of our end-to-end SLU approach to address the problem of domain portability.	[ { "created": "Tue, 18 Jun 2019 14:19:52 GMT", "version": "v1" } ]	2019-06-19	[ [ "Caubrière", "Antoine", "" ], [ "Tomashenko", "Natalia", "" ], [ "Laurent", "Antoine", "" ], [ "Morin", "Emmanuel", "" ], [ "Camelin", "Nathalie", "" ], [ "Estève", "Yannick", "" ] ]	We present an end-to-end approach to extract semantic concepts directly from the speech audio signal. To overcome the lack of data available for this spoken language understanding approach, we investigate the use of a transfer learning strategy based on the principles of curriculum learning. This approach allows us to exploit out-of-domain data that can help to prepare a fully neural architecture. Experiments are carried out on the French MEDIA and PORTMEDIA corpora and show that this end-to-end SLU approach reaches the best results ever published on this task. We compare our approach to a classical pipeline approach that uses ASR, POS tagging, lemmatizer, chunker... and other NLP tools that aim to enrich ASR outputs that feed an SLU text to concepts system. Last, we explore the promising capacity of our end-to-end SLU approach to address the problem of domain portability.
2305.06985	Alexander Fengler	Alexander Fengler, Alejandro Lancho, Krishna Narayanan, and Yury Polyanskiy	On the Advantages of Asynchrony in the Unsourced MAC	Accepted for presentation at IEEE ISIT 2023	null	null	null	cs.IT math.IT	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	In this work we demonstrate how a lack of synchronization can in fact be advantageous in the problem of random access. Specifically, we consider a multiple-access problem over a frame-asynchronous 2-user binary-input adder channel in the unsourced setup (2-UBAC). Previous work has shown that under perfect synchronization the per-user rates achievable with linear codes over the 2-UBAC are limited by 0.5 bit per channel use (compared to the capacity of 0.75). In this paper, we first demonstrate that arbitrary small (even single-bit) shift between the user's frames enables (random) linear codes to attain full capacity of 0.75 bit/user. Furthermore, we derive density evolution equations for irregular LDPC codes, and prove (via concentration arguments) that they correctly track the asymptotic bit-error rate of a BP decoder. Optimizing the degree distributions we construct LDPC codes achieving per-user rates of 0.73 bit per channel use.	[ { "created": "Thu, 11 May 2023 17:16:57 GMT", "version": "v1" } ]	2023-05-12	[ [ "Fengler", "Alexander", "" ], [ "Lancho", "Alejandro", "" ], [ "Narayanan", "Krishna", "" ], [ "Polyanskiy", "Yury", "" ] ]	In this work we demonstrate how a lack of synchronization can in fact be advantageous in the problem of random access. Specifically, we consider a multiple-access problem over a frame-asynchronous 2-user binary-input adder channel in the unsourced setup (2-UBAC). Previous work has shown that under perfect synchronization the per-user rates achievable with linear codes over the 2-UBAC are limited by 0.5 bit per channel use (compared to the capacity of 0.75). In this paper, we first demonstrate that arbitrary small (even single-bit) shift between the user's frames enables (random) linear codes to attain full capacity of 0.75 bit/user. Furthermore, we derive density evolution equations for irregular LDPC codes, and prove (via concentration arguments) that they correctly track the asymptotic bit-error rate of a BP decoder. Optimizing the degree distributions we construct LDPC codes achieving per-user rates of 0.73 bit per channel use.
1703.00159	Yong Wang	Yong Wang	A Calculus for True Concurrency	31 pages, 1 figures. arXiv admin note: substantial text overlap with arXiv:1611.09035	null	null	null	cs.LO	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We design a calculus for true concurrency called CTC, including its syntax and operational semantics. CTC has good properties modulo several kinds of strongly truly concurrent bisimulations and weakly truly concurrent bisimulations, such as monoid laws, static laws, new expansion law for strongly truly concurrent bisimulations, $\tau$ laws for weakly truly concurrent bisimulations, and full congruences for strongly and weakly truly concurrent bisimulations, and also unique solution for recursion.	[ { "created": "Wed, 1 Mar 2017 07:25:23 GMT", "version": "v1" }, { "created": "Wed, 22 Apr 2020 11:55:29 GMT", "version": "v2" } ]	2020-04-24	[ [ "Wang", "Yong", "" ] ]	We design a calculus for true concurrency called CTC, including its syntax and operational semantics. CTC has good properties modulo several kinds of strongly truly concurrent bisimulations and weakly truly concurrent bisimulations, such as monoid laws, static laws, new expansion law for strongly truly concurrent bisimulations, $\tau$ laws for weakly truly concurrent bisimulations, and full congruences for strongly and weakly truly concurrent bisimulations, and also unique solution for recursion.
2101.02527	Christian Hesch	Ustim Khristenko, Stefan Schu{\ss}, Melanie Kr\"uger, Felix Schmidt, Barbara Wohlmuth, Christian Hesch	Multidimensional coupling: A variationally consistent approach to fiber-reinforced materials	null	null	10.1016/j.cma.2021.113869	null	cs.CE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	A novel mathematical model for fiber-reinforced materials is proposed. It is based on a 1-dimensional beam model for the thin fiber structures, a flexible and general 3-dimensional elasticity model for the matrix and an overlapping domain decomposition approach. From a computational point of view, this is motivated by the fact that matrix and fibers can easily meshed independently. Our main interest is in fiber reinforce polymers where the Young's modulus are quite different. Thus the modeling error from the overlapping approach is of no significance. The coupling conditions acknowledge both, the forces and the moments of the beam model and transfer them to the background material. A suitable static condensation procedure is applied to remove the beam balance equations. The condensed system then forms our starting point for a numerical approximation in terms of isogeometric analysis. The choice of our discrete basis functions of higher regularity is motivated by the fact, that as a result of the static condensation, we obtain second gradient terms in fiber direction. Eventually, a series of benchmark tests demonstrate the flexibility and robustness of the proposed methodology. As a proof-of-concept, we show that our new model is able to capture bending, torsion and shear dominated situations.	[ { "created": "Thu, 7 Jan 2021 13:03:03 GMT", "version": "v1" }, { "created": "Wed, 24 Feb 2021 14:29:01 GMT", "version": "v2" }, { "created": "Sat, 10 Apr 2021 06:42:55 GMT", "version": "v3" } ]	2021-05-12	[ [ "Khristenko", "Ustim", "" ], [ "Schuß", "Stefan", "" ], [ "Krüger", "Melanie", "" ], [ "Schmidt", "Felix", "" ], [ "Wohlmuth", "Barbara", "" ], [ "Hesch", "Christian", "" ] ]	A novel mathematical model for fiber-reinforced materials is proposed. It is based on a 1-dimensional beam model for the thin fiber structures, a flexible and general 3-dimensional elasticity model for the matrix and an overlapping domain decomposition approach. From a computational point of view, this is motivated by the fact that matrix and fibers can easily meshed independently. Our main interest is in fiber reinforce polymers where the Young's modulus are quite different. Thus the modeling error from the overlapping approach is of no significance. The coupling conditions acknowledge both, the forces and the moments of the beam model and transfer them to the background material. A suitable static condensation procedure is applied to remove the beam balance equations. The condensed system then forms our starting point for a numerical approximation in terms of isogeometric analysis. The choice of our discrete basis functions of higher regularity is motivated by the fact, that as a result of the static condensation, we obtain second gradient terms in fiber direction. Eventually, a series of benchmark tests demonstrate the flexibility and robustness of the proposed methodology. As a proof-of-concept, we show that our new model is able to capture bending, torsion and shear dominated situations.
1606.01178	Md. Reza	Md. Alimoor Reza and Jana Kosecka	Reinforcement Learning for Semantic Segmentation in Indoor Scenes	null	null	null	null	cs.CV cs.RO	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Future advancements in robot autonomy and sophistication of robotics tasks rest on robust, efficient, and task-dependent semantic understanding of the environment. Semantic segmentation is the problem of simultaneous segmentation and categorization of a partition of sensory data. The majority of current approaches tackle this using multi-class segmentation and labeling in a Conditional Random Field (CRF) framework or by generating multiple object hypotheses and combining them sequentially. In practical settings, the subset of semantic labels that are needed depend on the task and particular scene and labelling every single pixel is not always necessary. We pursue these observations in developing a more modular and flexible approach to multi-class parsing of RGBD data based on learning strategies for combining independent binary object-vs-background segmentations in place of the usual monolithic multi-label CRF approach. Parameters for the independent binary segmentation models can be learned very efficiently, and the combination strategy---learned using reinforcement learning---can be set independently and can vary over different tasks and environments. Accuracy is comparable to state-of-art methods on a subset of the NYU-V2 dataset of indoor scenes, while providing additional flexibility and modularity.	[ { "created": "Fri, 3 Jun 2016 16:35:58 GMT", "version": "v1" } ]	2016-06-06	[ [ "Reza", "Md. Alimoor", "" ], [ "Kosecka", "Jana", "" ] ]	Future advancements in robot autonomy and sophistication of robotics tasks rest on robust, efficient, and task-dependent semantic understanding of the environment. Semantic segmentation is the problem of simultaneous segmentation and categorization of a partition of sensory data. The majority of current approaches tackle this using multi-class segmentation and labeling in a Conditional Random Field (CRF) framework or by generating multiple object hypotheses and combining them sequentially. In practical settings, the subset of semantic labels that are needed depend on the task and particular scene and labelling every single pixel is not always necessary. We pursue these observations in developing a more modular and flexible approach to multi-class parsing of RGBD data based on learning strategies for combining independent binary object-vs-background segmentations in place of the usual monolithic multi-label CRF approach. Parameters for the independent binary segmentation models can be learned very efficiently, and the combination strategy---learned using reinforcement learning---can be set independently and can vary over different tasks and environments. Accuracy is comparable to state-of-art methods on a subset of the NYU-V2 dataset of indoor scenes, while providing additional flexibility and modularity.
1905.09045	Lorenzo Cerrone	Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht	End-to-End Learned Random Walker for Seeded Image Segmentation	null	null	null	null	cs.CV cs.LG	http://creativecommons.org/licenses/by/4.0/	We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the underlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusivities for a linear diffusion process. Besides calculating the exact gradient for optimizing these diffusivities, we also propose simplifications that sparsely sample the gradient and still yield competitive results. The proposed method achieves the currently best results on a seeded version of the CREMI neuron segmentation challenge.	[ { "created": "Wed, 22 May 2019 09:56:04 GMT", "version": "v1" } ]	2019-05-23	[ [ "Cerrone", "Lorenzo", "" ], [ "Zeilmann", "Alexander", "" ], [ "Hamprecht", "Fred A.", "" ] ]	We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the underlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusivities for a linear diffusion process. Besides calculating the exact gradient for optimizing these diffusivities, we also propose simplifications that sparsely sample the gradient and still yield competitive results. The proposed method achieves the currently best results on a seeded version of the CREMI neuron segmentation challenge.
2009.10679	Manish Bhattarai	Manish Bhattarai, Aura Rose Jensen-Curtis, Manel Mart\'iNez-Ram\'on	An embedded deep learning system for augmented reality in firefighting applications	Accepted to ICMLA Special Session on Deep Learning	null	null	null	cs.CV cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Firefighting is a dynamic activity, in which numerous operations occur simultaneously. Maintaining situational awareness (i.e., knowledge of current conditions and activities at the scene) is critical to the accurate decision-making necessary for the safe and successful navigation of a fire environment by firefighters. Conversely, the disorientation caused by hazards such as smoke and extreme heat can lead to injury or even fatality. This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms to improve a firefighter's situational awareness and scene navigation through improved interpretation of that scene. We have designed and built a prototype embedded system that can leverage data streamed from cameras built into a firefighter's personal protective equipment (PPE) to capture thermal, RGB color, and depth imagery and then deploy already developed deep learning models to analyze the input data in real time. The embedded system analyzes and returns the processed images via wireless streaming, where they can be viewed remotely and relayed back to the firefighter using an augmented reality platform that visualizes the results of the analyzed inputs and draws the firefighter's attention to objects of interest, such as doors and windows otherwise invisible through smoke and flames.	[ { "created": "Tue, 22 Sep 2020 16:55:44 GMT", "version": "v1" } ]	2021-07-23	[ [ "Bhattarai", "Manish", "" ], [ "Jensen-Curtis", "Aura Rose", "" ], [ "MartíNez-Ramón", "Manel", "" ] ]	Firefighting is a dynamic activity, in which numerous operations occur simultaneously. Maintaining situational awareness (i.e., knowledge of current conditions and activities at the scene) is critical to the accurate decision-making necessary for the safe and successful navigation of a fire environment by firefighters. Conversely, the disorientation caused by hazards such as smoke and extreme heat can lead to injury or even fatality. This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms to improve a firefighter's situational awareness and scene navigation through improved interpretation of that scene. We have designed and built a prototype embedded system that can leverage data streamed from cameras built into a firefighter's personal protective equipment (PPE) to capture thermal, RGB color, and depth imagery and then deploy already developed deep learning models to analyze the input data in real time. The embedded system analyzes and returns the processed images via wireless streaming, where they can be viewed remotely and relayed back to the firefighter using an augmented reality platform that visualizes the results of the analyzed inputs and draws the firefighter's attention to objects of interest, such as doors and windows otherwise invisible through smoke and flames.
2006.10587	Yisroel Mirsky Dr.	Yisroel Mirsky, Tomer Golomb, Yuval Elovici	Lightweight Collaborative Anomaly Detection for the IoT using Blockchain	Preprint of accepted publication, June 2020: Journal of Parallel and Distributed Computing, Elsevier, ISSN: 0743-7315	null	null	null	cs.CR cs.DC cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives. Unfortunately, IoT devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. However, anomaly detection models must be trained for a long time in order to capture all benign behaviors. Furthermore, the anomaly detection model is vulnerable to adversarial attacks since, during the training phase, all observations are assumed to be benign. In this paper, we propose (1) a novel approach for anomaly detection and (2) a lightweight framework that utilizes the blockchain to ensemble an anomaly detection model in a distributed environment. Blockchain framework incrementally updates a trusted anomaly detection model via self-attestation and consensus among the IoT devices. We evaluate our method on a distributed IoT simulation platform, which consists of 48 Raspberry Pis. The simulation demonstrates how the approach can enhance the security of each device and the security of the network as a whole.	[ { "created": "Thu, 18 Jun 2020 14:50:08 GMT", "version": "v1" } ]	2020-06-19	[ [ "Mirsky", "Yisroel", "" ], [ "Golomb", "Tomer", "" ], [ "Elovici", "Yuval", "" ] ]	Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives. Unfortunately, IoT devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. However, anomaly detection models must be trained for a long time in order to capture all benign behaviors. Furthermore, the anomaly detection model is vulnerable to adversarial attacks since, during the training phase, all observations are assumed to be benign. In this paper, we propose (1) a novel approach for anomaly detection and (2) a lightweight framework that utilizes the blockchain to ensemble an anomaly detection model in a distributed environment. Blockchain framework incrementally updates a trusted anomaly detection model via self-attestation and consensus among the IoT devices. We evaluate our method on a distributed IoT simulation platform, which consists of 48 Raspberry Pis. The simulation demonstrates how the approach can enhance the security of each device and the security of the network as a whole.
1908.08774	Yikun Ban	Yikun Ban, Yuchen Zhou, Xu Cheng, Jiangfang Yi	Coalesced TLB to Exploit Diverse Contiguity of Memory Mapping	null	null	null	null	cs.DC	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The miss rate of TLB is crucial to the performance of address translation for virtual memory. To reduce the TLB misses, improving translation coverage of TLB has been an primary approach. Many previous works focus on coalescing multiple contiguously mapped pages of the memory mapping into a modified entry, which function well if the assumed contiguity of memory mapping is given. Unfortunately, scenarios of applications are complicated and the produced contiguity diversify. To gain better performance of translation, in this paper, we first introduce a complex but prevalent type of contiguity, mixed contiguity. Then we propose a HW-SW hybrid coalesced TLB structure which works well on all observed types of contiguity including this type. In our evaluation, the proposed scheme, K-bit Aligned TLB, outperforms the state-of-the-art work by reducing at lease 27% TLB misses on average over it using 16 benchmarks.	[ { "created": "Thu, 22 Aug 2019 11:34:43 GMT", "version": "v1" }, { "created": "Mon, 2 Dec 2019 22:21:59 GMT", "version": "v2" } ]	2019-12-04	[ [ "Ban", "Yikun", "" ], [ "Zhou", "Yuchen", "" ], [ "Cheng", "Xu", "" ], [ "Yi", "Jiangfang", "" ] ]	The miss rate of TLB is crucial to the performance of address translation for virtual memory. To reduce the TLB misses, improving translation coverage of TLB has been an primary approach. Many previous works focus on coalescing multiple contiguously mapped pages of the memory mapping into a modified entry, which function well if the assumed contiguity of memory mapping is given. Unfortunately, scenarios of applications are complicated and the produced contiguity diversify. To gain better performance of translation, in this paper, we first introduce a complex but prevalent type of contiguity, mixed contiguity. Then we propose a HW-SW hybrid coalesced TLB structure which works well on all observed types of contiguity including this type. In our evaluation, the proposed scheme, K-bit Aligned TLB, outperforms the state-of-the-art work by reducing at lease 27% TLB misses on average over it using 16 benchmarks.
2311.08383	Priyanka Kaswan	Priyanka Kaswan and Sennur Ulukus	Choosing Outdated Information to Achieve Reliability in Age-Based Gossiping	null	null	null	null	cs.IT cs.NI eess.SP math.IT	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We consider a system model with two sources, a reliable source and an unreliable source, who are responsible for disseminating updates regarding a process to an age-based gossip network of $n$ nodes. Nodes wish to have fresh information, however, they have preference for packets that originated at the reliable source and are willing to sacrifice their version age of information by up to $G$ versions to switch from an unreliable packet to a reliable packet. We study how this protocol impacts the prevalence of unreliable packets at nodes in the network and their version age. Using a stochastic hybrid system (SHS) framework, we formulate analytical equations to characterize two quantities: expected fraction of nodes with unreliable packets and expected version age of information at network nodes. We show that as $G$ increases, fewer nodes have unreliable packet, however, their version age increases as well, thereby inducing a freshness-reliability trade-off in the network. We present numerical results to support our findings.	[ { "created": "Tue, 14 Nov 2023 18:45:29 GMT", "version": "v1" } ]	2023-11-15	[ [ "Kaswan", "Priyanka", "" ], [ "Ulukus", "Sennur", "" ] ]	We consider a system model with two sources, a reliable source and an unreliable source, who are responsible for disseminating updates regarding a process to an age-based gossip network of $n$ nodes. Nodes wish to have fresh information, however, they have preference for packets that originated at the reliable source and are willing to sacrifice their version age of information by up to $G$ versions to switch from an unreliable packet to a reliable packet. We study how this protocol impacts the prevalence of unreliable packets at nodes in the network and their version age. Using a stochastic hybrid system (SHS) framework, we formulate analytical equations to characterize two quantities: expected fraction of nodes with unreliable packets and expected version age of information at network nodes. We show that as $G$ increases, fewer nodes have unreliable packet, however, their version age increases as well, thereby inducing a freshness-reliability trade-off in the network. We present numerical results to support our findings.
2110.13052	Noah Golowich	Noah Golowich, Ankur Moitra	Can Q-Learning be Improved with Advice?	null	null	null	null	cs.LG cs.AI cs.DS math.OC stat.ML	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Despite rapid progress in theoretical reinforcement learning (RL) over the last few years, most of the known guarantees are worst-case in nature, failing to take advantage of structure that may be known a priori about a given RL problem at hand. In this paper we address the question of whether worst-case lower bounds for regret in online learning of Markov decision processes (MDPs) can be circumvented when information about the MDP, in the form of predictions about its optimal $Q$-value function, is given to the algorithm. We show that when the predictions about the optimal $Q$-value function satisfy a reasonably weak condition we call distillation, then we can improve regret bounds by replacing the set of state-action pairs with the set of state-action pairs on which the predictions are grossly inaccurate. This improvement holds for both uniform regret bounds and gap-based ones. Further, we are able to achieve this property with an algorithm that achieves sublinear regret when given arbitrary predictions (i.e., even those which are not a distillation). Our work extends a recent line of work on algorithms with predictions, which has typically focused on simple online problems such as caching and scheduling, to the more complex and general problem of reinforcement learning.	[ { "created": "Mon, 25 Oct 2021 15:44:20 GMT", "version": "v1" } ]	2021-10-26	[ [ "Golowich", "Noah", "" ], [ "Moitra", "Ankur", "" ] ]	Despite rapid progress in theoretical reinforcement learning (RL) over the last few years, most of the known guarantees are worst-case in nature, failing to take advantage of structure that may be known a priori about a given RL problem at hand. In this paper we address the question of whether worst-case lower bounds for regret in online learning of Markov decision processes (MDPs) can be circumvented when information about the MDP, in the form of predictions about its optimal $Q$-value function, is given to the algorithm. We show that when the predictions about the optimal $Q$-value function satisfy a reasonably weak condition we call distillation, then we can improve regret bounds by replacing the set of state-action pairs with the set of state-action pairs on which the predictions are grossly inaccurate. This improvement holds for both uniform regret bounds and gap-based ones. Further, we are able to achieve this property with an algorithm that achieves sublinear regret when given arbitrary predictions (i.e., even those which are not a distillation). Our work extends a recent line of work on algorithms with predictions, which has typically focused on simple online problems such as caching and scheduling, to the more complex and general problem of reinforcement learning.
1709.00112	Swanand Kadhe	Swanand Kadhe, Brenden Garcia, Anoosheh Heidarzadeh, Salim El Rouayheb, Alex Sprintson	Private Information Retrieval with Side Information	Shorter version of the paper is accepted in Allerton Conference 2017	null	null	null	cs.IT cs.CR math.IT	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We study the problem of Private Information Retrieval (PIR) in the presence of prior side information. The problem setup includes a database of $K$ independent messages possibly replicated on several servers, and a user that needs to retrieve one of these messages. In addition, the user has some prior side information in the form of a subset of $M$ messages, not containing the desired message and unknown to the servers. This problem is motivated by practical settings in which the user can obtain side information opportunistically from other users or has previously downloaded some messages using classical PIR schemes. The objective of the user is to retrieve the required message without revealing its identity while minimizing the amount of data downloaded from the servers. We focus on achieving information-theoretic privacy in two scenarios: (i) the user wants to protect jointly its demand and side information; (ii) the user wants to protect only the information about its demand, but not the side information. To highlight the role of side information, we focus first on the case of a single server (single database). In the first scenario, we prove that the minimum download cost is $K-M$ messages, and in the second scenario it is $\lceil \frac{K}{M+1}\rceil$ messages, which should be compared to $K$ messages, the minimum download cost in the case of no side information. Then, we extend some of our results to the case of the database replicated on multiple servers. Our proof techniques relate PIR with side information to the index coding problem. We leverage this connection to prove converse results, as well as to design achievability schemes.	[ { "created": "Fri, 1 Sep 2017 00:04:11 GMT", "version": "v1" } ]	2017-09-04	[ [ "Kadhe", "Swanand", "" ], [ "Garcia", "Brenden", "" ], [ "Heidarzadeh", "Anoosheh", "" ], [ "Rouayheb", "Salim El", "" ], [ "Sprintson", "Alex", "" ] ]	We study the problem of Private Information Retrieval (PIR) in the presence of prior side information. The problem setup includes a database of $K$ independent messages possibly replicated on several servers, and a user that needs to retrieve one of these messages. In addition, the user has some prior side information in the form of a subset of $M$ messages, not containing the desired message and unknown to the servers. This problem is motivated by practical settings in which the user can obtain side information opportunistically from other users or has previously downloaded some messages using classical PIR schemes. The objective of the user is to retrieve the required message without revealing its identity while minimizing the amount of data downloaded from the servers. We focus on achieving information-theoretic privacy in two scenarios: (i) the user wants to protect jointly its demand and side information; (ii) the user wants to protect only the information about its demand, but not the side information. To highlight the role of side information, we focus first on the case of a single server (single database). In the first scenario, we prove that the minimum download cost is $K-M$ messages, and in the second scenario it is $\lceil \frac{K}{M+1}\rceil$ messages, which should be compared to $K$ messages, the minimum download cost in the case of no side information. Then, we extend some of our results to the case of the database replicated on multiple servers. Our proof techniques relate PIR with side information to the index coding problem. We leverage this connection to prove converse results, as well as to design achievability schemes.
1910.11791	Linchao Bao	Yajing Chen, Fanzi Wu, Zeyu Wang, Yibing Song, Yonggen Ling, Linchao Bao	Self-supervised Learning of Detailed 3D Face Reconstruction	Accepted by IEEE Transactions on Image Processing (TIP)	null	10.1109/TIP.2020.3017347	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	In this paper, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement map in UV-space to represent a 3D face. Unlike previous work addressing the problem, our learning framework does not require supervision of surrogate ground-truth 3D models computed with traditional approaches. Instead, we utilize the input image itself as supervision during learning. In the first stage, we combine a photometric loss and a facial perceptual loss between the input face and the rendered face, to regress a 3DMM-based coarse model. In the second stage, both the input image and the regressed texture of the coarse model are unwrapped into UV-space, and then sent through an image-toimage translation network to predict a displacement map in UVspace. The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage. The advantage of learning displacement map in UV-space is that face alignment can be explicitly done during the unwrapping, thus facial details are easier to learn from large amount of data. Extensive experiments demonstrate the superiority of the proposed method over previous work.	[ { "created": "Fri, 25 Oct 2019 15:16:20 GMT", "version": "v1" }, { "created": "Wed, 2 Sep 2020 03:58:23 GMT", "version": "v2" } ]	2020-09-03	[ [ "Chen", "Yajing", "" ], [ "Wu", "Fanzi", "" ], [ "Wang", "Zeyu", "" ], [ "Song", "Yibing", "" ], [ "Ling", "Yonggen", "" ], [ "Bao", "Linchao", "" ] ]	In this paper, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement map in UV-space to represent a 3D face. Unlike previous work addressing the problem, our learning framework does not require supervision of surrogate ground-truth 3D models computed with traditional approaches. Instead, we utilize the input image itself as supervision during learning. In the first stage, we combine a photometric loss and a facial perceptual loss between the input face and the rendered face, to regress a 3DMM-based coarse model. In the second stage, both the input image and the regressed texture of the coarse model are unwrapped into UV-space, and then sent through an image-toimage translation network to predict a displacement map in UVspace. The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage. The advantage of learning displacement map in UV-space is that face alignment can be explicitly done during the unwrapping, thus facial details are easier to learn from large amount of data. Extensive experiments demonstrate the superiority of the proposed method over previous work.
1804.06996	Gaurav Bharaj	Gaurav Bharaj, Danny Kaufman, Etienne Vouga, Hanspeter Pfister	Metamorphs: Bistable Planar Structures	null	null	null	null	cs.GR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Extreme deformation can drastically morph a structure from one structural form into another. Programming such deformation properties into the structure is often challenging and in many cases an impossible task. The morphed forms do not hold and usually relapse to the original form, where the structure is in its lowest energy state. For example, a stick, when bent, resists its bent form and tends to go back to its initial straight form, where it holds the least amount of potential energy. In this project, we present a computational design method which can create fabricable planar structure that can morph into two different bistable forms. Once the user provides the initial desired forms, the method automatically creates support structures (internal springs), such that, the structure can not only morph, but also hold the respective forms under external force application. We achieve this through an iterative nonlinear optimization strategy for shaping the potential energy of the structure in the two forms simultaneously. Our approach guarantees first and second-order stability with respect to the potential energy of the bistable structure.	[ { "created": "Thu, 19 Apr 2018 05:15:03 GMT", "version": "v1" } ]	2018-04-20	[ [ "Bharaj", "Gaurav", "" ], [ "Kaufman", "Danny", "" ], [ "Vouga", "Etienne", "" ], [ "Pfister", "Hanspeter", "" ] ]	Extreme deformation can drastically morph a structure from one structural form into another. Programming such deformation properties into the structure is often challenging and in many cases an impossible task. The morphed forms do not hold and usually relapse to the original form, where the structure is in its lowest energy state. For example, a stick, when bent, resists its bent form and tends to go back to its initial straight form, where it holds the least amount of potential energy. In this project, we present a computational design method which can create fabricable planar structure that can morph into two different bistable forms. Once the user provides the initial desired forms, the method automatically creates support structures (internal springs), such that, the structure can not only morph, but also hold the respective forms under external force application. We achieve this through an iterative nonlinear optimization strategy for shaping the potential energy of the structure in the two forms simultaneously. Our approach guarantees first and second-order stability with respect to the potential energy of the bistable structure.
1510.01891	Adam Kurpisz	Adam Kurpisz, Samuli Lepp\"anen, Monaldo Mastrolilli	On the Hardest Problem Formulations for the 0/1 Lasserre Hierarchy	null	null	null	null	cs.CC cs.DS	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The Lasserre/Sum-of-Squares (SoS) hierarchy is a systematic procedure for constructing a sequence of increasingly tight semidefinite relaxations. It is known that the hierarchy converges to the 0/1 polytope in n levels and captures the convex relaxations used in the best available approximation algorithms for a wide variety of optimization problems. In this paper we characterize the set of 0/1 integer linear problems and unconstrained 0/1 polynomial optimization problems that can still have an integrality gap at level n-1. These problems are the hardest for the Lasserre hierarchy in this sense.	[ { "created": "Wed, 7 Oct 2015 10:57:45 GMT", "version": "v1" } ]	2015-10-08	[ [ "Kurpisz", "Adam", "" ], [ "Leppänen", "Samuli", "" ], [ "Mastrolilli", "Monaldo", "" ] ]	The Lasserre/Sum-of-Squares (SoS) hierarchy is a systematic procedure for constructing a sequence of increasingly tight semidefinite relaxations. It is known that the hierarchy converges to the 0/1 polytope in n levels and captures the convex relaxations used in the best available approximation algorithms for a wide variety of optimization problems. In this paper we characterize the set of 0/1 integer linear problems and unconstrained 0/1 polynomial optimization problems that can still have an integrality gap at level n-1. These problems are the hardest for the Lasserre hierarchy in this sense.
2203.06870	Cl\'ement Canonne	Jayadev Acharya and Cl\'ement L. Canonne and Ziteng Sun and Himanshu Tyagi	The Role of Interactivity in Structured Estimation	null	null	null	null	cs.DS cs.DM cs.IT cs.LG math.IT math.ST stat.TH	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We study high-dimensional sparse estimation under three natural constraints: communication constraints, local privacy constraints, and linear measurements (compressive sensing). Without sparsity assumptions, it has been established that interactivity cannot improve the minimax rates of estimation under these information constraints. The question of whether interactivity helps with natural inference tasks has been a topic of active research. We settle this question in the affirmative for the prototypical problems of high-dimensional sparse mean estimation and compressive sensing, by demonstrating a gap between interactive and noninteractive protocols. We further establish that the gap increases when we have more structured sparsity: for block sparsity this gap can be as large as polynomial in the dimensionality. Thus, the more structured the sparsity is, the greater is the advantage of interaction. Proving the lower bounds requires a careful breaking of a sum of correlated random variables into independent components using Baranyai's theorem on decomposition of hypergraphs, which might be of independent interest.	[ { "created": "Mon, 14 Mar 2022 05:54:42 GMT", "version": "v1" } ]	2022-03-15	[ [ "Acharya", "Jayadev", "" ], [ "Canonne", "Clément L.", "" ], [ "Sun", "Ziteng", "" ], [ "Tyagi", "Himanshu", "" ] ]	We study high-dimensional sparse estimation under three natural constraints: communication constraints, local privacy constraints, and linear measurements (compressive sensing). Without sparsity assumptions, it has been established that interactivity cannot improve the minimax rates of estimation under these information constraints. The question of whether interactivity helps with natural inference tasks has been a topic of active research. We settle this question in the affirmative for the prototypical problems of high-dimensional sparse mean estimation and compressive sensing, by demonstrating a gap between interactive and noninteractive protocols. We further establish that the gap increases when we have more structured sparsity: for block sparsity this gap can be as large as polynomial in the dimensionality. Thus, the more structured the sparsity is, the greater is the advantage of interaction. Proving the lower bounds requires a careful breaking of a sum of correlated random variables into independent components using Baranyai's theorem on decomposition of hypergraphs, which might be of independent interest.
1903.00553	Binghui Wang	Binghui Wang, Neil Zhenqiang Gong	Attacking Graph-based Classification via Manipulating the Graph Structure	To appear in The 26th ACM Conference on Computer and Communications Security, Nov 2019	null	null	null	cs.CR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Graph-based classification methods are widely used for security and privacy analytics. Roughly speaking, graph-based classification methods include collective classification and graph neural network. Evading a graph-based classification method enables an attacker to evade detection in security analytics and can be used as a privacy defense against inference attacks. Existing adversarial machine learning studies mainly focused on machine learning for non-graph data. Only a few recent studies touched adversarial graph-based classification methods. However, they focused on graph neural network methods, leaving adversarial collective classification largely unexplored. We aim to bridge this gap in this work. We first propose a threat model to characterize the attack surface of a collective classification method. Specifically, we characterize an attacker's background knowledge along three dimensions: parameters of the method, training dataset, and the complete graph; an attacker's goal is to evade detection via manipulating the graph structure. We formulate our attack as a graph-based optimization problem, solving which produces the edges that an attacker needs to manipulate to achieve its attack goal. Moreover, we propose several approximation techniques to solve the optimization problem. We evaluate our attacks and compare them with a recent attack designed for graph neural networks. Results show that our attacks 1) can effectively evade graph-based classification methods; 2) do not require access to the true parameters, true training dataset, and/or complete graph; and 3) outperform the existing attack for evading collective classification methods and some graph neural network methods. We also apply our attacks to evade Sybil detection using a large-scale Twitter dataset and apply our attacks as a defense against attribute inference attacks using a large-scale Google+ dataset.	[ { "created": "Fri, 1 Mar 2019 21:59:17 GMT", "version": "v1" }, { "created": "Tue, 13 Aug 2019 02:00:12 GMT", "version": "v2" } ]	2019-08-14	[ [ "Wang", "Binghui", "" ], [ "Gong", "Neil Zhenqiang", "" ] ]	Graph-based classification methods are widely used for security and privacy analytics. Roughly speaking, graph-based classification methods include collective classification and graph neural network. Evading a graph-based classification method enables an attacker to evade detection in security analytics and can be used as a privacy defense against inference attacks. Existing adversarial machine learning studies mainly focused on machine learning for non-graph data. Only a few recent studies touched adversarial graph-based classification methods. However, they focused on graph neural network methods, leaving adversarial collective classification largely unexplored. We aim to bridge this gap in this work. We first propose a threat model to characterize the attack surface of a collective classification method. Specifically, we characterize an attacker's background knowledge along three dimensions: parameters of the method, training dataset, and the complete graph; an attacker's goal is to evade detection via manipulating the graph structure. We formulate our attack as a graph-based optimization problem, solving which produces the edges that an attacker needs to manipulate to achieve its attack goal. Moreover, we propose several approximation techniques to solve the optimization problem. We evaluate our attacks and compare them with a recent attack designed for graph neural networks. Results show that our attacks 1) can effectively evade graph-based classification methods; 2) do not require access to the true parameters, true training dataset, and/or complete graph; and 3) outperform the existing attack for evading collective classification methods and some graph neural network methods. We also apply our attacks to evade Sybil detection using a large-scale Twitter dataset and apply our attacks as a defense against attribute inference attacks using a large-scale Google+ dataset.
2308.03312	Kexin Pei	Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng, Lorenzo Cavallaro, Junfeng Yang, Suman Jana	Exploiting Code Symmetries for Learning Program Semantics	null	null	null	null	cs.LG cs.CR cs.PL	http://creativecommons.org/licenses/by/4.0/	This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We introduce a group-theoretic framework that defines code symmetries as semantics-preserving transformations, where forming a code symmetry group enables precise and efficient reasoning of code semantics. Our solution, SymC, develops a novel variant of self-attention that is provably equivariant to code symmetries from the permutation group defined over the program dependence graph. SymC obtains superior performance on five program analysis tasks, outperforming state-of-the-art code models without any pre-training. Our results suggest that code LLMs that encode the code structural prior via the code symmetry group generalize better and faster.	[ { "created": "Mon, 7 Aug 2023 05:40:58 GMT", "version": "v1" }, { "created": "Fri, 25 Aug 2023 16:08:51 GMT", "version": "v2" }, { "created": "Mon, 28 Aug 2023 04:53:52 GMT", "version": "v3" }, { "created": "Tue, 29 Aug 2023 01:44:39 GMT", "version": "v4" }, { "created": "Thu, 31 Aug 2023 02:29:36 GMT", "version": "v5" }, { "created": "Tue, 27 Feb 2024 21:18:17 GMT", "version": "v6" }, { "created": "Thu, 29 Feb 2024 05:16:24 GMT", "version": "v7" }, { "created": "Thu, 6 Jun 2024 16:35:20 GMT", "version": "v8" } ]	2024-06-07	[ [ "Pei", "Kexin", "" ], [ "Li", "Weichen", "" ], [ "Jin", "Qirui", "" ], [ "Liu", "Shuyang", "" ], [ "Geng", "Scott", "" ], [ "Cavallaro", "Lorenzo", "" ], [ "Yang", "Junfeng", "" ], [ "Jana", "Suman", "" ] ]	This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We introduce a group-theoretic framework that defines code symmetries as semantics-preserving transformations, where forming a code symmetry group enables precise and efficient reasoning of code semantics. Our solution, SymC, develops a novel variant of self-attention that is provably equivariant to code symmetries from the permutation group defined over the program dependence graph. SymC obtains superior performance on five program analysis tasks, outperforming state-of-the-art code models without any pre-training. Our results suggest that code LLMs that encode the code structural prior via the code symmetry group generalize better and faster.
2202.05329	Simon Lars\'en	Simon Lars\'en (1), Jean-R\'emy Falleri (2), Benoit Baudry (1), Martin Monperrus (1) ((1) KTH Royal Institute of Technology, (2) Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, IUF)	Spork: Structured Merge for Java with Formatting Preservation	21 pages, 18 figures, 11 tables, accepted for publication in IEEE Transactions on Software Engineering	IEEE Transactions on Software Engineering, 2022	10.1109/TSE.2022.3143766	null	cs.SE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The highly parallel workflows of modern software development have made merging of source code a common activity for developers. The state of the practice is based on line-based merge, which is ubiquitously used with "git merge". Line-based merge is however a generalized technique for any text that cannot leverage the structured nature of source code, making merge conflicts a common occurrence. As a remedy, research has proposed structured merge tools, which typically operate on abstract syntax trees instead of raw text. Structured merging greatly reduces the prevalence of merge conflicts but suffers from important limitations, the main ones being a tendency to alter the formatting of the merged code and being prone to excessive running times. In this paper, we present SPORK, a novel structured merge tool for JAVA. SPORK is unique as it preserves formatting to a significantly greater degree than comparable state-of-the-art tools. SPORK is also overall faster than the state of the art, in particular significantly reducing worst-case running times in practice. We demonstrate these properties by replaying 1740 real-world file merges collected from 119 open-source projects, and further demonstrate several key differences between SPORK and the state of the art with in-depth case studies.	[ { "created": "Thu, 10 Feb 2022 21:15:49 GMT", "version": "v1" } ]	2022-02-21	[ [ "Larsén", "Simon", "" ], [ "Falleri", "Jean-Rémy", "" ], [ "Baudry", "Benoit", "" ], [ "Monperrus", "Martin", "" ] ]	The highly parallel workflows of modern software development have made merging of source code a common activity for developers. The state of the practice is based on line-based merge, which is ubiquitously used with "git merge". Line-based merge is however a generalized technique for any text that cannot leverage the structured nature of source code, making merge conflicts a common occurrence. As a remedy, research has proposed structured merge tools, which typically operate on abstract syntax trees instead of raw text. Structured merging greatly reduces the prevalence of merge conflicts but suffers from important limitations, the main ones being a tendency to alter the formatting of the merged code and being prone to excessive running times. In this paper, we present SPORK, a novel structured merge tool for JAVA. SPORK is unique as it preserves formatting to a significantly greater degree than comparable state-of-the-art tools. SPORK is also overall faster than the state of the art, in particular significantly reducing worst-case running times in practice. We demonstrate these properties by replaying 1740 real-world file merges collected from 119 open-source projects, and further demonstrate several key differences between SPORK and the state of the art with in-depth case studies.
1501.04985	Konstantinos Georgiou	Jurek Czyzowicz, Konstantinos Georgiou, Evangelos Kranakis, Lata Narayanan, Jarda Opatrny, Birgit Vogtenhuber	Evacuating Robots from a Disk Using Face-to-Face Communication	22 pages, 8 figures. An extended abstract of this work was accepted for publication in the LNCS proceedings of the 9th International Conference on Algorithms and Complexity (CIAC15)	Discrete Mathematics & Theoretical Computer Science, vol. 22 no. 4, Distributed Computing and Networking (August 27, 2020) dmtcs:6198	10.23638/DMTCS-22-4-4	null	cs.DS	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Assume that two robots are located at the centre of a unit disk. Their goal is to evacuate from the disk through an exit at an unknown location on the boundary of the disk. At any time the robots can move anywhere they choose on the disk, independently of each other, with maximum speed $1$. The robots can cooperate by exchanging information whenever they meet. We study algorithms for the two robots to minimize the evacuation time: the time when both robots reach the exit. In [CGGKMP14] the authors gave an algorithm defining trajectories for the two robots yielding evacuation time at most $5.740$ and also proved that any algorithm has evacuation time at least $3+ \frac{\pi}{4} + \sqrt{2} \approx 5.199$. We improve both the upper and lower bound on the evacuation time of a unit disk. Namely, we present a new non-trivial algorithm whose evacuation time is at most $5.628$ and show that any algorithm has evacuation time at least $3+ \frac{\pi}{6} + \sqrt{3} \approx 5.255$. To achieve the upper bound, we designed an algorithm which proposes a forced meeting between the two robots, even if the exit has not been found by either of them. We also show that such a strategy is provably optimal for a related problem of searching for an exit placed at the vertices of a regular hexagon.	[ { "created": "Tue, 20 Jan 2015 21:36:08 GMT", "version": "v1" }, { "created": "Thu, 12 Mar 2020 02:07:34 GMT", "version": "v2" }, { "created": "Tue, 9 Jun 2020 02:12:31 GMT", "version": "v3" }, { "created": "Tue, 21 Jul 2020 04:08:18 GMT", "version": "v4" }, { "created": "Mon, 24 Aug 2020 04:13:48 GMT", "version": "v5" } ]	2023-06-22	[ [ "Czyzowicz", "Jurek", "" ], [ "Georgiou", "Konstantinos", "" ], [ "Kranakis", "Evangelos", "" ], [ "Narayanan", "Lata", "" ], [ "Opatrny", "Jarda", "" ], [ "Vogtenhuber", "Birgit", "" ] ]	Assume that two robots are located at the centre of a unit disk. Their goal is to evacuate from the disk through an exit at an unknown location on the boundary of the disk. At any time the robots can move anywhere they choose on the disk, independently of each other, with maximum speed $1$. The robots can cooperate by exchanging information whenever they meet. We study algorithms for the two robots to minimize the evacuation time: the time when both robots reach the exit. In [CGGKMP14] the authors gave an algorithm defining trajectories for the two robots yielding evacuation time at most $5.740$ and also proved that any algorithm has evacuation time at least $3+ \frac{\pi}{4} + \sqrt{2} \approx 5.199$. We improve both the upper and lower bound on the evacuation time of a unit disk. Namely, we present a new non-trivial algorithm whose evacuation time is at most $5.628$ and show that any algorithm has evacuation time at least $3+ \frac{\pi}{6} + \sqrt{3} \approx 5.255$. To achieve the upper bound, we designed an algorithm which proposes a forced meeting between the two robots, even if the exit has not been found by either of them. We also show that such a strategy is provably optimal for a related problem of searching for an exit placed at the vertices of a regular hexagon.
2310.11450	Thomas Decker	Thomas Decker, Michael Lebacher and Volker Tresp	Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts	2023 IEEE 21st International Conference on Industrial Informatics (INDIN)	null	10.1109/INDIN51400.2023.10218170	null	cs.LG cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industrial prediction problems is challenging as it is not immediately clear how to define and access appropriate concepts for individual use cases and specific data types. In this work, we investigate how to leverage established concept-based explanation techniques in the context of bearing fault detection with deep neural networks trained on vibration signals. Since bearings are prevalent in almost every rotating equipment, ensuring the reliability of intransparent fault detection models is crucial to prevent costly repairs and downtimes of industrial machinery. Our evaluations demonstrate that explaining opaque models in terms of vibration concepts enables human-comprehensible and intuitive insights about their inner workings, but the underlying assumptions need to be carefully validated first.	[ { "created": "Tue, 17 Oct 2023 17:58:19 GMT", "version": "v1" } ]	2023-10-18	[ [ "Decker", "Thomas", "" ], [ "Lebacher", "Michael", "" ], [ "Tresp", "Volker", "" ] ]	Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industrial prediction problems is challenging as it is not immediately clear how to define and access appropriate concepts for individual use cases and specific data types. In this work, we investigate how to leverage established concept-based explanation techniques in the context of bearing fault detection with deep neural networks trained on vibration signals. Since bearings are prevalent in almost every rotating equipment, ensuring the reliability of intransparent fault detection models is crucial to prevent costly repairs and downtimes of industrial machinery. Our evaluations demonstrate that explaining opaque models in terms of vibration concepts enables human-comprehensible and intuitive insights about their inner workings, but the underlying assumptions need to be carefully validated first.
1403.3286	Sadegh Esmaeil Zadeh Soudjani	S. Esmaeil Zadeh Soudjani, C. Gevaerts, A. Abate	FAUST$^2$: Formal Abstractions of Uncountable-STate STochastic processes	This paper is submitted to the 26th International Conference on Computer Aided Verification (CAV 2014)	null	null	null	cs.SY	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	FAUST$^2$ is a software tool that generates formal abstractions of (possibly non-deterministic) discrete-time Markov processes (dtMP) defined over uncountable (continuous) state spaces. A dtMP model is specified in MATLAB and abstracted as a finite-state Markov chain or Markov decision processes. The abstraction procedure runs in MATLAB and employs parallel computations and fast manipulations based on vector calculus. The abstract model is formally put in relationship with the concrete dtMP via a user-defined maximum threshold on the approximation error introduced by the abstraction procedure. FAUST$^2$ allows exporting the abstract model to well-known probabilistic model checkers, such as PRISM or MRMC. Alternatively, it can handle internally the computation of PCTL properties (e.g. safety or reach-avoid) over the abstract model, and refine the outcomes over the concrete dtMP via a quantified error that depends on the abstraction procedure and the given formula. The toolbox is available at http://sourceforge.net/projects/faust2/	[ { "created": "Thu, 13 Mar 2014 14:53:46 GMT", "version": "v1" } ]	2014-03-14	[ [ "Soudjani", "S. Esmaeil Zadeh", "" ], [ "Gevaerts", "C.", "" ], [ "Abate", "A.", "" ] ]	FAUST$^2$ is a software tool that generates formal abstractions of (possibly non-deterministic) discrete-time Markov processes (dtMP) defined over uncountable (continuous) state spaces. A dtMP model is specified in MATLAB and abstracted as a finite-state Markov chain or Markov decision processes. The abstraction procedure runs in MATLAB and employs parallel computations and fast manipulations based on vector calculus. The abstract model is formally put in relationship with the concrete dtMP via a user-defined maximum threshold on the approximation error introduced by the abstraction procedure. FAUST$^2$ allows exporting the abstract model to well-known probabilistic model checkers, such as PRISM or MRMC. Alternatively, it can handle internally the computation of PCTL properties (e.g. safety or reach-avoid) over the abstract model, and refine the outcomes over the concrete dtMP via a quantified error that depends on the abstraction procedure and the given formula. The toolbox is available at http://sourceforge.net/projects/faust2/
2210.16046	Masakazu Yoshimura	Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, Takeshi Ohashi	Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments	Accepted to CVPR2023	null	null	null	cs.CV eess.IV	http://creativecommons.org/licenses/by/4.0/	Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the need for hard-to-obtain datasets. One simple approach is to apply data augmentation such as color jitter and blur to standard RGB (sRGB) images in simple scenes. Unfortunately, this approach struggles to yield realistic images in terms of pixel intensity and noise distribution due to not considering the non-linearity of Image Signal Processors (ISPs) and noise characteristics of image sensors. Instead, we propose a noise-accounted RAW image augmentation method. In essence, color jitter and blur augmentation are applied to a RAW image before applying non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a noise amount alignment method that calibrates the domain gap in the noise property caused by the augmentation. We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data.	[ { "created": "Fri, 28 Oct 2022 10:33:45 GMT", "version": "v1" }, { "created": "Mon, 27 Mar 2023 06:17:13 GMT", "version": "v2" } ]	2023-03-28	[ [ "Yoshimura", "Masakazu", "" ], [ "Otsuka", "Junji", "" ], [ "Irie", "Atsushi", "" ], [ "Ohashi", "Takeshi", "" ] ]	Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the need for hard-to-obtain datasets. One simple approach is to apply data augmentation such as color jitter and blur to standard RGB (sRGB) images in simple scenes. Unfortunately, this approach struggles to yield realistic images in terms of pixel intensity and noise distribution due to not considering the non-linearity of Image Signal Processors (ISPs) and noise characteristics of image sensors. Instead, we propose a noise-accounted RAW image augmentation method. In essence, color jitter and blur augmentation are applied to a RAW image before applying non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a noise amount alignment method that calibrates the domain gap in the noise property caused by the augmentation. We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data.
1305.2755	Issam Sahmoudi issam sahmoudi	Issam Sahmoudi and Abdelmonaime Lachkar	Clustering Web Search Results For Effective Arabic Language Browsing	null	International Journal on Natural Language Computing (IJNLC) Vol. 2, No.2, April 2013	10.5121/ijnlc.2013.2202	null	cs.IR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The process of browsing Search Results is one of the major problems with traditional Web search engines for English, European, and any other languages generally, and for Arabic Language particularly. This process is absolutely time consuming and the browsing style seems to be unattractive. Organizing Web search results into clusters facilitates users quick browsing through search results. Traditional clustering techniques (data-centric clustering algorithms) are inadequate since they don't generate clusters with highly readable names or cluster labels. To solve this problem, Description-centric algorithms such as Suffix Tree Clustering (STC) algorithm have been introduced and used successfully and extensively with different adapted versions for English, European, and Chinese Languages. However, till the day of writing this paper, in our knowledge, STC algorithm has been never applied for Arabic Web Snippets Search Results Clustering.In this paper, we propose first, to study how STC can be applied for Arabic Language? We then illustrate by example that is impossible to apply STC after Arabic Snippets pre-processing (stem or root extraction) because the Merging process yields many redundant clusters. Secondly, to overcome this problem, we propose to integrate STC in a new scheme taking into a count the Arabic language properties in order to get the web more and more adapted to Arabic users. The proposed approach automatically clusters the web search results into high quality, and high significant clusters labels. The obtained clusters not only are coherent, but also can convey the contents to the users concisely and accurately. Therefore the Arabic users can decide at a glance whether the contents of a cluster are of interest.....	[ { "created": "Mon, 13 May 2013 12:28:34 GMT", "version": "v1" } ]	2013-05-14	[ [ "Sahmoudi", "Issam", "" ], [ "Lachkar", "Abdelmonaime", "" ] ]	The process of browsing Search Results is one of the major problems with traditional Web search engines for English, European, and any other languages generally, and for Arabic Language particularly. This process is absolutely time consuming and the browsing style seems to be unattractive. Organizing Web search results into clusters facilitates users quick browsing through search results. Traditional clustering techniques (data-centric clustering algorithms) are inadequate since they don't generate clusters with highly readable names or cluster labels. To solve this problem, Description-centric algorithms such as Suffix Tree Clustering (STC) algorithm have been introduced and used successfully and extensively with different adapted versions for English, European, and Chinese Languages. However, till the day of writing this paper, in our knowledge, STC algorithm has been never applied for Arabic Web Snippets Search Results Clustering.In this paper, we propose first, to study how STC can be applied for Arabic Language? We then illustrate by example that is impossible to apply STC after Arabic Snippets pre-processing (stem or root extraction) because the Merging process yields many redundant clusters. Secondly, to overcome this problem, we propose to integrate STC in a new scheme taking into a count the Arabic language properties in order to get the web more and more adapted to Arabic users. The proposed approach automatically clusters the web search results into high quality, and high significant clusters labels. The obtained clusters not only are coherent, but also can convey the contents to the users concisely and accurately. Therefore the Arabic users can decide at a glance whether the contents of a cluster are of interest.....
1802.07983	Miroslav Bures	Miroslav Bures and Karel Frajtak and Bestoun S. Ahmed	Tapir: Automation Support of Exploratory Testing Using Model Reconstruction of the System Under Test	null	null	10.1109/TR.2018.2799957	null	cs.SE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	For a considerable number of software projects, the creation of effective test cases is hindered by design documentation that is either lacking, incomplete or obsolete. The exploratory testing approach can serve as a sound method in such situations. However, the efficiency of this testing approach strongly depends on the method, the documentation of explored parts of a system, the organization and distribution of work among individual testers on a team, and the minimization of potential (very probable) duplicities in performed tests. In this paper, we present a framework for replacing and automating a portion of these tasks. A screen-flow-based model of the tested system is incrementally reconstructed during the exploratory testing process by tracking testers' activities. With additional metadata, the model serves for an automated navigation process for a tester. Compared with the exploratory testing approach, which is manually performed in two case studies, the proposed framework allows the testers to explore a greater extent of the tested system and enables greater detection of the defects present in the system. The results show that the time efficiency of the testing process improved with framework support. This efficiency can be increased by team-based navigational strategies that are implemented within the proposed framework, which is documented by another case study presented in this paper.	[ { "created": "Thu, 22 Feb 2018 11:27:14 GMT", "version": "v1" } ]	2019-12-05	[ [ "Bures", "Miroslav", "" ], [ "Frajtak", "Karel", "" ], [ "Ahmed", "Bestoun S.", "" ] ]	For a considerable number of software projects, the creation of effective test cases is hindered by design documentation that is either lacking, incomplete or obsolete. The exploratory testing approach can serve as a sound method in such situations. However, the efficiency of this testing approach strongly depends on the method, the documentation of explored parts of a system, the organization and distribution of work among individual testers on a team, and the minimization of potential (very probable) duplicities in performed tests. In this paper, we present a framework for replacing and automating a portion of these tasks. A screen-flow-based model of the tested system is incrementally reconstructed during the exploratory testing process by tracking testers' activities. With additional metadata, the model serves for an automated navigation process for a tester. Compared with the exploratory testing approach, which is manually performed in two case studies, the proposed framework allows the testers to explore a greater extent of the tested system and enables greater detection of the defects present in the system. The results show that the time efficiency of the testing process improved with framework support. This efficiency can be increased by team-based navigational strategies that are implemented within the proposed framework, which is documented by another case study presented in this paper.
0910.1123	Arvind Yedla	Arvind Yedla, Henry D. Pfister, Krishna R. Narayanan	Can Iterative Decoding for Erasure Correlated Sources be Universal?	8 pages, to appear in Allerton '09	null	null	null	cs.IT math.IT	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	In this paper, we consider a few iterative decoding schemes for the joint source-channel coding of correlated sources. Specifically, we consider the joint source-channel coding of two erasure correlated sources with transmission over different erasure channels. Our main interest is in determining whether or not various code ensembles can achieve the capacity region universally over varying channel conditions. We consider two ensembles in the class of low-density generator-matrix (LDGM) codes known as Luby-Transform (LT) codes and one ensemble of low-density parity-check (LDPC) codes. We analyze them using density evolution and show that optimized LT codes can achieve the extremal symmetric point of the capacity region. We also show that LT codes are not universal under iterative decoding for this problem because they cannot simultaneously achieve the extremal symmetric point and a corner point of the capacity region. The sub-universality of iterative decoding is characterized by studying the density evolution for LT codes.	[ { "created": "Tue, 6 Oct 2009 22:10:56 GMT", "version": "v1" } ]	2009-10-08	[ [ "Yedla", "Arvind", "" ], [ "Pfister", "Henry D.", "" ], [ "Narayanan", "Krishna R.", "" ] ]	In this paper, we consider a few iterative decoding schemes for the joint source-channel coding of correlated sources. Specifically, we consider the joint source-channel coding of two erasure correlated sources with transmission over different erasure channels. Our main interest is in determining whether or not various code ensembles can achieve the capacity region universally over varying channel conditions. We consider two ensembles in the class of low-density generator-matrix (LDGM) codes known as Luby-Transform (LT) codes and one ensemble of low-density parity-check (LDPC) codes. We analyze them using density evolution and show that optimized LT codes can achieve the extremal symmetric point of the capacity region. We also show that LT codes are not universal under iterative decoding for this problem because they cannot simultaneously achieve the extremal symmetric point and a corner point of the capacity region. The sub-universality of iterative decoding is characterized by studying the density evolution for LT codes.
2206.00227	Junbo Zhang	Junbo Zhang, Kaisheng Ma	Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views	Accepted to CVPR 2022	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable. However, the predetermined composition of multiple data augmentations brings two drawbacks. First, the artificial choice of augmentation types brings specific representational invariances to the model, which have different degrees of positive and negative effects on different downstream tasks. Treating each type of augmentation equally during training makes the model learn non-optimal representations for various downstream tasks and limits the flexibility to choose augmentation types beforehand. Second, the strong data augmentations used in classic contrastive learning methods may bring too much invariance in some cases, and fine-grained information that is essential to some downstream tasks may be lost. This paper proposes a general method to alleviate these two problems by considering where and what to contrast in a general contrastive learning framework. We first propose to learn different augmentation invariances at different depths of the model according to the importance of each data augmentation instead of learning representational invariances evenly in the backbone. We then propose to expand the contrast content with augmentation embeddings to reduce the misleading effects of strong data augmentations. Experiments based on several baseline methods demonstrate that we learn better representations for various benchmarks on classification, detection, and segmentation downstream tasks.	[ { "created": "Wed, 1 Jun 2022 04:30:46 GMT", "version": "v1" }, { "created": "Mon, 22 Aug 2022 03:28:52 GMT", "version": "v2" } ]	2022-08-23	[ [ "Zhang", "Junbo", "" ], [ "Ma", "Kaisheng", "" ] ]	A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable. However, the predetermined composition of multiple data augmentations brings two drawbacks. First, the artificial choice of augmentation types brings specific representational invariances to the model, which have different degrees of positive and negative effects on different downstream tasks. Treating each type of augmentation equally during training makes the model learn non-optimal representations for various downstream tasks and limits the flexibility to choose augmentation types beforehand. Second, the strong data augmentations used in classic contrastive learning methods may bring too much invariance in some cases, and fine-grained information that is essential to some downstream tasks may be lost. This paper proposes a general method to alleviate these two problems by considering where and what to contrast in a general contrastive learning framework. We first propose to learn different augmentation invariances at different depths of the model according to the importance of each data augmentation instead of learning representational invariances evenly in the backbone. We then propose to expand the contrast content with augmentation embeddings to reduce the misleading effects of strong data augmentations. Experiments based on several baseline methods demonstrate that we learn better representations for various benchmarks on classification, detection, and segmentation downstream tasks.
0706.3132	Paulo Condado	Paulo A. Condado and Fernando G. Lobo	EasyVoice: Integrating voice synthesis with Skype	null	null	null	null	cs.CY cs.HC	null	This paper presents EasyVoice, a system that integrates voice synthesis with Skype. EasyVoice allows a person with voice disabilities to talk with another person located anywhere in the world, removing an important obstacle that affect these people during a phone or VoIP-based conversation.	[ { "created": "Thu, 21 Jun 2007 12:04:40 GMT", "version": "v1" } ]	2007-06-22	[ [ "Condado", "Paulo A.", "" ], [ "Lobo", "Fernando G.", "" ] ]	This paper presents EasyVoice, a system that integrates voice synthesis with Skype. EasyVoice allows a person with voice disabilities to talk with another person located anywhere in the world, removing an important obstacle that affect these people during a phone or VoIP-based conversation.
1512.03866	Fei Li	Qiuyan Wang, Fei Li and Dongdai Lin	A Class of Linear Codes With Three Weights	11 pages,2 tables	null	null	null	cs.IT math.IT	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Linear codes have been an interesting subject of study for many years. Recently, linear codes with few weights have been constructed and extensively studied. In this paper, for an odd prime p, a class of three-weight linear codes over Fp are constructed. The weight distributions of the linear codes are settled. These codes have applications in authentication codes, association schemes and data storage systems.	[ { "created": "Sat, 12 Dec 2015 02:42:24 GMT", "version": "v1" }, { "created": "Wed, 23 Dec 2015 03:32:36 GMT", "version": "v2" } ]	2015-12-24	[ [ "Wang", "Qiuyan", "" ], [ "Li", "Fei", "" ], [ "Lin", "Dongdai", "" ] ]	Linear codes have been an interesting subject of study for many years. Recently, linear codes with few weights have been constructed and extensively studied. In this paper, for an odd prime p, a class of three-weight linear codes over Fp are constructed. The weight distributions of the linear codes are settled. These codes have applications in authentication codes, association schemes and data storage systems.
2206.12011	Zeynep K	Zeynep K and Bobak Nazer	Detecting Correlated Gaussian Databases	26 pages, 4 figures	null	null	null	cs.IT math.IT math.ST stat.TH	http://creativecommons.org/licenses/by/4.0/	This paper considers the problem of detecting whether two databases, each consisting of $n$ users with $d$ Gaussian features, are correlated. Under the null hypothesis, the databases are independent. Under the alternate hypothesis, the features are correlated across databases, under an unknown row permutation. A simple test is developed to show that detection is achievable above $\rho^2 \approx \frac{1}{d}$. For the converse, the truncated second moment method is used to establish that detection is impossible below roughly $\rho^2 \approx \frac{1}{d\sqrt{n}}$. These results are compared to the corresponding recovery problem, where the goal is to decode the row permutation, and a converse bound of roughly $\rho^2 \approx 1 - n^{-4/d}$ has been previously shown. For certain choices of parameters, the detection achievability bound outperforms this recovery converse bound, demonstrating that detection can be easier than recovery in this scenario.	[ { "created": "Thu, 23 Jun 2022 23:08:24 GMT", "version": "v1" } ]	2022-06-27	[ [ "K", "Zeynep", "" ], [ "Nazer", "Bobak", "" ] ]	This paper considers the problem of detecting whether two databases, each consisting of $n$ users with $d$ Gaussian features, are correlated. Under the null hypothesis, the databases are independent. Under the alternate hypothesis, the features are correlated across databases, under an unknown row permutation. A simple test is developed to show that detection is achievable above $\rho^2 \approx \frac{1}{d}$. For the converse, the truncated second moment method is used to establish that detection is impossible below roughly $\rho^2 \approx \frac{1}{d\sqrt{n}}$. These results are compared to the corresponding recovery problem, where the goal is to decode the row permutation, and a converse bound of roughly $\rho^2 \approx 1 - n^{-4/d}$ has been previously shown. For certain choices of parameters, the detection achievability bound outperforms this recovery converse bound, demonstrating that detection can be easier than recovery in this scenario.
2208.05777	Shaina Raza Dr.	Shaina Raza, Deepak John Reji, Chen Ding	Dbias: Detecting biases and ensuring Fairness in news articles	Accepted for publication in International Journal of Data Science and Analytics	null	null	null	cs.IR	http://creativecommons.org/licenses/by/4.0/	Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature. This paper introduces Dbias (https://pypi.org/project/Dbias/), an open-source Python package for ensuring fairness in news articles. Dbias can take any text to determine if it is biased. Then, it detects biased words in the text, masks them, and suggests a set of sentences with new words that are bias-free or at least less biased. We conduct extensive experiments to assess the performance of Dbias. To see how well our approach works, we compare it to the existing fairness models. We also test the individual components of Dbias to see how effective they are. The experimental results show that Dbias outperforms all the baselines in terms of accuracy and fairness. We make this package (Dbias) as publicly available for the developers and practitioners to mitigate biases in textual data (such as news articles), as well as to encourage extension of this work.	[ { "created": "Thu, 11 Aug 2022 12:14:06 GMT", "version": "v1" } ]	2022-08-12	[ [ "Raza", "Shaina", "" ], [ "Reji", "Deepak John", "" ], [ "Ding", "Chen", "" ] ]	Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature. This paper introduces Dbias (https://pypi.org/project/Dbias/), an open-source Python package for ensuring fairness in news articles. Dbias can take any text to determine if it is biased. Then, it detects biased words in the text, masks them, and suggests a set of sentences with new words that are bias-free or at least less biased. We conduct extensive experiments to assess the performance of Dbias. To see how well our approach works, we compare it to the existing fairness models. We also test the individual components of Dbias to see how effective they are. The experimental results show that Dbias outperforms all the baselines in terms of accuracy and fairness. We make this package (Dbias) as publicly available for the developers and practitioners to mitigate biases in textual data (such as news articles), as well as to encourage extension of this work.
2104.00837	Pingchuan Ma	Pingchuan Ma, Tao Du, John Z. Zhang, Kui Wu, Andrew Spielberg, Robert K. Katzschmann, Wojciech Matusik	DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation	ACM SIGGRAPH 2021. Homepage: http://diffaqua.csail.mit.edu/	null	10.1145/3450626.3459832	null	cs.LG cs.GR cs.RO	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer's geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes via optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer's performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.	[ { "created": "Fri, 2 Apr 2021 01:18:15 GMT", "version": "v1" }, { "created": "Wed, 5 May 2021 18:58:41 GMT", "version": "v2" } ]	2021-05-07	[ [ "Ma", "Pingchuan", "" ], [ "Du", "Tao", "" ], [ "Zhang", "John Z.", "" ], [ "Wu", "Kui", "" ], [ "Spielberg", "Andrew", "" ], [ "Katzschmann", "Robert K.", "" ], [ "Matusik", "Wojciech", "" ] ]	The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer's geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes via optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer's performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.
2111.03144	Abhinav Agrawal	Abhinav Agrawal, Justin Domke	Amortized Variational Inference for Simple Hierarchical Models	Neural Information Processing Systems (NeurIPS) 2021	null	null	null	cs.LG stat.ML	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	It is difficult to use subsampling with variational inference in hierarchical models since the number of local latent variables scales with the dataset. Thus, inference in hierarchical models remains a challenge at large scale. It is helpful to use a variational family with structure matching the posterior, but optimization is still slow due to the huge number of local distributions. Instead, this paper suggests an amortized approach where shared parameters simultaneously represent all local distributions. This approach is similarly accurate as using a given joint distribution (e.g., a full-rank Gaussian) but is feasible on datasets that are several orders of magnitude larger. It is also dramatically faster than using a structured variational distribution.	[ { "created": "Thu, 4 Nov 2021 20:29:12 GMT", "version": "v1" } ]	2021-11-08	[ [ "Agrawal", "Abhinav", "" ], [ "Domke", "Justin", "" ] ]	It is difficult to use subsampling with variational inference in hierarchical models since the number of local latent variables scales with the dataset. Thus, inference in hierarchical models remains a challenge at large scale. It is helpful to use a variational family with structure matching the posterior, but optimization is still slow due to the huge number of local distributions. Instead, this paper suggests an amortized approach where shared parameters simultaneously represent all local distributions. This approach is similarly accurate as using a given joint distribution (e.g., a full-rank Gaussian) but is feasible on datasets that are several orders of magnitude larger. It is also dramatically faster than using a structured variational distribution.
1802.07779	Daniel DeFreez	Daniel DeFreez, Aditya V. Thakur, Cindy Rubio-Gonz\'alez	Path-Based Function Embedding and its Application to Specification Mining	11 pages, 8 figures	null	null	null	cs.SE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Identifying the relationships among program elements is useful for program understanding, debugging, and analysis. One such relationship is synonymy. Function synonyms are functions that play a similar role in code, e.g. functions that perform initialization for different device drivers, or functions that implement different symmetric-key encryption schemes. Function synonyms are not necessarily semantically equivalent and can be syntactically dissimilar; consequently, approaches for identifying code clones or functional equivalence cannot be used to identify them. This paper presents func2vec, an algorithm that maps each function to a vector in a vector space such that function synonyms are grouped together. We compute the function embedding by training a neural network on sentences generated from random walks over an encoding of the program as a labeled pushdown system (l-PDS). We demonstrate that func2vec is effective at identifying function synonyms in the Linux kernel. Furthermore, we show how function synonyms enable mining error-handling specifications with high support in Linux file systems and drivers.	[ { "created": "Wed, 21 Feb 2018 20:02:52 GMT", "version": "v1" }, { "created": "Sun, 25 Feb 2018 04:22:50 GMT", "version": "v2" } ]	2018-02-27	[ [ "DeFreez", "Daniel", "" ], [ "Thakur", "Aditya V.", "" ], [ "Rubio-González", "Cindy", "" ] ]	Identifying the relationships among program elements is useful for program understanding, debugging, and analysis. One such relationship is synonymy. Function synonyms are functions that play a similar role in code, e.g. functions that perform initialization for different device drivers, or functions that implement different symmetric-key encryption schemes. Function synonyms are not necessarily semantically equivalent and can be syntactically dissimilar; consequently, approaches for identifying code clones or functional equivalence cannot be used to identify them. This paper presents func2vec, an algorithm that maps each function to a vector in a vector space such that function synonyms are grouped together. We compute the function embedding by training a neural network on sentences generated from random walks over an encoding of the program as a labeled pushdown system (l-PDS). We demonstrate that func2vec is effective at identifying function synonyms in the Linux kernel. Furthermore, we show how function synonyms enable mining error-handling specifications with high support in Linux file systems and drivers.
1404.3920	Aske Plaat	Catholijn Jonker, Joost Broekens, Aske Plaat	Virtual Reflexes	null	null	null	null	cs.CY cs.HC	http://creativecommons.org/licenses/by/3.0/	Virtual Reality is used successfully to treat people for regular phobias. A new challenge is to develop Virtual Reality Exposure Training for social skills. Virtual actors in such systems have to show appropriate social behavior including emotions, gaze, and keeping distance. The behavior must be realistic and real-time. Current approaches consist of four steps: 1) trainee social signal detection, 2) cognitive-affective interpretation, 3) determination of the appropriate bodily responses, and 4) actuation. The "cognitive" detour of such approaches does not match the directness of human bodily reflexes and causes unrealistic responses and delay. Instead, we propose virtual reflexes as concurrent sensory-motor processes to control virtual actors. Here we present a virtual reflexes architecture, explain how emotion and cognitive modulation are embedded, detail its workings, and give an example description of an aggression training application.	[ { "created": "Mon, 14 Apr 2014 14:07:09 GMT", "version": "v1" } ]	2014-04-16	[ [ "Jonker", "Catholijn", "" ], [ "Broekens", "Joost", "" ], [ "Plaat", "Aske", "" ] ]	Virtual Reality is used successfully to treat people for regular phobias. A new challenge is to develop Virtual Reality Exposure Training for social skills. Virtual actors in such systems have to show appropriate social behavior including emotions, gaze, and keeping distance. The behavior must be realistic and real-time. Current approaches consist of four steps: 1) trainee social signal detection, 2) cognitive-affective interpretation, 3) determination of the appropriate bodily responses, and 4) actuation. The "cognitive" detour of such approaches does not match the directness of human bodily reflexes and causes unrealistic responses and delay. Instead, we propose virtual reflexes as concurrent sensory-motor processes to control virtual actors. Here we present a virtual reflexes architecture, explain how emotion and cognitive modulation are embedded, detail its workings, and give an example description of an aggression training application.
1908.07018	Ayush Maheshwari	Ayush Maheshwari, Hrishikesh Patel, Nandan Rathod, Ritesh Kumar, Ganesh Ramakrishnan and Pushpak Bhattacharyya	Tale of tails using rule augmented sequence labeling for event extraction	9 pages, 4 figures, 6 tables	StarAI Workshop at AAAI 2020	null	null	cs.IR cs.CL cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data. Moreover, the task becomes complex for tail (rarely occurring) labels wherein extremely less data is available. In this paper, we present a new dataset (InDEE-2019) in the disaster domain for multiple Indic languages, collected from news websites. Using this dataset, we evaluate several rule-based mechanisms to augment deep learning based models. We formulate our problem of event extraction as a sequence labeling task and perform extensive experiments to study and understand the effectiveness of different approaches. We further show that tail labels can be easily incorporated by creating new rules without the requirement of large annotated data.	[ { "created": "Mon, 19 Aug 2019 18:43:06 GMT", "version": "v1" }, { "created": "Thu, 22 Aug 2019 06:10:02 GMT", "version": "v2" }, { "created": "Fri, 31 Jan 2020 07:36:09 GMT", "version": "v3" } ]	2020-11-23	[ [ "Maheshwari", "Ayush", "" ], [ "Patel", "Hrishikesh", "" ], [ "Rathod", "Nandan", "" ], [ "Kumar", "Ritesh", "" ], [ "Ramakrishnan", "Ganesh", "" ], [ "Bhattacharyya", "Pushpak", "" ] ]	The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data. Moreover, the task becomes complex for tail (rarely occurring) labels wherein extremely less data is available. In this paper, we present a new dataset (InDEE-2019) in the disaster domain for multiple Indic languages, collected from news websites. Using this dataset, we evaluate several rule-based mechanisms to augment deep learning based models. We formulate our problem of event extraction as a sequence labeling task and perform extensive experiments to study and understand the effectiveness of different approaches. We further show that tail labels can be easily incorporated by creating new rules without the requirement of large annotated data.
1204.0535	S Muthukrishnan	Yishay Mansour, S. Muthukrishnan and Noam Nisan	Doubleclick Ad Exchange Auction	null	null	null	null	cs.GT	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Display advertisements on the web are sold via ad exchanges that use real time auction. We describe the challenges of designing a suitable auction, and present a simple auction called the Optional Second Price (OSP) auction that is currently used in Doubleclick Ad Exchange.	[ { "created": "Mon, 2 Apr 2012 20:56:53 GMT", "version": "v1" } ]	2012-04-04	[ [ "Mansour", "Yishay", "" ], [ "Muthukrishnan", "S.", "" ], [ "Nisan", "Noam", "" ] ]	Display advertisements on the web are sold via ad exchanges that use real time auction. We describe the challenges of designing a suitable auction, and present a simple auction called the Optional Second Price (OSP) auction that is currently used in Doubleclick Ad Exchange.
1410.4011	EPTCS	Amir M. Ben-Amram, Aviad Pineles	Flowchart Programs, Regular Expressions, and Decidability of Polynomial Growth-Rate	In Proceedings VPT 2016, arXiv:1607.01835	EPTCS 216, 2016, pp. 24-49	10.4204/EPTCS.216.2	null	cs.PL cs.FL cs.LO	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We present a new method for inferring complexity properties for a class of programs in the form of flowcharts annotated with loop information. Specifically, our method can (soundly and completely) decide if computed values are polynomially bounded as a function of the input; and similarly for the running time. Such complexity properties are undecidable for a Turing-complete programming language, and a common work-around in program analysis is to settle for sound but incomplete solutions. In contrast, we consider a class of programs that is Turing-incomplete, but strong enough to include several challenges for this kind of analysis. For a related language that has well-structured syntax, similar to Meyer and Ritchie's LOOP programs, the problem has been previously proved to be decidable. The analysis relied on the compositionality of programs, hence the challenge in obtaining similar results for flowchart programs with arbitrary control-flow graphs. Our answer to the challenge is twofold: first, we propose a class of loop-annotated flowcharts, which is more general than the class of flowcharts that directly represent structured programs; secondly, we present a technique to reuse the ideas from the work on tructured programs and apply them to such flowcharts. The technique is inspired by the classic translation of non-deterministic automata to regular expressions, but we obviate the exponential cost of constructing such an expression, obtaining a polynomial-time analysis. These ideas may well be applicable to other analysis problems.	[ { "created": "Wed, 15 Oct 2014 11:16:16 GMT", "version": "v1" }, { "created": "Sun, 13 Mar 2016 14:40:47 GMT", "version": "v2" }, { "created": "Sun, 20 Mar 2016 10:01:27 GMT", "version": "v3" }, { "created": "Wed, 1 Jun 2016 16:30:18 GMT", "version": "v4" }, { "created": "Fri, 8 Jul 2016 05:30:33 GMT", "version": "v5" } ]	2016-07-11	[ [ "Ben-Amram", "Amir M.", "" ], [ "Pineles", "Aviad", "" ] ]	We present a new method for inferring complexity properties for a class of programs in the form of flowcharts annotated with loop information. Specifically, our method can (soundly and completely) decide if computed values are polynomially bounded as a function of the input; and similarly for the running time. Such complexity properties are undecidable for a Turing-complete programming language, and a common work-around in program analysis is to settle for sound but incomplete solutions. In contrast, we consider a class of programs that is Turing-incomplete, but strong enough to include several challenges for this kind of analysis. For a related language that has well-structured syntax, similar to Meyer and Ritchie's LOOP programs, the problem has been previously proved to be decidable. The analysis relied on the compositionality of programs, hence the challenge in obtaining similar results for flowchart programs with arbitrary control-flow graphs. Our answer to the challenge is twofold: first, we propose a class of loop-annotated flowcharts, which is more general than the class of flowcharts that directly represent structured programs; secondly, we present a technique to reuse the ideas from the work on tructured programs and apply them to such flowcharts. The technique is inspired by the classic translation of non-deterministic automata to regular expressions, but we obviate the exponential cost of constructing such an expression, obtaining a polynomial-time analysis. These ideas may well be applicable to other analysis problems.
2312.01656	Yilin Ye	Yilin Ye, Qian Zhu, Shishi Xiao, Kang Zhang, Wei Zeng	The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model	Accepted by The 2024 ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) (Proc. CSCW 2024)	null	null	null	cs.IR cs.AI cs.CV cs.HC	http://creativecommons.org/licenses/by/4.0/	Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.	[ { "created": "Mon, 4 Dec 2023 06:14:25 GMT", "version": "v1" }, { "created": "Tue, 5 Dec 2023 02:24:38 GMT", "version": "v2" } ]	2023-12-06	[ [ "Ye", "Yilin", "" ], [ "Zhu", "Qian", "" ], [ "Xiao", "Shishi", "" ], [ "Zhang", "Kang", "" ], [ "Zeng", "Wei", "" ] ]	Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.
2302.05743	Zian Li	Zian Li, Xiyuan Wang, Yinan Huang, Muhan Zhang	Is Distance Matrix Enough for Geometric Deep Learning?	To be published in NeurIPS2023	null	null	null	cs.LG cs.AI	http://creativecommons.org/licenses/by/4.0/	Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry of a given graph, such as molecular dynamics simulation. While incorporating Euclidean distance into Message Passing Neural Networks (referred to as Vanilla DisGNN) is a straightforward way to learn the geometry, it has been demonstrated that Vanilla DisGNN is geometrically incomplete. In this work, we first construct families of novel and symmetric geometric graphs that Vanilla DisGNN cannot distinguish even when considering all-pair distances, which greatly expands the existing counterexample families. Our counterexamples show the inherent limitation of Vanilla DisGNN to capture symmetric geometric structures. We then propose $k$-DisGNNs, which can effectively exploit the rich geometry contained in the distance matrix. We demonstrate the high expressive power of $k$-DisGNNs from three perspectives: 1. They can learn high-order geometric information that cannot be captured by Vanilla DisGNN. 2. They can unify some existing well-designed geometric models. 3. They are universal function approximators from geometric graphs to scalars (when $k\geq 2$) and vectors (when $k\geq 3$). Most importantly, we establish a connection between geometric deep learning (GDL) and traditional graph representation learning (GRL), showing that those highly expressive GNN models originally designed for GRL can also be applied to GDL with impressive performance, and that existing complicated, equivariant models are not the only solution. Experiments verify our theory. Our $k$-DisGNNs achieve many new state-of-the-art results on MD17.	[ { "created": "Sat, 11 Feb 2023 16:54:20 GMT", "version": "v1" }, { "created": "Mon, 3 Apr 2023 15:31:56 GMT", "version": "v2" }, { "created": "Fri, 14 Apr 2023 14:37:06 GMT", "version": "v3" }, { "created": "Fri, 2 Jun 2023 08:12:40 GMT", "version": "v4" }, { "created": "Tue, 31 Oct 2023 03:07:53 GMT", "version": "v5" } ]	2023-11-01	[ [ "Li", "Zian", "" ], [ "Wang", "Xiyuan", "" ], [ "Huang", "Yinan", "" ], [ "Zhang", "Muhan", "" ] ]	Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry of a given graph, such as molecular dynamics simulation. While incorporating Euclidean distance into Message Passing Neural Networks (referred to as Vanilla DisGNN) is a straightforward way to learn the geometry, it has been demonstrated that Vanilla DisGNN is geometrically incomplete. In this work, we first construct families of novel and symmetric geometric graphs that Vanilla DisGNN cannot distinguish even when considering all-pair distances, which greatly expands the existing counterexample families. Our counterexamples show the inherent limitation of Vanilla DisGNN to capture symmetric geometric structures. We then propose $k$-DisGNNs, which can effectively exploit the rich geometry contained in the distance matrix. We demonstrate the high expressive power of $k$-DisGNNs from three perspectives: 1. They can learn high-order geometric information that cannot be captured by Vanilla DisGNN. 2. They can unify some existing well-designed geometric models. 3. They are universal function approximators from geometric graphs to scalars (when $k\geq 2$) and vectors (when $k\geq 3$). Most importantly, we establish a connection between geometric deep learning (GDL) and traditional graph representation learning (GRL), showing that those highly expressive GNN models originally designed for GRL can also be applied to GDL with impressive performance, and that existing complicated, equivariant models are not the only solution. Experiments verify our theory. Our $k$-DisGNNs achieve many new state-of-the-art results on MD17.
1803.02123	Per Skarin	Per Skarin, William T\"arneberg, Karl-Erik {\AA}rzen, Maria Kihl	Towards Mission-Critical Control at the Edge and Over 5G	June 18th: Upload the final version as submitted to IEEE Services [EDGE] 2018 on May 16th (updated abstract and some wording, results unchanged)	null	null	null	cs.SY cs.DC	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	With the emergence of industrial IoT and cloud computing, and the advent of 5G and edge clouds, there are ambitious expectations on elasticity, economies of scale, and fast time to market for demanding use cases in the next generation of ICT networks. Responsiveness and reliability of wireless communication links and services in the cloud are set to improve significantly as the concept of edge clouds is becoming more prevalent. To enable industrial uptake we must provide cloud capacity in the networks but also a sufficient level of simplicity and self-sustainability in the software platforms. In this paper, we present a research test-bed built to study mission-critical control over the distributed edge cloud. We evaluate system properties using a conventional control application in the form of a Model Predictive Controller. Our cloud platform provides the means to continuously operate our mission-critical application while seamlessly relocating computations across geographically dispersed compute nodes. Through our use of 5G wireless radio, we allow for mobility and reliably provide compute resources with low latency, at the edge. The primary contribution of this paper is a state-of-the art, fully operational test-bed showing the potential for merged IoT, 5G, and cloud. We also provide an evaluation of the system while operating a mission-critical application and provide an outlook on a novel research direction.	[ { "created": "Tue, 6 Mar 2018 11:31:59 GMT", "version": "v1" }, { "created": "Mon, 18 Jun 2018 08:25:56 GMT", "version": "v2" } ]	2018-06-19	[ [ "Skarin", "Per", "" ], [ "Tärneberg", "William", "" ], [ "Årzen", "Karl-Erik", "" ], [ "Kihl", "Maria", "" ] ]	With the emergence of industrial IoT and cloud computing, and the advent of 5G and edge clouds, there are ambitious expectations on elasticity, economies of scale, and fast time to market for demanding use cases in the next generation of ICT networks. Responsiveness and reliability of wireless communication links and services in the cloud are set to improve significantly as the concept of edge clouds is becoming more prevalent. To enable industrial uptake we must provide cloud capacity in the networks but also a sufficient level of simplicity and self-sustainability in the software platforms. In this paper, we present a research test-bed built to study mission-critical control over the distributed edge cloud. We evaluate system properties using a conventional control application in the form of a Model Predictive Controller. Our cloud platform provides the means to continuously operate our mission-critical application while seamlessly relocating computations across geographically dispersed compute nodes. Through our use of 5G wireless radio, we allow for mobility and reliably provide compute resources with low latency, at the edge. The primary contribution of this paper is a state-of-the art, fully operational test-bed showing the potential for merged IoT, 5G, and cloud. We also provide an evaluation of the system while operating a mission-critical application and provide an outlook on a novel research direction.
2205.14198	Marina Knittel	Marina Knittel, Max Springer, John P. Dickerson, MohammadTaghi Hajiaghayi	Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost	null	null	null	null	cs.LG cs.DS	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering, after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation. Our work vastly improves the previous $O(n^{5/6}poly\log(n))$ fair approximation for cost to a near polylogarithmic $O(n^\delta poly\log(n))$ fair approximation for any constant $\delta\in(0,1)$. This result establishes a cost-fairness tradeoff and extends to broader fairness constraints than the previous work. We also show how to alter existing hierarchical clusterings to guarantee fairness and cluster balance across any level in the hierarchy.	[ { "created": "Fri, 27 May 2022 19:04:00 GMT", "version": "v1" }, { "created": "Tue, 9 May 2023 21:59:01 GMT", "version": "v2" } ]	2023-05-11	[ [ "Knittel", "Marina", "" ], [ "Springer", "Max", "" ], [ "Dickerson", "John P.", "" ], [ "Hajiaghayi", "MohammadTaghi", "" ] ]	Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering, after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation. Our work vastly improves the previous $O(n^{5/6}poly\log(n))$ fair approximation for cost to a near polylogarithmic $O(n^\delta poly\log(n))$ fair approximation for any constant $\delta\in(0,1)$. This result establishes a cost-fairness tradeoff and extends to broader fairness constraints than the previous work. We also show how to alter existing hierarchical clusterings to guarantee fairness and cluster balance across any level in the hierarchy.
2307.03003	Johannes Jakubik	Johannes Jakubik, Daniel Weber, Patrick Hemmer, Michael V\"ossing, Gerhard Satzger	Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts	Accepted at International Conference on Wirtschaftsinformatik, 2023	null	null	null	cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification.	[ { "created": "Thu, 6 Jul 2023 14:06:23 GMT", "version": "v1" }, { "created": "Fri, 7 Jul 2023 06:39:38 GMT", "version": "v2" } ]	2023-07-10	[ [ "Jakubik", "Johannes", "" ], [ "Weber", "Daniel", "" ], [ "Hemmer", "Patrick", "" ], [ "Vössing", "Michael", "" ], [ "Satzger", "Gerhard", "" ] ]	Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification.
1411.6749	Tie (Tony) Luo	Tie Luo and Mehul Motani and Vikram Srinivasan	Analyzing DISH for Multi-Channel MAC Protocols in Wireless Networks	Multi-channel multi-hop networks, availability of cooperation, cooperative protocol, distributed information sharing, ACM MobiHoc, May 2008	null	null	null	cs.NI cs.PF	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	For long, node cooperation has been exploited as a data relaying mechanism. However, the wireless channel allows for much richer interaction between nodes. One such scenario is in a multi-channel environment, where transmitter-receiver pairs may make incorrect decisions (e.g., in selecting channels) but idle neighbors could help by sharing information to prevent undesirable consequences (e.g., data collisions). This represents a Distributed Information SHaring (DISH) mechanism for cooperation and suggests new ways of designing cooperative protocols. However, what is lacking is a theoretical understanding of this new notion of cooperation. In this paper, we view cooperation as a network resource and evaluate the availability of cooperation via a metric, $p_{co}$, the probability of obtaining cooperation. First, we analytically evaluate $p_{co}$ in the context of multi-channel multi-hop wireless networks. Second, we verify our analysis via simulations and the results show that our analysis accurately characterizes the behavior of $p_{co}$ as a function of underlying network parameters. This step also yields important insights into DISH with respect to network dynamics. Third, we investigate the correlation between $p_{co}$ and network performance in terms of collision rate, packet delay, and throughput. The results indicate a near-linear relationship, which may significantly simplify performance analysis for cooperative networks and suggests that $p_{co}$ be used as an appropriate performance indicator itself. Throughout this work, we utilize, as appropriate, three different DISH contexts --- model-based DISH, ideal DISH, and real DISH --- to explore $p_{co}$.	[ { "created": "Tue, 25 Nov 2014 06:58:51 GMT", "version": "v1" } ]	2014-11-26	[ [ "Luo", "Tie", "" ], [ "Motani", "Mehul", "" ], [ "Srinivasan", "Vikram", "" ] ]	For long, node cooperation has been exploited as a data relaying mechanism. However, the wireless channel allows for much richer interaction between nodes. One such scenario is in a multi-channel environment, where transmitter-receiver pairs may make incorrect decisions (e.g., in selecting channels) but idle neighbors could help by sharing information to prevent undesirable consequences (e.g., data collisions). This represents a Distributed Information SHaring (DISH) mechanism for cooperation and suggests new ways of designing cooperative protocols. However, what is lacking is a theoretical understanding of this new notion of cooperation. In this paper, we view cooperation as a network resource and evaluate the availability of cooperation via a metric, $p_{co}$, the probability of obtaining cooperation. First, we analytically evaluate $p_{co}$ in the context of multi-channel multi-hop wireless networks. Second, we verify our analysis via simulations and the results show that our analysis accurately characterizes the behavior of $p_{co}$ as a function of underlying network parameters. This step also yields important insights into DISH with respect to network dynamics. Third, we investigate the correlation between $p_{co}$ and network performance in terms of collision rate, packet delay, and throughput. The results indicate a near-linear relationship, which may significantly simplify performance analysis for cooperative networks and suggests that $p_{co}$ be used as an appropriate performance indicator itself. Throughout this work, we utilize, as appropriate, three different DISH contexts --- model-based DISH, ideal DISH, and real DISH --- to explore $p_{co}$.
1803.01166	Seonwook Park	Seonwook Park and Christoph Gebhardt and Roman R\"adle and Anna Feit and Hana Vrzakova and Niraj Dayama and Hui-Shyong Yeo and Clemens Klokmose and Aaron Quigley and Antti Oulasvirta and Otmar Hilliges	AdaM: Adapting Multi-User Interfaces for Collaborative Environments in Real-Time	formatting tweaks	null	10.1145/3173574.3173758	null	cs.HC	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Developing cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows real-time applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study.	[ { "created": "Sat, 3 Mar 2018 14:05:07 GMT", "version": "v1" }, { "created": "Thu, 29 Mar 2018 11:22:23 GMT", "version": "v2" } ]	2018-03-30	[ [ "Park", "Seonwook", "" ], [ "Gebhardt", "Christoph", "" ], [ "Rädle", "Roman", "" ], [ "Feit", "Anna", "" ], [ "Vrzakova", "Hana", "" ], [ "Dayama", "Niraj", "" ], [ "Yeo", "Hui-Shyong", "" ], [ "Klokmose", "Clemens", "" ], [ "Quigley", "Aaron", "" ], [ "Oulasvirta", "Antti", "" ], [ "Hilliges", "Otmar", "" ] ]	Developing cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows real-time applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study.
2312.03357	Doriand Petit	Doriand Petit, Steve Bourgeois, Dumitru Pavel, Vincent Gay-Bellile, Florian Chabot and Loic Barthe	RING-NeRF : Rethinking Inductive Biases for Versatile and Efficient Neural Fields	This publication has been accepted at ECCV'24	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models. Rather than developing hard-to-combine and specific modules, another approach generally overlooked is to directly inject generic priors on the scene representation (also called inductive biases) into the NeRF architecture. Based on this idea, we propose the RING-NeRF architecture which includes two inductive biases : a continuous multi-scale representation of the scene and an invariance of the decoder's latent space over spatial and scale domains. We also design a single reconstruction process that takes advantage of those inductive biases and experimentally demonstrates on-par performances in terms of quality with dedicated architecture on multiple tasks (anti-aliasing, few view reconstruction, SDF reconstruction without scene-specific initialization) while being more efficient. Moreover, RING-NeRF has the distinctive ability to dynamically increase the resolution of the model, opening the way to adaptive reconstruction.	[ { "created": "Wed, 6 Dec 2023 08:54:04 GMT", "version": "v1" }, { "created": "Thu, 14 Mar 2024 13:58:06 GMT", "version": "v2" }, { "created": "Wed, 17 Jul 2024 07:47:30 GMT", "version": "v3" } ]	2024-07-18	[ [ "Petit", "Doriand", "" ], [ "Bourgeois", "Steve", "" ], [ "Pavel", "Dumitru", "" ], [ "Gay-Bellile", "Vincent", "" ], [ "Chabot", "Florian", "" ], [ "Barthe", "Loic", "" ] ]	Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models. Rather than developing hard-to-combine and specific modules, another approach generally overlooked is to directly inject generic priors on the scene representation (also called inductive biases) into the NeRF architecture. Based on this idea, we propose the RING-NeRF architecture which includes two inductive biases : a continuous multi-scale representation of the scene and an invariance of the decoder's latent space over spatial and scale domains. We also design a single reconstruction process that takes advantage of those inductive biases and experimentally demonstrates on-par performances in terms of quality with dedicated architecture on multiple tasks (anti-aliasing, few view reconstruction, SDF reconstruction without scene-specific initialization) while being more efficient. Moreover, RING-NeRF has the distinctive ability to dynamically increase the resolution of the model, opening the way to adaptive reconstruction.
2402.02399	Hao Wang	Hao Wang, Licheng Pan, Zhichao Chen, Degui Yang, Sen Zhang, Yifei Yang, Xinggao Liu, Haoxuan Li, Dacheng Tao	FreDF: Learning to Forecast in Frequency Domain	null	null	null	null	cs.LG cs.AI stat.AP stat.ML	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. Current research predominantly focuses on handling autocorrelation within the historical sequence but often neglects its presence in the label sequence. Specifically, emerging forecast models mainly conform to the direct forecast (DF) paradigm, generating multi-step forecasts under the assumption of conditional independence within the label sequence. This assumption disregards the inherent autocorrelation in the label sequence, thereby limiting the performance of DF-based models. In response to this gap, we introduce the Frequency-enhanced Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain. Our experiments demonstrate that FreDF substantially outperforms existing state-of-the-art methods including iTransformer and is compatible with a variety of forecast models.	[ { "created": "Sun, 4 Feb 2024 08:23:41 GMT", "version": "v1" } ]	2024-02-06	[ [ "Wang", "Hao", "" ], [ "Pan", "Licheng", "" ], [ "Chen", "Zhichao", "" ], [ "Yang", "Degui", "" ], [ "Zhang", "Sen", "" ], [ "Yang", "Yifei", "" ], [ "Liu", "Xinggao", "" ], [ "Li", "Haoxuan", "" ], [ "Tao", "Dacheng", "" ] ]	Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. Current research predominantly focuses on handling autocorrelation within the historical sequence but often neglects its presence in the label sequence. Specifically, emerging forecast models mainly conform to the direct forecast (DF) paradigm, generating multi-step forecasts under the assumption of conditional independence within the label sequence. This assumption disregards the inherent autocorrelation in the label sequence, thereby limiting the performance of DF-based models. In response to this gap, we introduce the Frequency-enhanced Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain. Our experiments demonstrate that FreDF substantially outperforms existing state-of-the-art methods including iTransformer and is compatible with a variety of forecast models.
2311.15260	Adam Tonderski	Adam Tonderski, Carl Lindstr\"om, Georg Hess, William Ljungbergh, Lennart Svensson, Christoffer Petersson	NeuRAD: Neural Rendering for Autonomous Driving	null	null	null	null	cs.CV	http://creativecommons.org/licenses/by-nc-sa/4.0/	Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training data augmentation technique. However, existing methods often require long training times, dense semantic supervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both camera and lidar -- including rolling shutter, beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets, achieving state-of-the-art performance across the board. To encourage further development, we will openly release the NeuRAD source code. See https://github.com/georghess/NeuRAD .	[ { "created": "Sun, 26 Nov 2023 10:27:22 GMT", "version": "v1" }, { "created": "Tue, 5 Dec 2023 09:53:18 GMT", "version": "v2" }, { "created": "Thu, 18 Apr 2024 12:44:56 GMT", "version": "v3" } ]	2024-04-19	[ [ "Tonderski", "Adam", "" ], [ "Lindström", "Carl", "" ], [ "Hess", "Georg", "" ], [ "Ljungbergh", "William", "" ], [ "Svensson", "Lennart", "" ], [ "Petersson", "Christoffer", "" ] ]	Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training data augmentation technique. However, existing methods often require long training times, dense semantic supervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both camera and lidar -- including rolling shutter, beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets, achieving state-of-the-art performance across the board. To encourage further development, we will openly release the NeuRAD source code. See https://github.com/georghess/NeuRAD .
1207.2847	Kai Liu	Kai Liu, Hock Beng Lim	Positioning Accuracy Improvement via Distributed Location Estimate in Cooperative Vehicular Networks	To appear in Proc. of the 15th International IEEE Conference on Intelligent Transportation Systems (IEEE ITSC'12)	null	10.1109/ITSC.2012.6338743	null	cs.DC cs.DS	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The development of cooperative vehicle safety (CVS) applications, such as collision warnings, turning assistants, and speed advisories, etc., has received great attention in the past few years. Accurate vehicular localization is essential to enable these applications. In this study, motivated by the proliferation of the Global Positioning System (GPS) devices, and the increasing sophistication of wireless communication technologies in vehicular networks, we propose a distributed location estimate algorithm to improve the positioning accuracy via cooperative inter-vehicle distance measurement. In particular, we compute the inter-vehicle distance based on raw GPS pseudorange measurements, instead of depending on traditional radio-based ranging techniques, which usually either suffer from high hardware cost or have inadequate positioning accuracy. In addition, we improve the estimation of the vehicles' locations only based on the inaccurate GPS fixes, without using any anchors with known exact locations. The algorithm is decentralized, which enhances its practicability in highly dynamic vehicular networks. We have developed a simulation model to evaluate the performance of the proposed algorithm, and the results demonstrate that the algorithm can significantly improve the positioning accuracy.	[ { "created": "Thu, 12 Jul 2012 05:27:16 GMT", "version": "v1" }, { "created": "Sat, 14 Jul 2012 06:32:37 GMT", "version": "v2" }, { "created": "Fri, 20 Jul 2012 08:33:23 GMT", "version": "v3" } ]	2016-11-15	[ [ "Liu", "Kai", "" ], [ "Lim", "Hock Beng", "" ] ]	The development of cooperative vehicle safety (CVS) applications, such as collision warnings, turning assistants, and speed advisories, etc., has received great attention in the past few years. Accurate vehicular localization is essential to enable these applications. In this study, motivated by the proliferation of the Global Positioning System (GPS) devices, and the increasing sophistication of wireless communication technologies in vehicular networks, we propose a distributed location estimate algorithm to improve the positioning accuracy via cooperative inter-vehicle distance measurement. In particular, we compute the inter-vehicle distance based on raw GPS pseudorange measurements, instead of depending on traditional radio-based ranging techniques, which usually either suffer from high hardware cost or have inadequate positioning accuracy. In addition, we improve the estimation of the vehicles' locations only based on the inaccurate GPS fixes, without using any anchors with known exact locations. The algorithm is decentralized, which enhances its practicability in highly dynamic vehicular networks. We have developed a simulation model to evaluate the performance of the proposed algorithm, and the results demonstrate that the algorithm can significantly improve the positioning accuracy.
0802.0820	Jonathan Hayman	Jonathan Hayman and Glynn Winskel	Independence and concurrent separation logic	null	Logical Methods in Computer Science, Volume 4, Issue 1 (March 19, 2008) lmcs:1100	10.2168/LMCS-4(1:6)2008	null	cs.LO cs.PL	null	A compositional Petri net-based semantics is given to a simple language allowing pointer manipulation and parallelism. The model is then applied to give a notion of validity to the judgements made by concurrent separation logic that emphasizes the process-environment duality inherent in such rely-guarantee reasoning. Soundness of the rules of concurrent separation logic with respect to this definition of validity is shown. The independence information retained by the Petri net model is then exploited to characterize the independence of parallel processes enforced by the logic. This is shown to permit a refinement operation capable of changing the granularity of atomic actions.	[ { "created": "Wed, 6 Feb 2008 15:39:20 GMT", "version": "v1" }, { "created": "Wed, 19 Mar 2008 15:26:51 GMT", "version": "v2" } ]	2015-07-01	[ [ "Hayman", "Jonathan", "" ], [ "Winskel", "Glynn", "" ] ]	A compositional Petri net-based semantics is given to a simple language allowing pointer manipulation and parallelism. The model is then applied to give a notion of validity to the judgements made by concurrent separation logic that emphasizes the process-environment duality inherent in such rely-guarantee reasoning. Soundness of the rules of concurrent separation logic with respect to this definition of validity is shown. The independence information retained by the Petri net model is then exploited to characterize the independence of parallel processes enforced by the logic. This is shown to permit a refinement operation capable of changing the granularity of atomic actions.
2101.00318	Xiaofeng Liu	Xiaofeng Liu, Xiongchang Liu, Bo Hu, Wenxuan Ji, Fangxu Xing, Jun Lu, Jane You, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo	Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis	Accepted to AAAI 2021	null	null	null	cs.CV cs.AI cs.LG	http://creativecommons.org/licenses/by/4.0/	Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases of with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on medical diagnosis tasks.	[ { "created": "Fri, 1 Jan 2021 21:04:50 GMT", "version": "v1" }, { "created": "Mon, 11 Jan 2021 15:09:03 GMT", "version": "v2" } ]	2021-01-12	[ [ "Liu", "Xiaofeng", "" ], [ "Liu", "Xiongchang", "" ], [ "Hu", "Bo", "" ], [ "Ji", "Wenxuan", "" ], [ "Xing", "Fangxu", "" ], [ "Lu", "Jun", "" ], [ "You", "Jane", "" ], [ "Kuo", "C. -C. Jay", "" ], [ "Fakhri", "Georges El", "" ], [ "Woo", "Jonghye", "" ] ]	Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases of with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on medical diagnosis tasks.
1104.4668	Massimiliano Vasile Massimiliano Vasile	Matteo Ceriotti and Massimiliano Vasile	MGA trajectory planning with an ACO-inspired algorithm	null	Acta Astronautica, 67 (9-10). pp. 1202-1217, ISSN 0094-5765, 2010	null	null	cs.CE cs.NE cs.SY math.OC	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem. Its automated solution would greatly improve the design of future space missions, allowing the assessment of a large number of alternative mission options in a short time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the optimal planetary sequence and a good estimation of the set of associated optimal trajectories. The trajectory model consists of a sequence of celestial bodies connected by twodimensional transfer arcs containing one DSM. For each transfer arc, the position of the planet and the spacecraft, at the time of arrival, are matched by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by Ant Colony Optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select a feasible direction. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter.	[ { "created": "Mon, 25 Apr 2011 00:58:35 GMT", "version": "v1" } ]	2011-04-26	[ [ "Ceriotti", "Matteo", "" ], [ "Vasile", "Massimiliano", "" ] ]	Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem. Its automated solution would greatly improve the design of future space missions, allowing the assessment of a large number of alternative mission options in a short time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the optimal planetary sequence and a good estimation of the set of associated optimal trajectories. The trajectory model consists of a sequence of celestial bodies connected by twodimensional transfer arcs containing one DSM. For each transfer arc, the position of the planet and the spacecraft, at the time of arrival, are matched by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by Ant Colony Optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select a feasible direction. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter.
2310.02264	Mingyu Ding	Haoyu Zhou, Mingyu Ding, Weikun Peng, Masayoshi Tomizuka, Lin Shao, Chuang Gan	Generalizable Long-Horizon Manipulations with Large Language Models	null	null	null	null	cs.RO cs.CL cs.CV cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks. These task conditions serve as guides for the generation and adjustment of Dynamic Movement Primitives (DMP) trajectories for long-horizon task execution. We further create a challenging robotic manipulation task suite based on Pybullet for long-horizon task evaluation. Extensive experiments in both simulated and real-world environments demonstrate the effectiveness of our framework on both familiar tasks involving new objects and novel but related tasks, highlighting the potential of LLMs in enhancing robotic system versatility and adaptability. Project website: https://object814.github.io/Task-Condition-With-LLM/	[ { "created": "Tue, 3 Oct 2023 17:59:46 GMT", "version": "v1" } ]	2023-10-04	[ [ "Zhou", "Haoyu", "" ], [ "Ding", "Mingyu", "" ], [ "Peng", "Weikun", "" ], [ "Tomizuka", "Masayoshi", "" ], [ "Shao", "Lin", "" ], [ "Gan", "Chuang", "" ] ]	This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks. These task conditions serve as guides for the generation and adjustment of Dynamic Movement Primitives (DMP) trajectories for long-horizon task execution. We further create a challenging robotic manipulation task suite based on Pybullet for long-horizon task evaluation. Extensive experiments in both simulated and real-world environments demonstrate the effectiveness of our framework on both familiar tasks involving new objects and novel but related tasks, highlighting the potential of LLMs in enhancing robotic system versatility and adaptability. Project website: https://object814.github.io/Task-Condition-With-LLM/
1706.06239	Hao Wang	Hao Wang, Yanmei Fu, Qinyong Wang, Hongzhi Yin, Changying Du, Hui Xiong	A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users	Accepted by KDD 2017	null	null	null	cs.SI cs.IR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.	[ { "created": "Tue, 20 Jun 2017 01:54:01 GMT", "version": "v1" } ]	2017-06-21	[ [ "Wang", "Hao", "" ], [ "Fu", "Yanmei", "" ], [ "Wang", "Qinyong", "" ], [ "Yin", "Hongzhi", "" ], [ "Du", "Changying", "" ], [ "Xiong", "Hui", "" ] ]	Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.
1305.3354	Sandip Chakraborty	Sandip Chakraborty, Soumyadip Majumder, Diganta Goswami	Approximate Congestion Games for Load Balancing in Distributed Environment	A version of this work has been presented at International Workshop on Distributed System (IWDS) 2010, IIT Kanpur, India, as a "work-in-progress" report	null	null	null	cs.NI cs.DC cs.GT	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The use of game theoretic models has been quite successful in describing various cooperative and non-cooperative optimization problems in networks and other domains of computer systems. In this paper, we study an application of game theoretic models in the domain of distributed system, where nodes play a game to balance the total processing loads among themselves. We have used congestion gaming model, a model of game theory where many agents compete for allocating resources, and studied the existence of Nash Equilibrium for such types of games. As the classical congestion game is known to be PLS-Complete, we use an approximation, called the \epsilon-Congestion game, which converges to \epsilon-Nash equilibrium within finite number of steps under selected conditions. Our focus is to define the load balancing problem using the model of \epsilon-congestion games, and finally provide a greedy algorithm for load balancing in distributed systems. We have simulated our proposed system to show the effect of \epsilon-congestion game, and the distribution of load at equilibrium state.	[ { "created": "Wed, 15 May 2013 05:06:02 GMT", "version": "v1" } ]	2013-05-16	[ [ "Chakraborty", "Sandip", "" ], [ "Majumder", "Soumyadip", "" ], [ "Goswami", "Diganta", "" ] ]	The use of game theoretic models has been quite successful in describing various cooperative and non-cooperative optimization problems in networks and other domains of computer systems. In this paper, we study an application of game theoretic models in the domain of distributed system, where nodes play a game to balance the total processing loads among themselves. We have used congestion gaming model, a model of game theory where many agents compete for allocating resources, and studied the existence of Nash Equilibrium for such types of games. As the classical congestion game is known to be PLS-Complete, we use an approximation, called the \epsilon-Congestion game, which converges to \epsilon-Nash equilibrium within finite number of steps under selected conditions. Our focus is to define the load balancing problem using the model of \epsilon-congestion games, and finally provide a greedy algorithm for load balancing in distributed systems. We have simulated our proposed system to show the effect of \epsilon-congestion game, and the distribution of load at equilibrium state.
2309.13438	Tingyu Zhao	Tingyu Zhao, Bo Peng, Yuan Sun, Daipeng Yang, Zhenguang Zhang, and Xi Wu	Rethinking Superpixel Segmentation from Biologically Inspired Mechanisms	null	null	null	null	cs.CV cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Recently, advancements in deep learning-based superpixel segmentation methods have brought about improvements in both the efficiency and the performance of segmentation. However, a significant challenge remains in generating superpixels that strictly adhere to object boundaries while conveying rich visual significance, especially when cross-surface color correlations may interfere with objects. Drawing inspiration from neural structure and visual mechanisms, we propose a biological network architecture comprising an Enhanced Screening Module (ESM) and a novel Boundary-Aware Label (BAL) for superpixel segmentation. The ESM enhances semantic information by simulating the interactive projection mechanisms of the visual cortex. Additionally, the BAL emulates the spatial frequency characteristics of visual cortical cells to facilitate the generation of superpixels with strong boundary adherence. We demonstrate the effectiveness of our approach through evaluations on both the BSDS500 dataset and the NYUv2 dataset.	[ { "created": "Sat, 23 Sep 2023 17:29:38 GMT", "version": "v1" }, { "created": "Wed, 4 Oct 2023 12:13:53 GMT", "version": "v2" }, { "created": "Wed, 11 Oct 2023 06:43:08 GMT", "version": "v3" } ]	2023-10-12	[ [ "Zhao", "Tingyu", "" ], [ "Peng", "Bo", "" ], [ "Sun", "Yuan", "" ], [ "Yang", "Daipeng", "" ], [ "Zhang", "Zhenguang", "" ], [ "Wu", "Xi", "" ] ]	Recently, advancements in deep learning-based superpixel segmentation methods have brought about improvements in both the efficiency and the performance of segmentation. However, a significant challenge remains in generating superpixels that strictly adhere to object boundaries while conveying rich visual significance, especially when cross-surface color correlations may interfere with objects. Drawing inspiration from neural structure and visual mechanisms, we propose a biological network architecture comprising an Enhanced Screening Module (ESM) and a novel Boundary-Aware Label (BAL) for superpixel segmentation. The ESM enhances semantic information by simulating the interactive projection mechanisms of the visual cortex. Additionally, the BAL emulates the spatial frequency characteristics of visual cortical cells to facilitate the generation of superpixels with strong boundary adherence. We demonstrate the effectiveness of our approach through evaluations on both the BSDS500 dataset and the NYUv2 dataset.
2211.17059	Yiyang Liu	Yiyang Liu, Chenxin Li, Xiaotong Tu, Xinghao Ding, Yue Huang	Hint-dynamic Knowledge Distillation	5 pages	null	null	null	cs.CV cs.LG	http://creativecommons.org/licenses/by/4.0/	Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher model to promote a smaller student model. Existing efforts guide the distillation by matching their prediction logits, feature embedding, etc., while leaving how to efficiently utilize them in junction less explored. In this paper, we propose Hint-dynamic Knowledge Distillation, dubbed HKD, which excavates the knowledge from the teacher' s hints in a dynamic scheme. The guidance effect from the knowledge hints usually varies in different instances and learning stages, which motivates us to customize a specific hint-learning manner for each instance adaptively. Specifically, a meta-weight network is introduced to generate the instance-wise weight coefficients about knowledge hints in the perception of the dynamical learning progress of the student model. We further present a weight ensembling strategy to eliminate the potential bias of coefficient estimation by exploiting the historical statics. Experiments on standard benchmarks of CIFAR-100 and Tiny-ImageNet manifest that the proposed HKD well boost the effect of knowledge distillation tasks.	[ { "created": "Wed, 30 Nov 2022 15:03:53 GMT", "version": "v1" } ]	2022-12-01	[ [ "Liu", "Yiyang", "" ], [ "Li", "Chenxin", "" ], [ "Tu", "Xiaotong", "" ], [ "Ding", "Xinghao", "" ], [ "Huang", "Yue", "" ] ]	Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher model to promote a smaller student model. Existing efforts guide the distillation by matching their prediction logits, feature embedding, etc., while leaving how to efficiently utilize them in junction less explored. In this paper, we propose Hint-dynamic Knowledge Distillation, dubbed HKD, which excavates the knowledge from the teacher' s hints in a dynamic scheme. The guidance effect from the knowledge hints usually varies in different instances and learning stages, which motivates us to customize a specific hint-learning manner for each instance adaptively. Specifically, a meta-weight network is introduced to generate the instance-wise weight coefficients about knowledge hints in the perception of the dynamical learning progress of the student model. We further present a weight ensembling strategy to eliminate the potential bias of coefficient estimation by exploiting the historical statics. Experiments on standard benchmarks of CIFAR-100 and Tiny-ImageNet manifest that the proposed HKD well boost the effect of knowledge distillation tasks.
1301.4478	Chaitanya Swamy	Sara Ahmadian, Zachary Friggstad, and Chaitanya Swamy	Local-Search based Approximation Algorithms for Mobile Facility Location Problems	null	null	null	null	cs.DS cs.DM	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We consider the {\em mobile facility location} (\mfl) problem. We are given a set of facilities and clients located in a common metric space. The goal is to move each facility from its initial location to a destination and assign each client to the destination of some facility so as to minimize the sum of the movement-costs of the facilities and the client-assignment costs. This abstracts facility-location settings where one has the flexibility of moving facilities from their current locations to other destinations so as to serve clients more efficiently by reducing their assignment costs. We give the first {\em local-search based} approximation algorithm for this problem and achieve the best-known approximation guarantee. Our main result is $(3+\epsilon)$-approximation for this problem for any constant $\epsilon>0$ using local search. The previous best guarantee was an 8-approximation algorithm based on LP-rounding. Our guarantee {\em matches} the best-known approximation guarantee for the $k$-median problem. Since there is an approximation-preserving reduction from the $k$-median problem to \mfl, any improvement of our result would imply an analogous improvement for the $k$-median problem. Furthermore, {\em our analysis is tight} (up to $o(1)$ factors) since the tight example for the local-search based 3-approximation algorithm for $k$-median can be easily adapted to show that our local-search algorithm has a tight approximation ratio of 3. One of the chief novelties of the analysis is that in order to generate a suitable collection of local-search moves whose resulting inequalities yield the desired bound on the cost of a local-optimum, we define a tree-like structure that (loosely speaking) functions as a "recursion tree", using which we spawn off local-search moves by exploring this tree to a constant depth.	[ { "created": "Fri, 18 Jan 2013 20:05:12 GMT", "version": "v1" } ]	2013-01-21	[ [ "Ahmadian", "Sara", "" ], [ "Friggstad", "Zachary", "" ], [ "Swamy", "Chaitanya", "" ] ]	We consider the {\em mobile facility location} (\mfl) problem. We are given a set of facilities and clients located in a common metric space. The goal is to move each facility from its initial location to a destination and assign each client to the destination of some facility so as to minimize the sum of the movement-costs of the facilities and the client-assignment costs. This abstracts facility-location settings where one has the flexibility of moving facilities from their current locations to other destinations so as to serve clients more efficiently by reducing their assignment costs. We give the first {\em local-search based} approximation algorithm for this problem and achieve the best-known approximation guarantee. Our main result is $(3+\epsilon)$-approximation for this problem for any constant $\epsilon>0$ using local search. The previous best guarantee was an 8-approximation algorithm based on LP-rounding. Our guarantee {\em matches} the best-known approximation guarantee for the $k$-median problem. Since there is an approximation-preserving reduction from the $k$-median problem to \mfl, any improvement of our result would imply an analogous improvement for the $k$-median problem. Furthermore, {\em our analysis is tight} (up to $o(1)$ factors) since the tight example for the local-search based 3-approximation algorithm for $k$-median can be easily adapted to show that our local-search algorithm has a tight approximation ratio of 3. One of the chief novelties of the analysis is that in order to generate a suitable collection of local-search moves whose resulting inequalities yield the desired bound on the cost of a local-optimum, we define a tree-like structure that (loosely speaking) functions as a "recursion tree", using which we spawn off local-search moves by exploring this tree to a constant depth.
2009.04656	Yian Li	Yian Li, Hai Zhao	Learning Universal Representations from Word to Sentence	null	null	null	null	cs.CL	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space through a task-independent evaluation. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space. Then we empirically verify that well pre-trained Transformer models incorporated with appropriate training settings may effectively yield universal representation. Especially, our implementation of fine-tuning ALBERT on NLI and PPDB datasets achieves the highest accuracy on analogy tasks in different language levels. Further experiments on the insurance FAQ task show effectiveness of universal representation models in real-world applications.	[ { "created": "Thu, 10 Sep 2020 03:53:18 GMT", "version": "v1" } ]	2020-09-11	[ [ "Li", "Yian", "" ], [ "Zhao", "Hai", "" ] ]	Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space through a task-independent evaluation. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space. Then we empirically verify that well pre-trained Transformer models incorporated with appropriate training settings may effectively yield universal representation. Especially, our implementation of fine-tuning ALBERT on NLI and PPDB datasets achieves the highest accuracy on analogy tasks in different language levels. Further experiments on the insurance FAQ task show effectiveness of universal representation models in real-world applications.
2303.07831	Yu Zhou	Yu Zhou, Liyuan Guo, Lianghai Jin	Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild	This paper has been accepted to ICASSP2023	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Facial expression recognition (FER) is a challenging topic in artificial intelligence. Recently, many researchers have attempted to introduce Vision Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional features extracted from raw images and requires a lot of computing resources. To overcome these problems, we propose a quaternion orthogonal transformer (QOT) for FER. Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features. Secondly, three orthogonal sub-features are integrated into a quaternion matrix, which maintains the correlations between different orthogonal components. Finally, we develop a quaternion vision transformer (Q-ViT) for feature classification. The Q-ViT adopts quaternion operations instead of the original operations in ViT, which improves the final accuracies with fewer parameters. Experimental results on three in-the-wild FER datasets show that the proposed QOT outperforms several state-of-the-art models and reduces the computations.	[ { "created": "Tue, 14 Mar 2023 12:07:48 GMT", "version": "v1" } ]	2023-03-15	[ [ "Zhou", "Yu", "" ], [ "Guo", "Liyuan", "" ], [ "Jin", "Lianghai", "" ] ]	Facial expression recognition (FER) is a challenging topic in artificial intelligence. Recently, many researchers have attempted to introduce Vision Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional features extracted from raw images and requires a lot of computing resources. To overcome these problems, we propose a quaternion orthogonal transformer (QOT) for FER. Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features. Secondly, three orthogonal sub-features are integrated into a quaternion matrix, which maintains the correlations between different orthogonal components. Finally, we develop a quaternion vision transformer (Q-ViT) for feature classification. The Q-ViT adopts quaternion operations instead of the original operations in ViT, which improves the final accuracies with fewer parameters. Experimental results on three in-the-wild FER datasets show that the proposed QOT outperforms several state-of-the-art models and reduces the computations.
2001.08328	Yuwei Tu	Yuwei Tu, Weiyu Chen, Christopher G. Brinton	A Deep Learning Approach to Behavior-Based Learner Modeling	null	null	null	null	cs.LG cs.HC	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The increasing popularity of e-learning has created demand for improving online education through techniques such as predictive analytics and content recommendations. In this paper, we study learner outcome predictions, i.e., predictions of how they will perform at the end of a course. We propose a novel Two Branch Decision Network for performance prediction that incorporates two important factors: how learners progress through the course and how the content progresses through the course. We combine clickstream features which log every action the learner takes while learning, and textual features which are generated through pre-trained GloVe word embeddings. To assess the performance of our proposed network, we collect data from a short online course designed for corporate training and evaluate both neural network and non-neural network based algorithms on it. Our proposed algorithm achieves 95.7% accuracy and 0.958 AUC score, which outperforms all other models. The results also indicate the combination of behavior features and text features are more predictive than behavior features only and neural network models are powerful in capturing the joint relationship between user behavior and course content.	[ { "created": "Thu, 23 Jan 2020 01:26:52 GMT", "version": "v1" } ]	2020-01-24	[ [ "Tu", "Yuwei", "" ], [ "Chen", "Weiyu", "" ], [ "Brinton", "Christopher G.", "" ] ]	The increasing popularity of e-learning has created demand for improving online education through techniques such as predictive analytics and content recommendations. In this paper, we study learner outcome predictions, i.e., predictions of how they will perform at the end of a course. We propose a novel Two Branch Decision Network for performance prediction that incorporates two important factors: how learners progress through the course and how the content progresses through the course. We combine clickstream features which log every action the learner takes while learning, and textual features which are generated through pre-trained GloVe word embeddings. To assess the performance of our proposed network, we collect data from a short online course designed for corporate training and evaluate both neural network and non-neural network based algorithms on it. Our proposed algorithm achieves 95.7% accuracy and 0.958 AUC score, which outperforms all other models. The results also indicate the combination of behavior features and text features are more predictive than behavior features only and neural network models are powerful in capturing the joint relationship between user behavior and course content.
1809.07912	Meng Shen	Meng Shen, Baoli Ma, Liehuang Zhu, Rashid Mijumbi, Xiaojiang Du, and Jiankun Hu	Cloud-Based Approximate Constrained Shortest Distance Queries Over Encrypted Graphs With Privacy Protection	null	null	null	null	cs.CR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a given threshold. CSD querying has a wide range of applications, such as routing in telecommunications and transportation. With an increasing prevalence of cloud computing paradigm, graph owners desire to outsource their graphs to cloud servers. In order to protect sensitive information, these graphs are usually encrypted before being outsourced to the cloud. This, however, imposes a great challenge to CSD querying over encrypted graphs. Since performing constraint filtering is an intractable task, existing work mainly focuses on unconstrained shortest distance queries. CSD querying over encrypted graphs remains an open research problem. In this paper, we propose Connor, a novel graph encryption scheme that enables approximate CSD querying. Connor is built based on an efficient, tree-based ciphertext comparison protocol, and makes use of symmetric-key primitives and the somewhat homomorphic encryption, making it computationally efficient. Using Connor, a graph owner can first encrypt privacy-sensitive graphs and then outsource them to the cloud server, achieving the necessary privacy without losing the ability of querying. Extensive experiments with real-world datasets demonstrate the effectiveness and efficiency of the proposed graph encryption scheme.	[ { "created": "Fri, 21 Sep 2018 01:58:00 GMT", "version": "v1" } ]	2018-09-24	[ [ "Shen", "Meng", "" ], [ "Ma", "Baoli", "" ], [ "Zhu", "Liehuang", "" ], [ "Mijumbi", "Rashid", "" ], [ "Du", "Xiaojiang", "" ], [ "Hu", "Jiankun", "" ] ]	Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a given threshold. CSD querying has a wide range of applications, such as routing in telecommunications and transportation. With an increasing prevalence of cloud computing paradigm, graph owners desire to outsource their graphs to cloud servers. In order to protect sensitive information, these graphs are usually encrypted before being outsourced to the cloud. This, however, imposes a great challenge to CSD querying over encrypted graphs. Since performing constraint filtering is an intractable task, existing work mainly focuses on unconstrained shortest distance queries. CSD querying over encrypted graphs remains an open research problem. In this paper, we propose Connor, a novel graph encryption scheme that enables approximate CSD querying. Connor is built based on an efficient, tree-based ciphertext comparison protocol, and makes use of symmetric-key primitives and the somewhat homomorphic encryption, making it computationally efficient. Using Connor, a graph owner can first encrypt privacy-sensitive graphs and then outsource them to the cloud server, achieving the necessary privacy without losing the ability of querying. Extensive experiments with real-world datasets demonstrate the effectiveness and efficiency of the proposed graph encryption scheme.
2403.07238	Mostafa Jamshidian	Farah Alkhatib, Mostafa Jamshidian, Donatien Le Liepvre, Florian Bernard, Ludovic Minvielle, Adam Wittek, Karol Miller	Towards Full Automation of Geometry Extraction for Biomechanical Analysis of Abdominal Aortic Aneurysm; Neural Network-Based versus Classical Methodologies	32 pages, 9 figures	null	null	null	cs.CE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	In this study we investigated the impact of image segmentation methods on the results of stress computation in the wall of abdominal aortic aneurysms (AAAs). We compared wall stress distributions and magnitudes calculated from geometry models obtained from classical semi-automated segmentation versus automated neural network-based segmentation. Ten different AAA contrast-enhanced computed tomography (CT) images were semi-automatically segmented by an analyst, taking, depending on the quality of an image, between 15 and 40 minutes of human effort per patient. The same images were automatically segmented using PRAEVAorta 2, commercial software by NUREA (https://www.nurea-soft.com/), developed based on artificial intelligence (AI) algorithms, requiring only 1-2 minutes of computer time per patient. Aneurysm wall stress calculations performed using the BioPARR software (https://bioparr.mech.uwa.edu.au/) revealed that, compared to the classical semi-automated segmentation, the automatic neural network-based segmentation leads to equivalent stress distributions, and slightly higher peak and 99th percentile maximum principal stress values. This difference is due to consistently larger lumen surface areas in automatically segmented models as compared to classical semi-automated segmentations, resulting in greater total pressure load on the wall. Our findings are a steppingstone toward a fully automated pipeline for biomechanical analysis of AAAs, starting with CT scans and concluding with wall stress assessment, while at the same time highlighting the critical importance of the repeatable and accurate segmentation of the lumen, the difficult problem often underestimated by the literature.	[ { "created": "Tue, 12 Mar 2024 01:20:34 GMT", "version": "v1" } ]	2024-03-13	[ [ "Alkhatib", "Farah", "" ], [ "Jamshidian", "Mostafa", "" ], [ "Liepvre", "Donatien Le", "" ], [ "Bernard", "Florian", "" ], [ "Minvielle", "Ludovic", "" ], [ "Wittek", "Adam", "" ], [ "Miller", "Karol", "" ] ]	In this study we investigated the impact of image segmentation methods on the results of stress computation in the wall of abdominal aortic aneurysms (AAAs). We compared wall stress distributions and magnitudes calculated from geometry models obtained from classical semi-automated segmentation versus automated neural network-based segmentation. Ten different AAA contrast-enhanced computed tomography (CT) images were semi-automatically segmented by an analyst, taking, depending on the quality of an image, between 15 and 40 minutes of human effort per patient. The same images were automatically segmented using PRAEVAorta 2, commercial software by NUREA (https://www.nurea-soft.com/), developed based on artificial intelligence (AI) algorithms, requiring only 1-2 minutes of computer time per patient. Aneurysm wall stress calculations performed using the BioPARR software (https://bioparr.mech.uwa.edu.au/) revealed that, compared to the classical semi-automated segmentation, the automatic neural network-based segmentation leads to equivalent stress distributions, and slightly higher peak and 99th percentile maximum principal stress values. This difference is due to consistently larger lumen surface areas in automatically segmented models as compared to classical semi-automated segmentations, resulting in greater total pressure load on the wall. Our findings are a steppingstone toward a fully automated pipeline for biomechanical analysis of AAAs, starting with CT scans and concluding with wall stress assessment, while at the same time highlighting the critical importance of the repeatable and accurate segmentation of the lumen, the difficult problem often underestimated by the literature.
2406.17873	Zhongtao Miao	Zhongtao Miao, Kaiyan Zhao, Yoshimasa Tsuruoka	Improving Arithmetic Reasoning Ability of Large Language Models through Relation Tuples, Verification and Dynamic Feedback	Under review, 25 figures, 8 tables, 29 pages	null	null	null	cs.CL cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Current representations used in reasoning steps of large language models can mostly be categorized into two main types: (1) natural language, which is difficult to verify; and (2) non-natural language, usually programming code, which is difficult for people who are unfamiliar with coding to read. In this paper, we propose to use a semi-structured form to represent reasoning steps of large language models. Specifically, we use relation tuples, which are not only human-readable but also machine-friendly and easier to verify than natural language. We implement a framework that includes three main components: (1) introducing relation tuples into the reasoning steps of large language models; (2) implementing an automatic verification process of reasoning steps with a local code interpreter based on relation tuples; and (3) integrating a simple and effective dynamic feedback mechanism, which we found helpful for self-improvement of large language models. The experimental results on various arithmetic datasets demonstrate the effectiveness of our method in improving the arithmetic reasoning ability of large language models. The source code is available at https://github.com/gpgg/art.	[ { "created": "Tue, 25 Jun 2024 18:21:00 GMT", "version": "v1" } ]	2024-06-27	[ [ "Miao", "Zhongtao", "" ], [ "Zhao", "Kaiyan", "" ], [ "Tsuruoka", "Yoshimasa", "" ] ]	Current representations used in reasoning steps of large language models can mostly be categorized into two main types: (1) natural language, which is difficult to verify; and (2) non-natural language, usually programming code, which is difficult for people who are unfamiliar with coding to read. In this paper, we propose to use a semi-structured form to represent reasoning steps of large language models. Specifically, we use relation tuples, which are not only human-readable but also machine-friendly and easier to verify than natural language. We implement a framework that includes three main components: (1) introducing relation tuples into the reasoning steps of large language models; (2) implementing an automatic verification process of reasoning steps with a local code interpreter based on relation tuples; and (3) integrating a simple and effective dynamic feedback mechanism, which we found helpful for self-improvement of large language models. The experimental results on various arithmetic datasets demonstrate the effectiveness of our method in improving the arithmetic reasoning ability of large language models. The source code is available at https://github.com/gpgg/art.
2306.17411	Yanjiang Guo	Yanjiang Guo, Zheyuan Jiang, Yen-Jen Wang, Jingyue Gao, Jianyu Chen	Decentralized Motor Skill Learning for Complex Robotic Systems	8 pages, 7 figures	null	null	null	cs.RO cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Reinforcement learning (RL) has achieved remarkable success in complex robotic systems (eg. quadruped locomotion). In previous works, the RL-based controller was typically implemented as a single neural network with concatenated observation input. However, the corresponding learned policy is highly task-specific. Since all motors are controlled in a centralized way, out-of-distribution local observations can impact global motors through the single coupled neural network policy. In contrast, animals and humans can control their limbs separately. Inspired by this biological phenomenon, we propose a Decentralized motor skill (DEMOS) learning algorithm to automatically discover motor groups that can be decoupled from each other while preserving essential connections and then learn a decentralized motor control policy. Our method improves the robustness and generalization of the policy without sacrificing performance. Experiments on quadruped and humanoid robots demonstrate that the learned policy is robust against local motor malfunctions and can be transferred to new tasks.	[ { "created": "Fri, 30 Jun 2023 05:55:34 GMT", "version": "v1" } ]	2023-07-03	[ [ "Guo", "Yanjiang", "" ], [ "Jiang", "Zheyuan", "" ], [ "Wang", "Yen-Jen", "" ], [ "Gao", "Jingyue", "" ], [ "Chen", "Jianyu", "" ] ]	Reinforcement learning (RL) has achieved remarkable success in complex robotic systems (eg. quadruped locomotion). In previous works, the RL-based controller was typically implemented as a single neural network with concatenated observation input. However, the corresponding learned policy is highly task-specific. Since all motors are controlled in a centralized way, out-of-distribution local observations can impact global motors through the single coupled neural network policy. In contrast, animals and humans can control their limbs separately. Inspired by this biological phenomenon, we propose a Decentralized motor skill (DEMOS) learning algorithm to automatically discover motor groups that can be decoupled from each other while preserving essential connections and then learn a decentralized motor control policy. Our method improves the robustness and generalization of the policy without sacrificing performance. Experiments on quadruped and humanoid robots demonstrate that the learned policy is robust against local motor malfunctions and can be transferred to new tasks.
2210.02287	Luyuan Xie	Luyuan Xie, Yan Zhong, Lin Yang, Zhaoyu Yan, Zhonghai Wu, Junjie Wang	TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene	Accepted to APSIPA ASC 2022	null	null	null	cs.SD cs.LG eess.AS	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Convolution neural networks (CNNs) have good performance in low-complexity classification tasks such as acoustic scene classifications (ASCs). However, there are few studies on the relationship between the length of target speech and the size of the convolution kernels. In this paper, we combine Selective Kernel Network with Temporal-Convolution (TC-SKNet) to adjust the receptive field of convolution kernels to solve the problem of variable length of target voice while keeping low-complexity. GridMask is a data augmentation strategy by masking part of the raw data or feature area. It can enhance the generalization of the model as the role of dropout. In our experiments, the performance gain brought by GridMask is stronger than spectrum augmentation in ASCs. Finally, we adopt AutoML to search best structure of TC-SKNet and hyperparameters of GridMask for improving the classification performance. As a result, a peak accuracy of 59.87% TC-SKNet is equivalent to that of SOTA, but the parameters only use 20.9 K.	[ { "created": "Wed, 5 Oct 2022 14:24:17 GMT", "version": "v1" } ]	2022-10-06	[ [ "Xie", "Luyuan", "" ], [ "Zhong", "Yan", "" ], [ "Yang", "Lin", "" ], [ "Yan", "Zhaoyu", "" ], [ "Wu", "Zhonghai", "" ], [ "Wang", "Junjie", "" ] ]	Convolution neural networks (CNNs) have good performance in low-complexity classification tasks such as acoustic scene classifications (ASCs). However, there are few studies on the relationship between the length of target speech and the size of the convolution kernels. In this paper, we combine Selective Kernel Network with Temporal-Convolution (TC-SKNet) to adjust the receptive field of convolution kernels to solve the problem of variable length of target voice while keeping low-complexity. GridMask is a data augmentation strategy by masking part of the raw data or feature area. It can enhance the generalization of the model as the role of dropout. In our experiments, the performance gain brought by GridMask is stronger than spectrum augmentation in ASCs. Finally, we adopt AutoML to search best structure of TC-SKNet and hyperparameters of GridMask for improving the classification performance. As a result, a peak accuracy of 59.87% TC-SKNet is equivalent to that of SOTA, but the parameters only use 20.9 K.
cs/0208044	Stephen A. Fenner	Stephen A. Fenner	Gales and supergales are equivalent for defining constructive Hausdorff dimension	7 pages, no figures	null	null	null	cs.CC	null	We show that for a wide range of probability measures, constructive gales are interchangable with constructive supergales for defining constructive Hausdorff dimension, thus generalizing a previous independent result of Hitchcock (cs.CC/0208043) and partially answering an open question of Lutz (cs.CC/0203017).	[ { "created": "Thu, 29 Aug 2002 21:25:47 GMT", "version": "v1" } ]	2007-05-23	[ [ "Fenner", "Stephen A.", "" ] ]	We show that for a wide range of probability measures, constructive gales are interchangable with constructive supergales for defining constructive Hausdorff dimension, thus generalizing a previous independent result of Hitchcock (cs.CC/0208043) and partially answering an open question of Lutz (cs.CC/0203017).
1803.06904	SeyedMajid Azimi	Seyed Majid Azimi, Peter Fischer, Marco K\"orner, Peter Reinartz	Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks	IEEE TGRS 2018 - Accepted	null	10.1109/TGRS.2018.2878510	null	cs.CV	http://creativecommons.org/licenses/by-nc-sa/4.0/	The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lane-wise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery which can capture a large area in a short period of time by introducing an aerial lane marking dataset. In this work, we propose a Symmetric Fully Convolutional Neural Network enhanced by Wavelet Transform in order to automatically carry out lane marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of number of lane marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a very high accuracy in pixel-wise localization of lane markings without using 3rd-party information. In this work, we introduce the first high-quality dataset used within our experiments which contains a broad range of situations and classes of lane markings representative of current transportation systems. This dataset will be publicly available and hence, it can be used as the benchmark dataset for future algorithms within this domain.	[ { "created": "Mon, 19 Mar 2018 13:32:27 GMT", "version": "v1" }, { "created": "Thu, 1 Nov 2018 16:16:51 GMT", "version": "v2" } ]	2019-08-26	[ [ "Azimi", "Seyed Majid", "" ], [ "Fischer", "Peter", "" ], [ "Körner", "Marco", "" ], [ "Reinartz", "Peter", "" ] ]	The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lane-wise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery which can capture a large area in a short period of time by introducing an aerial lane marking dataset. In this work, we propose a Symmetric Fully Convolutional Neural Network enhanced by Wavelet Transform in order to automatically carry out lane marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of number of lane marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a very high accuracy in pixel-wise localization of lane markings without using 3rd-party information. In this work, we introduce the first high-quality dataset used within our experiments which contains a broad range of situations and classes of lane markings representative of current transportation systems. This dataset will be publicly available and hence, it can be used as the benchmark dataset for future algorithms within this domain.
2405.00287	Jeongwhan Choi	Chaejeong Lee, Jeongwhan Choi, Hyowon Wi, Sung-Bae Cho, Noseong Park	Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering	null	null	null	null	cs.IR cs.AI cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Graph-based collaborative filtering (CF) has emerged as a promising approach in recommendation systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. By considering that they are both sampling tasks, we generate dynamic augmented views and diverse hard negative samples via our unified stochastic sampling framework based on score-based generative models. In our comprehensive evaluations with 6 benchmark datasets, our proposed SCONE significantly improves recommendation accuracy and robustness, and demonstrates the superiority of our approach over existing CF models. Furthermore, we prove the efficacy of user-item specific stochastic sampling for addressing the user sparsity and item popularity issues. The integration of the stochastic sampling and graph-based CF obtains the state-of-the-art in personalized recommendation systems, making significant strides in information-rich environments.	[ { "created": "Wed, 1 May 2024 02:27:59 GMT", "version": "v1" } ]	2024-05-02	[ [ "Lee", "Chaejeong", "" ], [ "Choi", "Jeongwhan", "" ], [ "Wi", "Hyowon", "" ], [ "Cho", "Sung-Bae", "" ], [ "Park", "Noseong", "" ] ]	Graph-based collaborative filtering (CF) has emerged as a promising approach in recommendation systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. By considering that they are both sampling tasks, we generate dynamic augmented views and diverse hard negative samples via our unified stochastic sampling framework based on score-based generative models. In our comprehensive evaluations with 6 benchmark datasets, our proposed SCONE significantly improves recommendation accuracy and robustness, and demonstrates the superiority of our approach over existing CF models. Furthermore, we prove the efficacy of user-item specific stochastic sampling for addressing the user sparsity and item popularity issues. The integration of the stochastic sampling and graph-based CF obtains the state-of-the-art in personalized recommendation systems, making significant strides in information-rich environments.
1811.00677	Rafael Menelau Oliveira E Cruz	Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti	Analyzing different prototype selection techniques for dynamic classifier and ensemble selection	null	Published on the International Joint Conference on Neural Networks, 2017, 3959-3966	10.1109/IJCNN.2017.7966355	null	cs.LG stat.ML	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence is usually estimated using the neighborhood of the test sample defined on the validation samples, called the region of competence. Thus, the performance of DS techniques is sensitive to the distribution of the validation set. In this paper, we evaluate six prototype selection techniques that work by editing the validation data in order to remove noise and redundant instances. Experiments conducted using several state-of-the-art DS techniques over 30 classification problems demonstrate that by using prototype selection techniques we can improve the classification accuracy of DS techniques and also significantly reduce the computational cost involved.	[ { "created": "Thu, 1 Nov 2018 23:34:10 GMT", "version": "v1" } ]	2018-11-05	[ [ "Cruz", "Rafael M. O.", "" ], [ "Sabourin", "Robert", "" ], [ "Cavalcanti", "George D. C.", "" ] ]	In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence is usually estimated using the neighborhood of the test sample defined on the validation samples, called the region of competence. Thus, the performance of DS techniques is sensitive to the distribution of the validation set. In this paper, we evaluate six prototype selection techniques that work by editing the validation data in order to remove noise and redundant instances. Experiments conducted using several state-of-the-art DS techniques over 30 classification problems demonstrate that by using prototype selection techniques we can improve the classification accuracy of DS techniques and also significantly reduce the computational cost involved.
2009.05487	Timo Freiesleben	Timo Freiesleben	The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples	null	null	10.1007/s11023-021-09580-9	null	cs.AI cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current methods for generating CEs and AEs, and estimate that the fields will merge more and more as the number of common use-cases grows.	[ { "created": "Fri, 11 Sep 2020 15:09:12 GMT", "version": "v1" }, { "created": "Thu, 3 Dec 2020 10:05:17 GMT", "version": "v2" }, { "created": "Thu, 26 Aug 2021 08:40:29 GMT", "version": "v3" } ]	2021-11-03	[ [ "Freiesleben", "Timo", "" ] ]	The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current methods for generating CEs and AEs, and estimate that the fields will merge more and more as the number of common use-cases grows.
2406.16527	Miguel Arana-Catania	Zheng Fang, Miguel Arana-Catania, Felix-Anselm van Lier, Juliana Outes Velarde, Harry Bregazzi, Mara Airoldi, Eleanor Carter, Rob Procter	SyROCCo: Enhancing Systematic Reviews using Machine Learning	28 pages, 5 figures. To appear in Data & Policy journal	null	null	null	cs.CL cs.CY cs.DL cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previously been used to reliably 'screen' articles for review - that is, identify relevant articles based on reviewers' inclusion criteria. The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We therefore set out to develop a series of tools that would assist in the profiling and analysis of 1,952 publications on the theme of 'outcomes-based contracting'. Tools were developed for the following tasks: assign publications into 'policy area' categories; identify and extract key information for evidence mapping, such as organisations, laws, and geographical information; connect the evidence base to an existing dataset on the same topic; and identify subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of ML techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While ML techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analysing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.	[ { "created": "Mon, 24 Jun 2024 11:04:43 GMT", "version": "v1" } ]	2024-06-25	[ [ "Fang", "Zheng", "" ], [ "Arana-Catania", "Miguel", "" ], [ "van Lier", "Felix-Anselm", "" ], [ "Velarde", "Juliana Outes", "" ], [ "Bregazzi", "Harry", "" ], [ "Airoldi", "Mara", "" ], [ "Carter", "Eleanor", "" ], [ "Procter", "Rob", "" ] ]	The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previously been used to reliably 'screen' articles for review - that is, identify relevant articles based on reviewers' inclusion criteria. The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We therefore set out to develop a series of tools that would assist in the profiling and analysis of 1,952 publications on the theme of 'outcomes-based contracting'. Tools were developed for the following tasks: assign publications into 'policy area' categories; identify and extract key information for evidence mapping, such as organisations, laws, and geographical information; connect the evidence base to an existing dataset on the same topic; and identify subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of ML techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While ML techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analysing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.

2305.06185

Chidi Agbo

Chidi Agbo, Hoda Mehrpouyan

Conflict Analysis and Resolution of Safety and Security Boundary Conditions for Industrial Control Systems

12 pages, 10 figures, 2022 6th International Conference on System Reliability and Safety (ICSRS)|978-1-6654-7092-6 @IEEE

null

10.1109/ICSRS56243.2022.10067393

null

cs.CR

http://creativecommons.org/licenses/by-nc-sa/4.0/

Safety and security are the two most important properties of industrial control systems (ICS), and their integration is necessary to ensure that safety goals do not undermine security goals and vice versa. Sometimes, safety and security co-engineering leads to conflicting requirements or violations capable of impacting the normal behavior of the system. Identification, analysis, and resolution of conflicts arising from safety and security co-engineering is a major challenge, an under-researched area in safety-critical systems(ICS). This paper presents an STPA-SafeSec-CDCL approach that addresses the challenge. Our proposed methodology combines the STPA-SafeSec approach for safety and security analysis and the Conflict-Driven Clause Learning (CDCL) approach for the identification, analysis, and resolution of conflicts where conflicting constraints are encoded in satisfiability (SAT) problems. We apply our framework to the Tennessee Eastman Plant process model, a chemical process model developed specifically for the study of industrial control processes, to demonstrate how to use the proposed method. Our methodology goes beyond the requirement analysis phase and can be applied to the early stages of system design and development to increase system reliability, robustness, and resilience.

[ { "created": "Wed, 10 May 2023 14:16:49 GMT", "version": "v1" } ]

2023-05-11

[ [ "Agbo", "Chidi", "" ], [ "Mehrpouyan", "Hoda", "" ] ]

Safety and security are the two most important properties of industrial control systems (ICS), and their integration is necessary to ensure that safety goals do not undermine security goals and vice versa. Sometimes, safety and security co-engineering leads to conflicting requirements or violations capable of impacting the normal behavior of the system. Identification, analysis, and resolution of conflicts arising from safety and security co-engineering is a major challenge, an under-researched area in safety-critical systems(ICS). This paper presents an STPA-SafeSec-CDCL approach that addresses the challenge. Our proposed methodology combines the STPA-SafeSec approach for safety and security analysis and the Conflict-Driven Clause Learning (CDCL) approach for the identification, analysis, and resolution of conflicts where conflicting constraints are encoded in satisfiability (SAT) problems. We apply our framework to the Tennessee Eastman Plant process model, a chemical process model developed specifically for the study of industrial control processes, to demonstrate how to use the proposed method. Our methodology goes beyond the requirement analysis phase and can be applied to the early stages of system design and development to increase system reliability, robustness, and resilience.

cs/0504044

Richard McClatchey

Michael Thomas, Conrad Steenberg, Frank van Lingen, Harvey Newman, Julian Bunn, Arshad Ali, Richard McClatchey, Ashiq Anjum, Tahir Azim, Waqas ur Rehman, Faisal Khan, Jang Uk In

JClarens: A Java Framework for Developing and Deploying Web Services for Grid Computing

8 pages, 4 figures. Paper at the 3rd IEEE International Conference on Web Services (ICWS05). Florida, USA. July 2005

null

cs.DC

null

High Energy Physics (HEP) and other scientific communities have adopted Service Oriented Architectures (SOA) as part of a larger Grid computing effort. This effort involves the integration of many legacy applications and programming libraries into a SOA framework. The Grid Analysis Environment (GAE) is such a service oriented architecture based on the Clarens Grid Services Framework and is being developed as part of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at European Laboratory for Particle Physics (CERN). Clarens provides a set of authorization, access control, and discovery services, as well as XMLRPC and SOAP access to all deployed services. Two implementations of the Clarens Web Services Framework (Python and Java) offer integration possibilities for a wide range of programming languages. This paper describes the Java implementation of the Clarens Web Services Framework called JClarens. and several web services of interest to the scientific and Grid community that have been deployed using JClarens.

[ { "created": "Mon, 11 Apr 2005 21:45:07 GMT", "version": "v1" } ]

2007-05-23

[ [ "Thomas", "Michael", "" ], [ "Steenberg", "Conrad", "" ], [ "van Lingen", "Frank", "" ], [ "Newman", "Harvey", "" ], [ "Bunn", "Julian", "" ], [ "Ali", "Arshad", "" ], [ "McClatchey", "Richard", "" ], [ "Anjum", "Ashiq", "" ], [ "Azim", "Tahir", "" ], [ "Rehman", "Waqas ur", "" ], [ "Khan", "Faisal", "" ], [ "In", "Jang Uk", "" ] ]

High Energy Physics (HEP) and other scientific communities have adopted Service Oriented Architectures (SOA) as part of a larger Grid computing effort. This effort involves the integration of many legacy applications and programming libraries into a SOA framework. The Grid Analysis Environment (GAE) is such a service oriented architecture based on the Clarens Grid Services Framework and is being developed as part of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at European Laboratory for Particle Physics (CERN). Clarens provides a set of authorization, access control, and discovery services, as well as XMLRPC and SOAP access to all deployed services. Two implementations of the Clarens Web Services Framework (Python and Java) offer integration possibilities for a wide range of programming languages. This paper describes the Java implementation of the Clarens Web Services Framework called JClarens. and several web services of interest to the scientific and Grid community that have been deployed using JClarens.

1409.5872

Peter Schrammel

Peter Schrammel, Daniel Kroening, Martin Brain, Ruben Martins, Tino Teige, Tom Bienm\"uller

Incremental Bounded Model Checking for Embedded Software (extended version)

extended version of paper submitted to EMSOFT'14

null

cs.SE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Program analysis is on the brink of mainstream in embedded systems development. Formal verification of behavioural requirements, finding runtime errors and automated test case generation are some of the most common applications of automated verification tools based on Bounded Model Checking. Existing industrial tools for embedded software use an off-the-shelf Bounded Model Checker and apply it iteratively to verify the program with an increasing number of unwindings. This approach unnecessarily wastes time repeating work that has already been done and fails to exploit the power of incremental SAT solving. This paper reports on the extension of the software model checker CBMC to support incremental Bounded Model Checking and its successful integration with the industrial embedded software verification tool BTC EmbeddedTester. We present an extensive evaluation over large industrial embedded programs, which shows that incremental Bounded Model Checking cuts runtimes by one order of magnitude in comparison to the standard non-incremental approach, enabling the application of formal verification to large and complex embedded software.

[ { "created": "Sat, 20 Sep 2014 09:14:04 GMT", "version": "v1" } ]

2014-09-23

[ [ "Schrammel", "Peter", "" ], [ "Kroening", "Daniel", "" ], [ "Brain", "Martin", "" ], [ "Martins", "Ruben", "" ], [ "Teige", "Tino", "" ], [ "Bienmüller", "Tom", "" ] ]

Program analysis is on the brink of mainstream in embedded systems development. Formal verification of behavioural requirements, finding runtime errors and automated test case generation are some of the most common applications of automated verification tools based on Bounded Model Checking. Existing industrial tools for embedded software use an off-the-shelf Bounded Model Checker and apply it iteratively to verify the program with an increasing number of unwindings. This approach unnecessarily wastes time repeating work that has already been done and fails to exploit the power of incremental SAT solving. This paper reports on the extension of the software model checker CBMC to support incremental Bounded Model Checking and its successful integration with the industrial embedded software verification tool BTC EmbeddedTester. We present an extensive evaluation over large industrial embedded programs, which shows that incremental Bounded Model Checking cuts runtimes by one order of magnitude in comparison to the standard non-incremental approach, enabling the application of formal verification to large and complex embedded software.

2110.03522

Jules Leguy

Jules Leguy, Thomas Cauchy, Beatrice Duval, Benoit Da Mota

Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties

Submitted to ICTAI 2021

null

cs.LG cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials. An important difficulty is that the properties to be optimized rely on costly evaluations. Machine learning methods are investigated with success to predict these properties, but show generalization issues on less known areas of the chemical space. We propose here a surrogate-based black box optimization method, to tackle jointly the optimization and machine learning problems. It consists in optimizing the expected improvement of the surrogate of a molecular property using an evolutionary algorithm. The surrogate is defined as a Gaussian Process Regression (GPR) model, learned on a relevant area of the search space with respect to the property to be optimized. We show that our approach can successfully optimize a costly property of interest much faster than a purely metaheuristic approach.

[ { "created": "Fri, 1 Oct 2021 15:28:15 GMT", "version": "v1" } ]

2021-10-08

[ [ "Leguy", "Jules", "" ], [ "Cauchy", "Thomas", "" ], [ "Duval", "Beatrice", "" ], [ "Da Mota", "Benoit", "" ] ]

AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials. An important difficulty is that the properties to be optimized rely on costly evaluations. Machine learning methods are investigated with success to predict these properties, but show generalization issues on less known areas of the chemical space. We propose here a surrogate-based black box optimization method, to tackle jointly the optimization and machine learning problems. It consists in optimizing the expected improvement of the surrogate of a molecular property using an evolutionary algorithm. The surrogate is defined as a Gaussian Process Regression (GPR) model, learned on a relevant area of the search space with respect to the property to be optimized. We show that our approach can successfully optimize a costly property of interest much faster than a purely metaheuristic approach.

1610.08597

Sanjaya Wijeratne

Sanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth

Word Embeddings to Enhance Twitter Gang Member Profile Identification

7 pages, 1 figure, 2 tables, Published at IJCAI Workshop on Semantic Machine Learning (SML 2016)

IJCAI Workshop on Semantic Machine Learning (SML 2016). pp. 18-24. CEUR-WS, New York City, NY (07 2016)

null

cs.SI cs.CL cs.CY cs.IR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members social media posts.

[ { "created": "Thu, 27 Oct 2016 03:21:49 GMT", "version": "v1" } ]

2016-10-28

[ [ "Wijeratne", "Sanjaya", "" ], [ "Balasuriya", "Lakshika", "" ], [ "Doran", "Derek", "" ], [ "Sheth", "Amit", "" ] ]

Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members social media posts.

1206.6030

Sundararajan Sellamanickam

Sundararajan Sellamanickam, Shirish Shevade

An Additive Model View to Sparse Gaussian Process Classifier Design

14 pages, 3 figures

null

cs.LG stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new methods for two key components viz., site parameter estimation and basis vector selection in any SGPC design. The proposed adaptive sampling based basis vector selection method aids in achieving improved generalization performance at a reduced computational cost. This method can also be used in conjunction with any other site parameter estimation methods. It has similar computational and storage complexities as the well-known information vector machine and is suitable for large datasets. The hyperparameters can be determined by optimizing a predictive loss function. The experimental results show better generalization performance of the proposed basis vector selection method on several benchmark datasets, particularly for relatively smaller basis vector set sizes or on difficult datasets.

[ { "created": "Tue, 26 Jun 2012 15:58:21 GMT", "version": "v1" } ]

2012-06-27

[ [ "Sellamanickam", "Sundararajan", "" ], [ "Shevade", "Shirish", "" ] ]

We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new methods for two key components viz., site parameter estimation and basis vector selection in any SGPC design. The proposed adaptive sampling based basis vector selection method aids in achieving improved generalization performance at a reduced computational cost. This method can also be used in conjunction with any other site parameter estimation methods. It has similar computational and storage complexities as the well-known information vector machine and is suitable for large datasets. The hyperparameters can be determined by optimizing a predictive loss function. The experimental results show better generalization performance of the proposed basis vector selection method on several benchmark datasets, particularly for relatively smaller basis vector set sizes or on difficult datasets.

2405.04095

Yiling He

Yiling He, Junchi Lei, Zhan Qin and Kui Ren

DREAM: Combating Concept Drift with Explanatory Detection and Adaptation in Malware Classification

null

cs.CR cs.AI

http://creativecommons.org/licenses/by-sa/4.0/

Deep learning-based malware classifiers face significant challenges due to concept drift. The rapid evolution of malware, especially with new families, can depress classification accuracy to near-random levels. Previous research has primarily focused on detecting drift samples, relying on expert-led analysis and labeling for model retraining. However, these methods often lack a comprehensive understanding of malware concepts and provide limited guidance for effective drift adaptation, leading to unstable detection performance and high human labeling costs. To address these limitations, we introduce DREAM, a novel system designed to surpass the capabilities of existing drift detectors and to establish an explanatory drift adaptation process. DREAM enhances drift detection through model sensitivity and data autonomy. The detector, trained in a semi-supervised approach, proactively captures malware behavior concepts through classifier feedback. During testing, it utilizes samples generated by the detector itself, eliminating reliance on extensive training data. For drift adaptation, DREAM enlarges human intervention, enabling revisions of malware labels and concept explanations embedded within the detector's latent space. To ensure a comprehensive response to concept drift, it facilitates a coordinated update process for both the classifier and the detector. Our evaluation shows that DREAM can effectively improve the drift detection accuracy and reduce the expert analysis effort in adaptation across different malware datasets and classifiers.

[ { "created": "Tue, 7 May 2024 07:55:45 GMT", "version": "v1" }, { "created": "Thu, 8 Aug 2024 05:45:56 GMT", "version": "v2" } ]

2024-08-09

[ [ "He", "Yiling", "" ], [ "Lei", "Junchi", "" ], [ "Qin", "Zhan", "" ], [ "Ren", "Kui", "" ] ]

Deep learning-based malware classifiers face significant challenges due to concept drift. The rapid evolution of malware, especially with new families, can depress classification accuracy to near-random levels. Previous research has primarily focused on detecting drift samples, relying on expert-led analysis and labeling for model retraining. However, these methods often lack a comprehensive understanding of malware concepts and provide limited guidance for effective drift adaptation, leading to unstable detection performance and high human labeling costs. To address these limitations, we introduce DREAM, a novel system designed to surpass the capabilities of existing drift detectors and to establish an explanatory drift adaptation process. DREAM enhances drift detection through model sensitivity and data autonomy. The detector, trained in a semi-supervised approach, proactively captures malware behavior concepts through classifier feedback. During testing, it utilizes samples generated by the detector itself, eliminating reliance on extensive training data. For drift adaptation, DREAM enlarges human intervention, enabling revisions of malware labels and concept explanations embedded within the detector's latent space. To ensure a comprehensive response to concept drift, it facilitates a coordinated update process for both the classifier and the detector. Our evaluation shows that DREAM can effectively improve the drift detection accuracy and reduce the expert analysis effort in adaptation across different malware datasets and classifiers.

2012.02516

Patrick Esser

Patrick Esser and Robin Rombach and Bj\"orn Ommer

A Note on Data Biases in Generative Models

Extended Abstract for the NeurIPS 2020 Workshop on Machine Learning for Creativity and Design

null

cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net .

[ { "created": "Fri, 4 Dec 2020 10:46:37 GMT", "version": "v1" } ]

2020-12-07

[ [ "Esser", "Patrick", "" ], [ "Rombach", "Robin", "" ], [ "Ommer", "Björn", "" ] ]

It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net .

2001.08014

Hongyu Jin

Hongyu Jin and Mohammad Khodaei and Panos Papadimitratos

Security and Privacy in Vehicular Social Networks

A chapter for the book "Vehicular Social Networks"

A. M. Vegni, V. Loscr\`i, and A. V. Vasilakos, Eds. CRC Press, Taylor & Francis Group, March 2017

null

cs.CR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We surveyed and presented the state-of-the-art VC systems, security and privacy architectures and technologies, emphasizing on security and privacy challenges and their solutions for P2P interactions in VSNs towards standardization and deployment. We note that beyond safety applications that have drawn a lot of attention in VC systems, there is significant and rising interest in vehicle-to-vehicle interaction for a range of transportation efficiency and infotainment applications, notably LBS as well as a gamut of services by mobile providers. While this enriches the VC systems and the user experience, security and privacy concerns are also intensified. This is especially so, considering (i) the privacy risk from the exposure of the users to the service providers, and (ii) the security risk from the interaction with malicious or selfish and thus misbehaving users or infrastructure. We showed existing solutions can in fact evolve and address the VSN-specific challenges, and improve or even accelerate the adoption of VSN applications.

[ { "created": "Wed, 22 Jan 2020 13:49:23 GMT", "version": "v1" } ]

2020-01-23

[ [ "Jin", "Hongyu", "" ], [ "Khodaei", "Mohammad", "" ], [ "Papadimitratos", "Panos", "" ] ]

We surveyed and presented the state-of-the-art VC systems, security and privacy architectures and technologies, emphasizing on security and privacy challenges and their solutions for P2P interactions in VSNs towards standardization and deployment. We note that beyond safety applications that have drawn a lot of attention in VC systems, there is significant and rising interest in vehicle-to-vehicle interaction for a range of transportation efficiency and infotainment applications, notably LBS as well as a gamut of services by mobile providers. While this enriches the VC systems and the user experience, security and privacy concerns are also intensified. This is especially so, considering (i) the privacy risk from the exposure of the users to the service providers, and (ii) the security risk from the interaction with malicious or selfish and thus misbehaving users or infrastructure. We showed existing solutions can in fact evolve and address the VSN-specific challenges, and improve or even accelerate the adoption of VSN applications.

2312.04590

Alexander Ziller

Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging

null

cs.CR cs.AI cs.CV cs.LG

http://creativecommons.org/licenses/by/4.0/

Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing Technologies (PETs), such as Differential Privacy (DP), aim to circumvent these susceptibilities. DP is the strongest possible protection for training models while bounding the risks of inferring the inclusion of training samples or reconstructing the original data. DP achieves this by setting a quantifiable privacy budget. Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models. This imposes a trade-off between robust performance and stringent privacy. Additionally, the interpretation of a privacy budget remains abstract and challenging to contextualize. In this study, we contrast the performance of AI models at various privacy budgets against both, theoretical risk bounds and empirical success of reconstruction attacks. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible. We thus conclude that not using DP -- at all -- is negligent when applying AI models to sensitive data. We deem those results to lie a foundation for further debates on striking a balance between privacy risks and model performance.

[ { "created": "Tue, 5 Dec 2023 12:21:30 GMT", "version": "v1" } ]

2023-12-11

[ [ "Ziller", "Alexander", "" ], [ "Mueller", "Tamara T.", "" ], [ "Stieger", "Simon", "" ], [ "Feiner", "Leonhard", "" ], [ "Brandt", "Johannes", "" ], [ "Braren", "Rickmer", "" ], [ "Rueckert", "Daniel", "" ], [ "Kaissis", "Georgios", "" ] ]

Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing Technologies (PETs), such as Differential Privacy (DP), aim to circumvent these susceptibilities. DP is the strongest possible protection for training models while bounding the risks of inferring the inclusion of training samples or reconstructing the original data. DP achieves this by setting a quantifiable privacy budget. Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models. This imposes a trade-off between robust performance and stringent privacy. Additionally, the interpretation of a privacy budget remains abstract and challenging to contextualize. In this study, we contrast the performance of AI models at various privacy budgets against both, theoretical risk bounds and empirical success of reconstruction attacks. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible. We thus conclude that not using DP -- at all -- is negligent when applying AI models to sensitive data. We deem those results to lie a foundation for further debates on striking a balance between privacy risks and model performance.

2210.13024

Martin Lentschat

Puthineath Lay, Martin Lentschat and Cyril Labb\'e

Investigating the detection of Tortured Phrases in Scientific Literature

null

cs.CL cs.IR

http://creativecommons.org/licenses/by-nc-sa/4.0/

With the help of online tools, unscrupulous authors can today generate a pseudo-scientific article and attempt to publish it. Some of these tools work by replacing or paraphrasing existing texts to produce new content, but they have a tendency to generate nonsensical expressions. A recent study introduced the concept of 'tortured phrase', an unexpected odd phrase that appears instead of the fixed expression. E.g. counterfeit consciousness instead of artificial intelligence. The present study aims at investigating how tortured phrases, that are not yet listed, can be detected automatically. We conducted several experiments, including non-neural binary classification, neural binary classification and cosine similarity comparison of the phrase tokens, yielding noticeable results.

[ { "created": "Mon, 24 Oct 2022 08:15:22 GMT", "version": "v1" } ]

2022-10-25

[ [ "Lay", "Puthineath", "" ], [ "Lentschat", "Martin", "" ], [ "Labbé", "Cyril", "" ] ]

With the help of online tools, unscrupulous authors can today generate a pseudo-scientific article and attempt to publish it. Some of these tools work by replacing or paraphrasing existing texts to produce new content, but they have a tendency to generate nonsensical expressions. A recent study introduced the concept of 'tortured phrase', an unexpected odd phrase that appears instead of the fixed expression. E.g. counterfeit consciousness instead of artificial intelligence. The present study aims at investigating how tortured phrases, that are not yet listed, can be detected automatically. We conducted several experiments, including non-neural binary classification, neural binary classification and cosine similarity comparison of the phrase tokens, yielding noticeable results.

1308.3136

Richard Preen

Richard J. Preen and Larry Bull

Toward the Coevolution of Novel Vertical-Axis Wind Turbines

appears in IEEE Transactions on Evolutionary Computation (2014). arXiv admin note: substantial text overlap with arXiv:1212.5271, arXiv:1204.4107

IEEE Transactions on Evolutionary Computation (2015), 19(2):284-294

10.1109/TEVC.2014.2316199

null

cs.NE cs.AI cs.CE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under fan generated wind conditions. Initially a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.

[ { "created": "Tue, 13 Aug 2013 14:02:34 GMT", "version": "v1" }, { "created": "Thu, 15 Jan 2015 16:25:33 GMT", "version": "v2" } ]

2015-07-02

[ [ "Preen", "Richard J.", "" ], [ "Bull", "Larry", "" ] ]

The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under fan generated wind conditions. Initially a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.

2208.11885

Melissa Swift

Melissa E. Swift (1 and 2), Wyatt Ayers (1), Sophie Pallanck (1), Scott Wehrwein (1) ((1) Western Washington University, (2) Pacific Northwest National Laboratory)

Visualizing the Passage of Time with Video Temporal Pyramids

11 pages, 9 figures, accepted for presentation at IEEE VIS 2022, will be published in conference proceedings, supplementary material and more can be found on our project page at https://fw.cs.wwu.edu/~wehrwes/TemporalPyramids

null

10.1109/TVCG.2022.3209454

null

cs.CV eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

What can we learn about a scene by watching it for months or years? A video recorded over a long timespan will depict interesting phenomena at multiple timescales, but identifying and viewing them presents a challenge. The video is too long to watch in full, and some occurrences are too slow to experience in real-time, such as glacial retreat. Timelapse videography is a common approach to summarizing long videos and visualizing slow timescales. However, a timelapse is limited to a single chosen temporal frequency, and often appears flickery due to aliasing and temporal discontinuities between frames. In this paper, we propose Video Temporal Pyramids, a technique that addresses these limitations and expands the possibilities for visualizing the passage of time. Inspired by spatial image pyramids from computer vision, we developed an algorithm that builds video pyramids in the temporal domain. Each level of a Video Temporal Pyramid visualizes a different timescale; for instance, videos from the monthly timescale are usually good for visualizing seasonal changes, while videos from the one-minute timescale are best for visualizing sunrise or the movement of clouds across the sky. To help explore the different pyramid levels, we also propose a Video Spectrogram to visualize the amount of activity across the entire pyramid, providing a holistic overview of the scene dynamics and the ability to explore and discover phenomena across time and timescales. To demonstrate our approach, we have built Video Temporal Pyramids from ten outdoor scenes, each containing months or years of data. We compare Video Temporal Pyramid layers to naive timelapse and find that our pyramids enable alias-free viewing of longer-term changes. We also demonstrate that the Video Spectrogram facilitates exploration and discovery of phenomena across pyramid levels, by enabling both overview and detail-focused perspectives.

[ { "created": "Thu, 25 Aug 2022 06:19:02 GMT", "version": "v1" } ]

2023-05-02

[ [ "Swift", "Melissa E.", "", "1 and 2" ], [ "Ayers", "Wyatt", "" ], [ "Pallanck", "Sophie", "" ], [ "Wehrwein", "Scott", "" ] ]

What can we learn about a scene by watching it for months or years? A video recorded over a long timespan will depict interesting phenomena at multiple timescales, but identifying and viewing them presents a challenge. The video is too long to watch in full, and some occurrences are too slow to experience in real-time, such as glacial retreat. Timelapse videography is a common approach to summarizing long videos and visualizing slow timescales. However, a timelapse is limited to a single chosen temporal frequency, and often appears flickery due to aliasing and temporal discontinuities between frames. In this paper, we propose Video Temporal Pyramids, a technique that addresses these limitations and expands the possibilities for visualizing the passage of time. Inspired by spatial image pyramids from computer vision, we developed an algorithm that builds video pyramids in the temporal domain. Each level of a Video Temporal Pyramid visualizes a different timescale; for instance, videos from the monthly timescale are usually good for visualizing seasonal changes, while videos from the one-minute timescale are best for visualizing sunrise or the movement of clouds across the sky. To help explore the different pyramid levels, we also propose a Video Spectrogram to visualize the amount of activity across the entire pyramid, providing a holistic overview of the scene dynamics and the ability to explore and discover phenomena across time and timescales. To demonstrate our approach, we have built Video Temporal Pyramids from ten outdoor scenes, each containing months or years of data. We compare Video Temporal Pyramid layers to naive timelapse and find that our pyramids enable alias-free viewing of longer-term changes. We also demonstrate that the Video Spectrogram facilitates exploration and discovery of phenomena across pyramid levels, by enabling both overview and detail-focused perspectives.

1810.05716

Yanting Pei

Yanting Pei, Yaping Huang, Qi Zou, Yuhang Lu and Song Wang

Does Haze Removal Help CNN-based Image Classification?

null

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance.

[ { "created": "Fri, 12 Oct 2018 20:46:29 GMT", "version": "v1" } ]

2018-10-16

[ [ "Pei", "Yanting", "" ], [ "Huang", "Yaping", "" ], [ "Zou", "Qi", "" ], [ "Lu", "Yuhang", "" ], [ "Wang", "Song", "" ] ]

Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance.

1905.02025

Grigorios Kalliatakis

Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Klaus McDonald-Maier

DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level

To be published in CVPR Workshop on Computer Vision for Global Challenges (CV4GC). arXiv admin note: substantial text overlap with arXiv:1902.03817

null

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at https://github.com/GKalliatakis/DisplaceNet.

[ { "created": "Fri, 3 May 2019 11:07:27 GMT", "version": "v1" } ]

2019-05-07

[ [ "Kalliatakis", "Grigorios", "" ], [ "Ehsan", "Shoaib", "" ], [ "Fasli", "Maria", "" ], [ "McDonald-Maier", "Klaus", "" ] ]

Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at https://github.com/GKalliatakis/DisplaceNet.

2208.02885

Zilin Si

Zilin Si, Zirui Zhu, Arpit Agarwal, Stuart Anderson and Wenzhen Yuan

Grasp Stability Prediction with Sim-to-Real Transfer from Tactile Sensing

null

cs.RO

http://creativecommons.org/licenses/by-nc-sa/4.0/

Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the sim-to-real transfer for tactile-based manipulation tasks still challenging. In this work, we integrate simulation of robot dynamics and vision-based tactile sensors by modeling the physics of contact. This contact model uses simulated contact forces at the robot's end-effector to inform the generation of realistic tactile outputs. To eliminate the sim-to-real transfer gap, we calibrate our physics simulator of robot dynamics, contact model, and tactile optical simulator with real-world data, and then we demonstrate the effectiveness of our system on a zero-shot sim-to-real grasp stability prediction task where we achieve an average accuracy of 90.7% on various objects. Experiments reveal the potential of applying our simulation framework to more complicated manipulation tasks. We open-source our simulation framework at https://github.com/CMURoboTouch/Taxim/tree/taxim-robot.

[ { "created": "Thu, 4 Aug 2022 20:55:09 GMT", "version": "v1" } ]

2022-08-08

[ [ "Si", "Zilin", "" ], [ "Zhu", "Zirui", "" ], [ "Agarwal", "Arpit", "" ], [ "Anderson", "Stuart", "" ], [ "Yuan", "Wenzhen", "" ] ]

Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the sim-to-real transfer for tactile-based manipulation tasks still challenging. In this work, we integrate simulation of robot dynamics and vision-based tactile sensors by modeling the physics of contact. This contact model uses simulated contact forces at the robot's end-effector to inform the generation of realistic tactile outputs. To eliminate the sim-to-real transfer gap, we calibrate our physics simulator of robot dynamics, contact model, and tactile optical simulator with real-world data, and then we demonstrate the effectiveness of our system on a zero-shot sim-to-real grasp stability prediction task where we achieve an average accuracy of 90.7% on various objects. Experiments reveal the potential of applying our simulation framework to more complicated manipulation tasks. We open-source our simulation framework at https://github.com/CMURoboTouch/Taxim/tree/taxim-robot.

1907.02703

Yong Min

Yong Min, Tingjun Jiang, Cheng Jin, Qu Li and Xiaogang Jin

Intelligent social bots uncover the link between user preference and diversity of news consumption

The refined manuscript is under review in Royal Society Open Science

Roy. Soc. Open Sci. 6 (2019) 190868

10.1098/rsos.190868

null

cs.SI physics.soc-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The boom of online social media and microblogging platforms has rapidly alter the way we consume news and exchange opinions. Even though considerable efforts try to recommend various contents to users, loss of information diversity and the polarization of interest groups are still an enormous challenge for industry and academia. Here, we take advantage of benign social bots to design a controlled experiment on Weibo (the largest microblogging platform in China). These software bots can exhibit human-like behavior (e.g., preferring particular content) and simulate the formation of personal social networks and news consumption under two well-accepted sociological hypotheses (i.e., homophily and triadic closure). We deployed 68 bots to Weibo, and each bot ran for at least 2 months and followed 100 to 120 accounts. In total, we observed 5,318 users and recorded about 630,000 messages exposed to these bots. Our results show, even with the same selection behaviors, bots preferring entertainment content are more likely to form polarized communities with their peers, in which about 80\% of the information they consume is of the same type, which is a significant difference for bots preferring sci-tech content. The result suggests that users preference played a more crucial role in limiting themselves access to diverse content by compared with the two well-known drivers (self-selection and pre-selection). Furthermore, our results reveal an ingenious connection between specific content and its propagating sub-structures in the same social network. In the Weibo network, entertainment news favors a unidirectional star-like sub-structure, while sci-tech news spreads on a bidirectional clustering sub-structure. This connection can amplify the diversity effect of user preference. The discovery may have important implications for diffusion dynamics study and recommendation system design.

[ { "created": "Fri, 5 Jul 2019 07:20:48 GMT", "version": "v1" } ]

2020-03-05

[ [ "Min", "Yong", "" ], [ "Jiang", "Tingjun", "" ], [ "Jin", "Cheng", "" ], [ "Li", "Qu", "" ], [ "Jin", "Xiaogang", "" ] ]

The boom of online social media and microblogging platforms has rapidly alter the way we consume news and exchange opinions. Even though considerable efforts try to recommend various contents to users, loss of information diversity and the polarization of interest groups are still an enormous challenge for industry and academia. Here, we take advantage of benign social bots to design a controlled experiment on Weibo (the largest microblogging platform in China). These software bots can exhibit human-like behavior (e.g., preferring particular content) and simulate the formation of personal social networks and news consumption under two well-accepted sociological hypotheses (i.e., homophily and triadic closure). We deployed 68 bots to Weibo, and each bot ran for at least 2 months and followed 100 to 120 accounts. In total, we observed 5,318 users and recorded about 630,000 messages exposed to these bots. Our results show, even with the same selection behaviors, bots preferring entertainment content are more likely to form polarized communities with their peers, in which about 80\% of the information they consume is of the same type, which is a significant difference for bots preferring sci-tech content. The result suggests that users preference played a more crucial role in limiting themselves access to diverse content by compared with the two well-known drivers (self-selection and pre-selection). Furthermore, our results reveal an ingenious connection between specific content and its propagating sub-structures in the same social network. In the Weibo network, entertainment news favors a unidirectional star-like sub-structure, while sci-tech news spreads on a bidirectional clustering sub-structure. This connection can amplify the diversity effect of user preference. The discovery may have important implications for diffusion dynamics study and recommendation system design.

1906.00284

Ehsan Aryafar

Ehsan Aryafar, Alireza Keshavarz-Haddad, Carlee Joe-Wong

Proportional Fair RAT Aggregation in HetNets

Extended version of the 31st International Teletraffic Congress (ITC 2019) conference paper

null

cs.NI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Heterogeneity in wireless network architectures (i.e., the coexistence of 3G, LTE, 5G, WiFi, etc.) has become a key component of current and future generation cellular networks. Simultaneous aggregation of each client's traffic across multiple such radio access technologies (RATs) / base stations (BSs) can significantly increase the system throughput, and has become an important feature of cellular standards on multi-RAT integration. Distributed algorithms that can realize the full potential of this aggregation are thus of great importance to operators. In this paper, we study the problem of resource allocation for multi-RAT traffic aggregation in HetNets (heterogeneous networks). Our goal is to ensure that the resources at each BS are allocated so that the aggregate throughput achieved by each client across its RATs satisfies a proportional fairness (PF) criterion. In particular, we provide a simple distributed algorithm for resource allocation at each BS that extends the PF allocation algorithm for a single BS. Despite its simplicity and lack of coordination across the BSs, we show that our algorithm converges to the desired PF solution and provide (tight) bounds on its convergence speed. We also study the characteristics of the optimal solution and use its properties to prove the optimality of our algorithm's outcomes.

[ { "created": "Sat, 1 Jun 2019 20:22:14 GMT", "version": "v1" } ]

2019-06-04

[ [ "Aryafar", "Ehsan", "" ], [ "Keshavarz-Haddad", "Alireza", "" ], [ "Joe-Wong", "Carlee", "" ] ]

Heterogeneity in wireless network architectures (i.e., the coexistence of 3G, LTE, 5G, WiFi, etc.) has become a key component of current and future generation cellular networks. Simultaneous aggregation of each client's traffic across multiple such radio access technologies (RATs) / base stations (BSs) can significantly increase the system throughput, and has become an important feature of cellular standards on multi-RAT integration. Distributed algorithms that can realize the full potential of this aggregation are thus of great importance to operators. In this paper, we study the problem of resource allocation for multi-RAT traffic aggregation in HetNets (heterogeneous networks). Our goal is to ensure that the resources at each BS are allocated so that the aggregate throughput achieved by each client across its RATs satisfies a proportional fairness (PF) criterion. In particular, we provide a simple distributed algorithm for resource allocation at each BS that extends the PF allocation algorithm for a single BS. Despite its simplicity and lack of coordination across the BSs, we show that our algorithm converges to the desired PF solution and provide (tight) bounds on its convergence speed. We also study the characteristics of the optimal solution and use its properties to prove the optimality of our algorithm's outcomes.

2101.06409

Ashish Kumar

Ashish Kumar, L. Behera

Shape Back-Projection In 3D Scenes

7 pages, 7 figures, 3 tables

null

cs.CV cs.RO

http://creativecommons.org/licenses/by-sa/4.0/

In this work, we propose a novel framework shape back-projection for computationally efficient point cloud processing in a probabilistic manner. The primary component of the technique is shape histogram and a back-projection procedure. The technique measures similarity between 3D surfaces, by analyzing their geometrical properties. It is analogous to color back-projection which measures similarity between images, simply by looking at their color distributions. In the overall process, first, shape histogram of a sample surface (e.g. planar) is computed, which captures the profile of surface normals around a point in form of a probability distribution. Later, the histogram is back-projected onto a test surface and a likelihood score is obtained. The score depicts that how likely a point in the test surface behaves similar to the sample surface, geometrically. Shape back-projection finds its application in binary surface classification, high curvature edge detection in unorganized point cloud, automated point cloud labeling for 3D-CNNs (convolutional neural network) etc. The algorithm can also be used for real-time robotic operations such as autonomous object picking in warehouse automation, ground plane extraction for autonomous vehicles and can be deployed easily on computationally limited platforms (UAVs).

[ { "created": "Sat, 16 Jan 2021 09:00:34 GMT", "version": "v1" } ]

2021-01-19

[ [ "Kumar", "Ashish", "" ], [ "Behera", "L.", "" ] ]

In this work, we propose a novel framework shape back-projection for computationally efficient point cloud processing in a probabilistic manner. The primary component of the technique is shape histogram and a back-projection procedure. The technique measures similarity between 3D surfaces, by analyzing their geometrical properties. It is analogous to color back-projection which measures similarity between images, simply by looking at their color distributions. In the overall process, first, shape histogram of a sample surface (e.g. planar) is computed, which captures the profile of surface normals around a point in form of a probability distribution. Later, the histogram is back-projected onto a test surface and a likelihood score is obtained. The score depicts that how likely a point in the test surface behaves similar to the sample surface, geometrically. Shape back-projection finds its application in binary surface classification, high curvature edge detection in unorganized point cloud, automated point cloud labeling for 3D-CNNs (convolutional neural network) etc. The algorithm can also be used for real-time robotic operations such as autonomous object picking in warehouse automation, ground plane extraction for autonomous vehicles and can be deployed easily on computationally limited platforms (UAVs).

2205.14280

Li Niu

Li Niu, Qingyang Liu, Zhenchen Liu, Jiangtong Li

Fast Object Placement Assessment

null

cs.CV

http://creativecommons.org/publicdomain/zero/1.0/

Object placement assessment (OPA) aims to predict the rationality score of a composite image in terms of the placement (e.g., scale, location) of inserted foreground object. However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background and pass the obtained composite image through the model one at a time, which is very time-consuming. In this work, we investigate a new task named as fast OPA. Specifically, provided with a scaled foreground and a background, we only pass them through the model once and predict the rationality scores for all locations. To accomplish this task, we propose a pioneering fast OPA model with several innovations (i.e., foreground dynamic filter, background prior transfer, and composite feature mimicking) to bridge the performance gap between slow OPA model and fast OPA model. Extensive experiments on OPA dataset show that our proposed fast OPA model performs on par with slow OPA model but runs significantly faster.

[ { "created": "Sat, 28 May 2022 00:28:32 GMT", "version": "v1" } ]

2022-05-31

[ [ "Niu", "Li", "" ], [ "Liu", "Qingyang", "" ], [ "Liu", "Zhenchen", "" ], [ "Li", "Jiangtong", "" ] ]

Object placement assessment (OPA) aims to predict the rationality score of a composite image in terms of the placement (e.g., scale, location) of inserted foreground object. However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background and pass the obtained composite image through the model one at a time, which is very time-consuming. In this work, we investigate a new task named as fast OPA. Specifically, provided with a scaled foreground and a background, we only pass them through the model once and predict the rationality scores for all locations. To accomplish this task, we propose a pioneering fast OPA model with several innovations (i.e., foreground dynamic filter, background prior transfer, and composite feature mimicking) to bridge the performance gap between slow OPA model and fast OPA model. Extensive experiments on OPA dataset show that our proposed fast OPA model performs on par with slow OPA model but runs significantly faster.

2405.06652

Yuhongmo Mo

Yuhong Mo, Hao Qin, Yushan Dong, Ziyi Zhu, Zhenglin Li

Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm

6 pages

null

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, a tool for detecting LLM AI text generation is developed based on the Transformer model, aiming to improve the accuracy of AI text generation detection and provide reference for subsequent research. Firstly the text is Unicode normalised, converted to lowercase form, characters other than non-alphabetic characters and punctuation marks are removed by regular expressions, spaces are added around punctuation marks, first and last spaces are removed, consecutive ellipses are replaced with single spaces and the text is connected using the specified delimiter. Next remove non-alphabetic characters and extra whitespace characters, replace multiple consecutive whitespace characters with a single space and again convert to lowercase form. The deep learning model combines layers such as LSTM, Transformer and CNN for text classification or sequence labelling tasks. The training and validation sets show that the model loss decreases from 0.127 to 0.005 and accuracy increases from 94.96 to 99.8, indicating that the model has good detection and classification ability for AI generated text. The test set confusion matrix and accuracy show that the model has 99% prediction accuracy for AI-generated text, with a precision of 0.99, a recall of 1, and an f1 score of 0.99, achieving a very high classification accuracy. Looking forward, it has the prospect of wide application in the field of AI text detection.

[ { "created": "Sat, 6 Apr 2024 06:22:45 GMT", "version": "v1" } ]

2024-05-14

[ [ "Mo", "Yuhong", "" ], [ "Qin", "Hao", "" ], [ "Dong", "Yushan", "" ], [ "Zhu", "Ziyi", "" ], [ "Li", "Zhenglin", "" ] ]

In this paper, a tool for detecting LLM AI text generation is developed based on the Transformer model, aiming to improve the accuracy of AI text generation detection and provide reference for subsequent research. Firstly the text is Unicode normalised, converted to lowercase form, characters other than non-alphabetic characters and punctuation marks are removed by regular expressions, spaces are added around punctuation marks, first and last spaces are removed, consecutive ellipses are replaced with single spaces and the text is connected using the specified delimiter. Next remove non-alphabetic characters and extra whitespace characters, replace multiple consecutive whitespace characters with a single space and again convert to lowercase form. The deep learning model combines layers such as LSTM, Transformer and CNN for text classification or sequence labelling tasks. The training and validation sets show that the model loss decreases from 0.127 to 0.005 and accuracy increases from 94.96 to 99.8, indicating that the model has good detection and classification ability for AI generated text. The test set confusion matrix and accuracy show that the model has 99% prediction accuracy for AI-generated text, with a precision of 0.99, a recall of 1, and an f1 score of 0.99, achieving a very high classification accuracy. Looking forward, it has the prospect of wide application in the field of AI text detection.

2112.01330

Moein Sorkhei

Moein Sorkhei, Yue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Athanasios Zouzos, Fredrik Strand, Kevin Smith

CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks

null

cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers -- without being explicitly trained for these tasks -- than its breast density counterparts.

[ { "created": "Thu, 2 Dec 2021 15:31:51 GMT", "version": "v1" } ]

2021-12-03

[ [ "Sorkhei", "Moein", "" ], [ "Liu", "Yue", "" ], [ "Azizpour", "Hossein", "" ], [ "Azavedo", "Edward", "" ], [ "Dembrower", "Karin", "" ], [ "Ntoula", "Dimitra", "" ], [ "Zouzos", "Athanasios", "" ], [ "Strand", "Fredrik", "" ], [ "Smith", "Kevin", "" ] ]

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers -- without being explicitly trained for these tasks -- than its breast density counterparts.

2311.07761

Maximilian Luz

Maximilian Luz, Rohit Mohan, Ahmed Rida Sekkat, Oliver Sawade, Elmar Matthes, Thomas Brox, Abhinav Valada

Amodal Optical Flow

null

cs.CV cs.AI cs.RO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Optical flow estimation is very challenging in situations with transparent or occluded objects. In this work, we address these challenges at the task level by introducing Amodal Optical Flow, which integrates optical flow with amodal perception. Instead of only representing the visible regions, we define amodal optical flow as a multi-layered pixel-level motion field that encompasses both visible and occluded regions of the scene. To facilitate research on this new task, we extend the AmodalSynthDrive dataset to include pixel-level labels for amodal optical flow estimation. We present several strong baselines, along with the Amodal Flow Quality metric to quantify the performance in an interpretable manner. Furthermore, we propose the novel AmodalFlowNet as an initial step toward addressing this task. AmodalFlowNet consists of a transformer-based cost-volume encoder paired with a recurrent transformer decoder which facilitates recurrent hierarchical feature propagation and amodal semantic grounding. We demonstrate the tractability of amodal optical flow in extensive experiments and show its utility for downstream tasks such as panoptic tracking. We make the dataset, code, and trained models publicly available at http://amodal-flow.cs.uni-freiburg.de.

[ { "created": "Mon, 13 Nov 2023 21:21:43 GMT", "version": "v1" }, { "created": "Tue, 7 May 2024 17:36:29 GMT", "version": "v2" } ]

2024-05-08

[ [ "Luz", "Maximilian", "" ], [ "Mohan", "Rohit", "" ], [ "Sekkat", "Ahmed Rida", "" ], [ "Sawade", "Oliver", "" ], [ "Matthes", "Elmar", "" ], [ "Brox", "Thomas", "" ], [ "Valada", "Abhinav", "" ] ]

Optical flow estimation is very challenging in situations with transparent or occluded objects. In this work, we address these challenges at the task level by introducing Amodal Optical Flow, which integrates optical flow with amodal perception. Instead of only representing the visible regions, we define amodal optical flow as a multi-layered pixel-level motion field that encompasses both visible and occluded regions of the scene. To facilitate research on this new task, we extend the AmodalSynthDrive dataset to include pixel-level labels for amodal optical flow estimation. We present several strong baselines, along with the Amodal Flow Quality metric to quantify the performance in an interpretable manner. Furthermore, we propose the novel AmodalFlowNet as an initial step toward addressing this task. AmodalFlowNet consists of a transformer-based cost-volume encoder paired with a recurrent transformer decoder which facilitates recurrent hierarchical feature propagation and amodal semantic grounding. We demonstrate the tractability of amodal optical flow in extensive experiments and show its utility for downstream tasks such as panoptic tracking. We make the dataset, code, and trained models publicly available at http://amodal-flow.cs.uni-freiburg.de.

2102.11057

Guillaume Jaume

Pushpak Pati and Guillaume Jaume and Antonio Foncubierta and Florinda Feroce and Anna Maria Anniciello and Giosu\`e Scognamiglio and Nadia Brancati and Maryse Fiche and Estelle Dubruc and Daniel Riccio and Maurizio Di Bonito and Giuseppe De Pietro and Gerardo Botti and Jean-Philippe Thiran and Maria Frucci and Orcun Goksel and Maria Gabrani

Hierarchical Graph Representations in Digital Pathology

null

cs.CV

http://creativecommons.org/publicdomain/zero/1.0/

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs that encode cell morphology and organization to denote the tissue information. These allow for utilizing machine learning to map tissue representations to tissue functionality to help quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a graph neural network is proposed to operate on the hierarchical entity-graph representation to map the tissue structure to tissue functionality. Specifically, for input histology images we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a graph neural network, to classify such HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of H&E stained breast tumor images, to evaluate our proposed methodology against pathologists and state-of-the-art approaches. Through comparative assessment and ablation studies, our method is demonstrated to yield superior classification results compared to alternative methods as well as pathologists.

[ { "created": "Mon, 22 Feb 2021 14:30:57 GMT", "version": "v1" }, { "created": "Wed, 17 Mar 2021 09:09:02 GMT", "version": "v2" } ]

2021-03-18

[ [ "Pati", "Pushpak", "" ], [ "Jaume", "Guillaume", "" ], [ "Foncubierta", "Antonio", "" ], [ "Feroce", "Florinda", "" ], [ "Anniciello", "Anna Maria", "" ], [ "Scognamiglio", "Giosuè", "" ], [ "Brancati", "Nadia", "" ], [ "Fiche", "Maryse", "" ], [ "Dubruc", "Estelle", "" ], [ "Riccio", "Daniel", "" ], [ "Di Bonito", "Maurizio", "" ], [ "De Pietro", "Giuseppe", "" ], [ "Botti", "Gerardo", "" ], [ "Thiran", "Jean-Philippe", "" ], [ "Frucci", "Maria", "" ], [ "Goksel", "Orcun", "" ], [ "Gabrani", "Maria", "" ] ]

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs that encode cell morphology and organization to denote the tissue information. These allow for utilizing machine learning to map tissue representations to tissue functionality to help quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a graph neural network is proposed to operate on the hierarchical entity-graph representation to map the tissue structure to tissue functionality. Specifically, for input histology images we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a graph neural network, to classify such HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of H&E stained breast tumor images, to evaluate our proposed methodology against pathologists and state-of-the-art approaches. Through comparative assessment and ablation studies, our method is demonstrated to yield superior classification results compared to alternative methods as well as pathologists.

cs/0507029

Samuel Landau

Samuel Landau (INRIA Futurs), Olivier Sigaud (LIP6), Marc Schoenauer (INRIA Futurs)

ATNoSFERES revisited

null

Dans Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2005 [OAI: oai:hal.inria.fr:inria-00000158_v1] - http://hal.inria.fr/inria-00000158

null

cs.AI

null

ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which the rules are represented as edges of an Augmented Transition Network. Genotypes are strings of tokens of a stack-based language, whose execution builds the labeled graph. The original ATNoSFERES, using a bitstring to represent the language tokens, has been favorably compared in previous work to several Michigan style LCSs architectures in the context of Non Markov problems. Several modifications of ATNoSFERES are proposed here: the most important one conceptually being a representational change: each token is now represented by an integer, hence the genotype is a string of integers; several other modifications of the underlying grammar language are also proposed. The resulting ATNoSFERES-II is validated on several standard animat Non Markov problems, on which it outperforms all previously published results in the LCS literature. The reasons for these improvement are carefully analyzed, and some assumptions are proposed on the underlying mechanisms in order to explain these good results.

[ { "created": "Mon, 11 Jul 2005 13:11:25 GMT", "version": "v1" } ]

2019-05-01

[ [ "Landau", "Samuel", "", "INRIA Futurs" ], [ "Sigaud", "Olivier", "", "LIP6" ], [ "Schoenauer", "Marc", "", "INRIA Futurs" ] ]

ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which the rules are represented as edges of an Augmented Transition Network. Genotypes are strings of tokens of a stack-based language, whose execution builds the labeled graph. The original ATNoSFERES, using a bitstring to represent the language tokens, has been favorably compared in previous work to several Michigan style LCSs architectures in the context of Non Markov problems. Several modifications of ATNoSFERES are proposed here: the most important one conceptually being a representational change: each token is now represented by an integer, hence the genotype is a string of integers; several other modifications of the underlying grammar language are also proposed. The resulting ATNoSFERES-II is validated on several standard animat Non Markov problems, on which it outperforms all previously published results in the LCS literature. The reasons for these improvement are carefully analyzed, and some assumptions are proposed on the underlying mechanisms in order to explain these good results.

2407.13594

Nils Palumbo

Nils Palumbo, Ravi Mangal, Zifan Wang, Saranya Vijayakumar, Corina S. Pasareanu, Somesh Jha

Mechanistically Interpreting a Transformer-based 2-SAT Solver: An Axiomatic Approach

null

cs.LG

http://creativecommons.org/licenses/by/4.0/

Mechanistic interpretability aims to reverse engineer the computation performed by a neural network in terms of its internal components. Although there is a growing body of research on mechanistic interpretation of neural networks, the notion of a mechanistic interpretation itself is often ad-hoc. Inspired by the notion of abstract interpretation from the program analysis literature that aims to develop approximate semantics for programs, we give a set of axioms that formally characterize a mechanistic interpretation as a description that approximately captures the semantics of the neural network under analysis in a compositional manner. We use these axioms to guide the mechanistic interpretability analysis of a Transformer-based model trained to solve the well-known 2-SAT problem. We are able to reverse engineer the algorithm learned by the model -- the model first parses the input formulas and then evaluates their satisfiability via enumeration of different possible valuations of the Boolean input variables. We also present evidence to support that the mechanistic interpretation of the analyzed model indeed satisfies the stated axioms.

[ { "created": "Thu, 18 Jul 2024 15:32:44 GMT", "version": "v1" } ]

2024-07-19

[ [ "Palumbo", "Nils", "" ], [ "Mangal", "Ravi", "" ], [ "Wang", "Zifan", "" ], [ "Vijayakumar", "Saranya", "" ], [ "Pasareanu", "Corina S.", "" ], [ "Jha", "Somesh", "" ] ]

Mechanistic interpretability aims to reverse engineer the computation performed by a neural network in terms of its internal components. Although there is a growing body of research on mechanistic interpretation of neural networks, the notion of a mechanistic interpretation itself is often ad-hoc. Inspired by the notion of abstract interpretation from the program analysis literature that aims to develop approximate semantics for programs, we give a set of axioms that formally characterize a mechanistic interpretation as a description that approximately captures the semantics of the neural network under analysis in a compositional manner. We use these axioms to guide the mechanistic interpretability analysis of a Transformer-based model trained to solve the well-known 2-SAT problem. We are able to reverse engineer the algorithm learned by the model -- the model first parses the input formulas and then evaluates their satisfiability via enumeration of different possible valuations of the Boolean input variables. We also present evidence to support that the mechanistic interpretation of the analyzed model indeed satisfies the stated axioms.

1905.09958

Ren\'ee Burton

Characterizing Certain DNS DDoS Attacks

25 pages, 21 figures

null

cs.CR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper details data science research in the area of Cyber Threat Intelligence applied to a specific type of Distributed Denial of Service (DDoS) attack. We study a DDoS technique prevalent in the Domain Name System (DNS) for which little malware have been recovered. Using data from a globally distributed set of a passive collectors (pDNS), we create a statistical classifier to identify these attacks and then use unsupervised learning to investigate the attack events and the malware that generates them. The first known major study of this technique, we discovered that current attacks have little resemblance to published descriptions and identify several previously unpublished features of the attacks. Through a combination of text and time series features, we are able to characterize the dominant malware and demonstrate that the number of global-scale attack systems is relatively small.

[ { "created": "Thu, 23 May 2019 22:38:11 GMT", "version": "v1" }, { "created": "Sat, 20 Jul 2019 16:21:23 GMT", "version": "v2" } ]

2019-07-23

[ [ "Burton", "Renée", "" ] ]

This paper details data science research in the area of Cyber Threat Intelligence applied to a specific type of Distributed Denial of Service (DDoS) attack. We study a DDoS technique prevalent in the Domain Name System (DNS) for which little malware have been recovered. Using data from a globally distributed set of a passive collectors (pDNS), we create a statistical classifier to identify these attacks and then use unsupervised learning to investigate the attack events and the malware that generates them. The first known major study of this technique, we discovered that current attacks have little resemblance to published descriptions and identify several previously unpublished features of the attacks. Through a combination of text and time series features, we are able to characterize the dominant malware and demonstrate that the number of global-scale attack systems is relatively small.

2309.15418

Hengchang Hu

Hengchang Hu, Yiming Cao, Zhankui He, Samson Tan, Min-Yen Kan

Automatic Feature Fairness in Recommendation via Adversaries

SIGIR-AP'23

null

10.1145/3624918.3625318

null

cs.IR cs.LG

http://creativecommons.org/licenses/by/4.0/

Fairness is a widely discussed topic in recommender systems, but its practical implementation faces challenges in defining sensitive features while maintaining recommendation accuracy. We propose feature fairness as the foundation to achieve equitable treatment across diverse groups defined by various feature combinations. This improves overall accuracy through balanced feature generalizability. We introduce unbiased feature learning through adversarial training, using adversarial perturbation to enhance feature representation. The adversaries improve model generalization for under-represented features. We adapt adversaries automatically based on two forms of feature biases: frequency and combination variety of feature values. This allows us to dynamically adjust perturbation strengths and adversarial training weights. Stronger perturbations are applied to feature values with fewer combination varieties to improve generalization, while higher weights for low-frequency features address training imbalances. We leverage the Adaptive Adversarial perturbation based on the widely-applied Factorization Machine (AAFM) as our backbone model. In experiments, AAFM surpasses strong baselines in both fairness and accuracy measures. AAFM excels in providing item- and user-fairness for single- and multi-feature tasks, showcasing their versatility and scalability. To maintain good accuracy, we find that adversarial perturbation must be well-managed: during training, perturbations should not overly persist and their strengths should decay.

[ { "created": "Wed, 27 Sep 2023 05:48:05 GMT", "version": "v1" } ]

2023-09-28

[ [ "Hu", "Hengchang", "" ], [ "Cao", "Yiming", "" ], [ "He", "Zhankui", "" ], [ "Tan", "Samson", "" ], [ "Kan", "Min-Yen", "" ] ]

Fairness is a widely discussed topic in recommender systems, but its practical implementation faces challenges in defining sensitive features while maintaining recommendation accuracy. We propose feature fairness as the foundation to achieve equitable treatment across diverse groups defined by various feature combinations. This improves overall accuracy through balanced feature generalizability. We introduce unbiased feature learning through adversarial training, using adversarial perturbation to enhance feature representation. The adversaries improve model generalization for under-represented features. We adapt adversaries automatically based on two forms of feature biases: frequency and combination variety of feature values. This allows us to dynamically adjust perturbation strengths and adversarial training weights. Stronger perturbations are applied to feature values with fewer combination varieties to improve generalization, while higher weights for low-frequency features address training imbalances. We leverage the Adaptive Adversarial perturbation based on the widely-applied Factorization Machine (AAFM) as our backbone model. In experiments, AAFM surpasses strong baselines in both fairness and accuracy measures. AAFM excels in providing item- and user-fairness for single- and multi-feature tasks, showcasing their versatility and scalability. To maintain good accuracy, we find that adversarial perturbation must be well-managed: during training, perturbations should not overly persist and their strengths should decay.

1906.07601

Yannick Est\`eve

Antoine Caubri\`ere, Natalia Tomashenko, Antoine Laurent, Emmanuel Morin, Nathalie Camelin, Yannick Est\`eve

Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability

Accepted to the INTERSPEECH 2019 conference. Submitted on March 29, 2019 (Paper submission deadline)

null

cs.CL cs.SD eess.AS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present an end-to-end approach to extract semantic concepts directly from the speech audio signal. To overcome the lack of data available for this spoken language understanding approach, we investigate the use of a transfer learning strategy based on the principles of curriculum learning. This approach allows us to exploit out-of-domain data that can help to prepare a fully neural architecture. Experiments are carried out on the French MEDIA and PORTMEDIA corpora and show that this end-to-end SLU approach reaches the best results ever published on this task. We compare our approach to a classical pipeline approach that uses ASR, POS tagging, lemmatizer, chunker... and other NLP tools that aim to enrich ASR outputs that feed an SLU text to concepts system. Last, we explore the promising capacity of our end-to-end SLU approach to address the problem of domain portability.

[ { "created": "Tue, 18 Jun 2019 14:19:52 GMT", "version": "v1" } ]

2019-06-19

[ [ "Caubrière", "Antoine", "" ], [ "Tomashenko", "Natalia", "" ], [ "Laurent", "Antoine", "" ], [ "Morin", "Emmanuel", "" ], [ "Camelin", "Nathalie", "" ], [ "Estève", "Yannick", "" ] ]

We present an end-to-end approach to extract semantic concepts directly from the speech audio signal. To overcome the lack of data available for this spoken language understanding approach, we investigate the use of a transfer learning strategy based on the principles of curriculum learning. This approach allows us to exploit out-of-domain data that can help to prepare a fully neural architecture. Experiments are carried out on the French MEDIA and PORTMEDIA corpora and show that this end-to-end SLU approach reaches the best results ever published on this task. We compare our approach to a classical pipeline approach that uses ASR, POS tagging, lemmatizer, chunker... and other NLP tools that aim to enrich ASR outputs that feed an SLU text to concepts system. Last, we explore the promising capacity of our end-to-end SLU approach to address the problem of domain portability.

2305.06985

Alexander Fengler

Alexander Fengler, Alejandro Lancho, Krishna Narayanan, and Yury Polyanskiy

On the Advantages of Asynchrony in the Unsourced MAC

Accepted for presentation at IEEE ISIT 2023

null

cs.IT math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this work we demonstrate how a lack of synchronization can in fact be advantageous in the problem of random access. Specifically, we consider a multiple-access problem over a frame-asynchronous 2-user binary-input adder channel in the unsourced setup (2-UBAC). Previous work has shown that under perfect synchronization the per-user rates achievable with linear codes over the 2-UBAC are limited by 0.5 bit per channel use (compared to the capacity of 0.75). In this paper, we first demonstrate that arbitrary small (even single-bit) shift between the user's frames enables (random) linear codes to attain full capacity of 0.75 bit/user. Furthermore, we derive density evolution equations for irregular LDPC codes, and prove (via concentration arguments) that they correctly track the asymptotic bit-error rate of a BP decoder. Optimizing the degree distributions we construct LDPC codes achieving per-user rates of 0.73 bit per channel use.

[ { "created": "Thu, 11 May 2023 17:16:57 GMT", "version": "v1" } ]

2023-05-12

[ [ "Fengler", "Alexander", "" ], [ "Lancho", "Alejandro", "" ], [ "Narayanan", "Krishna", "" ], [ "Polyanskiy", "Yury", "" ] ]

In this work we demonstrate how a lack of synchronization can in fact be advantageous in the problem of random access. Specifically, we consider a multiple-access problem over a frame-asynchronous 2-user binary-input adder channel in the unsourced setup (2-UBAC). Previous work has shown that under perfect synchronization the per-user rates achievable with linear codes over the 2-UBAC are limited by 0.5 bit per channel use (compared to the capacity of 0.75). In this paper, we first demonstrate that arbitrary small (even single-bit) shift between the user's frames enables (random) linear codes to attain full capacity of 0.75 bit/user. Furthermore, we derive density evolution equations for irregular LDPC codes, and prove (via concentration arguments) that they correctly track the asymptotic bit-error rate of a BP decoder. Optimizing the degree distributions we construct LDPC codes achieving per-user rates of 0.73 bit per channel use.

1703.00159

Yong Wang

A Calculus for True Concurrency

31 pages, 1 figures. arXiv admin note: substantial text overlap with arXiv:1611.09035

null

cs.LO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We design a calculus for true concurrency called CTC, including its syntax and operational semantics. CTC has good properties modulo several kinds of strongly truly concurrent bisimulations and weakly truly concurrent bisimulations, such as monoid laws, static laws, new expansion law for strongly truly concurrent bisimulations, $\tau$ laws for weakly truly concurrent bisimulations, and full congruences for strongly and weakly truly concurrent bisimulations, and also unique solution for recursion.

[ { "created": "Wed, 1 Mar 2017 07:25:23 GMT", "version": "v1" }, { "created": "Wed, 22 Apr 2020 11:55:29 GMT", "version": "v2" } ]

2020-04-24

[ [ "Wang", "Yong", "" ] ]

We design a calculus for true concurrency called CTC, including its syntax and operational semantics. CTC has good properties modulo several kinds of strongly truly concurrent bisimulations and weakly truly concurrent bisimulations, such as monoid laws, static laws, new expansion law for strongly truly concurrent bisimulations, $\tau$ laws for weakly truly concurrent bisimulations, and full congruences for strongly and weakly truly concurrent bisimulations, and also unique solution for recursion.

2101.02527

Christian Hesch

Ustim Khristenko, Stefan Schu{\ss}, Melanie Kr\"uger, Felix Schmidt, Barbara Wohlmuth, Christian Hesch

Multidimensional coupling: A variationally consistent approach to fiber-reinforced materials

null

10.1016/j.cma.2021.113869

null

cs.CE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

A novel mathematical model for fiber-reinforced materials is proposed. It is based on a 1-dimensional beam model for the thin fiber structures, a flexible and general 3-dimensional elasticity model for the matrix and an overlapping domain decomposition approach. From a computational point of view, this is motivated by the fact that matrix and fibers can easily meshed independently. Our main interest is in fiber reinforce polymers where the Young's modulus are quite different. Thus the modeling error from the overlapping approach is of no significance. The coupling conditions acknowledge both, the forces and the moments of the beam model and transfer them to the background material. A suitable static condensation procedure is applied to remove the beam balance equations. The condensed system then forms our starting point for a numerical approximation in terms of isogeometric analysis. The choice of our discrete basis functions of higher regularity is motivated by the fact, that as a result of the static condensation, we obtain second gradient terms in fiber direction. Eventually, a series of benchmark tests demonstrate the flexibility and robustness of the proposed methodology. As a proof-of-concept, we show that our new model is able to capture bending, torsion and shear dominated situations.

[ { "created": "Thu, 7 Jan 2021 13:03:03 GMT", "version": "v1" }, { "created": "Wed, 24 Feb 2021 14:29:01 GMT", "version": "v2" }, { "created": "Sat, 10 Apr 2021 06:42:55 GMT", "version": "v3" } ]

2021-05-12

[ [ "Khristenko", "Ustim", "" ], [ "Schuß", "Stefan", "" ], [ "Krüger", "Melanie", "" ], [ "Schmidt", "Felix", "" ], [ "Wohlmuth", "Barbara", "" ], [ "Hesch", "Christian", "" ] ]

A novel mathematical model for fiber-reinforced materials is proposed. It is based on a 1-dimensional beam model for the thin fiber structures, a flexible and general 3-dimensional elasticity model for the matrix and an overlapping domain decomposition approach. From a computational point of view, this is motivated by the fact that matrix and fibers can easily meshed independently. Our main interest is in fiber reinforce polymers where the Young's modulus are quite different. Thus the modeling error from the overlapping approach is of no significance. The coupling conditions acknowledge both, the forces and the moments of the beam model and transfer them to the background material. A suitable static condensation procedure is applied to remove the beam balance equations. The condensed system then forms our starting point for a numerical approximation in terms of isogeometric analysis. The choice of our discrete basis functions of higher regularity is motivated by the fact, that as a result of the static condensation, we obtain second gradient terms in fiber direction. Eventually, a series of benchmark tests demonstrate the flexibility and robustness of the proposed methodology. As a proof-of-concept, we show that our new model is able to capture bending, torsion and shear dominated situations.

1606.01178

Md. Reza

Md. Alimoor Reza and Jana Kosecka

Reinforcement Learning for Semantic Segmentation in Indoor Scenes

null

cs.CV cs.RO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Future advancements in robot autonomy and sophistication of robotics tasks rest on robust, efficient, and task-dependent semantic understanding of the environment. Semantic segmentation is the problem of simultaneous segmentation and categorization of a partition of sensory data. The majority of current approaches tackle this using multi-class segmentation and labeling in a Conditional Random Field (CRF) framework or by generating multiple object hypotheses and combining them sequentially. In practical settings, the subset of semantic labels that are needed depend on the task and particular scene and labelling every single pixel is not always necessary. We pursue these observations in developing a more modular and flexible approach to multi-class parsing of RGBD data based on learning strategies for combining independent binary object-vs-background segmentations in place of the usual monolithic multi-label CRF approach. Parameters for the independent binary segmentation models can be learned very efficiently, and the combination strategy---learned using reinforcement learning---can be set independently and can vary over different tasks and environments. Accuracy is comparable to state-of-art methods on a subset of the NYU-V2 dataset of indoor scenes, while providing additional flexibility and modularity.

[ { "created": "Fri, 3 Jun 2016 16:35:58 GMT", "version": "v1" } ]

2016-06-06

[ [ "Reza", "Md. Alimoor", "" ], [ "Kosecka", "Jana", "" ] ]

Future advancements in robot autonomy and sophistication of robotics tasks rest on robust, efficient, and task-dependent semantic understanding of the environment. Semantic segmentation is the problem of simultaneous segmentation and categorization of a partition of sensory data. The majority of current approaches tackle this using multi-class segmentation and labeling in a Conditional Random Field (CRF) framework or by generating multiple object hypotheses and combining them sequentially. In practical settings, the subset of semantic labels that are needed depend on the task and particular scene and labelling every single pixel is not always necessary. We pursue these observations in developing a more modular and flexible approach to multi-class parsing of RGBD data based on learning strategies for combining independent binary object-vs-background segmentations in place of the usual monolithic multi-label CRF approach. Parameters for the independent binary segmentation models can be learned very efficiently, and the combination strategy---learned using reinforcement learning---can be set independently and can vary over different tasks and environments. Accuracy is comparable to state-of-art methods on a subset of the NYU-V2 dataset of indoor scenes, while providing additional flexibility and modularity.

1905.09045

Lorenzo Cerrone

Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht

End-to-End Learned Random Walker for Seeded Image Segmentation

null

cs.CV cs.LG

http://creativecommons.org/licenses/by/4.0/

We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the underlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusivities for a linear diffusion process. Besides calculating the exact gradient for optimizing these diffusivities, we also propose simplifications that sparsely sample the gradient and still yield competitive results. The proposed method achieves the currently best results on a seeded version of the CREMI neuron segmentation challenge.

[ { "created": "Wed, 22 May 2019 09:56:04 GMT", "version": "v1" } ]

2019-05-23

[ [ "Cerrone", "Lorenzo", "" ], [ "Zeilmann", "Alexander", "" ], [ "Hamprecht", "Fred A.", "" ] ]

We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the underlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusivities for a linear diffusion process. Besides calculating the exact gradient for optimizing these diffusivities, we also propose simplifications that sparsely sample the gradient and still yield competitive results. The proposed method achieves the currently best results on a seeded version of the CREMI neuron segmentation challenge.

2009.10679

Manish Bhattarai

Manish Bhattarai, Aura Rose Jensen-Curtis, Manel Mart\'iNez-Ram\'on

An embedded deep learning system for augmented reality in firefighting applications

Accepted to ICMLA Special Session on Deep Learning

null

cs.CV cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Firefighting is a dynamic activity, in which numerous operations occur simultaneously. Maintaining situational awareness (i.e., knowledge of current conditions and activities at the scene) is critical to the accurate decision-making necessary for the safe and successful navigation of a fire environment by firefighters. Conversely, the disorientation caused by hazards such as smoke and extreme heat can lead to injury or even fatality. This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms to improve a firefighter's situational awareness and scene navigation through improved interpretation of that scene. We have designed and built a prototype embedded system that can leverage data streamed from cameras built into a firefighter's personal protective equipment (PPE) to capture thermal, RGB color, and depth imagery and then deploy already developed deep learning models to analyze the input data in real time. The embedded system analyzes and returns the processed images via wireless streaming, where they can be viewed remotely and relayed back to the firefighter using an augmented reality platform that visualizes the results of the analyzed inputs and draws the firefighter's attention to objects of interest, such as doors and windows otherwise invisible through smoke and flames.

[ { "created": "Tue, 22 Sep 2020 16:55:44 GMT", "version": "v1" } ]

2021-07-23

[ [ "Bhattarai", "Manish", "" ], [ "Jensen-Curtis", "Aura Rose", "" ], [ "MartíNez-Ramón", "Manel", "" ] ]

Firefighting is a dynamic activity, in which numerous operations occur simultaneously. Maintaining situational awareness (i.e., knowledge of current conditions and activities at the scene) is critical to the accurate decision-making necessary for the safe and successful navigation of a fire environment by firefighters. Conversely, the disorientation caused by hazards such as smoke and extreme heat can lead to injury or even fatality. This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms to improve a firefighter's situational awareness and scene navigation through improved interpretation of that scene. We have designed and built a prototype embedded system that can leverage data streamed from cameras built into a firefighter's personal protective equipment (PPE) to capture thermal, RGB color, and depth imagery and then deploy already developed deep learning models to analyze the input data in real time. The embedded system analyzes and returns the processed images via wireless streaming, where they can be viewed remotely and relayed back to the firefighter using an augmented reality platform that visualizes the results of the analyzed inputs and draws the firefighter's attention to objects of interest, such as doors and windows otherwise invisible through smoke and flames.

2006.10587

Yisroel Mirsky Dr.

Yisroel Mirsky, Tomer Golomb, Yuval Elovici

Lightweight Collaborative Anomaly Detection for the IoT using Blockchain

Preprint of accepted publication, June 2020: Journal of Parallel and Distributed Computing, Elsevier, ISSN: 0743-7315

null

cs.CR cs.DC cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives. Unfortunately, IoT devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. However, anomaly detection models must be trained for a long time in order to capture all benign behaviors. Furthermore, the anomaly detection model is vulnerable to adversarial attacks since, during the training phase, all observations are assumed to be benign. In this paper, we propose (1) a novel approach for anomaly detection and (2) a lightweight framework that utilizes the blockchain to ensemble an anomaly detection model in a distributed environment. Blockchain framework incrementally updates a trusted anomaly detection model via self-attestation and consensus among the IoT devices. We evaluate our method on a distributed IoT simulation platform, which consists of 48 Raspberry Pis. The simulation demonstrates how the approach can enhance the security of each device and the security of the network as a whole.

[ { "created": "Thu, 18 Jun 2020 14:50:08 GMT", "version": "v1" } ]

2020-06-19

[ [ "Mirsky", "Yisroel", "" ], [ "Golomb", "Tomer", "" ], [ "Elovici", "Yuval", "" ] ]

Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives. Unfortunately, IoT devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. However, anomaly detection models must be trained for a long time in order to capture all benign behaviors. Furthermore, the anomaly detection model is vulnerable to adversarial attacks since, during the training phase, all observations are assumed to be benign. In this paper, we propose (1) a novel approach for anomaly detection and (2) a lightweight framework that utilizes the blockchain to ensemble an anomaly detection model in a distributed environment. Blockchain framework incrementally updates a trusted anomaly detection model via self-attestation and consensus among the IoT devices. We evaluate our method on a distributed IoT simulation platform, which consists of 48 Raspberry Pis. The simulation demonstrates how the approach can enhance the security of each device and the security of the network as a whole.

1908.08774

Yikun Ban

Yikun Ban, Yuchen Zhou, Xu Cheng, Jiangfang Yi

Coalesced TLB to Exploit Diverse Contiguity of Memory Mapping

null

cs.DC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The miss rate of TLB is crucial to the performance of address translation for virtual memory. To reduce the TLB misses, improving translation coverage of TLB has been an primary approach. Many previous works focus on coalescing multiple contiguously mapped pages of the memory mapping into a modified entry, which function well if the assumed contiguity of memory mapping is given. Unfortunately, scenarios of applications are complicated and the produced contiguity diversify. To gain better performance of translation, in this paper, we first introduce a complex but prevalent type of contiguity, mixed contiguity. Then we propose a HW-SW hybrid coalesced TLB structure which works well on all observed types of contiguity including this type. In our evaluation, the proposed scheme, K-bit Aligned TLB, outperforms the state-of-the-art work by reducing at lease 27% TLB misses on average over it using 16 benchmarks.

[ { "created": "Thu, 22 Aug 2019 11:34:43 GMT", "version": "v1" }, { "created": "Mon, 2 Dec 2019 22:21:59 GMT", "version": "v2" } ]

2019-12-04

[ [ "Ban", "Yikun", "" ], [ "Zhou", "Yuchen", "" ], [ "Cheng", "Xu", "" ], [ "Yi", "Jiangfang", "" ] ]

The miss rate of TLB is crucial to the performance of address translation for virtual memory. To reduce the TLB misses, improving translation coverage of TLB has been an primary approach. Many previous works focus on coalescing multiple contiguously mapped pages of the memory mapping into a modified entry, which function well if the assumed contiguity of memory mapping is given. Unfortunately, scenarios of applications are complicated and the produced contiguity diversify. To gain better performance of translation, in this paper, we first introduce a complex but prevalent type of contiguity, mixed contiguity. Then we propose a HW-SW hybrid coalesced TLB structure which works well on all observed types of contiguity including this type. In our evaluation, the proposed scheme, K-bit Aligned TLB, outperforms the state-of-the-art work by reducing at lease 27% TLB misses on average over it using 16 benchmarks.

2311.08383

Priyanka Kaswan

Priyanka Kaswan and Sennur Ulukus

Choosing Outdated Information to Achieve Reliability in Age-Based Gossiping

null

cs.IT cs.NI eess.SP math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We consider a system model with two sources, a reliable source and an unreliable source, who are responsible for disseminating updates regarding a process to an age-based gossip network of $n$ nodes. Nodes wish to have fresh information, however, they have preference for packets that originated at the reliable source and are willing to sacrifice their version age of information by up to $G$ versions to switch from an unreliable packet to a reliable packet. We study how this protocol impacts the prevalence of unreliable packets at nodes in the network and their version age. Using a stochastic hybrid system (SHS) framework, we formulate analytical equations to characterize two quantities: expected fraction of nodes with unreliable packets and expected version age of information at network nodes. We show that as $G$ increases, fewer nodes have unreliable packet, however, their version age increases as well, thereby inducing a freshness-reliability trade-off in the network. We present numerical results to support our findings.

[ { "created": "Tue, 14 Nov 2023 18:45:29 GMT", "version": "v1" } ]

2023-11-15

[ [ "Kaswan", "Priyanka", "" ], [ "Ulukus", "Sennur", "" ] ]

We consider a system model with two sources, a reliable source and an unreliable source, who are responsible for disseminating updates regarding a process to an age-based gossip network of $n$ nodes. Nodes wish to have fresh information, however, they have preference for packets that originated at the reliable source and are willing to sacrifice their version age of information by up to $G$ versions to switch from an unreliable packet to a reliable packet. We study how this protocol impacts the prevalence of unreliable packets at nodes in the network and their version age. Using a stochastic hybrid system (SHS) framework, we formulate analytical equations to characterize two quantities: expected fraction of nodes with unreliable packets and expected version age of information at network nodes. We show that as $G$ increases, fewer nodes have unreliable packet, however, their version age increases as well, thereby inducing a freshness-reliability trade-off in the network. We present numerical results to support our findings.

2110.13052

Noah Golowich

Noah Golowich, Ankur Moitra

Can Q-Learning be Improved with Advice?

null

cs.LG cs.AI cs.DS math.OC stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Despite rapid progress in theoretical reinforcement learning (RL) over the last few years, most of the known guarantees are worst-case in nature, failing to take advantage of structure that may be known a priori about a given RL problem at hand. In this paper we address the question of whether worst-case lower bounds for regret in online learning of Markov decision processes (MDPs) can be circumvented when information about the MDP, in the form of predictions about its optimal $Q$-value function, is given to the algorithm. We show that when the predictions about the optimal $Q$-value function satisfy a reasonably weak condition we call distillation, then we can improve regret bounds by replacing the set of state-action pairs with the set of state-action pairs on which the predictions are grossly inaccurate. This improvement holds for both uniform regret bounds and gap-based ones. Further, we are able to achieve this property with an algorithm that achieves sublinear regret when given arbitrary predictions (i.e., even those which are not a distillation). Our work extends a recent line of work on algorithms with predictions, which has typically focused on simple online problems such as caching and scheduling, to the more complex and general problem of reinforcement learning.

[ { "created": "Mon, 25 Oct 2021 15:44:20 GMT", "version": "v1" } ]

2021-10-26

[ [ "Golowich", "Noah", "" ], [ "Moitra", "Ankur", "" ] ]

Despite rapid progress in theoretical reinforcement learning (RL) over the last few years, most of the known guarantees are worst-case in nature, failing to take advantage of structure that may be known a priori about a given RL problem at hand. In this paper we address the question of whether worst-case lower bounds for regret in online learning of Markov decision processes (MDPs) can be circumvented when information about the MDP, in the form of predictions about its optimal $Q$-value function, is given to the algorithm. We show that when the predictions about the optimal $Q$-value function satisfy a reasonably weak condition we call distillation, then we can improve regret bounds by replacing the set of state-action pairs with the set of state-action pairs on which the predictions are grossly inaccurate. This improvement holds for both uniform regret bounds and gap-based ones. Further, we are able to achieve this property with an algorithm that achieves sublinear regret when given arbitrary predictions (i.e., even those which are not a distillation). Our work extends a recent line of work on algorithms with predictions, which has typically focused on simple online problems such as caching and scheduling, to the more complex and general problem of reinforcement learning.

1709.00112

Swanand Kadhe

Swanand Kadhe, Brenden Garcia, Anoosheh Heidarzadeh, Salim El Rouayheb, Alex Sprintson

Private Information Retrieval with Side Information

Shorter version of the paper is accepted in Allerton Conference 2017

null

cs.IT cs.CR math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We study the problem of Private Information Retrieval (PIR) in the presence of prior side information. The problem setup includes a database of $K$ independent messages possibly replicated on several servers, and a user that needs to retrieve one of these messages. In addition, the user has some prior side information in the form of a subset of $M$ messages, not containing the desired message and unknown to the servers. This problem is motivated by practical settings in which the user can obtain side information opportunistically from other users or has previously downloaded some messages using classical PIR schemes. The objective of the user is to retrieve the required message without revealing its identity while minimizing the amount of data downloaded from the servers. We focus on achieving information-theoretic privacy in two scenarios: (i) the user wants to protect jointly its demand and side information; (ii) the user wants to protect only the information about its demand, but not the side information. To highlight the role of side information, we focus first on the case of a single server (single database). In the first scenario, we prove that the minimum download cost is $K-M$ messages, and in the second scenario it is $\lceil \frac{K}{M+1}\rceil$ messages, which should be compared to $K$ messages, the minimum download cost in the case of no side information. Then, we extend some of our results to the case of the database replicated on multiple servers. Our proof techniques relate PIR with side information to the index coding problem. We leverage this connection to prove converse results, as well as to design achievability schemes.

[ { "created": "Fri, 1 Sep 2017 00:04:11 GMT", "version": "v1" } ]

2017-09-04

[ [ "Kadhe", "Swanand", "" ], [ "Garcia", "Brenden", "" ], [ "Heidarzadeh", "Anoosheh", "" ], [ "Rouayheb", "Salim El", "" ], [ "Sprintson", "Alex", "" ] ]

We study the problem of Private Information Retrieval (PIR) in the presence of prior side information. The problem setup includes a database of $K$ independent messages possibly replicated on several servers, and a user that needs to retrieve one of these messages. In addition, the user has some prior side information in the form of a subset of $M$ messages, not containing the desired message and unknown to the servers. This problem is motivated by practical settings in which the user can obtain side information opportunistically from other users or has previously downloaded some messages using classical PIR schemes. The objective of the user is to retrieve the required message without revealing its identity while minimizing the amount of data downloaded from the servers. We focus on achieving information-theoretic privacy in two scenarios: (i) the user wants to protect jointly its demand and side information; (ii) the user wants to protect only the information about its demand, but not the side information. To highlight the role of side information, we focus first on the case of a single server (single database). In the first scenario, we prove that the minimum download cost is $K-M$ messages, and in the second scenario it is $\lceil \frac{K}{M+1}\rceil$ messages, which should be compared to $K$ messages, the minimum download cost in the case of no side information. Then, we extend some of our results to the case of the database replicated on multiple servers. Our proof techniques relate PIR with side information to the index coding problem. We leverage this connection to prove converse results, as well as to design achievability schemes.

1910.11791

Linchao Bao

Yajing Chen, Fanzi Wu, Zeyu Wang, Yibing Song, Yonggen Ling, Linchao Bao

Self-supervised Learning of Detailed 3D Face Reconstruction

Accepted by IEEE Transactions on Image Processing (TIP)

null

10.1109/TIP.2020.3017347

null

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement map in UV-space to represent a 3D face. Unlike previous work addressing the problem, our learning framework does not require supervision of surrogate ground-truth 3D models computed with traditional approaches. Instead, we utilize the input image itself as supervision during learning. In the first stage, we combine a photometric loss and a facial perceptual loss between the input face and the rendered face, to regress a 3DMM-based coarse model. In the second stage, both the input image and the regressed texture of the coarse model are unwrapped into UV-space, and then sent through an image-toimage translation network to predict a displacement map in UVspace. The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage. The advantage of learning displacement map in UV-space is that face alignment can be explicitly done during the unwrapping, thus facial details are easier to learn from large amount of data. Extensive experiments demonstrate the superiority of the proposed method over previous work.

[ { "created": "Fri, 25 Oct 2019 15:16:20 GMT", "version": "v1" }, { "created": "Wed, 2 Sep 2020 03:58:23 GMT", "version": "v2" } ]

2020-09-03

[ [ "Chen", "Yajing", "" ], [ "Wu", "Fanzi", "" ], [ "Wang", "Zeyu", "" ], [ "Song", "Yibing", "" ], [ "Ling", "Yonggen", "" ], [ "Bao", "Linchao", "" ] ]

In this paper, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement map in UV-space to represent a 3D face. Unlike previous work addressing the problem, our learning framework does not require supervision of surrogate ground-truth 3D models computed with traditional approaches. Instead, we utilize the input image itself as supervision during learning. In the first stage, we combine a photometric loss and a facial perceptual loss between the input face and the rendered face, to regress a 3DMM-based coarse model. In the second stage, both the input image and the regressed texture of the coarse model are unwrapped into UV-space, and then sent through an image-toimage translation network to predict a displacement map in UVspace. The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage. The advantage of learning displacement map in UV-space is that face alignment can be explicitly done during the unwrapping, thus facial details are easier to learn from large amount of data. Extensive experiments demonstrate the superiority of the proposed method over previous work.

1804.06996

Gaurav Bharaj

Gaurav Bharaj, Danny Kaufman, Etienne Vouga, Hanspeter Pfister

Metamorphs: Bistable Planar Structures

null

cs.GR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Extreme deformation can drastically morph a structure from one structural form into another. Programming such deformation properties into the structure is often challenging and in many cases an impossible task. The morphed forms do not hold and usually relapse to the original form, where the structure is in its lowest energy state. For example, a stick, when bent, resists its bent form and tends to go back to its initial straight form, where it holds the least amount of potential energy. In this project, we present a computational design method which can create fabricable planar structure that can morph into two different bistable forms. Once the user provides the initial desired forms, the method automatically creates support structures (internal springs), such that, the structure can not only morph, but also hold the respective forms under external force application. We achieve this through an iterative nonlinear optimization strategy for shaping the potential energy of the structure in the two forms simultaneously. Our approach guarantees first and second-order stability with respect to the potential energy of the bistable structure.

[ { "created": "Thu, 19 Apr 2018 05:15:03 GMT", "version": "v1" } ]

2018-04-20

[ [ "Bharaj", "Gaurav", "" ], [ "Kaufman", "Danny", "" ], [ "Vouga", "Etienne", "" ], [ "Pfister", "Hanspeter", "" ] ]

Extreme deformation can drastically morph a structure from one structural form into another. Programming such deformation properties into the structure is often challenging and in many cases an impossible task. The morphed forms do not hold and usually relapse to the original form, where the structure is in its lowest energy state. For example, a stick, when bent, resists its bent form and tends to go back to its initial straight form, where it holds the least amount of potential energy. In this project, we present a computational design method which can create fabricable planar structure that can morph into two different bistable forms. Once the user provides the initial desired forms, the method automatically creates support structures (internal springs), such that, the structure can not only morph, but also hold the respective forms under external force application. We achieve this through an iterative nonlinear optimization strategy for shaping the potential energy of the structure in the two forms simultaneously. Our approach guarantees first and second-order stability with respect to the potential energy of the bistable structure.

1510.01891

Adam Kurpisz

Adam Kurpisz, Samuli Lepp\"anen, Monaldo Mastrolilli

On the Hardest Problem Formulations for the 0/1 Lasserre Hierarchy

null

cs.CC cs.DS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The Lasserre/Sum-of-Squares (SoS) hierarchy is a systematic procedure for constructing a sequence of increasingly tight semidefinite relaxations. It is known that the hierarchy converges to the 0/1 polytope in n levels and captures the convex relaxations used in the best available approximation algorithms for a wide variety of optimization problems. In this paper we characterize the set of 0/1 integer linear problems and unconstrained 0/1 polynomial optimization problems that can still have an integrality gap at level n-1. These problems are the hardest for the Lasserre hierarchy in this sense.

[ { "created": "Wed, 7 Oct 2015 10:57:45 GMT", "version": "v1" } ]

2015-10-08

[ [ "Kurpisz", "Adam", "" ], [ "Leppänen", "Samuli", "" ], [ "Mastrolilli", "Monaldo", "" ] ]

The Lasserre/Sum-of-Squares (SoS) hierarchy is a systematic procedure for constructing a sequence of increasingly tight semidefinite relaxations. It is known that the hierarchy converges to the 0/1 polytope in n levels and captures the convex relaxations used in the best available approximation algorithms for a wide variety of optimization problems. In this paper we characterize the set of 0/1 integer linear problems and unconstrained 0/1 polynomial optimization problems that can still have an integrality gap at level n-1. These problems are the hardest for the Lasserre hierarchy in this sense.

2203.06870

Cl\'ement Canonne

Jayadev Acharya and Cl\'ement L. Canonne and Ziteng Sun and Himanshu Tyagi

The Role of Interactivity in Structured Estimation

null

cs.DS cs.DM cs.IT cs.LG math.IT math.ST stat.TH

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We study high-dimensional sparse estimation under three natural constraints: communication constraints, local privacy constraints, and linear measurements (compressive sensing). Without sparsity assumptions, it has been established that interactivity cannot improve the minimax rates of estimation under these information constraints. The question of whether interactivity helps with natural inference tasks has been a topic of active research. We settle this question in the affirmative for the prototypical problems of high-dimensional sparse mean estimation and compressive sensing, by demonstrating a gap between interactive and noninteractive protocols. We further establish that the gap increases when we have more structured sparsity: for block sparsity this gap can be as large as polynomial in the dimensionality. Thus, the more structured the sparsity is, the greater is the advantage of interaction. Proving the lower bounds requires a careful breaking of a sum of correlated random variables into independent components using Baranyai's theorem on decomposition of hypergraphs, which might be of independent interest.

[ { "created": "Mon, 14 Mar 2022 05:54:42 GMT", "version": "v1" } ]

2022-03-15

[ [ "Acharya", "Jayadev", "" ], [ "Canonne", "Clément L.", "" ], [ "Sun", "Ziteng", "" ], [ "Tyagi", "Himanshu", "" ] ]

We study high-dimensional sparse estimation under three natural constraints: communication constraints, local privacy constraints, and linear measurements (compressive sensing). Without sparsity assumptions, it has been established that interactivity cannot improve the minimax rates of estimation under these information constraints. The question of whether interactivity helps with natural inference tasks has been a topic of active research. We settle this question in the affirmative for the prototypical problems of high-dimensional sparse mean estimation and compressive sensing, by demonstrating a gap between interactive and noninteractive protocols. We further establish that the gap increases when we have more structured sparsity: for block sparsity this gap can be as large as polynomial in the dimensionality. Thus, the more structured the sparsity is, the greater is the advantage of interaction. Proving the lower bounds requires a careful breaking of a sum of correlated random variables into independent components using Baranyai's theorem on decomposition of hypergraphs, which might be of independent interest.

1903.00553

Binghui Wang

Binghui Wang, Neil Zhenqiang Gong

Attacking Graph-based Classification via Manipulating the Graph Structure

To appear in The 26th ACM Conference on Computer and Communications Security, Nov 2019

null

cs.CR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Graph-based classification methods are widely used for security and privacy analytics. Roughly speaking, graph-based classification methods include collective classification and graph neural network. Evading a graph-based classification method enables an attacker to evade detection in security analytics and can be used as a privacy defense against inference attacks. Existing adversarial machine learning studies mainly focused on machine learning for non-graph data. Only a few recent studies touched adversarial graph-based classification methods. However, they focused on graph neural network methods, leaving adversarial collective classification largely unexplored. We aim to bridge this gap in this work. We first propose a threat model to characterize the attack surface of a collective classification method. Specifically, we characterize an attacker's background knowledge along three dimensions: parameters of the method, training dataset, and the complete graph; an attacker's goal is to evade detection via manipulating the graph structure. We formulate our attack as a graph-based optimization problem, solving which produces the edges that an attacker needs to manipulate to achieve its attack goal. Moreover, we propose several approximation techniques to solve the optimization problem. We evaluate our attacks and compare them with a recent attack designed for graph neural networks. Results show that our attacks 1) can effectively evade graph-based classification methods; 2) do not require access to the true parameters, true training dataset, and/or complete graph; and 3) outperform the existing attack for evading collective classification methods and some graph neural network methods. We also apply our attacks to evade Sybil detection using a large-scale Twitter dataset and apply our attacks as a defense against attribute inference attacks using a large-scale Google+ dataset.

[ { "created": "Fri, 1 Mar 2019 21:59:17 GMT", "version": "v1" }, { "created": "Tue, 13 Aug 2019 02:00:12 GMT", "version": "v2" } ]

2019-08-14

[ [ "Wang", "Binghui", "" ], [ "Gong", "Neil Zhenqiang", "" ] ]

Graph-based classification methods are widely used for security and privacy analytics. Roughly speaking, graph-based classification methods include collective classification and graph neural network. Evading a graph-based classification method enables an attacker to evade detection in security analytics and can be used as a privacy defense against inference attacks. Existing adversarial machine learning studies mainly focused on machine learning for non-graph data. Only a few recent studies touched adversarial graph-based classification methods. However, they focused on graph neural network methods, leaving adversarial collective classification largely unexplored. We aim to bridge this gap in this work. We first propose a threat model to characterize the attack surface of a collective classification method. Specifically, we characterize an attacker's background knowledge along three dimensions: parameters of the method, training dataset, and the complete graph; an attacker's goal is to evade detection via manipulating the graph structure. We formulate our attack as a graph-based optimization problem, solving which produces the edges that an attacker needs to manipulate to achieve its attack goal. Moreover, we propose several approximation techniques to solve the optimization problem. We evaluate our attacks and compare them with a recent attack designed for graph neural networks. Results show that our attacks 1) can effectively evade graph-based classification methods; 2) do not require access to the true parameters, true training dataset, and/or complete graph; and 3) outperform the existing attack for evading collective classification methods and some graph neural network methods. We also apply our attacks to evade Sybil detection using a large-scale Twitter dataset and apply our attacks as a defense against attribute inference attacks using a large-scale Google+ dataset.

2308.03312

Kexin Pei

Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng, Lorenzo Cavallaro, Junfeng Yang, Suman Jana

Exploiting Code Symmetries for Learning Program Semantics

null

cs.LG cs.CR cs.PL

http://creativecommons.org/licenses/by/4.0/

This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We introduce a group-theoretic framework that defines code symmetries as semantics-preserving transformations, where forming a code symmetry group enables precise and efficient reasoning of code semantics. Our solution, SymC, develops a novel variant of self-attention that is provably equivariant to code symmetries from the permutation group defined over the program dependence graph. SymC obtains superior performance on five program analysis tasks, outperforming state-of-the-art code models without any pre-training. Our results suggest that code LLMs that encode the code structural prior via the code symmetry group generalize better and faster.

[ { "created": "Mon, 7 Aug 2023 05:40:58 GMT", "version": "v1" }, { "created": "Fri, 25 Aug 2023 16:08:51 GMT", "version": "v2" }, { "created": "Mon, 28 Aug 2023 04:53:52 GMT", "version": "v3" }, { "created": "Tue, 29 Aug 2023 01:44:39 GMT", "version": "v4" }, { "created": "Thu, 31 Aug 2023 02:29:36 GMT", "version": "v5" }, { "created": "Tue, 27 Feb 2024 21:18:17 GMT", "version": "v6" }, { "created": "Thu, 29 Feb 2024 05:16:24 GMT", "version": "v7" }, { "created": "Thu, 6 Jun 2024 16:35:20 GMT", "version": "v8" } ]

2024-06-07

[ [ "Pei", "Kexin", "" ], [ "Li", "Weichen", "" ], [ "Jin", "Qirui", "" ], [ "Liu", "Shuyang", "" ], [ "Geng", "Scott", "" ], [ "Cavallaro", "Lorenzo", "" ], [ "Yang", "Junfeng", "" ], [ "Jana", "Suman", "" ] ]

This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We introduce a group-theoretic framework that defines code symmetries as semantics-preserving transformations, where forming a code symmetry group enables precise and efficient reasoning of code semantics. Our solution, SymC, develops a novel variant of self-attention that is provably equivariant to code symmetries from the permutation group defined over the program dependence graph. SymC obtains superior performance on five program analysis tasks, outperforming state-of-the-art code models without any pre-training. Our results suggest that code LLMs that encode the code structural prior via the code symmetry group generalize better and faster.

2202.05329

Simon Lars\'en

Simon Lars\'en (1), Jean-R\'emy Falleri (2), Benoit Baudry (1), Martin Monperrus (1) ((1) KTH Royal Institute of Technology, (2) Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, IUF)

Spork: Structured Merge for Java with Formatting Preservation

21 pages, 18 figures, 11 tables, accepted for publication in IEEE Transactions on Software Engineering

IEEE Transactions on Software Engineering, 2022

10.1109/TSE.2022.3143766

null

cs.SE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The highly parallel workflows of modern software development have made merging of source code a common activity for developers. The state of the practice is based on line-based merge, which is ubiquitously used with "git merge". Line-based merge is however a generalized technique for any text that cannot leverage the structured nature of source code, making merge conflicts a common occurrence. As a remedy, research has proposed structured merge tools, which typically operate on abstract syntax trees instead of raw text. Structured merging greatly reduces the prevalence of merge conflicts but suffers from important limitations, the main ones being a tendency to alter the formatting of the merged code and being prone to excessive running times. In this paper, we present SPORK, a novel structured merge tool for JAVA. SPORK is unique as it preserves formatting to a significantly greater degree than comparable state-of-the-art tools. SPORK is also overall faster than the state of the art, in particular significantly reducing worst-case running times in practice. We demonstrate these properties by replaying 1740 real-world file merges collected from 119 open-source projects, and further demonstrate several key differences between SPORK and the state of the art with in-depth case studies.

[ { "created": "Thu, 10 Feb 2022 21:15:49 GMT", "version": "v1" } ]

2022-02-21

[ [ "Larsén", "Simon", "" ], [ "Falleri", "Jean-Rémy", "" ], [ "Baudry", "Benoit", "" ], [ "Monperrus", "Martin", "" ] ]

The highly parallel workflows of modern software development have made merging of source code a common activity for developers. The state of the practice is based on line-based merge, which is ubiquitously used with "git merge". Line-based merge is however a generalized technique for any text that cannot leverage the structured nature of source code, making merge conflicts a common occurrence. As a remedy, research has proposed structured merge tools, which typically operate on abstract syntax trees instead of raw text. Structured merging greatly reduces the prevalence of merge conflicts but suffers from important limitations, the main ones being a tendency to alter the formatting of the merged code and being prone to excessive running times. In this paper, we present SPORK, a novel structured merge tool for JAVA. SPORK is unique as it preserves formatting to a significantly greater degree than comparable state-of-the-art tools. SPORK is also overall faster than the state of the art, in particular significantly reducing worst-case running times in practice. We demonstrate these properties by replaying 1740 real-world file merges collected from 119 open-source projects, and further demonstrate several key differences between SPORK and the state of the art with in-depth case studies.

1501.04985

Konstantinos Georgiou

Jurek Czyzowicz, Konstantinos Georgiou, Evangelos Kranakis, Lata Narayanan, Jarda Opatrny, Birgit Vogtenhuber

Evacuating Robots from a Disk Using Face-to-Face Communication

22 pages, 8 figures. An extended abstract of this work was accepted for publication in the LNCS proceedings of the 9th International Conference on Algorithms and Complexity (CIAC15)

Discrete Mathematics & Theoretical Computer Science, vol. 22 no. 4, Distributed Computing and Networking (August 27, 2020) dmtcs:6198

10.23638/DMTCS-22-4-4

null

cs.DS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Assume that two robots are located at the centre of a unit disk. Their goal is to evacuate from the disk through an exit at an unknown location on the boundary of the disk. At any time the robots can move anywhere they choose on the disk, independently of each other, with maximum speed $1$. The robots can cooperate by exchanging information whenever they meet. We study algorithms for the two robots to minimize the evacuation time: the time when both robots reach the exit. In [CGGKMP14] the authors gave an algorithm defining trajectories for the two robots yielding evacuation time at most $5.740$ and also proved that any algorithm has evacuation time at least $3+ \frac{\pi}{4} + \sqrt{2} \approx 5.199$. We improve both the upper and lower bound on the evacuation time of a unit disk. Namely, we present a new non-trivial algorithm whose evacuation time is at most $5.628$ and show that any algorithm has evacuation time at least $3+ \frac{\pi}{6} + \sqrt{3} \approx 5.255$. To achieve the upper bound, we designed an algorithm which proposes a forced meeting between the two robots, even if the exit has not been found by either of them. We also show that such a strategy is provably optimal for a related problem of searching for an exit placed at the vertices of a regular hexagon.

[ { "created": "Tue, 20 Jan 2015 21:36:08 GMT", "version": "v1" }, { "created": "Thu, 12 Mar 2020 02:07:34 GMT", "version": "v2" }, { "created": "Tue, 9 Jun 2020 02:12:31 GMT", "version": "v3" }, { "created": "Tue, 21 Jul 2020 04:08:18 GMT", "version": "v4" }, { "created": "Mon, 24 Aug 2020 04:13:48 GMT", "version": "v5" } ]

2023-06-22

[ [ "Czyzowicz", "Jurek", "" ], [ "Georgiou", "Konstantinos", "" ], [ "Kranakis", "Evangelos", "" ], [ "Narayanan", "Lata", "" ], [ "Opatrny", "Jarda", "" ], [ "Vogtenhuber", "Birgit", "" ] ]

Assume that two robots are located at the centre of a unit disk. Their goal is to evacuate from the disk through an exit at an unknown location on the boundary of the disk. At any time the robots can move anywhere they choose on the disk, independently of each other, with maximum speed $1$. The robots can cooperate by exchanging information whenever they meet. We study algorithms for the two robots to minimize the evacuation time: the time when both robots reach the exit. In [CGGKMP14] the authors gave an algorithm defining trajectories for the two robots yielding evacuation time at most $5.740$ and also proved that any algorithm has evacuation time at least $3+ \frac{\pi}{4} + \sqrt{2} \approx 5.199$. We improve both the upper and lower bound on the evacuation time of a unit disk. Namely, we present a new non-trivial algorithm whose evacuation time is at most $5.628$ and show that any algorithm has evacuation time at least $3+ \frac{\pi}{6} + \sqrt{3} \approx 5.255$. To achieve the upper bound, we designed an algorithm which proposes a forced meeting between the two robots, even if the exit has not been found by either of them. We also show that such a strategy is provably optimal for a related problem of searching for an exit placed at the vertices of a regular hexagon.

2310.11450

Thomas Decker

Thomas Decker, Michael Lebacher and Volker Tresp

Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts

2023 IEEE 21st International Conference on Industrial Informatics (INDIN)

null

10.1109/INDIN51400.2023.10218170

null

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industrial prediction problems is challenging as it is not immediately clear how to define and access appropriate concepts for individual use cases and specific data types. In this work, we investigate how to leverage established concept-based explanation techniques in the context of bearing fault detection with deep neural networks trained on vibration signals. Since bearings are prevalent in almost every rotating equipment, ensuring the reliability of intransparent fault detection models is crucial to prevent costly repairs and downtimes of industrial machinery. Our evaluations demonstrate that explaining opaque models in terms of vibration concepts enables human-comprehensible and intuitive insights about their inner workings, but the underlying assumptions need to be carefully validated first.

[ { "created": "Tue, 17 Oct 2023 17:58:19 GMT", "version": "v1" } ]

2023-10-18

[ [ "Decker", "Thomas", "" ], [ "Lebacher", "Michael", "" ], [ "Tresp", "Volker", "" ] ]

Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industrial prediction problems is challenging as it is not immediately clear how to define and access appropriate concepts for individual use cases and specific data types. In this work, we investigate how to leverage established concept-based explanation techniques in the context of bearing fault detection with deep neural networks trained on vibration signals. Since bearings are prevalent in almost every rotating equipment, ensuring the reliability of intransparent fault detection models is crucial to prevent costly repairs and downtimes of industrial machinery. Our evaluations demonstrate that explaining opaque models in terms of vibration concepts enables human-comprehensible and intuitive insights about their inner workings, but the underlying assumptions need to be carefully validated first.

1403.3286

Sadegh Esmaeil Zadeh Soudjani

S. Esmaeil Zadeh Soudjani, C. Gevaerts, A. Abate

FAUST$^2$: Formal Abstractions of Uncountable-STate STochastic processes

This paper is submitted to the 26th International Conference on Computer Aided Verification (CAV 2014)

null

cs.SY

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

FAUST$^2$ is a software tool that generates formal abstractions of (possibly non-deterministic) discrete-time Markov processes (dtMP) defined over uncountable (continuous) state spaces. A dtMP model is specified in MATLAB and abstracted as a finite-state Markov chain or Markov decision processes. The abstraction procedure runs in MATLAB and employs parallel computations and fast manipulations based on vector calculus. The abstract model is formally put in relationship with the concrete dtMP via a user-defined maximum threshold on the approximation error introduced by the abstraction procedure. FAUST$^2$ allows exporting the abstract model to well-known probabilistic model checkers, such as PRISM or MRMC. Alternatively, it can handle internally the computation of PCTL properties (e.g. safety or reach-avoid) over the abstract model, and refine the outcomes over the concrete dtMP via a quantified error that depends on the abstraction procedure and the given formula. The toolbox is available at http://sourceforge.net/projects/faust2/

[ { "created": "Thu, 13 Mar 2014 14:53:46 GMT", "version": "v1" } ]

2014-03-14

[ [ "Soudjani", "S. Esmaeil Zadeh", "" ], [ "Gevaerts", "C.", "" ], [ "Abate", "A.", "" ] ]

FAUST$^2$ is a software tool that generates formal abstractions of (possibly non-deterministic) discrete-time Markov processes (dtMP) defined over uncountable (continuous) state spaces. A dtMP model is specified in MATLAB and abstracted as a finite-state Markov chain or Markov decision processes. The abstraction procedure runs in MATLAB and employs parallel computations and fast manipulations based on vector calculus. The abstract model is formally put in relationship with the concrete dtMP via a user-defined maximum threshold on the approximation error introduced by the abstraction procedure. FAUST$^2$ allows exporting the abstract model to well-known probabilistic model checkers, such as PRISM or MRMC. Alternatively, it can handle internally the computation of PCTL properties (e.g. safety or reach-avoid) over the abstract model, and refine the outcomes over the concrete dtMP via a quantified error that depends on the abstraction procedure and the given formula. The toolbox is available at http://sourceforge.net/projects/faust2/

2210.16046

Masakazu Yoshimura

Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, Takeshi Ohashi

Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments

Accepted to CVPR2023

null

cs.CV eess.IV

http://creativecommons.org/licenses/by/4.0/

Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the need for hard-to-obtain datasets. One simple approach is to apply data augmentation such as color jitter and blur to standard RGB (sRGB) images in simple scenes. Unfortunately, this approach struggles to yield realistic images in terms of pixel intensity and noise distribution due to not considering the non-linearity of Image Signal Processors (ISPs) and noise characteristics of image sensors. Instead, we propose a noise-accounted RAW image augmentation method. In essence, color jitter and blur augmentation are applied to a RAW image before applying non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a noise amount alignment method that calibrates the domain gap in the noise property caused by the augmentation. We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data.

[ { "created": "Fri, 28 Oct 2022 10:33:45 GMT", "version": "v1" }, { "created": "Mon, 27 Mar 2023 06:17:13 GMT", "version": "v2" } ]

2023-03-28

[ [ "Yoshimura", "Masakazu", "" ], [ "Otsuka", "Junji", "" ], [ "Irie", "Atsushi", "" ], [ "Ohashi", "Takeshi", "" ] ]

Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the need for hard-to-obtain datasets. One simple approach is to apply data augmentation such as color jitter and blur to standard RGB (sRGB) images in simple scenes. Unfortunately, this approach struggles to yield realistic images in terms of pixel intensity and noise distribution due to not considering the non-linearity of Image Signal Processors (ISPs) and noise characteristics of image sensors. Instead, we propose a noise-accounted RAW image augmentation method. In essence, color jitter and blur augmentation are applied to a RAW image before applying non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a noise amount alignment method that calibrates the domain gap in the noise property caused by the augmentation. We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data.

1305.2755

Issam Sahmoudi issam sahmoudi

Issam Sahmoudi and Abdelmonaime Lachkar

Clustering Web Search Results For Effective Arabic Language Browsing

null

International Journal on Natural Language Computing (IJNLC) Vol. 2, No.2, April 2013

10.5121/ijnlc.2013.2202

null

cs.IR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The process of browsing Search Results is one of the major problems with traditional Web search engines for English, European, and any other languages generally, and for Arabic Language particularly. This process is absolutely time consuming and the browsing style seems to be unattractive. Organizing Web search results into clusters facilitates users quick browsing through search results. Traditional clustering techniques (data-centric clustering algorithms) are inadequate since they don't generate clusters with highly readable names or cluster labels. To solve this problem, Description-centric algorithms such as Suffix Tree Clustering (STC) algorithm have been introduced and used successfully and extensively with different adapted versions for English, European, and Chinese Languages. However, till the day of writing this paper, in our knowledge, STC algorithm has been never applied for Arabic Web Snippets Search Results Clustering.In this paper, we propose first, to study how STC can be applied for Arabic Language? We then illustrate by example that is impossible to apply STC after Arabic Snippets pre-processing (stem or root extraction) because the Merging process yields many redundant clusters. Secondly, to overcome this problem, we propose to integrate STC in a new scheme taking into a count the Arabic language properties in order to get the web more and more adapted to Arabic users. The proposed approach automatically clusters the web search results into high quality, and high significant clusters labels. The obtained clusters not only are coherent, but also can convey the contents to the users concisely and accurately. Therefore the Arabic users can decide at a glance whether the contents of a cluster are of interest.....

[ { "created": "Mon, 13 May 2013 12:28:34 GMT", "version": "v1" } ]

2013-05-14

[ [ "Sahmoudi", "Issam", "" ], [ "Lachkar", "Abdelmonaime", "" ] ]

The process of browsing Search Results is one of the major problems with traditional Web search engines for English, European, and any other languages generally, and for Arabic Language particularly. This process is absolutely time consuming and the browsing style seems to be unattractive. Organizing Web search results into clusters facilitates users quick browsing through search results. Traditional clustering techniques (data-centric clustering algorithms) are inadequate since they don't generate clusters with highly readable names or cluster labels. To solve this problem, Description-centric algorithms such as Suffix Tree Clustering (STC) algorithm have been introduced and used successfully and extensively with different adapted versions for English, European, and Chinese Languages. However, till the day of writing this paper, in our knowledge, STC algorithm has been never applied for Arabic Web Snippets Search Results Clustering.In this paper, we propose first, to study how STC can be applied for Arabic Language? We then illustrate by example that is impossible to apply STC after Arabic Snippets pre-processing (stem or root extraction) because the Merging process yields many redundant clusters. Secondly, to overcome this problem, we propose to integrate STC in a new scheme taking into a count the Arabic language properties in order to get the web more and more adapted to Arabic users. The proposed approach automatically clusters the web search results into high quality, and high significant clusters labels. The obtained clusters not only are coherent, but also can convey the contents to the users concisely and accurately. Therefore the Arabic users can decide at a glance whether the contents of a cluster are of interest.....

1802.07983

Miroslav Bures

Miroslav Bures and Karel Frajtak and Bestoun S. Ahmed

Tapir: Automation Support of Exploratory Testing Using Model Reconstruction of the System Under Test

null

10.1109/TR.2018.2799957

null

cs.SE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

For a considerable number of software projects, the creation of effective test cases is hindered by design documentation that is either lacking, incomplete or obsolete. The exploratory testing approach can serve as a sound method in such situations. However, the efficiency of this testing approach strongly depends on the method, the documentation of explored parts of a system, the organization and distribution of work among individual testers on a team, and the minimization of potential (very probable) duplicities in performed tests. In this paper, we present a framework for replacing and automating a portion of these tasks. A screen-flow-based model of the tested system is incrementally reconstructed during the exploratory testing process by tracking testers' activities. With additional metadata, the model serves for an automated navigation process for a tester. Compared with the exploratory testing approach, which is manually performed in two case studies, the proposed framework allows the testers to explore a greater extent of the tested system and enables greater detection of the defects present in the system. The results show that the time efficiency of the testing process improved with framework support. This efficiency can be increased by team-based navigational strategies that are implemented within the proposed framework, which is documented by another case study presented in this paper.

[ { "created": "Thu, 22 Feb 2018 11:27:14 GMT", "version": "v1" } ]

2019-12-05

[ [ "Bures", "Miroslav", "" ], [ "Frajtak", "Karel", "" ], [ "Ahmed", "Bestoun S.", "" ] ]

For a considerable number of software projects, the creation of effective test cases is hindered by design documentation that is either lacking, incomplete or obsolete. The exploratory testing approach can serve as a sound method in such situations. However, the efficiency of this testing approach strongly depends on the method, the documentation of explored parts of a system, the organization and distribution of work among individual testers on a team, and the minimization of potential (very probable) duplicities in performed tests. In this paper, we present a framework for replacing and automating a portion of these tasks. A screen-flow-based model of the tested system is incrementally reconstructed during the exploratory testing process by tracking testers' activities. With additional metadata, the model serves for an automated navigation process for a tester. Compared with the exploratory testing approach, which is manually performed in two case studies, the proposed framework allows the testers to explore a greater extent of the tested system and enables greater detection of the defects present in the system. The results show that the time efficiency of the testing process improved with framework support. This efficiency can be increased by team-based navigational strategies that are implemented within the proposed framework, which is documented by another case study presented in this paper.

0910.1123

Arvind Yedla

Arvind Yedla, Henry D. Pfister, Krishna R. Narayanan

Can Iterative Decoding for Erasure Correlated Sources be Universal?

8 pages, to appear in Allerton '09

null

cs.IT math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, we consider a few iterative decoding schemes for the joint source-channel coding of correlated sources. Specifically, we consider the joint source-channel coding of two erasure correlated sources with transmission over different erasure channels. Our main interest is in determining whether or not various code ensembles can achieve the capacity region universally over varying channel conditions. We consider two ensembles in the class of low-density generator-matrix (LDGM) codes known as Luby-Transform (LT) codes and one ensemble of low-density parity-check (LDPC) codes. We analyze them using density evolution and show that optimized LT codes can achieve the extremal symmetric point of the capacity region. We also show that LT codes are not universal under iterative decoding for this problem because they cannot simultaneously achieve the extremal symmetric point and a corner point of the capacity region. The sub-universality of iterative decoding is characterized by studying the density evolution for LT codes.

[ { "created": "Tue, 6 Oct 2009 22:10:56 GMT", "version": "v1" } ]

2009-10-08

[ [ "Yedla", "Arvind", "" ], [ "Pfister", "Henry D.", "" ], [ "Narayanan", "Krishna R.", "" ] ]

In this paper, we consider a few iterative decoding schemes for the joint source-channel coding of correlated sources. Specifically, we consider the joint source-channel coding of two erasure correlated sources with transmission over different erasure channels. Our main interest is in determining whether or not various code ensembles can achieve the capacity region universally over varying channel conditions. We consider two ensembles in the class of low-density generator-matrix (LDGM) codes known as Luby-Transform (LT) codes and one ensemble of low-density parity-check (LDPC) codes. We analyze them using density evolution and show that optimized LT codes can achieve the extremal symmetric point of the capacity region. We also show that LT codes are not universal under iterative decoding for this problem because they cannot simultaneously achieve the extremal symmetric point and a corner point of the capacity region. The sub-universality of iterative decoding is characterized by studying the density evolution for LT codes.

2206.00227

Junbo Zhang

Junbo Zhang, Kaisheng Ma

Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views

Accepted to CVPR 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

null

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable. However, the predetermined composition of multiple data augmentations brings two drawbacks. First, the artificial choice of augmentation types brings specific representational invariances to the model, which have different degrees of positive and negative effects on different downstream tasks. Treating each type of augmentation equally during training makes the model learn non-optimal representations for various downstream tasks and limits the flexibility to choose augmentation types beforehand. Second, the strong data augmentations used in classic contrastive learning methods may bring too much invariance in some cases, and fine-grained information that is essential to some downstream tasks may be lost. This paper proposes a general method to alleviate these two problems by considering where and what to contrast in a general contrastive learning framework. We first propose to learn different augmentation invariances at different depths of the model according to the importance of each data augmentation instead of learning representational invariances evenly in the backbone. We then propose to expand the contrast content with augmentation embeddings to reduce the misleading effects of strong data augmentations. Experiments based on several baseline methods demonstrate that we learn better representations for various benchmarks on classification, detection, and segmentation downstream tasks.

[ { "created": "Wed, 1 Jun 2022 04:30:46 GMT", "version": "v1" }, { "created": "Mon, 22 Aug 2022 03:28:52 GMT", "version": "v2" } ]

2022-08-23

[ [ "Zhang", "Junbo", "" ], [ "Ma", "Kaisheng", "" ] ]

A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable. However, the predetermined composition of multiple data augmentations brings two drawbacks. First, the artificial choice of augmentation types brings specific representational invariances to the model, which have different degrees of positive and negative effects on different downstream tasks. Treating each type of augmentation equally during training makes the model learn non-optimal representations for various downstream tasks and limits the flexibility to choose augmentation types beforehand. Second, the strong data augmentations used in classic contrastive learning methods may bring too much invariance in some cases, and fine-grained information that is essential to some downstream tasks may be lost. This paper proposes a general method to alleviate these two problems by considering where and what to contrast in a general contrastive learning framework. We first propose to learn different augmentation invariances at different depths of the model according to the importance of each data augmentation instead of learning representational invariances evenly in the backbone. We then propose to expand the contrast content with augmentation embeddings to reduce the misleading effects of strong data augmentations. Experiments based on several baseline methods demonstrate that we learn better representations for various benchmarks on classification, detection, and segmentation downstream tasks.

0706.3132

Paulo Condado

Paulo A. Condado and Fernando G. Lobo

EasyVoice: Integrating voice synthesis with Skype

null

cs.CY cs.HC

null

This paper presents EasyVoice, a system that integrates voice synthesis with Skype. EasyVoice allows a person with voice disabilities to talk with another person located anywhere in the world, removing an important obstacle that affect these people during a phone or VoIP-based conversation.

[ { "created": "Thu, 21 Jun 2007 12:04:40 GMT", "version": "v1" } ]

2007-06-22

[ [ "Condado", "Paulo A.", "" ], [ "Lobo", "Fernando G.", "" ] ]

This paper presents EasyVoice, a system that integrates voice synthesis with Skype. EasyVoice allows a person with voice disabilities to talk with another person located anywhere in the world, removing an important obstacle that affect these people during a phone or VoIP-based conversation.

1512.03866

Fei Li

Qiuyan Wang, Fei Li and Dongdai Lin

A Class of Linear Codes With Three Weights

11 pages,2 tables

null

cs.IT math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Linear codes have been an interesting subject of study for many years. Recently, linear codes with few weights have been constructed and extensively studied. In this paper, for an odd prime p, a class of three-weight linear codes over Fp are constructed. The weight distributions of the linear codes are settled. These codes have applications in authentication codes, association schemes and data storage systems.

[ { "created": "Sat, 12 Dec 2015 02:42:24 GMT", "version": "v1" }, { "created": "Wed, 23 Dec 2015 03:32:36 GMT", "version": "v2" } ]

2015-12-24

[ [ "Wang", "Qiuyan", "" ], [ "Li", "Fei", "" ], [ "Lin", "Dongdai", "" ] ]

Linear codes have been an interesting subject of study for many years. Recently, linear codes with few weights have been constructed and extensively studied. In this paper, for an odd prime p, a class of three-weight linear codes over Fp are constructed. The weight distributions of the linear codes are settled. These codes have applications in authentication codes, association schemes and data storage systems.

2206.12011

Zeynep K

Zeynep K and Bobak Nazer

Detecting Correlated Gaussian Databases

26 pages, 4 figures

null

cs.IT math.IT math.ST stat.TH

http://creativecommons.org/licenses/by/4.0/

This paper considers the problem of detecting whether two databases, each consisting of $n$ users with $d$ Gaussian features, are correlated. Under the null hypothesis, the databases are independent. Under the alternate hypothesis, the features are correlated across databases, under an unknown row permutation. A simple test is developed to show that detection is achievable above $\rho^2 \approx \frac{1}{d}$. For the converse, the truncated second moment method is used to establish that detection is impossible below roughly $\rho^2 \approx \frac{1}{d\sqrt{n}}$. These results are compared to the corresponding recovery problem, where the goal is to decode the row permutation, and a converse bound of roughly $\rho^2 \approx 1 - n^{-4/d}$ has been previously shown. For certain choices of parameters, the detection achievability bound outperforms this recovery converse bound, demonstrating that detection can be easier than recovery in this scenario.

[ { "created": "Thu, 23 Jun 2022 23:08:24 GMT", "version": "v1" } ]

2022-06-27

[ [ "K", "Zeynep", "" ], [ "Nazer", "Bobak", "" ] ]

This paper considers the problem of detecting whether two databases, each consisting of $n$ users with $d$ Gaussian features, are correlated. Under the null hypothesis, the databases are independent. Under the alternate hypothesis, the features are correlated across databases, under an unknown row permutation. A simple test is developed to show that detection is achievable above $\rho^2 \approx \frac{1}{d}$. For the converse, the truncated second moment method is used to establish that detection is impossible below roughly $\rho^2 \approx \frac{1}{d\sqrt{n}}$. These results are compared to the corresponding recovery problem, where the goal is to decode the row permutation, and a converse bound of roughly $\rho^2 \approx 1 - n^{-4/d}$ has been previously shown. For certain choices of parameters, the detection achievability bound outperforms this recovery converse bound, demonstrating that detection can be easier than recovery in this scenario.

2208.05777

Shaina Raza Dr.

Shaina Raza, Deepak John Reji, Chen Ding

Dbias: Detecting biases and ensuring Fairness in news articles

Accepted for publication in International Journal of Data Science and Analytics

null

cs.IR

http://creativecommons.org/licenses/by/4.0/

Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature. This paper introduces Dbias (https://pypi.org/project/Dbias/), an open-source Python package for ensuring fairness in news articles. Dbias can take any text to determine if it is biased. Then, it detects biased words in the text, masks them, and suggests a set of sentences with new words that are bias-free or at least less biased. We conduct extensive experiments to assess the performance of Dbias. To see how well our approach works, we compare it to the existing fairness models. We also test the individual components of Dbias to see how effective they are. The experimental results show that Dbias outperforms all the baselines in terms of accuracy and fairness. We make this package (Dbias) as publicly available for the developers and practitioners to mitigate biases in textual data (such as news articles), as well as to encourage extension of this work.

[ { "created": "Thu, 11 Aug 2022 12:14:06 GMT", "version": "v1" } ]

2022-08-12

[ [ "Raza", "Shaina", "" ], [ "Reji", "Deepak John", "" ], [ "Ding", "Chen", "" ] ]

Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature. This paper introduces Dbias (https://pypi.org/project/Dbias/), an open-source Python package for ensuring fairness in news articles. Dbias can take any text to determine if it is biased. Then, it detects biased words in the text, masks them, and suggests a set of sentences with new words that are bias-free or at least less biased. We conduct extensive experiments to assess the performance of Dbias. To see how well our approach works, we compare it to the existing fairness models. We also test the individual components of Dbias to see how effective they are. The experimental results show that Dbias outperforms all the baselines in terms of accuracy and fairness. We make this package (Dbias) as publicly available for the developers and practitioners to mitigate biases in textual data (such as news articles), as well as to encourage extension of this work.

2104.00837

Pingchuan Ma

Pingchuan Ma, Tao Du, John Z. Zhang, Kui Wu, Andrew Spielberg, Robert K. Katzschmann, Wojciech Matusik

DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation

ACM SIGGRAPH 2021. Homepage: http://diffaqua.csail.mit.edu/

null

10.1145/3450626.3459832

null

cs.LG cs.GR cs.RO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer's geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes via optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer's performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.

[ { "created": "Fri, 2 Apr 2021 01:18:15 GMT", "version": "v1" }, { "created": "Wed, 5 May 2021 18:58:41 GMT", "version": "v2" } ]

2021-05-07

[ [ "Ma", "Pingchuan", "" ], [ "Du", "Tao", "" ], [ "Zhang", "John Z.", "" ], [ "Wu", "Kui", "" ], [ "Spielberg", "Andrew", "" ], [ "Katzschmann", "Robert K.", "" ], [ "Matusik", "Wojciech", "" ] ]

The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer's geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes via optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer's performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.

2111.03144

Abhinav Agrawal

Abhinav Agrawal, Justin Domke

Amortized Variational Inference for Simple Hierarchical Models

Neural Information Processing Systems (NeurIPS) 2021

null

cs.LG stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

It is difficult to use subsampling with variational inference in hierarchical models since the number of local latent variables scales with the dataset. Thus, inference in hierarchical models remains a challenge at large scale. It is helpful to use a variational family with structure matching the posterior, but optimization is still slow due to the huge number of local distributions. Instead, this paper suggests an amortized approach where shared parameters simultaneously represent all local distributions. This approach is similarly accurate as using a given joint distribution (e.g., a full-rank Gaussian) but is feasible on datasets that are several orders of magnitude larger. It is also dramatically faster than using a structured variational distribution.

[ { "created": "Thu, 4 Nov 2021 20:29:12 GMT", "version": "v1" } ]

2021-11-08

[ [ "Agrawal", "Abhinav", "" ], [ "Domke", "Justin", "" ] ]

It is difficult to use subsampling with variational inference in hierarchical models since the number of local latent variables scales with the dataset. Thus, inference in hierarchical models remains a challenge at large scale. It is helpful to use a variational family with structure matching the posterior, but optimization is still slow due to the huge number of local distributions. Instead, this paper suggests an amortized approach where shared parameters simultaneously represent all local distributions. This approach is similarly accurate as using a given joint distribution (e.g., a full-rank Gaussian) but is feasible on datasets that are several orders of magnitude larger. It is also dramatically faster than using a structured variational distribution.

1802.07779

Daniel DeFreez

Daniel DeFreez, Aditya V. Thakur, Cindy Rubio-Gonz\'alez

Path-Based Function Embedding and its Application to Specification Mining

11 pages, 8 figures

null

cs.SE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Identifying the relationships among program elements is useful for program understanding, debugging, and analysis. One such relationship is synonymy. Function synonyms are functions that play a similar role in code, e.g. functions that perform initialization for different device drivers, or functions that implement different symmetric-key encryption schemes. Function synonyms are not necessarily semantically equivalent and can be syntactically dissimilar; consequently, approaches for identifying code clones or functional equivalence cannot be used to identify them. This paper presents func2vec, an algorithm that maps each function to a vector in a vector space such that function synonyms are grouped together. We compute the function embedding by training a neural network on sentences generated from random walks over an encoding of the program as a labeled pushdown system (l-PDS). We demonstrate that func2vec is effective at identifying function synonyms in the Linux kernel. Furthermore, we show how function synonyms enable mining error-handling specifications with high support in Linux file systems and drivers.

[ { "created": "Wed, 21 Feb 2018 20:02:52 GMT", "version": "v1" }, { "created": "Sun, 25 Feb 2018 04:22:50 GMT", "version": "v2" } ]

2018-02-27

[ [ "DeFreez", "Daniel", "" ], [ "Thakur", "Aditya V.", "" ], [ "Rubio-González", "Cindy", "" ] ]

Identifying the relationships among program elements is useful for program understanding, debugging, and analysis. One such relationship is synonymy. Function synonyms are functions that play a similar role in code, e.g. functions that perform initialization for different device drivers, or functions that implement different symmetric-key encryption schemes. Function synonyms are not necessarily semantically equivalent and can be syntactically dissimilar; consequently, approaches for identifying code clones or functional equivalence cannot be used to identify them. This paper presents func2vec, an algorithm that maps each function to a vector in a vector space such that function synonyms are grouped together. We compute the function embedding by training a neural network on sentences generated from random walks over an encoding of the program as a labeled pushdown system (l-PDS). We demonstrate that func2vec is effective at identifying function synonyms in the Linux kernel. Furthermore, we show how function synonyms enable mining error-handling specifications with high support in Linux file systems and drivers.

1404.3920

Aske Plaat

Catholijn Jonker, Joost Broekens, Aske Plaat

Virtual Reflexes

null

cs.CY cs.HC

http://creativecommons.org/licenses/by/3.0/

Virtual Reality is used successfully to treat people for regular phobias. A new challenge is to develop Virtual Reality Exposure Training for social skills. Virtual actors in such systems have to show appropriate social behavior including emotions, gaze, and keeping distance. The behavior must be realistic and real-time. Current approaches consist of four steps: 1) trainee social signal detection, 2) cognitive-affective interpretation, 3) determination of the appropriate bodily responses, and 4) actuation. The "cognitive" detour of such approaches does not match the directness of human bodily reflexes and causes unrealistic responses and delay. Instead, we propose virtual reflexes as concurrent sensory-motor processes to control virtual actors. Here we present a virtual reflexes architecture, explain how emotion and cognitive modulation are embedded, detail its workings, and give an example description of an aggression training application.

[ { "created": "Mon, 14 Apr 2014 14:07:09 GMT", "version": "v1" } ]

2014-04-16

[ [ "Jonker", "Catholijn", "" ], [ "Broekens", "Joost", "" ], [ "Plaat", "Aske", "" ] ]

Virtual Reality is used successfully to treat people for regular phobias. A new challenge is to develop Virtual Reality Exposure Training for social skills. Virtual actors in such systems have to show appropriate social behavior including emotions, gaze, and keeping distance. The behavior must be realistic and real-time. Current approaches consist of four steps: 1) trainee social signal detection, 2) cognitive-affective interpretation, 3) determination of the appropriate bodily responses, and 4) actuation. The "cognitive" detour of such approaches does not match the directness of human bodily reflexes and causes unrealistic responses and delay. Instead, we propose virtual reflexes as concurrent sensory-motor processes to control virtual actors. Here we present a virtual reflexes architecture, explain how emotion and cognitive modulation are embedded, detail its workings, and give an example description of an aggression training application.

1908.07018

Ayush Maheshwari

Ayush Maheshwari, Hrishikesh Patel, Nandan Rathod, Ritesh Kumar, Ganesh Ramakrishnan and Pushpak Bhattacharyya

Tale of tails using rule augmented sequence labeling for event extraction

9 pages, 4 figures, 6 tables

StarAI Workshop at AAAI 2020

null

cs.IR cs.CL cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data. Moreover, the task becomes complex for tail (rarely occurring) labels wherein extremely less data is available. In this paper, we present a new dataset (InDEE-2019) in the disaster domain for multiple Indic languages, collected from news websites. Using this dataset, we evaluate several rule-based mechanisms to augment deep learning based models. We formulate our problem of event extraction as a sequence labeling task and perform extensive experiments to study and understand the effectiveness of different approaches. We further show that tail labels can be easily incorporated by creating new rules without the requirement of large annotated data.

[ { "created": "Mon, 19 Aug 2019 18:43:06 GMT", "version": "v1" }, { "created": "Thu, 22 Aug 2019 06:10:02 GMT", "version": "v2" }, { "created": "Fri, 31 Jan 2020 07:36:09 GMT", "version": "v3" } ]

2020-11-23

[ [ "Maheshwari", "Ayush", "" ], [ "Patel", "Hrishikesh", "" ], [ "Rathod", "Nandan", "" ], [ "Kumar", "Ritesh", "" ], [ "Ramakrishnan", "Ganesh", "" ], [ "Bhattacharyya", "Pushpak", "" ] ]

The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data. Moreover, the task becomes complex for tail (rarely occurring) labels wherein extremely less data is available. In this paper, we present a new dataset (InDEE-2019) in the disaster domain for multiple Indic languages, collected from news websites. Using this dataset, we evaluate several rule-based mechanisms to augment deep learning based models. We formulate our problem of event extraction as a sequence labeling task and perform extensive experiments to study and understand the effectiveness of different approaches. We further show that tail labels can be easily incorporated by creating new rules without the requirement of large annotated data.

1204.0535

S Muthukrishnan

Yishay Mansour, S. Muthukrishnan and Noam Nisan

Doubleclick Ad Exchange Auction

null

cs.GT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Display advertisements on the web are sold via ad exchanges that use real time auction. We describe the challenges of designing a suitable auction, and present a simple auction called the Optional Second Price (OSP) auction that is currently used in Doubleclick Ad Exchange.

[ { "created": "Mon, 2 Apr 2012 20:56:53 GMT", "version": "v1" } ]

2012-04-04

[ [ "Mansour", "Yishay", "" ], [ "Muthukrishnan", "S.", "" ], [ "Nisan", "Noam", "" ] ]

Display advertisements on the web are sold via ad exchanges that use real time auction. We describe the challenges of designing a suitable auction, and present a simple auction called the Optional Second Price (OSP) auction that is currently used in Doubleclick Ad Exchange.

1410.4011

EPTCS

Amir M. Ben-Amram, Aviad Pineles

Flowchart Programs, Regular Expressions, and Decidability of Polynomial Growth-Rate

In Proceedings VPT 2016, arXiv:1607.01835

EPTCS 216, 2016, pp. 24-49

10.4204/EPTCS.216.2

null

cs.PL cs.FL cs.LO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present a new method for inferring complexity properties for a class of programs in the form of flowcharts annotated with loop information. Specifically, our method can (soundly and completely) decide if computed values are polynomially bounded as a function of the input; and similarly for the running time. Such complexity properties are undecidable for a Turing-complete programming language, and a common work-around in program analysis is to settle for sound but incomplete solutions. In contrast, we consider a class of programs that is Turing-incomplete, but strong enough to include several challenges for this kind of analysis. For a related language that has well-structured syntax, similar to Meyer and Ritchie's LOOP programs, the problem has been previously proved to be decidable. The analysis relied on the compositionality of programs, hence the challenge in obtaining similar results for flowchart programs with arbitrary control-flow graphs. Our answer to the challenge is twofold: first, we propose a class of loop-annotated flowcharts, which is more general than the class of flowcharts that directly represent structured programs; secondly, we present a technique to reuse the ideas from the work on tructured programs and apply them to such flowcharts. The technique is inspired by the classic translation of non-deterministic automata to regular expressions, but we obviate the exponential cost of constructing such an expression, obtaining a polynomial-time analysis. These ideas may well be applicable to other analysis problems.

[ { "created": "Wed, 15 Oct 2014 11:16:16 GMT", "version": "v1" }, { "created": "Sun, 13 Mar 2016 14:40:47 GMT", "version": "v2" }, { "created": "Sun, 20 Mar 2016 10:01:27 GMT", "version": "v3" }, { "created": "Wed, 1 Jun 2016 16:30:18 GMT", "version": "v4" }, { "created": "Fri, 8 Jul 2016 05:30:33 GMT", "version": "v5" } ]

2016-07-11

[ [ "Ben-Amram", "Amir M.", "" ], [ "Pineles", "Aviad", "" ] ]

We present a new method for inferring complexity properties for a class of programs in the form of flowcharts annotated with loop information. Specifically, our method can (soundly and completely) decide if computed values are polynomially bounded as a function of the input; and similarly for the running time. Such complexity properties are undecidable for a Turing-complete programming language, and a common work-around in program analysis is to settle for sound but incomplete solutions. In contrast, we consider a class of programs that is Turing-incomplete, but strong enough to include several challenges for this kind of analysis. For a related language that has well-structured syntax, similar to Meyer and Ritchie's LOOP programs, the problem has been previously proved to be decidable. The analysis relied on the compositionality of programs, hence the challenge in obtaining similar results for flowchart programs with arbitrary control-flow graphs. Our answer to the challenge is twofold: first, we propose a class of loop-annotated flowcharts, which is more general than the class of flowcharts that directly represent structured programs; secondly, we present a technique to reuse the ideas from the work on tructured programs and apply them to such flowcharts. The technique is inspired by the classic translation of non-deterministic automata to regular expressions, but we obviate the exponential cost of constructing such an expression, obtaining a polynomial-time analysis. These ideas may well be applicable to other analysis problems.

2312.01656

Yilin Ye

Yilin Ye, Qian Zhu, Shishi Xiao, Kang Zhang, Wei Zeng

The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model

Accepted by The 2024 ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) (Proc. CSCW 2024)

null

cs.IR cs.AI cs.CV cs.HC

http://creativecommons.org/licenses/by/4.0/

Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.

[ { "created": "Mon, 4 Dec 2023 06:14:25 GMT", "version": "v1" }, { "created": "Tue, 5 Dec 2023 02:24:38 GMT", "version": "v2" } ]

2023-12-06

[ [ "Ye", "Yilin", "" ], [ "Zhu", "Qian", "" ], [ "Xiao", "Shishi", "" ], [ "Zhang", "Kang", "" ], [ "Zeng", "Wei", "" ] ]

Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.

2302.05743

Zian Li

Zian Li, Xiyuan Wang, Yinan Huang, Muhan Zhang

Is Distance Matrix Enough for Geometric Deep Learning?

To be published in NeurIPS2023

null

cs.LG cs.AI

http://creativecommons.org/licenses/by/4.0/

Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry of a given graph, such as molecular dynamics simulation. While incorporating Euclidean distance into Message Passing Neural Networks (referred to as Vanilla DisGNN) is a straightforward way to learn the geometry, it has been demonstrated that Vanilla DisGNN is geometrically incomplete. In this work, we first construct families of novel and symmetric geometric graphs that Vanilla DisGNN cannot distinguish even when considering all-pair distances, which greatly expands the existing counterexample families. Our counterexamples show the inherent limitation of Vanilla DisGNN to capture symmetric geometric structures. We then propose $k$-DisGNNs, which can effectively exploit the rich geometry contained in the distance matrix. We demonstrate the high expressive power of $k$-DisGNNs from three perspectives: 1. They can learn high-order geometric information that cannot be captured by Vanilla DisGNN. 2. They can unify some existing well-designed geometric models. 3. They are universal function approximators from geometric graphs to scalars (when $k\geq 2$) and vectors (when $k\geq 3$). Most importantly, we establish a connection between geometric deep learning (GDL) and traditional graph representation learning (GRL), showing that those highly expressive GNN models originally designed for GRL can also be applied to GDL with impressive performance, and that existing complicated, equivariant models are not the only solution. Experiments verify our theory. Our $k$-DisGNNs achieve many new state-of-the-art results on MD17.

[ { "created": "Sat, 11 Feb 2023 16:54:20 GMT", "version": "v1" }, { "created": "Mon, 3 Apr 2023 15:31:56 GMT", "version": "v2" }, { "created": "Fri, 14 Apr 2023 14:37:06 GMT", "version": "v3" }, { "created": "Fri, 2 Jun 2023 08:12:40 GMT", "version": "v4" }, { "created": "Tue, 31 Oct 2023 03:07:53 GMT", "version": "v5" } ]

2023-11-01

[ [ "Li", "Zian", "" ], [ "Wang", "Xiyuan", "" ], [ "Huang", "Yinan", "" ], [ "Zhang", "Muhan", "" ] ]

Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry of a given graph, such as molecular dynamics simulation. While incorporating Euclidean distance into Message Passing Neural Networks (referred to as Vanilla DisGNN) is a straightforward way to learn the geometry, it has been demonstrated that Vanilla DisGNN is geometrically incomplete. In this work, we first construct families of novel and symmetric geometric graphs that Vanilla DisGNN cannot distinguish even when considering all-pair distances, which greatly expands the existing counterexample families. Our counterexamples show the inherent limitation of Vanilla DisGNN to capture symmetric geometric structures. We then propose $k$-DisGNNs, which can effectively exploit the rich geometry contained in the distance matrix. We demonstrate the high expressive power of $k$-DisGNNs from three perspectives: 1. They can learn high-order geometric information that cannot be captured by Vanilla DisGNN. 2. They can unify some existing well-designed geometric models. 3. They are universal function approximators from geometric graphs to scalars (when $k\geq 2$) and vectors (when $k\geq 3$). Most importantly, we establish a connection between geometric deep learning (GDL) and traditional graph representation learning (GRL), showing that those highly expressive GNN models originally designed for GRL can also be applied to GDL with impressive performance, and that existing complicated, equivariant models are not the only solution. Experiments verify our theory. Our $k$-DisGNNs achieve many new state-of-the-art results on MD17.

1803.02123

Per Skarin

Per Skarin, William T\"arneberg, Karl-Erik {\AA}rzen, Maria Kihl

Towards Mission-Critical Control at the Edge and Over 5G

June 18th: Upload the final version as submitted to IEEE Services [EDGE] 2018 on May 16th (updated abstract and some wording, results unchanged)

null

cs.SY cs.DC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

With the emergence of industrial IoT and cloud computing, and the advent of 5G and edge clouds, there are ambitious expectations on elasticity, economies of scale, and fast time to market for demanding use cases in the next generation of ICT networks. Responsiveness and reliability of wireless communication links and services in the cloud are set to improve significantly as the concept of edge clouds is becoming more prevalent. To enable industrial uptake we must provide cloud capacity in the networks but also a sufficient level of simplicity and self-sustainability in the software platforms. In this paper, we present a research test-bed built to study mission-critical control over the distributed edge cloud. We evaluate system properties using a conventional control application in the form of a Model Predictive Controller. Our cloud platform provides the means to continuously operate our mission-critical application while seamlessly relocating computations across geographically dispersed compute nodes. Through our use of 5G wireless radio, we allow for mobility and reliably provide compute resources with low latency, at the edge. The primary contribution of this paper is a state-of-the art, fully operational test-bed showing the potential for merged IoT, 5G, and cloud. We also provide an evaluation of the system while operating a mission-critical application and provide an outlook on a novel research direction.

[ { "created": "Tue, 6 Mar 2018 11:31:59 GMT", "version": "v1" }, { "created": "Mon, 18 Jun 2018 08:25:56 GMT", "version": "v2" } ]

2018-06-19

[ [ "Skarin", "Per", "" ], [ "Tärneberg", "William", "" ], [ "Årzen", "Karl-Erik", "" ], [ "Kihl", "Maria", "" ] ]

With the emergence of industrial IoT and cloud computing, and the advent of 5G and edge clouds, there are ambitious expectations on elasticity, economies of scale, and fast time to market for demanding use cases in the next generation of ICT networks. Responsiveness and reliability of wireless communication links and services in the cloud are set to improve significantly as the concept of edge clouds is becoming more prevalent. To enable industrial uptake we must provide cloud capacity in the networks but also a sufficient level of simplicity and self-sustainability in the software platforms. In this paper, we present a research test-bed built to study mission-critical control over the distributed edge cloud. We evaluate system properties using a conventional control application in the form of a Model Predictive Controller. Our cloud platform provides the means to continuously operate our mission-critical application while seamlessly relocating computations across geographically dispersed compute nodes. Through our use of 5G wireless radio, we allow for mobility and reliably provide compute resources with low latency, at the edge. The primary contribution of this paper is a state-of-the art, fully operational test-bed showing the potential for merged IoT, 5G, and cloud. We also provide an evaluation of the system while operating a mission-critical application and provide an outlook on a novel research direction.

2205.14198

Marina Knittel

Marina Knittel, Max Springer, John P. Dickerson, MohammadTaghi Hajiaghayi

Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost

null

cs.LG cs.DS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering, after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation. Our work vastly improves the previous $O(n^{5/6}poly\log(n))$ fair approximation for cost to a near polylogarithmic $O(n^\delta poly\log(n))$ fair approximation for any constant $\delta\in(0,1)$. This result establishes a cost-fairness tradeoff and extends to broader fairness constraints than the previous work. We also show how to alter existing hierarchical clusterings to guarantee fairness and cluster balance across any level in the hierarchy.

[ { "created": "Fri, 27 May 2022 19:04:00 GMT", "version": "v1" }, { "created": "Tue, 9 May 2023 21:59:01 GMT", "version": "v2" } ]

2023-05-11

[ [ "Knittel", "Marina", "" ], [ "Springer", "Max", "" ], [ "Dickerson", "John P.", "" ], [ "Hajiaghayi", "MohammadTaghi", "" ] ]

Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering, after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation. Our work vastly improves the previous $O(n^{5/6}poly\log(n))$ fair approximation for cost to a near polylogarithmic $O(n^\delta poly\log(n))$ fair approximation for any constant $\delta\in(0,1)$. This result establishes a cost-fairness tradeoff and extends to broader fairness constraints than the previous work. We also show how to alter existing hierarchical clusterings to guarantee fairness and cluster balance across any level in the hierarchy.

2307.03003

Johannes Jakubik

Johannes Jakubik, Daniel Weber, Patrick Hemmer, Michael V\"ossing, Gerhard Satzger

Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts

Accepted at International Conference on Wirtschaftsinformatik, 2023

null

cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification.

[ { "created": "Thu, 6 Jul 2023 14:06:23 GMT", "version": "v1" }, { "created": "Fri, 7 Jul 2023 06:39:38 GMT", "version": "v2" } ]

2023-07-10

[ [ "Jakubik", "Johannes", "" ], [ "Weber", "Daniel", "" ], [ "Hemmer", "Patrick", "" ], [ "Vössing", "Michael", "" ], [ "Satzger", "Gerhard", "" ] ]

Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification.

1411.6749

Tie (Tony) Luo

Tie Luo and Mehul Motani and Vikram Srinivasan

Analyzing DISH for Multi-Channel MAC Protocols in Wireless Networks

Multi-channel multi-hop networks, availability of cooperation, cooperative protocol, distributed information sharing, ACM MobiHoc, May 2008

null

cs.NI cs.PF

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

For long, node cooperation has been exploited as a data relaying mechanism. However, the wireless channel allows for much richer interaction between nodes. One such scenario is in a multi-channel environment, where transmitter-receiver pairs may make incorrect decisions (e.g., in selecting channels) but idle neighbors could help by sharing information to prevent undesirable consequences (e.g., data collisions). This represents a Distributed Information SHaring (DISH) mechanism for cooperation and suggests new ways of designing cooperative protocols. However, what is lacking is a theoretical understanding of this new notion of cooperation. In this paper, we view cooperation as a network resource and evaluate the availability of cooperation via a metric, $p_{co}$, the probability of obtaining cooperation. First, we analytically evaluate $p_{co}$ in the context of multi-channel multi-hop wireless networks. Second, we verify our analysis via simulations and the results show that our analysis accurately characterizes the behavior of $p_{co}$ as a function of underlying network parameters. This step also yields important insights into DISH with respect to network dynamics. Third, we investigate the correlation between $p_{co}$ and network performance in terms of collision rate, packet delay, and throughput. The results indicate a near-linear relationship, which may significantly simplify performance analysis for cooperative networks and suggests that $p_{co}$ be used as an appropriate performance indicator itself. Throughout this work, we utilize, as appropriate, three different DISH contexts --- model-based DISH, ideal DISH, and real DISH --- to explore $p_{co}$.

[ { "created": "Tue, 25 Nov 2014 06:58:51 GMT", "version": "v1" } ]

2014-11-26

[ [ "Luo", "Tie", "" ], [ "Motani", "Mehul", "" ], [ "Srinivasan", "Vikram", "" ] ]

For long, node cooperation has been exploited as a data relaying mechanism. However, the wireless channel allows for much richer interaction between nodes. One such scenario is in a multi-channel environment, where transmitter-receiver pairs may make incorrect decisions (e.g., in selecting channels) but idle neighbors could help by sharing information to prevent undesirable consequences (e.g., data collisions). This represents a Distributed Information SHaring (DISH) mechanism for cooperation and suggests new ways of designing cooperative protocols. However, what is lacking is a theoretical understanding of this new notion of cooperation. In this paper, we view cooperation as a network resource and evaluate the availability of cooperation via a metric, $p_{co}$, the probability of obtaining cooperation. First, we analytically evaluate $p_{co}$ in the context of multi-channel multi-hop wireless networks. Second, we verify our analysis via simulations and the results show that our analysis accurately characterizes the behavior of $p_{co}$ as a function of underlying network parameters. This step also yields important insights into DISH with respect to network dynamics. Third, we investigate the correlation between $p_{co}$ and network performance in terms of collision rate, packet delay, and throughput. The results indicate a near-linear relationship, which may significantly simplify performance analysis for cooperative networks and suggests that $p_{co}$ be used as an appropriate performance indicator itself. Throughout this work, we utilize, as appropriate, three different DISH contexts --- model-based DISH, ideal DISH, and real DISH --- to explore $p_{co}$.

1803.01166

Seonwook Park

Seonwook Park and Christoph Gebhardt and Roman R\"adle and Anna Feit and Hana Vrzakova and Niraj Dayama and Hui-Shyong Yeo and Clemens Klokmose and Aaron Quigley and Antti Oulasvirta and Otmar Hilliges

AdaM: Adapting Multi-User Interfaces for Collaborative Environments in Real-Time

formatting tweaks

null

10.1145/3173574.3173758

null

cs.HC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Developing cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows real-time applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study.

[ { "created": "Sat, 3 Mar 2018 14:05:07 GMT", "version": "v1" }, { "created": "Thu, 29 Mar 2018 11:22:23 GMT", "version": "v2" } ]

2018-03-30

[ [ "Park", "Seonwook", "" ], [ "Gebhardt", "Christoph", "" ], [ "Rädle", "Roman", "" ], [ "Feit", "Anna", "" ], [ "Vrzakova", "Hana", "" ], [ "Dayama", "Niraj", "" ], [ "Yeo", "Hui-Shyong", "" ], [ "Klokmose", "Clemens", "" ], [ "Quigley", "Aaron", "" ], [ "Oulasvirta", "Antti", "" ], [ "Hilliges", "Otmar", "" ] ]

Developing cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows real-time applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study.

2312.03357

Doriand Petit

Doriand Petit, Steve Bourgeois, Dumitru Pavel, Vincent Gay-Bellile, Florian Chabot and Loic Barthe

RING-NeRF : Rethinking Inductive Biases for Versatile and Efficient Neural Fields

This publication has been accepted at ECCV'24

null

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models. Rather than developing hard-to-combine and specific modules, another approach generally overlooked is to directly inject generic priors on the scene representation (also called inductive biases) into the NeRF architecture. Based on this idea, we propose the RING-NeRF architecture which includes two inductive biases : a continuous multi-scale representation of the scene and an invariance of the decoder's latent space over spatial and scale domains. We also design a single reconstruction process that takes advantage of those inductive biases and experimentally demonstrates on-par performances in terms of quality with dedicated architecture on multiple tasks (anti-aliasing, few view reconstruction, SDF reconstruction without scene-specific initialization) while being more efficient. Moreover, RING-NeRF has the distinctive ability to dynamically increase the resolution of the model, opening the way to adaptive reconstruction.

[ { "created": "Wed, 6 Dec 2023 08:54:04 GMT", "version": "v1" }, { "created": "Thu, 14 Mar 2024 13:58:06 GMT", "version": "v2" }, { "created": "Wed, 17 Jul 2024 07:47:30 GMT", "version": "v3" } ]

2024-07-18

[ [ "Petit", "Doriand", "" ], [ "Bourgeois", "Steve", "" ], [ "Pavel", "Dumitru", "" ], [ "Gay-Bellile", "Vincent", "" ], [ "Chabot", "Florian", "" ], [ "Barthe", "Loic", "" ] ]

Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models. Rather than developing hard-to-combine and specific modules, another approach generally overlooked is to directly inject generic priors on the scene representation (also called inductive biases) into the NeRF architecture. Based on this idea, we propose the RING-NeRF architecture which includes two inductive biases : a continuous multi-scale representation of the scene and an invariance of the decoder's latent space over spatial and scale domains. We also design a single reconstruction process that takes advantage of those inductive biases and experimentally demonstrates on-par performances in terms of quality with dedicated architecture on multiple tasks (anti-aliasing, few view reconstruction, SDF reconstruction without scene-specific initialization) while being more efficient. Moreover, RING-NeRF has the distinctive ability to dynamically increase the resolution of the model, opening the way to adaptive reconstruction.

2402.02399

Hao Wang

Hao Wang, Licheng Pan, Zhichao Chen, Degui Yang, Sen Zhang, Yifei Yang, Xinggao Liu, Haoxuan Li, Dacheng Tao

FreDF: Learning to Forecast in Frequency Domain

null

cs.LG cs.AI stat.AP stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. Current research predominantly focuses on handling autocorrelation within the historical sequence but often neglects its presence in the label sequence. Specifically, emerging forecast models mainly conform to the direct forecast (DF) paradigm, generating multi-step forecasts under the assumption of conditional independence within the label sequence. This assumption disregards the inherent autocorrelation in the label sequence, thereby limiting the performance of DF-based models. In response to this gap, we introduce the Frequency-enhanced Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain. Our experiments demonstrate that FreDF substantially outperforms existing state-of-the-art methods including iTransformer and is compatible with a variety of forecast models.

[ { "created": "Sun, 4 Feb 2024 08:23:41 GMT", "version": "v1" } ]

2024-02-06

[ [ "Wang", "Hao", "" ], [ "Pan", "Licheng", "" ], [ "Chen", "Zhichao", "" ], [ "Yang", "Degui", "" ], [ "Zhang", "Sen", "" ], [ "Yang", "Yifei", "" ], [ "Liu", "Xinggao", "" ], [ "Li", "Haoxuan", "" ], [ "Tao", "Dacheng", "" ] ]

Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. Current research predominantly focuses on handling autocorrelation within the historical sequence but often neglects its presence in the label sequence. Specifically, emerging forecast models mainly conform to the direct forecast (DF) paradigm, generating multi-step forecasts under the assumption of conditional independence within the label sequence. This assumption disregards the inherent autocorrelation in the label sequence, thereby limiting the performance of DF-based models. In response to this gap, we introduce the Frequency-enhanced Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain. Our experiments demonstrate that FreDF substantially outperforms existing state-of-the-art methods including iTransformer and is compatible with a variety of forecast models.

2311.15260

Adam Tonderski

Adam Tonderski, Carl Lindstr\"om, Georg Hess, William Ljungbergh, Lennart Svensson, Christoffer Petersson

NeuRAD: Neural Rendering for Autonomous Driving

null

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training data augmentation technique. However, existing methods often require long training times, dense semantic supervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both camera and lidar -- including rolling shutter, beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets, achieving state-of-the-art performance across the board. To encourage further development, we will openly release the NeuRAD source code. See https://github.com/georghess/NeuRAD .

[ { "created": "Sun, 26 Nov 2023 10:27:22 GMT", "version": "v1" }, { "created": "Tue, 5 Dec 2023 09:53:18 GMT", "version": "v2" }, { "created": "Thu, 18 Apr 2024 12:44:56 GMT", "version": "v3" } ]

2024-04-19

[ [ "Tonderski", "Adam", "" ], [ "Lindström", "Carl", "" ], [ "Hess", "Georg", "" ], [ "Ljungbergh", "William", "" ], [ "Svensson", "Lennart", "" ], [ "Petersson", "Christoffer", "" ] ]

Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training data augmentation technique. However, existing methods often require long training times, dense semantic supervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both camera and lidar -- including rolling shutter, beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets, achieving state-of-the-art performance across the board. To encourage further development, we will openly release the NeuRAD source code. See https://github.com/georghess/NeuRAD .

1207.2847

Kai Liu

Kai Liu, Hock Beng Lim

Positioning Accuracy Improvement via Distributed Location Estimate in Cooperative Vehicular Networks

To appear in Proc. of the 15th International IEEE Conference on Intelligent Transportation Systems (IEEE ITSC'12)

null

10.1109/ITSC.2012.6338743

null

cs.DC cs.DS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The development of cooperative vehicle safety (CVS) applications, such as collision warnings, turning assistants, and speed advisories, etc., has received great attention in the past few years. Accurate vehicular localization is essential to enable these applications. In this study, motivated by the proliferation of the Global Positioning System (GPS) devices, and the increasing sophistication of wireless communication technologies in vehicular networks, we propose a distributed location estimate algorithm to improve the positioning accuracy via cooperative inter-vehicle distance measurement. In particular, we compute the inter-vehicle distance based on raw GPS pseudorange measurements, instead of depending on traditional radio-based ranging techniques, which usually either suffer from high hardware cost or have inadequate positioning accuracy. In addition, we improve the estimation of the vehicles' locations only based on the inaccurate GPS fixes, without using any anchors with known exact locations. The algorithm is decentralized, which enhances its practicability in highly dynamic vehicular networks. We have developed a simulation model to evaluate the performance of the proposed algorithm, and the results demonstrate that the algorithm can significantly improve the positioning accuracy.

[ { "created": "Thu, 12 Jul 2012 05:27:16 GMT", "version": "v1" }, { "created": "Sat, 14 Jul 2012 06:32:37 GMT", "version": "v2" }, { "created": "Fri, 20 Jul 2012 08:33:23 GMT", "version": "v3" } ]

2016-11-15

[ [ "Liu", "Kai", "" ], [ "Lim", "Hock Beng", "" ] ]

The development of cooperative vehicle safety (CVS) applications, such as collision warnings, turning assistants, and speed advisories, etc., has received great attention in the past few years. Accurate vehicular localization is essential to enable these applications. In this study, motivated by the proliferation of the Global Positioning System (GPS) devices, and the increasing sophistication of wireless communication technologies in vehicular networks, we propose a distributed location estimate algorithm to improve the positioning accuracy via cooperative inter-vehicle distance measurement. In particular, we compute the inter-vehicle distance based on raw GPS pseudorange measurements, instead of depending on traditional radio-based ranging techniques, which usually either suffer from high hardware cost or have inadequate positioning accuracy. In addition, we improve the estimation of the vehicles' locations only based on the inaccurate GPS fixes, without using any anchors with known exact locations. The algorithm is decentralized, which enhances its practicability in highly dynamic vehicular networks. We have developed a simulation model to evaluate the performance of the proposed algorithm, and the results demonstrate that the algorithm can significantly improve the positioning accuracy.

0802.0820

Jonathan Hayman

Jonathan Hayman and Glynn Winskel

Independence and concurrent separation logic

null

Logical Methods in Computer Science, Volume 4, Issue 1 (March 19, 2008) lmcs:1100

10.2168/LMCS-4(1:6)2008

null

cs.LO cs.PL

null

A compositional Petri net-based semantics is given to a simple language allowing pointer manipulation and parallelism. The model is then applied to give a notion of validity to the judgements made by concurrent separation logic that emphasizes the process-environment duality inherent in such rely-guarantee reasoning. Soundness of the rules of concurrent separation logic with respect to this definition of validity is shown. The independence information retained by the Petri net model is then exploited to characterize the independence of parallel processes enforced by the logic. This is shown to permit a refinement operation capable of changing the granularity of atomic actions.

[ { "created": "Wed, 6 Feb 2008 15:39:20 GMT", "version": "v1" }, { "created": "Wed, 19 Mar 2008 15:26:51 GMT", "version": "v2" } ]

2015-07-01

[ [ "Hayman", "Jonathan", "" ], [ "Winskel", "Glynn", "" ] ]

A compositional Petri net-based semantics is given to a simple language allowing pointer manipulation and parallelism. The model is then applied to give a notion of validity to the judgements made by concurrent separation logic that emphasizes the process-environment duality inherent in such rely-guarantee reasoning. Soundness of the rules of concurrent separation logic with respect to this definition of validity is shown. The independence information retained by the Petri net model is then exploited to characterize the independence of parallel processes enforced by the logic. This is shown to permit a refinement operation capable of changing the granularity of atomic actions.

2101.00318

Xiaofeng Liu

Xiaofeng Liu, Xiongchang Liu, Bo Hu, Wenxuan Ji, Fangxu Xing, Jun Lu, Jane You, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

Accepted to AAAI 2021

null

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases of with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on medical diagnosis tasks.

[ { "created": "Fri, 1 Jan 2021 21:04:50 GMT", "version": "v1" }, { "created": "Mon, 11 Jan 2021 15:09:03 GMT", "version": "v2" } ]

2021-01-12

[ [ "Liu", "Xiaofeng", "" ], [ "Liu", "Xiongchang", "" ], [ "Hu", "Bo", "" ], [ "Ji", "Wenxuan", "" ], [ "Xing", "Fangxu", "" ], [ "Lu", "Jun", "" ], [ "You", "Jane", "" ], [ "Kuo", "C. -C. Jay", "" ], [ "Fakhri", "Georges El", "" ], [ "Woo", "Jonghye", "" ] ]

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases of with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on medical diagnosis tasks.

1104.4668

Massimiliano Vasile Massimiliano Vasile

Matteo Ceriotti and Massimiliano Vasile

MGA trajectory planning with an ACO-inspired algorithm

null

Acta Astronautica, 67 (9-10). pp. 1202-1217, ISSN 0094-5765, 2010

null

cs.CE cs.NE cs.SY math.OC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem. Its automated solution would greatly improve the design of future space missions, allowing the assessment of a large number of alternative mission options in a short time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the optimal planetary sequence and a good estimation of the set of associated optimal trajectories. The trajectory model consists of a sequence of celestial bodies connected by twodimensional transfer arcs containing one DSM. For each transfer arc, the position of the planet and the spacecraft, at the time of arrival, are matched by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by Ant Colony Optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select a feasible direction. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter.

[ { "created": "Mon, 25 Apr 2011 00:58:35 GMT", "version": "v1" } ]

2011-04-26

[ [ "Ceriotti", "Matteo", "" ], [ "Vasile", "Massimiliano", "" ] ]

Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem. Its automated solution would greatly improve the design of future space missions, allowing the assessment of a large number of alternative mission options in a short time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the optimal planetary sequence and a good estimation of the set of associated optimal trajectories. The trajectory model consists of a sequence of celestial bodies connected by twodimensional transfer arcs containing one DSM. For each transfer arc, the position of the planet and the spacecraft, at the time of arrival, are matched by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by Ant Colony Optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select a feasible direction. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter.

2310.02264

Mingyu Ding

Haoyu Zhou, Mingyu Ding, Weikun Peng, Masayoshi Tomizuka, Lin Shao, Chuang Gan

Generalizable Long-Horizon Manipulations with Large Language Models

null

cs.RO cs.CL cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks. These task conditions serve as guides for the generation and adjustment of Dynamic Movement Primitives (DMP) trajectories for long-horizon task execution. We further create a challenging robotic manipulation task suite based on Pybullet for long-horizon task evaluation. Extensive experiments in both simulated and real-world environments demonstrate the effectiveness of our framework on both familiar tasks involving new objects and novel but related tasks, highlighting the potential of LLMs in enhancing robotic system versatility and adaptability. Project website: https://object814.github.io/Task-Condition-With-LLM/

[ { "created": "Tue, 3 Oct 2023 17:59:46 GMT", "version": "v1" } ]

2023-10-04

[ [ "Zhou", "Haoyu", "" ], [ "Ding", "Mingyu", "" ], [ "Peng", "Weikun", "" ], [ "Tomizuka", "Masayoshi", "" ], [ "Shao", "Lin", "" ], [ "Gan", "Chuang", "" ] ]

This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks. These task conditions serve as guides for the generation and adjustment of Dynamic Movement Primitives (DMP) trajectories for long-horizon task execution. We further create a challenging robotic manipulation task suite based on Pybullet for long-horizon task evaluation. Extensive experiments in both simulated and real-world environments demonstrate the effectiveness of our framework on both familiar tasks involving new objects and novel but related tasks, highlighting the potential of LLMs in enhancing robotic system versatility and adaptability. Project website: https://object814.github.io/Task-Condition-With-LLM/

1706.06239

Hao Wang

Hao Wang, Yanmei Fu, Qinyong Wang, Hongzhi Yin, Changying Du, Hui Xiong

A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

Accepted by KDD 2017

null

cs.SI cs.IR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.

[ { "created": "Tue, 20 Jun 2017 01:54:01 GMT", "version": "v1" } ]

2017-06-21

[ [ "Wang", "Hao", "" ], [ "Fu", "Yanmei", "" ], [ "Wang", "Qinyong", "" ], [ "Yin", "Hongzhi", "" ], [ "Du", "Changying", "" ], [ "Xiong", "Hui", "" ] ]

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.

1305.3354

Sandip Chakraborty

Sandip Chakraborty, Soumyadip Majumder, Diganta Goswami

Approximate Congestion Games for Load Balancing in Distributed Environment

A version of this work has been presented at International Workshop on Distributed System (IWDS) 2010, IIT Kanpur, India, as a "work-in-progress" report

null

cs.NI cs.DC cs.GT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The use of game theoretic models has been quite successful in describing various cooperative and non-cooperative optimization problems in networks and other domains of computer systems. In this paper, we study an application of game theoretic models in the domain of distributed system, where nodes play a game to balance the total processing loads among themselves. We have used congestion gaming model, a model of game theory where many agents compete for allocating resources, and studied the existence of Nash Equilibrium for such types of games. As the classical congestion game is known to be PLS-Complete, we use an approximation, called the \epsilon-Congestion game, which converges to \epsilon-Nash equilibrium within finite number of steps under selected conditions. Our focus is to define the load balancing problem using the model of \epsilon-congestion games, and finally provide a greedy algorithm for load balancing in distributed systems. We have simulated our proposed system to show the effect of \epsilon-congestion game, and the distribution of load at equilibrium state.

[ { "created": "Wed, 15 May 2013 05:06:02 GMT", "version": "v1" } ]

2013-05-16

[ [ "Chakraborty", "Sandip", "" ], [ "Majumder", "Soumyadip", "" ], [ "Goswami", "Diganta", "" ] ]

The use of game theoretic models has been quite successful in describing various cooperative and non-cooperative optimization problems in networks and other domains of computer systems. In this paper, we study an application of game theoretic models in the domain of distributed system, where nodes play a game to balance the total processing loads among themselves. We have used congestion gaming model, a model of game theory where many agents compete for allocating resources, and studied the existence of Nash Equilibrium for such types of games. As the classical congestion game is known to be PLS-Complete, we use an approximation, called the \epsilon-Congestion game, which converges to \epsilon-Nash equilibrium within finite number of steps under selected conditions. Our focus is to define the load balancing problem using the model of \epsilon-congestion games, and finally provide a greedy algorithm for load balancing in distributed systems. We have simulated our proposed system to show the effect of \epsilon-congestion game, and the distribution of load at equilibrium state.

2309.13438

Tingyu Zhao

Tingyu Zhao, Bo Peng, Yuan Sun, Daipeng Yang, Zhenguang Zhang, and Xi Wu

Rethinking Superpixel Segmentation from Biologically Inspired Mechanisms

null

cs.CV cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recently, advancements in deep learning-based superpixel segmentation methods have brought about improvements in both the efficiency and the performance of segmentation. However, a significant challenge remains in generating superpixels that strictly adhere to object boundaries while conveying rich visual significance, especially when cross-surface color correlations may interfere with objects. Drawing inspiration from neural structure and visual mechanisms, we propose a biological network architecture comprising an Enhanced Screening Module (ESM) and a novel Boundary-Aware Label (BAL) for superpixel segmentation. The ESM enhances semantic information by simulating the interactive projection mechanisms of the visual cortex. Additionally, the BAL emulates the spatial frequency characteristics of visual cortical cells to facilitate the generation of superpixels with strong boundary adherence. We demonstrate the effectiveness of our approach through evaluations on both the BSDS500 dataset and the NYUv2 dataset.

[ { "created": "Sat, 23 Sep 2023 17:29:38 GMT", "version": "v1" }, { "created": "Wed, 4 Oct 2023 12:13:53 GMT", "version": "v2" }, { "created": "Wed, 11 Oct 2023 06:43:08 GMT", "version": "v3" } ]

2023-10-12

[ [ "Zhao", "Tingyu", "" ], [ "Peng", "Bo", "" ], [ "Sun", "Yuan", "" ], [ "Yang", "Daipeng", "" ], [ "Zhang", "Zhenguang", "" ], [ "Wu", "Xi", "" ] ]

Recently, advancements in deep learning-based superpixel segmentation methods have brought about improvements in both the efficiency and the performance of segmentation. However, a significant challenge remains in generating superpixels that strictly adhere to object boundaries while conveying rich visual significance, especially when cross-surface color correlations may interfere with objects. Drawing inspiration from neural structure and visual mechanisms, we propose a biological network architecture comprising an Enhanced Screening Module (ESM) and a novel Boundary-Aware Label (BAL) for superpixel segmentation. The ESM enhances semantic information by simulating the interactive projection mechanisms of the visual cortex. Additionally, the BAL emulates the spatial frequency characteristics of visual cortical cells to facilitate the generation of superpixels with strong boundary adherence. We demonstrate the effectiveness of our approach through evaluations on both the BSDS500 dataset and the NYUv2 dataset.

2211.17059

Yiyang Liu

Yiyang Liu, Chenxin Li, Xiaotong Tu, Xinghao Ding, Yue Huang

Hint-dynamic Knowledge Distillation

5 pages

null

cs.CV cs.LG

http://creativecommons.org/licenses/by/4.0/

Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher model to promote a smaller student model. Existing efforts guide the distillation by matching their prediction logits, feature embedding, etc., while leaving how to efficiently utilize them in junction less explored. In this paper, we propose Hint-dynamic Knowledge Distillation, dubbed HKD, which excavates the knowledge from the teacher' s hints in a dynamic scheme. The guidance effect from the knowledge hints usually varies in different instances and learning stages, which motivates us to customize a specific hint-learning manner for each instance adaptively. Specifically, a meta-weight network is introduced to generate the instance-wise weight coefficients about knowledge hints in the perception of the dynamical learning progress of the student model. We further present a weight ensembling strategy to eliminate the potential bias of coefficient estimation by exploiting the historical statics. Experiments on standard benchmarks of CIFAR-100 and Tiny-ImageNet manifest that the proposed HKD well boost the effect of knowledge distillation tasks.

[ { "created": "Wed, 30 Nov 2022 15:03:53 GMT", "version": "v1" } ]

2022-12-01

[ [ "Liu", "Yiyang", "" ], [ "Li", "Chenxin", "" ], [ "Tu", "Xiaotong", "" ], [ "Ding", "Xinghao", "" ], [ "Huang", "Yue", "" ] ]

Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher model to promote a smaller student model. Existing efforts guide the distillation by matching their prediction logits, feature embedding, etc., while leaving how to efficiently utilize them in junction less explored. In this paper, we propose Hint-dynamic Knowledge Distillation, dubbed HKD, which excavates the knowledge from the teacher' s hints in a dynamic scheme. The guidance effect from the knowledge hints usually varies in different instances and learning stages, which motivates us to customize a specific hint-learning manner for each instance adaptively. Specifically, a meta-weight network is introduced to generate the instance-wise weight coefficients about knowledge hints in the perception of the dynamical learning progress of the student model. We further present a weight ensembling strategy to eliminate the potential bias of coefficient estimation by exploiting the historical statics. Experiments on standard benchmarks of CIFAR-100 and Tiny-ImageNet manifest that the proposed HKD well boost the effect of knowledge distillation tasks.

1301.4478

Chaitanya Swamy

Sara Ahmadian, Zachary Friggstad, and Chaitanya Swamy

Local-Search based Approximation Algorithms for Mobile Facility Location Problems

null

cs.DS cs.DM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We consider the {\em mobile facility location} (\mfl) problem. We are given a set of facilities and clients located in a common metric space. The goal is to move each facility from its initial location to a destination and assign each client to the destination of some facility so as to minimize the sum of the movement-costs of the facilities and the client-assignment costs. This abstracts facility-location settings where one has the flexibility of moving facilities from their current locations to other destinations so as to serve clients more efficiently by reducing their assignment costs. We give the first {\em local-search based} approximation algorithm for this problem and achieve the best-known approximation guarantee. Our main result is $(3+\epsilon)$-approximation for this problem for any constant $\epsilon>0$ using local search. The previous best guarantee was an 8-approximation algorithm based on LP-rounding. Our guarantee {\em matches} the best-known approximation guarantee for the $k$-median problem. Since there is an approximation-preserving reduction from the $k$-median problem to \mfl, any improvement of our result would imply an analogous improvement for the $k$-median problem. Furthermore, {\em our analysis is tight} (up to $o(1)$ factors) since the tight example for the local-search based 3-approximation algorithm for $k$-median can be easily adapted to show that our local-search algorithm has a tight approximation ratio of 3. One of the chief novelties of the analysis is that in order to generate a suitable collection of local-search moves whose resulting inequalities yield the desired bound on the cost of a local-optimum, we define a tree-like structure that (loosely speaking) functions as a "recursion tree", using which we spawn off local-search moves by exploring this tree to a constant depth.

[ { "created": "Fri, 18 Jan 2013 20:05:12 GMT", "version": "v1" } ]

2013-01-21

[ [ "Ahmadian", "Sara", "" ], [ "Friggstad", "Zachary", "" ], [ "Swamy", "Chaitanya", "" ] ]

We consider the {\em mobile facility location} (\mfl) problem. We are given a set of facilities and clients located in a common metric space. The goal is to move each facility from its initial location to a destination and assign each client to the destination of some facility so as to minimize the sum of the movement-costs of the facilities and the client-assignment costs. This abstracts facility-location settings where one has the flexibility of moving facilities from their current locations to other destinations so as to serve clients more efficiently by reducing their assignment costs. We give the first {\em local-search based} approximation algorithm for this problem and achieve the best-known approximation guarantee. Our main result is $(3+\epsilon)$-approximation for this problem for any constant $\epsilon>0$ using local search. The previous best guarantee was an 8-approximation algorithm based on LP-rounding. Our guarantee {\em matches} the best-known approximation guarantee for the $k$-median problem. Since there is an approximation-preserving reduction from the $k$-median problem to \mfl, any improvement of our result would imply an analogous improvement for the $k$-median problem. Furthermore, {\em our analysis is tight} (up to $o(1)$ factors) since the tight example for the local-search based 3-approximation algorithm for $k$-median can be easily adapted to show that our local-search algorithm has a tight approximation ratio of 3. One of the chief novelties of the analysis is that in order to generate a suitable collection of local-search moves whose resulting inequalities yield the desired bound on the cost of a local-optimum, we define a tree-like structure that (loosely speaking) functions as a "recursion tree", using which we spawn off local-search moves by exploring this tree to a constant depth.

2009.04656

Yian Li

Yian Li, Hai Zhao

Learning Universal Representations from Word to Sentence

null

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space through a task-independent evaluation. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space. Then we empirically verify that well pre-trained Transformer models incorporated with appropriate training settings may effectively yield universal representation. Especially, our implementation of fine-tuning ALBERT on NLI and PPDB datasets achieves the highest accuracy on analogy tasks in different language levels. Further experiments on the insurance FAQ task show effectiveness of universal representation models in real-world applications.

[ { "created": "Thu, 10 Sep 2020 03:53:18 GMT", "version": "v1" } ]

2020-09-11

[ [ "Li", "Yian", "" ], [ "Zhao", "Hai", "" ] ]

Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space through a task-independent evaluation. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space. Then we empirically verify that well pre-trained Transformer models incorporated with appropriate training settings may effectively yield universal representation. Especially, our implementation of fine-tuning ALBERT on NLI and PPDB datasets achieves the highest accuracy on analogy tasks in different language levels. Further experiments on the insurance FAQ task show effectiveness of universal representation models in real-world applications.

2303.07831

Yu Zhou

Yu Zhou, Liyuan Guo, Lianghai Jin

Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild

This paper has been accepted to ICASSP2023

null

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Facial expression recognition (FER) is a challenging topic in artificial intelligence. Recently, many researchers have attempted to introduce Vision Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional features extracted from raw images and requires a lot of computing resources. To overcome these problems, we propose a quaternion orthogonal transformer (QOT) for FER. Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features. Secondly, three orthogonal sub-features are integrated into a quaternion matrix, which maintains the correlations between different orthogonal components. Finally, we develop a quaternion vision transformer (Q-ViT) for feature classification. The Q-ViT adopts quaternion operations instead of the original operations in ViT, which improves the final accuracies with fewer parameters. Experimental results on three in-the-wild FER datasets show that the proposed QOT outperforms several state-of-the-art models and reduces the computations.

[ { "created": "Tue, 14 Mar 2023 12:07:48 GMT", "version": "v1" } ]

2023-03-15

[ [ "Zhou", "Yu", "" ], [ "Guo", "Liyuan", "" ], [ "Jin", "Lianghai", "" ] ]

Facial expression recognition (FER) is a challenging topic in artificial intelligence. Recently, many researchers have attempted to introduce Vision Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional features extracted from raw images and requires a lot of computing resources. To overcome these problems, we propose a quaternion orthogonal transformer (QOT) for FER. Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features. Secondly, three orthogonal sub-features are integrated into a quaternion matrix, which maintains the correlations between different orthogonal components. Finally, we develop a quaternion vision transformer (Q-ViT) for feature classification. The Q-ViT adopts quaternion operations instead of the original operations in ViT, which improves the final accuracies with fewer parameters. Experimental results on three in-the-wild FER datasets show that the proposed QOT outperforms several state-of-the-art models and reduces the computations.

2001.08328

Yuwei Tu

Yuwei Tu, Weiyu Chen, Christopher G. Brinton

A Deep Learning Approach to Behavior-Based Learner Modeling

null

cs.LG cs.HC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The increasing popularity of e-learning has created demand for improving online education through techniques such as predictive analytics and content recommendations. In this paper, we study learner outcome predictions, i.e., predictions of how they will perform at the end of a course. We propose a novel Two Branch Decision Network for performance prediction that incorporates two important factors: how learners progress through the course and how the content progresses through the course. We combine clickstream features which log every action the learner takes while learning, and textual features which are generated through pre-trained GloVe word embeddings. To assess the performance of our proposed network, we collect data from a short online course designed for corporate training and evaluate both neural network and non-neural network based algorithms on it. Our proposed algorithm achieves 95.7% accuracy and 0.958 AUC score, which outperforms all other models. The results also indicate the combination of behavior features and text features are more predictive than behavior features only and neural network models are powerful in capturing the joint relationship between user behavior and course content.

[ { "created": "Thu, 23 Jan 2020 01:26:52 GMT", "version": "v1" } ]

2020-01-24

[ [ "Tu", "Yuwei", "" ], [ "Chen", "Weiyu", "" ], [ "Brinton", "Christopher G.", "" ] ]

The increasing popularity of e-learning has created demand for improving online education through techniques such as predictive analytics and content recommendations. In this paper, we study learner outcome predictions, i.e., predictions of how they will perform at the end of a course. We propose a novel Two Branch Decision Network for performance prediction that incorporates two important factors: how learners progress through the course and how the content progresses through the course. We combine clickstream features which log every action the learner takes while learning, and textual features which are generated through pre-trained GloVe word embeddings. To assess the performance of our proposed network, we collect data from a short online course designed for corporate training and evaluate both neural network and non-neural network based algorithms on it. Our proposed algorithm achieves 95.7% accuracy and 0.958 AUC score, which outperforms all other models. The results also indicate the combination of behavior features and text features are more predictive than behavior features only and neural network models are powerful in capturing the joint relationship between user behavior and course content.

1809.07912

Meng Shen

Meng Shen, Baoli Ma, Liehuang Zhu, Rashid Mijumbi, Xiaojiang Du, and Jiankun Hu

Cloud-Based Approximate Constrained Shortest Distance Queries Over Encrypted Graphs With Privacy Protection

null

cs.CR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a given threshold. CSD querying has a wide range of applications, such as routing in telecommunications and transportation. With an increasing prevalence of cloud computing paradigm, graph owners desire to outsource their graphs to cloud servers. In order to protect sensitive information, these graphs are usually encrypted before being outsourced to the cloud. This, however, imposes a great challenge to CSD querying over encrypted graphs. Since performing constraint filtering is an intractable task, existing work mainly focuses on unconstrained shortest distance queries. CSD querying over encrypted graphs remains an open research problem. In this paper, we propose Connor, a novel graph encryption scheme that enables approximate CSD querying. Connor is built based on an efficient, tree-based ciphertext comparison protocol, and makes use of symmetric-key primitives and the somewhat homomorphic encryption, making it computationally efficient. Using Connor, a graph owner can first encrypt privacy-sensitive graphs and then outsource them to the cloud server, achieving the necessary privacy without losing the ability of querying. Extensive experiments with real-world datasets demonstrate the effectiveness and efficiency of the proposed graph encryption scheme.

[ { "created": "Fri, 21 Sep 2018 01:58:00 GMT", "version": "v1" } ]

2018-09-24

[ [ "Shen", "Meng", "" ], [ "Ma", "Baoli", "" ], [ "Zhu", "Liehuang", "" ], [ "Mijumbi", "Rashid", "" ], [ "Du", "Xiaojiang", "" ], [ "Hu", "Jiankun", "" ] ]

Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a given threshold. CSD querying has a wide range of applications, such as routing in telecommunications and transportation. With an increasing prevalence of cloud computing paradigm, graph owners desire to outsource their graphs to cloud servers. In order to protect sensitive information, these graphs are usually encrypted before being outsourced to the cloud. This, however, imposes a great challenge to CSD querying over encrypted graphs. Since performing constraint filtering is an intractable task, existing work mainly focuses on unconstrained shortest distance queries. CSD querying over encrypted graphs remains an open research problem. In this paper, we propose Connor, a novel graph encryption scheme that enables approximate CSD querying. Connor is built based on an efficient, tree-based ciphertext comparison protocol, and makes use of symmetric-key primitives and the somewhat homomorphic encryption, making it computationally efficient. Using Connor, a graph owner can first encrypt privacy-sensitive graphs and then outsource them to the cloud server, achieving the necessary privacy without losing the ability of querying. Extensive experiments with real-world datasets demonstrate the effectiveness and efficiency of the proposed graph encryption scheme.

2403.07238

Mostafa Jamshidian

Farah Alkhatib, Mostafa Jamshidian, Donatien Le Liepvre, Florian Bernard, Ludovic Minvielle, Adam Wittek, Karol Miller

Towards Full Automation of Geometry Extraction for Biomechanical Analysis of Abdominal Aortic Aneurysm; Neural Network-Based versus Classical Methodologies

32 pages, 9 figures

null

cs.CE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this study we investigated the impact of image segmentation methods on the results of stress computation in the wall of abdominal aortic aneurysms (AAAs). We compared wall stress distributions and magnitudes calculated from geometry models obtained from classical semi-automated segmentation versus automated neural network-based segmentation. Ten different AAA contrast-enhanced computed tomography (CT) images were semi-automatically segmented by an analyst, taking, depending on the quality of an image, between 15 and 40 minutes of human effort per patient. The same images were automatically segmented using PRAEVAorta 2, commercial software by NUREA (https://www.nurea-soft.com/), developed based on artificial intelligence (AI) algorithms, requiring only 1-2 minutes of computer time per patient. Aneurysm wall stress calculations performed using the BioPARR software (https://bioparr.mech.uwa.edu.au/) revealed that, compared to the classical semi-automated segmentation, the automatic neural network-based segmentation leads to equivalent stress distributions, and slightly higher peak and 99th percentile maximum principal stress values. This difference is due to consistently larger lumen surface areas in automatically segmented models as compared to classical semi-automated segmentations, resulting in greater total pressure load on the wall. Our findings are a steppingstone toward a fully automated pipeline for biomechanical analysis of AAAs, starting with CT scans and concluding with wall stress assessment, while at the same time highlighting the critical importance of the repeatable and accurate segmentation of the lumen, the difficult problem often underestimated by the literature.

[ { "created": "Tue, 12 Mar 2024 01:20:34 GMT", "version": "v1" } ]

2024-03-13

[ [ "Alkhatib", "Farah", "" ], [ "Jamshidian", "Mostafa", "" ], [ "Liepvre", "Donatien Le", "" ], [ "Bernard", "Florian", "" ], [ "Minvielle", "Ludovic", "" ], [ "Wittek", "Adam", "" ], [ "Miller", "Karol", "" ] ]

In this study we investigated the impact of image segmentation methods on the results of stress computation in the wall of abdominal aortic aneurysms (AAAs). We compared wall stress distributions and magnitudes calculated from geometry models obtained from classical semi-automated segmentation versus automated neural network-based segmentation. Ten different AAA contrast-enhanced computed tomography (CT) images were semi-automatically segmented by an analyst, taking, depending on the quality of an image, between 15 and 40 minutes of human effort per patient. The same images were automatically segmented using PRAEVAorta 2, commercial software by NUREA (https://www.nurea-soft.com/), developed based on artificial intelligence (AI) algorithms, requiring only 1-2 minutes of computer time per patient. Aneurysm wall stress calculations performed using the BioPARR software (https://bioparr.mech.uwa.edu.au/) revealed that, compared to the classical semi-automated segmentation, the automatic neural network-based segmentation leads to equivalent stress distributions, and slightly higher peak and 99th percentile maximum principal stress values. This difference is due to consistently larger lumen surface areas in automatically segmented models as compared to classical semi-automated segmentations, resulting in greater total pressure load on the wall. Our findings are a steppingstone toward a fully automated pipeline for biomechanical analysis of AAAs, starting with CT scans and concluding with wall stress assessment, while at the same time highlighting the critical importance of the repeatable and accurate segmentation of the lumen, the difficult problem often underestimated by the literature.

2406.17873

Zhongtao Miao

Zhongtao Miao, Kaiyan Zhao, Yoshimasa Tsuruoka

Improving Arithmetic Reasoning Ability of Large Language Models through Relation Tuples, Verification and Dynamic Feedback

Under review, 25 figures, 8 tables, 29 pages

null

cs.CL cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Current representations used in reasoning steps of large language models can mostly be categorized into two main types: (1) natural language, which is difficult to verify; and (2) non-natural language, usually programming code, which is difficult for people who are unfamiliar with coding to read. In this paper, we propose to use a semi-structured form to represent reasoning steps of large language models. Specifically, we use relation tuples, which are not only human-readable but also machine-friendly and easier to verify than natural language. We implement a framework that includes three main components: (1) introducing relation tuples into the reasoning steps of large language models; (2) implementing an automatic verification process of reasoning steps with a local code interpreter based on relation tuples; and (3) integrating a simple and effective dynamic feedback mechanism, which we found helpful for self-improvement of large language models. The experimental results on various arithmetic datasets demonstrate the effectiveness of our method in improving the arithmetic reasoning ability of large language models. The source code is available at https://github.com/gpgg/art.

[ { "created": "Tue, 25 Jun 2024 18:21:00 GMT", "version": "v1" } ]

2024-06-27

[ [ "Miao", "Zhongtao", "" ], [ "Zhao", "Kaiyan", "" ], [ "Tsuruoka", "Yoshimasa", "" ] ]

Current representations used in reasoning steps of large language models can mostly be categorized into two main types: (1) natural language, which is difficult to verify; and (2) non-natural language, usually programming code, which is difficult for people who are unfamiliar with coding to read. In this paper, we propose to use a semi-structured form to represent reasoning steps of large language models. Specifically, we use relation tuples, which are not only human-readable but also machine-friendly and easier to verify than natural language. We implement a framework that includes three main components: (1) introducing relation tuples into the reasoning steps of large language models; (2) implementing an automatic verification process of reasoning steps with a local code interpreter based on relation tuples; and (3) integrating a simple and effective dynamic feedback mechanism, which we found helpful for self-improvement of large language models. The experimental results on various arithmetic datasets demonstrate the effectiveness of our method in improving the arithmetic reasoning ability of large language models. The source code is available at https://github.com/gpgg/art.

2306.17411

Yanjiang Guo

Yanjiang Guo, Zheyuan Jiang, Yen-Jen Wang, Jingyue Gao, Jianyu Chen

Decentralized Motor Skill Learning for Complex Robotic Systems

8 pages, 7 figures

null

cs.RO cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Reinforcement learning (RL) has achieved remarkable success in complex robotic systems (eg. quadruped locomotion). In previous works, the RL-based controller was typically implemented as a single neural network with concatenated observation input. However, the corresponding learned policy is highly task-specific. Since all motors are controlled in a centralized way, out-of-distribution local observations can impact global motors through the single coupled neural network policy. In contrast, animals and humans can control their limbs separately. Inspired by this biological phenomenon, we propose a Decentralized motor skill (DEMOS) learning algorithm to automatically discover motor groups that can be decoupled from each other while preserving essential connections and then learn a decentralized motor control policy. Our method improves the robustness and generalization of the policy without sacrificing performance. Experiments on quadruped and humanoid robots demonstrate that the learned policy is robust against local motor malfunctions and can be transferred to new tasks.

[ { "created": "Fri, 30 Jun 2023 05:55:34 GMT", "version": "v1" } ]

2023-07-03

[ [ "Guo", "Yanjiang", "" ], [ "Jiang", "Zheyuan", "" ], [ "Wang", "Yen-Jen", "" ], [ "Gao", "Jingyue", "" ], [ "Chen", "Jianyu", "" ] ]

Reinforcement learning (RL) has achieved remarkable success in complex robotic systems (eg. quadruped locomotion). In previous works, the RL-based controller was typically implemented as a single neural network with concatenated observation input. However, the corresponding learned policy is highly task-specific. Since all motors are controlled in a centralized way, out-of-distribution local observations can impact global motors through the single coupled neural network policy. In contrast, animals and humans can control their limbs separately. Inspired by this biological phenomenon, we propose a Decentralized motor skill (DEMOS) learning algorithm to automatically discover motor groups that can be decoupled from each other while preserving essential connections and then learn a decentralized motor control policy. Our method improves the robustness and generalization of the policy without sacrificing performance. Experiments on quadruped and humanoid robots demonstrate that the learned policy is robust against local motor malfunctions and can be transferred to new tasks.

2210.02287

Luyuan Xie

Luyuan Xie, Yan Zhong, Lin Yang, Zhaoyu Yan, Zhonghai Wu, Junjie Wang

TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene

Accepted to APSIPA ASC 2022

null

cs.SD cs.LG eess.AS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Convolution neural networks (CNNs) have good performance in low-complexity classification tasks such as acoustic scene classifications (ASCs). However, there are few studies on the relationship between the length of target speech and the size of the convolution kernels. In this paper, we combine Selective Kernel Network with Temporal-Convolution (TC-SKNet) to adjust the receptive field of convolution kernels to solve the problem of variable length of target voice while keeping low-complexity. GridMask is a data augmentation strategy by masking part of the raw data or feature area. It can enhance the generalization of the model as the role of dropout. In our experiments, the performance gain brought by GridMask is stronger than spectrum augmentation in ASCs. Finally, we adopt AutoML to search best structure of TC-SKNet and hyperparameters of GridMask for improving the classification performance. As a result, a peak accuracy of 59.87% TC-SKNet is equivalent to that of SOTA, but the parameters only use 20.9 K.

[ { "created": "Wed, 5 Oct 2022 14:24:17 GMT", "version": "v1" } ]

2022-10-06

[ [ "Xie", "Luyuan", "" ], [ "Zhong", "Yan", "" ], [ "Yang", "Lin", "" ], [ "Yan", "Zhaoyu", "" ], [ "Wu", "Zhonghai", "" ], [ "Wang", "Junjie", "" ] ]

Convolution neural networks (CNNs) have good performance in low-complexity classification tasks such as acoustic scene classifications (ASCs). However, there are few studies on the relationship between the length of target speech and the size of the convolution kernels. In this paper, we combine Selective Kernel Network with Temporal-Convolution (TC-SKNet) to adjust the receptive field of convolution kernels to solve the problem of variable length of target voice while keeping low-complexity. GridMask is a data augmentation strategy by masking part of the raw data or feature area. It can enhance the generalization of the model as the role of dropout. In our experiments, the performance gain brought by GridMask is stronger than spectrum augmentation in ASCs. Finally, we adopt AutoML to search best structure of TC-SKNet and hyperparameters of GridMask for improving the classification performance. As a result, a peak accuracy of 59.87% TC-SKNet is equivalent to that of SOTA, but the parameters only use 20.9 K.

cs/0208044

Stephen A. Fenner

Gales and supergales are equivalent for defining constructive Hausdorff dimension

7 pages, no figures

null

cs.CC

null

We show that for a wide range of probability measures, constructive gales are interchangable with constructive supergales for defining constructive Hausdorff dimension, thus generalizing a previous independent result of Hitchcock (cs.CC/0208043) and partially answering an open question of Lutz (cs.CC/0203017).

[ { "created": "Thu, 29 Aug 2002 21:25:47 GMT", "version": "v1" } ]

2007-05-23

[ [ "Fenner", "Stephen A.", "" ] ]

We show that for a wide range of probability measures, constructive gales are interchangable with constructive supergales for defining constructive Hausdorff dimension, thus generalizing a previous independent result of Hitchcock (cs.CC/0208043) and partially answering an open question of Lutz (cs.CC/0203017).

1803.06904

SeyedMajid Azimi

Seyed Majid Azimi, Peter Fischer, Marco K\"orner, Peter Reinartz

Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks

IEEE TGRS 2018 - Accepted

null

10.1109/TGRS.2018.2878510

null

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lane-wise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery which can capture a large area in a short period of time by introducing an aerial lane marking dataset. In this work, we propose a Symmetric Fully Convolutional Neural Network enhanced by Wavelet Transform in order to automatically carry out lane marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of number of lane marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a very high accuracy in pixel-wise localization of lane markings without using 3rd-party information. In this work, we introduce the first high-quality dataset used within our experiments which contains a broad range of situations and classes of lane markings representative of current transportation systems. This dataset will be publicly available and hence, it can be used as the benchmark dataset for future algorithms within this domain.

[ { "created": "Mon, 19 Mar 2018 13:32:27 GMT", "version": "v1" }, { "created": "Thu, 1 Nov 2018 16:16:51 GMT", "version": "v2" } ]

2019-08-26

[ [ "Azimi", "Seyed Majid", "" ], [ "Fischer", "Peter", "" ], [ "Körner", "Marco", "" ], [ "Reinartz", "Peter", "" ] ]

The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lane-wise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery which can capture a large area in a short period of time by introducing an aerial lane marking dataset. In this work, we propose a Symmetric Fully Convolutional Neural Network enhanced by Wavelet Transform in order to automatically carry out lane marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of number of lane marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a very high accuracy in pixel-wise localization of lane markings without using 3rd-party information. In this work, we introduce the first high-quality dataset used within our experiments which contains a broad range of situations and classes of lane markings representative of current transportation systems. This dataset will be publicly available and hence, it can be used as the benchmark dataset for future algorithms within this domain.

2405.00287

Jeongwhan Choi

Chaejeong Lee, Jeongwhan Choi, Hyowon Wi, Sung-Bae Cho, Noseong Park

Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering

null

cs.IR cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Graph-based collaborative filtering (CF) has emerged as a promising approach in recommendation systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. By considering that they are both sampling tasks, we generate dynamic augmented views and diverse hard negative samples via our unified stochastic sampling framework based on score-based generative models. In our comprehensive evaluations with 6 benchmark datasets, our proposed SCONE significantly improves recommendation accuracy and robustness, and demonstrates the superiority of our approach over existing CF models. Furthermore, we prove the efficacy of user-item specific stochastic sampling for addressing the user sparsity and item popularity issues. The integration of the stochastic sampling and graph-based CF obtains the state-of-the-art in personalized recommendation systems, making significant strides in information-rich environments.

[ { "created": "Wed, 1 May 2024 02:27:59 GMT", "version": "v1" } ]

2024-05-02

[ [ "Lee", "Chaejeong", "" ], [ "Choi", "Jeongwhan", "" ], [ "Wi", "Hyowon", "" ], [ "Cho", "Sung-Bae", "" ], [ "Park", "Noseong", "" ] ]

Graph-based collaborative filtering (CF) has emerged as a promising approach in recommendation systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. By considering that they are both sampling tasks, we generate dynamic augmented views and diverse hard negative samples via our unified stochastic sampling framework based on score-based generative models. In our comprehensive evaluations with 6 benchmark datasets, our proposed SCONE significantly improves recommendation accuracy and robustness, and demonstrates the superiority of our approach over existing CF models. Furthermore, we prove the efficacy of user-item specific stochastic sampling for addressing the user sparsity and item popularity issues. The integration of the stochastic sampling and graph-based CF obtains the state-of-the-art in personalized recommendation systems, making significant strides in information-rich environments.

1811.00677

Rafael Menelau Oliveira E Cruz

Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti

Analyzing different prototype selection techniques for dynamic classifier and ensemble selection

null

Published on the International Joint Conference on Neural Networks, 2017, 3959-3966

10.1109/IJCNN.2017.7966355

null

cs.LG stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence is usually estimated using the neighborhood of the test sample defined on the validation samples, called the region of competence. Thus, the performance of DS techniques is sensitive to the distribution of the validation set. In this paper, we evaluate six prototype selection techniques that work by editing the validation data in order to remove noise and redundant instances. Experiments conducted using several state-of-the-art DS techniques over 30 classification problems demonstrate that by using prototype selection techniques we can improve the classification accuracy of DS techniques and also significantly reduce the computational cost involved.

[ { "created": "Thu, 1 Nov 2018 23:34:10 GMT", "version": "v1" } ]

2018-11-05

[ [ "Cruz", "Rafael M. O.", "" ], [ "Sabourin", "Robert", "" ], [ "Cavalcanti", "George D. C.", "" ] ]

In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence is usually estimated using the neighborhood of the test sample defined on the validation samples, called the region of competence. Thus, the performance of DS techniques is sensitive to the distribution of the validation set. In this paper, we evaluate six prototype selection techniques that work by editing the validation data in order to remove noise and redundant instances. Experiments conducted using several state-of-the-art DS techniques over 30 classification problems demonstrate that by using prototype selection techniques we can improve the classification accuracy of DS techniques and also significantly reduce the computational cost involved.

2009.05487

Timo Freiesleben

The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples

null

10.1007/s11023-021-09580-9

null

cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current methods for generating CEs and AEs, and estimate that the fields will merge more and more as the number of common use-cases grows.

[ { "created": "Fri, 11 Sep 2020 15:09:12 GMT", "version": "v1" }, { "created": "Thu, 3 Dec 2020 10:05:17 GMT", "version": "v2" }, { "created": "Thu, 26 Aug 2021 08:40:29 GMT", "version": "v3" } ]

2021-11-03

[ [ "Freiesleben", "Timo", "" ] ]

The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current methods for generating CEs and AEs, and estimate that the fields will merge more and more as the number of common use-cases grows.

2406.16527

Miguel Arana-Catania

Zheng Fang, Miguel Arana-Catania, Felix-Anselm van Lier, Juliana Outes Velarde, Harry Bregazzi, Mara Airoldi, Eleanor Carter, Rob Procter

SyROCCo: Enhancing Systematic Reviews using Machine Learning

28 pages, 5 figures. To appear in Data & Policy journal

null

cs.CL cs.CY cs.DL cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previously been used to reliably 'screen' articles for review - that is, identify relevant articles based on reviewers' inclusion criteria. The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We therefore set out to develop a series of tools that would assist in the profiling and analysis of 1,952 publications on the theme of 'outcomes-based contracting'. Tools were developed for the following tasks: assign publications into 'policy area' categories; identify and extract key information for evidence mapping, such as organisations, laws, and geographical information; connect the evidence base to an existing dataset on the same topic; and identify subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of ML techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While ML techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analysing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.

[ { "created": "Mon, 24 Jun 2024 11:04:43 GMT", "version": "v1" } ]

2024-06-25

[ [ "Fang", "Zheng", "" ], [ "Arana-Catania", "Miguel", "" ], [ "van Lier", "Felix-Anselm", "" ], [ "Velarde", "Juliana Outes", "" ], [ "Bregazzi", "Harry", "" ], [ "Airoldi", "Mara", "" ], [ "Carter", "Eleanor", "" ], [ "Procter", "Rob", "" ] ]

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previously been used to reliably 'screen' articles for review - that is, identify relevant articles based on reviewers' inclusion criteria. The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We therefore set out to develop a series of tools that would assist in the profiling and analysis of 1,952 publications on the theme of 'outcomes-based contracting'. Tools were developed for the following tasks: assign publications into 'policy area' categories; identify and extract key information for evidence mapping, such as organisations, laws, and geographical information; connect the evidence base to an existing dataset on the same topic; and identify subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of ML techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While ML techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analysing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.