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"title": "A Novel Anomaly Detection Approach for Executable Program Security",
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"abstract": "Anomaly detection of executable program is a security detection solution that examines whether security violation issues exist in programs. The paper presents a novel anomaly detection approach for executable program security (ADEPS), which monitors program executions and detects anomalous program behaviors. Through reverse analysis of executable program, critical behavior monitoring points can be extracted from binary code sequences and memory space. A hybrid neural network model is proposed to detect abnormal attacks and classify detected attacks from actual program behaviors. The experimental results demonstrate that the proposed approach can effectively and accurately perform anomaly detection.",
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"proceeding": {
"id": "1G9DtzCwrjW",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
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"abstract": "Unsupervised video anomaly detection (UVAD) intends to discern anomalous events from fully unlabeled videos. However, existing UVAD methods suffer from poor performance. Inspired by recent masked autoencoder (MAE) [1], we propose Temporal Masked Auto-Encoding (TMAE) as an effective end-to-end UVAD method. Specifically, we first denote video events by spatial-temporal cubes (STCs), which are built by temporally consecutive foreground patches from unlabeled videos. Then, half of patches in an STC are masked along the temporal dimension, while a vision transformer (ViT) is trained to exploit unmasked patches to predict masked patches. The rare and unusual nature of anomaly will result in a poorer prediction for anomalous events, which enables us to discriminate anomalies from unlabeled videos and compute the anomaly scores. Furthermore, to utilize motion clues in videos, we also propose to apply TMAE on optical flow, which can further boost performance. Experiments show that TMAE significantly outperforms existing UVAD methods by a notable margin (3.9%–6.6% AUC).",
"abstracts": [
{
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"content": "Unsupervised video anomaly detection (UVAD) intends to discern anomalous events from fully unlabeled videos. However, existing UVAD methods suffer from poor performance. Inspired by recent masked autoencoder (MAE) [1], we propose Temporal Masked Auto-Encoding (TMAE) as an effective end-to-end UVAD method. Specifically, we first denote video events by spatial-temporal cubes (STCs), which are built by temporally consecutive foreground patches from unlabeled videos. Then, half of patches in an STC are masked along the temporal dimension, while a vision transformer (ViT) is trained to exploit unmasked patches to predict masked patches. The rare and unusual nature of anomaly will result in a poorer prediction for anomalous events, which enables us to discriminate anomalies from unlabeled videos and compute the anomaly scores. Furthermore, to utilize motion clues in videos, we also propose to apply TMAE on optical flow, which can further boost performance. Experiments show that TMAE significantly outperforms existing UVAD methods by a notable margin (3.9%–6.6% AUC).",
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"normalizedAbstract": "Unsupervised video anomaly detection (UVAD) intends to discern anomalous events from fully unlabeled videos. However, existing UVAD methods suffer from poor performance. Inspired by recent masked autoencoder (MAE) [1], we propose Temporal Masked Auto-Encoding (TMAE) as an effective end-to-end UVAD method. Specifically, we first denote video events by spatial-temporal cubes (STCs), which are built by temporally consecutive foreground patches from unlabeled videos. Then, half of patches in an STC are masked along the temporal dimension, while a vision transformer (ViT) is trained to exploit unmasked patches to predict masked patches. The rare and unusual nature of anomaly will result in a poorer prediction for anomalous events, which enables us to discriminate anomalies from unlabeled videos and compute the anomaly scores. Furthermore, to utilize motion clues in videos, we also propose to apply TMAE on optical flow, which can further boost performance. Experiments show that TMAE significantly outperforms existing UVAD methods by a notable margin (3.9%–6.6% AUC).",
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"affiliation": "National University of Defense Technology,China",
"fullName": "Siqi Wang",
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"affiliation": "National University of Defense Technology,China",
"fullName": "En Zhu",
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"affiliation": "National University of Defense Technology,China",
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"affiliation": "Zhejiang Normal University,China",
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"title": "2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)",
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"title": "Detecting Anomalous Behaviour from Textual Content in Financial Records",
"normalizedTitle": "Detecting Anomalous Behaviour from Textual Content in Financial Records",
"abstract": "Most financial institutions mainly use numerical statistics to detect anomalous (malpractice) activity. The textual content in financial records however contains precious information which to date has not been effectively used for detection of anomalous behaviors by users because these are often unintelligible, cluttered with abbreviations, numbers and symbols, which makes it difficult to build a framework system that can coherently understand and draw conclusions. Rule-based techniques have been proposed but such systems are easy to elude, as they are difficult to generalize and do not scale up. The work presented in this paper differs from previous work in that we exclusively base anomalous activities on text (excluding numerical values) in financial records and treat this as a classification problem for a deep learning network. We propose four solutions using deep learning techniques on textual data to distinguish between normal with anomalous behaviors of the users. The results of our experiments convincingly show that use of the textual content in financial records yields greater accuracy in anomalous behavior detection. They also suggest that deep learning is a viable and effective solution for real time anomaly detection by financial institutions.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Most financial institutions mainly use numerical statistics to detect anomalous (malpractice) activity. The textual content in financial records however contains precious information which to date has not been effectively used for detection of anomalous behaviors by users because these are often unintelligible, cluttered with abbreviations, numbers and symbols, which makes it difficult to build a framework system that can coherently understand and draw conclusions. Rule-based techniques have been proposed but such systems are easy to elude, as they are difficult to generalize and do not scale up. The work presented in this paper differs from previous work in that we exclusively base anomalous activities on text (excluding numerical values) in financial records and treat this as a classification problem for a deep learning network. We propose four solutions using deep learning techniques on textual data to distinguish between normal with anomalous behaviors of the users. The results of our experiments convincingly show that use of the textual content in financial records yields greater accuracy in anomalous behavior detection. They also suggest that deep learning is a viable and effective solution for real time anomaly detection by financial institutions.",
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"normalizedAbstract": "Most financial institutions mainly use numerical statistics to detect anomalous (malpractice) activity. The textual content in financial records however contains precious information which to date has not been effectively used for detection of anomalous behaviors by users because these are often unintelligible, cluttered with abbreviations, numbers and symbols, which makes it difficult to build a framework system that can coherently understand and draw conclusions. Rule-based techniques have been proposed but such systems are easy to elude, as they are difficult to generalize and do not scale up. The work presented in this paper differs from previous work in that we exclusively base anomalous activities on text (excluding numerical values) in financial records and treat this as a classification problem for a deep learning network. We propose four solutions using deep learning techniques on textual data to distinguish between normal with anomalous behaviors of the users. The results of our experiments convincingly show that use of the textual content in financial records yields greater accuracy in anomalous behavior detection. They also suggest that deep learning is a viable and effective solution for real time anomaly detection by financial institutions.",
"fno": "08909570",
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"Financial Data Processing",
"Learning Artificial Intelligence",
"Security Of Data",
"Text Analysis",
"Textual Content",
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"Base Anomalous Activities",
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"authors": [
{
"affiliation": "Dept. of Computer Science and Software Engineering Concordia University,Montreal,Canada",
"fullName": "Jerry G. Thomas",
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"affiliation": "Dept. of Computer Science and Software Engineering Concordia University,Montreal,Canada",
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"affiliation": "Dept. of Computer Science and Software Engineering Concordia University,Montreal,Canada",
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"abstract": "Rule based intrusion detection depends on the attack signature database which has to be constantly updated, requiring time and efforts. Anomaly based intrusion detection through unsupervised methods does not require comparing with attack signatures. However, detecting anomalous behaviour is a complex task. In this paper, we have proposed an unsupervised approach for anomalous network traffic identification by combining dimensionality reduction with sub-space clustering. Our approach takes the attribute values from network traffics as input, performs principal component analysis on them, and then applies density-based clustering on each possible three dimensional sub-spaces to rank the outliers. Results show that our proposed approach detects a wide range of anomalous network session which included instances of intrusive sessions too. The evaluation of this approach showed significant accuracy and faster detection with a zero false negative rate, implying that no instance of the listed attacks went undetected.",
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"affiliation": "Bangladesh University of Engineering and Technology,Department of Computer Science and Engineering,Bangladesh",
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"abstract": "The increase in the quantity and questionable quality of the forensic information retrieved from the current virtualized data cloud system architectures has made it extremely difficult for law enforcement to resolve criminal activities within these logical domains. This paper poses the question of what kind of information is desired from virtual machine (VM) hosted operating systems (OS) investigated by a cloud forensic examiner. The authors gives an overview of the information that exists on current VM OS by looking at it's kernel hypervisor logs and discusses the shortcomings. An examination of the role that the VM kernel hypervisor logs provide as OS metadata in cloud investigations is also presented.",
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"abstract": "In this paper, we propose a method of measuring data persistence using the Ext4 journal. Digital Forensic tools and techniques are commonly used to extract data from media. A great deal of research has been dedicated to the recovery of deleted data, however, there is a lack of information on quantifying the chance that an investigator will be successful in this endeavor. To that end, we suggest the file system journal be used as a source to gather empirical evidence of data persistence, which can later be used to formulate the probability of recovering deleted data under various conditions. Knowing this probability can help investigators decide where to best invest their resources. We have implemented a proof of concept system that interrogates the Ext4 file system journal and logs relevant data. We then detail how this information can be used to track the reuse of data blocks from the examination of file system metadata structures. This preliminary design contributes a novel method of tracking deleted data persistence that can be used to generate the information necessary to formulate probability models regarding the full and/or partial recovery of deleted data.",
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"content": "In this paper, we propose a method of measuring data persistence using the Ext4 journal. Digital Forensic tools and techniques are commonly used to extract data from media. A great deal of research has been dedicated to the recovery of deleted data, however, there is a lack of information on quantifying the chance that an investigator will be successful in this endeavor. To that end, we suggest the file system journal be used as a source to gather empirical evidence of data persistence, which can later be used to formulate the probability of recovering deleted data under various conditions. Knowing this probability can help investigators decide where to best invest their resources. We have implemented a proof of concept system that interrogates the Ext4 file system journal and logs relevant data. We then detail how this information can be used to track the reuse of data blocks from the examination of file system metadata structures. This preliminary design contributes a novel method of tracking deleted data persistence that can be used to generate the information necessary to formulate probability models regarding the full and/or partial recovery of deleted data.",
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"title": "Forensic Analysis of Email on Android Volatile Memory",
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"abstract": "With the popularity of smart phones and the emergence of the mobile office mode, the traditional email forensics that works for computer has been already unable to satisfy the demands of reality, so forensic work needs to be expanded to a range of mobile devices, such as mobile phone, tablet, etc. In this paper, we will focus on examining if we can discover email-related information in the volatile memory of the mobile phone. Specifically, we choose Android mobile as a research focus, and two Chinese mainstream Android email applications—MailMaster and QQMail are as email client to the experimental test. Finally, we not only sort out the email-related information stored in the volatile memory, but also identify the patterns of the information saved in the memory. Moreover, based on these patterns, we also develop a tool named EmailFinder that can automatically extract the email-related information from memory dump. It can be utilized as a forensic tool on Android phones to assist forensic investigators retrieve email-related evidence from memory dump.",
"abstracts": [
{
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"content": "With the popularity of smart phones and the emergence of the mobile office mode, the traditional email forensics that works for computer has been already unable to satisfy the demands of reality, so forensic work needs to be expanded to a range of mobile devices, such as mobile phone, tablet, etc. In this paper, we will focus on examining if we can discover email-related information in the volatile memory of the mobile phone. Specifically, we choose Android mobile as a research focus, and two Chinese mainstream Android email applications—MailMaster and QQMail are as email client to the experimental test. Finally, we not only sort out the email-related information stored in the volatile memory, but also identify the patterns of the information saved in the memory. Moreover, based on these patterns, we also develop a tool named EmailFinder that can automatically extract the email-related information from memory dump. It can be utilized as a forensic tool on Android phones to assist forensic investigators retrieve email-related evidence from memory dump.",
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"fno": "07847043",
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{
"affiliation": null,
"fullName": "Long Chen",
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"fullName": "Yue Mao",
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"abstract": "This paper focuses on establishing the need for new architectures on which to build visualisation systems that enhance computer forensic investigation of digital evidence. The issues surrounding processing of large quantities of digital evidence are established. In addition, the current state of visualisation and data analysis techniques for computer forensics are highlighted. This paper suggests need for new visualisation techniques in order to display data in familiar visual forms that facilitate efficient insight gaining into digital evidence. Visualisations techniques also require a source of processed data that contains context relevant information to present to an investigator. To this end this paper introduces the notion of data exploitation as a way to describe techniques that provide opportunistic data analysis across multiple sources of digital evidence. Data exploitation techniques provide normalisation techniques, event correlation, relationship extraction and investigative domain knowledge processing to occur across a set of evidence. This enables a visual representation of digital evidence to highlight relationships and events across many data sources, support an investigator throughout the entire data analysis process and enable an investigator to focus on the context of the current crime.",
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"title": "Digital Forensic Analysis of Fitbit Wearable Technology: An Investigator’s Guide",
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"abstract": "Wearable technology, such as Fitbit devices, log a user’s daily activities, heart rate, calories burned, step count, and sleep activity. This information is valuable to digital forensic investigators as it may serve as evidence to a crime, to either support a suspect’s innocence or guilt. It is important for an investigator to find and analyze every piece of data for accuracy and integrity; however, there is no standard for conducting a forensic investigation for wearable technology. In this paper, we conduct a forensic analysis of two different Fitbit devices using open-source tools. It is the responsibility of the investigator to show how the data was obtained and to ensure that the data was not modified during the analysis. This paper will guide investigators in understanding what data is collected by a Fitbit device (specifically the Ionic smartwatch and Alta tracker), how to handle Fitbit devices, and how to extract and forensically analyze said devices using open-source tools, Autopsy Sleuth Kit and Bulk Extractor Viewer.",
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"abstract": "Identifying the labels of points of interest (POIs), aka POI labelling, provides significant benefits in location-based services. However, the quality of raw labels manually added by users or generated by artificial algorithms cannot be guaranteed. Such low-quality labels decrease the usability and result in bad user experiences. In this paper, by observing that crowdsourcing is a best-fit for computer-hard tasks, we leverage crowdsourcing to improve the quality of POI labelling. To our best knowledge, this is the first work on crowdsourced POI labelling tasks. In particular, there are two sub-problems: (1) how to infer the correct labels for each POI based on workers' answers, and (2) how to effectively assign proper tasks to workers in order to make more accurate inference for next available workers. To address these two problems, we propose a framework consisting of an inference model and an online task assigner. The inference model measures the quality of a worker on a POI by elaborately exploiting (i) worker's inherent quality, (ii) the spatial distance between the worker and the POI, and (iii) the POI influence, which can provide reliable inference results once a worker submits an answer. As workers are dynamically coming, the online task assigner judiciously assigns proper tasks to them so as to benefit the inference. The inference model and task assigner work alternately to continuously improve the overall quality. We conduct extensive experiments on a real crowdsourcing platform, and the results on two real datasets show that our method significantly outperforms state-of-the-art approaches.",
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"fullName": "Huiqi Hu",
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"title": "2021 IEEE International Conference on Data Mining (ICDM)",
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"doi": "10.1109/ICDM51629.2021.00065",
"title": "Truth Discovery in Sequence Labels from Crowds",
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"abstract": "Annotation quality and quantity positively affect the learning performance of sequence labeling, a vital task in Natural Language Processing. Hiring domain experts to annotate a corpus is very costly in terms of money and time. Crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), have been deployed to assist in this purpose. However, the annotations collected this way are prone to human errors due to the lack of expertise of the crowd workers. Existing literature in annotation aggregation assumes that annotations are independent and thus faces challenges when handling the sequential label aggregation tasks with complex dependencies. To conquer the challenges, we propose an optimization-based method that infers the ground truth labels using annotations provided by workers for sequential labeling tasks. The proposed Aggregation method for Sequential Labels from Crowds (AggSLC) jointly considers the characteristics of sequential labeling tasks, workers’ reliabilities, and advanced machine learning techniques. Theoretical analysis on the algorithm’s convergence further demonstrates that the proposed AggSLC halts after a finite number of iterations. We evaluate AggSLC on different crowdsourced datasets for Named Entity Recognition (NER) tasks and Information Extraction tasks in biomedical (PICO), as well as a simulated dataset. Our results show that the proposed method outperforms the state-of-the-art aggregation methods. To achieve insights into the framework, we study the effectiveness of AggSLC’s components through ablation studies.",
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"content": "Annotation quality and quantity positively affect the learning performance of sequence labeling, a vital task in Natural Language Processing. Hiring domain experts to annotate a corpus is very costly in terms of money and time. Crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), have been deployed to assist in this purpose. However, the annotations collected this way are prone to human errors due to the lack of expertise of the crowd workers. Existing literature in annotation aggregation assumes that annotations are independent and thus faces challenges when handling the sequential label aggregation tasks with complex dependencies. To conquer the challenges, we propose an optimization-based method that infers the ground truth labels using annotations provided by workers for sequential labeling tasks. The proposed Aggregation method for Sequential Labels from Crowds (AggSLC) jointly considers the characteristics of sequential labeling tasks, workers’ reliabilities, and advanced machine learning techniques. Theoretical analysis on the algorithm’s convergence further demonstrates that the proposed AggSLC halts after a finite number of iterations. We evaluate AggSLC on different crowdsourced datasets for Named Entity Recognition (NER) tasks and Information Extraction tasks in biomedical (PICO), as well as a simulated dataset. Our results show that the proposed method outperforms the state-of-the-art aggregation methods. To achieve insights into the framework, we study the effectiveness of AggSLC’s components through ablation studies.",
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"affiliation": "Iowa State University,Ames,Iowa,USA",
"fullName": "Nasim Sabetpour",
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"fullName": "Adithya Kulkarni",
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{
"affiliation": "Lehigh University,Bethlehem,PA,USA",
"fullName": "Sihong Xie",
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"surname": "Xie",
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{
"affiliation": "Iowa State University,Ames,Iowa,USA",
"fullName": "Qi Li",
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"surname": "Li",
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"abstract": "In spite of its wide usage in various applications, existing construction methods for Knowledge Base (KB) are still on their way to obtaining 100% correct facts. Thus, employing crowd workers to validate a KB has been proposed to improve its reliability. Most of the existing works focus on devising games with proper incentives to engage workers in validating more facts, but rarely consider matching facts with proper workers. Facts have diverse domains (topics), which naturally require workers of different expertise. In addition, they also generally have different utilities, i.e., some are more heavily used than others. Thus, distinguishing the facts in terms of utility to give them different validation priorities is meaningful, especially when the budget is limited. To this end, we study the crowdsourced fact validation problem which considers worker domains and fact utilities, and find that with some reductions, it can be solved by the existing minimum cost network flow method. However, directly employing that method requires a huge time cost. We thereby propose an optimized network flow method which reduces the network complexity to save the time cost by properly grouping the facts. Furthermore, we propose an incremental validation method, which utilizes the previous results for validating an evolving KB. We finally conduct extensive experiments to demonstrate the effectiveness of the proposed methods.",
"abstracts": [
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"content": "In spite of its wide usage in various applications, existing construction methods for Knowledge Base (KB) are still on their way to obtaining 100% correct facts. Thus, employing crowd workers to validate a KB has been proposed to improve its reliability. Most of the existing works focus on devising games with proper incentives to engage workers in validating more facts, but rarely consider matching facts with proper workers. Facts have diverse domains (topics), which naturally require workers of different expertise. In addition, they also generally have different utilities, i.e., some are more heavily used than others. Thus, distinguishing the facts in terms of utility to give them different validation priorities is meaningful, especially when the budget is limited. To this end, we study the crowdsourced fact validation problem which considers worker domains and fact utilities, and find that with some reductions, it can be solved by the existing minimum cost network flow method. However, directly employing that method requires a huge time cost. We thereby propose an optimized network flow method which reduces the network complexity to save the time cost by properly grouping the facts. Furthermore, we propose an incremental validation method, which utilizes the previous results for validating an evolving KB. We finally conduct extensive experiments to demonstrate the effectiveness of the proposed methods.",
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"affiliation": "Sun Yat-sen University,Guangdong Key Laboratory of Big Data Analysis and Processing,Guangzhou,China",
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"affiliation": "Sun Yat-sen University,Guangdong Key Laboratory of Big Data Analysis and Processing,Guangzhou,China",
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"title": "Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study",
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"abstract": "Learning representation has been proven to be helpful in numerous machine learning tasks. The success of the majority of existing representation learning approaches often requires a large amount of consistent and noise-free labels. However, labels are not accessible in many real-world scenarios and they are usually annotated by the crowds. In practice, the crowdsourced labels are usually inconsistent among crowd workers given their diverse expertise and the number of crowdsourced labels is very limited. Thus, directly adopting crowdsourced labels for existing representation learning algorithms is inappropriate and suboptimal. In this paper, we investigate the above problem and propose a novel framework of Representation Learning with crowdsourced Labels, i.e., \"RLL\", which learns representation of data with crowdsourced labels by jointly and coherently solving the challenges introduced by limited and inconsistent labels. The proposed representation learning framework is evaluated in two real-world education applications. The experimental results demonstrate the benefits of our approach on learning representation from limited labeled data from the crowds, and show RLL is able to outperform state-of-the-art baselines. Moreover, detailed experiments are conducted on RLL to fully understand its key components and the corresponding performance.",
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"content": "Learning representation has been proven to be helpful in numerous machine learning tasks. The success of the majority of existing representation learning approaches often requires a large amount of consistent and noise-free labels. However, labels are not accessible in many real-world scenarios and they are usually annotated by the crowds. In practice, the crowdsourced labels are usually inconsistent among crowd workers given their diverse expertise and the number of crowdsourced labels is very limited. Thus, directly adopting crowdsourced labels for existing representation learning algorithms is inappropriate and suboptimal. In this paper, we investigate the above problem and propose a novel framework of Representation Learning with crowdsourced Labels, i.e., \"RLL\", which learns representation of data with crowdsourced labels by jointly and coherently solving the challenges introduced by limited and inconsistent labels. The proposed representation learning framework is evaluated in two real-world education applications. The experimental results demonstrate the benefits of our approach on learning representation from limited labeled data from the crowds, and show RLL is able to outperform state-of-the-art baselines. Moreover, detailed experiments are conducted on RLL to fully understand its key components and the corresponding performance.",
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"content": "Reinforcement Learning is a kind of machine learning method which learns through agents' interaction with the environment. Deep Q-Network (DQN), which is a model of reinforcement learning based on deep neural networks, succeeded in learning human-level control policies on various kinds of Atari 2600 games with image pixel inputs. Because an input of DQN is the game frames of the last four steps, DQN had difficulty on mastering such games that need to remember events earlier than four steps in the past. To alleviate the problem, Deep Recurrent Q-Network (DRQN) and Deep Attention Recurrent Q-Network (DARQN) were proposed. In DRQN, the first fully-connected layer just after convolutional layers is replaced with an LSTM to incorporate past information. DARQN is a model with visual attention mechanisms on top of DRQN. We propose two new reinforcement learning models: Deep Recurrent Q-Network with Truncated History (T-DRQN) and Deep Attention Recurrent Q-Network with Truncated History (T-DARQN). T-DRQN uses a truncated history so that we can control the length of history to be considered. T-DARQN is a model with visual attention mechanism on top of T-DRQN. Experiments of our models on six games of Atari 2600 shows a level of performance between DQN and D(A) RQN. Furthermore, results show the necessity of using past information with a truncated length, rather than using only the current information or all of the past information.",
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"normalizedAbstract": "Reinforcement Learning is a kind of machine learning method which learns through agents' interaction with the environment. Deep Q-Network (DQN), which is a model of reinforcement learning based on deep neural networks, succeeded in learning human-level control policies on various kinds of Atari 2600 games with image pixel inputs. Because an input of DQN is the game frames of the last four steps, DQN had difficulty on mastering such games that need to remember events earlier than four steps in the past. To alleviate the problem, Deep Recurrent Q-Network (DRQN) and Deep Attention Recurrent Q-Network (DARQN) were proposed. In DRQN, the first fully-connected layer just after convolutional layers is replaced with an LSTM to incorporate past information. DARQN is a model with visual attention mechanisms on top of DRQN. We propose two new reinforcement learning models: Deep Recurrent Q-Network with Truncated History (T-DRQN) and Deep Attention Recurrent Q-Network with Truncated History (T-DARQN). T-DRQN uses a truncated history so that we can control the length of history to be considered. T-DARQN is a model with visual attention mechanism on top of T-DRQN. Experiments of our models on six games of Atari 2600 shows a level of performance between DQN and D(A) RQN. Furthermore, results show the necessity of using past information with a truncated length, rather than using only the current information or all of the past information.",
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"Computer Games",
"Learning Artificial Intelligence",
"Recurrent Neural Nets",
"D A RQN",
"T DARQN",
"T DRQN",
"Reinforcement Learning Models",
"Visual Attention Mechanism",
"Deep Attention Recurrent Q Network",
"Deep Recurrent Q Network",
"Game Frames",
"Image Pixel Inputs",
"Atari 2600 Games",
"Human Level Control Policies",
"Deep Neural Networks",
"DQN",
"Deep Q Network",
"Machine Learning Method",
"Truncated History",
"Games",
"History",
"Visualization",
"Network Architecture",
"Feature Extraction",
"Degradation",
"Reinforcement Learning",
"Recurrent Neural Network",
"Attention Mechanism",
"DQN",
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"DARQN"
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"affiliation": null,
"fullName": "Tomoyuki Kaneko",
"givenName": "Tomoyuki",
"surname": "Kaneko",
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"abstract": "These last few years have been big for the field of Reinforcement Learning. While is was long thought to be impossible for Reinforcement Learning to master complex environments, recent research has seen Reinforcement Learning agent master highly complex tasks like Atari Games, Go, Dota 2, Autonomous transportation among many others. This has sparked a new wave of interest into Reinforcement Learning, with most of that focus directed towards Deep Reinforcement Learning algorithms. This paper will explore the realm of aviation and apply Deep Reinforcement learning to it. To be more specific, we will train an agent with Double Deep Q-Learning to learn how to control the planes attitude control. The QPlane toolkit will be used for this, and we will utilize both simulators provided. The methodology and results will be presented in this paper.",
"abstracts": [
{
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"content": "These last few years have been big for the field of Reinforcement Learning. While is was long thought to be impossible for Reinforcement Learning to master complex environments, recent research has seen Reinforcement Learning agent master highly complex tasks like Atari Games, Go, Dota 2, Autonomous transportation among many others. This has sparked a new wave of interest into Reinforcement Learning, with most of that focus directed towards Deep Reinforcement Learning algorithms. This paper will explore the realm of aviation and apply Deep Reinforcement learning to it. To be more specific, we will train an agent with Double Deep Q-Learning to learn how to control the planes attitude control. The QPlane toolkit will be used for this, and we will utilize both simulators provided. The methodology and results will be presented in this paper.",
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"fno": "649500a646",
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"Aircraft Control",
"Attitude Control",
"Control Engineering Computing",
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"doi": "10.1109/MLBDBI48998.2019.00039",
"title": "Exploring Noise Deep Q Network Based on Cross-Connected",
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"abstract": "Deep Q Network, which is a combination of CNN and Q-Learning, is an important turning point in deep reinforcement learning. DQN algorithm has achieved great success on the Atari 2600 game platform. However, in some complex game environments, on the one hand, CNN is not accurate enough to extract the original environment feature information points, which leads to unreasonable action strategy selection in the later stage of DQN; on the other hand, the local ε-perturbation strategy adopted by DQN can not be effectively explored on a large scale, resulting in the optimal strategy of action space perturbation. To solve these problems, a new exploratory noise deep Q network based on cross-connected is proposed. Firstly, the model is a six-layer network structure, which includes the input layer, three convolution layer, one full-connection layer and the output layer. The second convolution layer is connected to the full-connection layer, making full use of the characteristics of low-level. Secondly, parameter noise is introduced into the whole connection layer and output layer, which adds parameter noise to its weight and causes network output changes. According to the changes, a large number of new states are explored, more abundant samples are provided, and effective decision information is provided. Finally, the experimental results show that the effectiveness of the model is validated in multiple game environments of Atari 2600.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Deep Q Network, which is a combination of CNN and Q-Learning, is an important turning point in deep reinforcement learning. DQN algorithm has achieved great success on the Atari 2600 game platform. However, in some complex game environments, on the one hand, CNN is not accurate enough to extract the original environment feature information points, which leads to unreasonable action strategy selection in the later stage of DQN; on the other hand, the local ε-perturbation strategy adopted by DQN can not be effectively explored on a large scale, resulting in the optimal strategy of action space perturbation. To solve these problems, a new exploratory noise deep Q network based on cross-connected is proposed. Firstly, the model is a six-layer network structure, which includes the input layer, three convolution layer, one full-connection layer and the output layer. The second convolution layer is connected to the full-connection layer, making full use of the characteristics of low-level. Secondly, parameter noise is introduced into the whole connection layer and output layer, which adds parameter noise to its weight and causes network output changes. According to the changes, a large number of new states are explored, more abundant samples are provided, and effective decision information is provided. Finally, the experimental results show that the effectiveness of the model is validated in multiple game environments of Atari 2600.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Deep Q Network, which is a combination of CNN and Q-Learning, is an important turning point in deep reinforcement learning. DQN algorithm has achieved great success on the Atari 2600 game platform. However, in some complex game environments, on the one hand, CNN is not accurate enough to extract the original environment feature information points, which leads to unreasonable action strategy selection in the later stage of DQN; on the other hand, the local ε-perturbation strategy adopted by DQN can not be effectively explored on a large scale, resulting in the optimal strategy of action space perturbation. To solve these problems, a new exploratory noise deep Q network based on cross-connected is proposed. Firstly, the model is a six-layer network structure, which includes the input layer, three convolution layer, one full-connection layer and the output layer. The second convolution layer is connected to the full-connection layer, making full use of the characteristics of low-level. Secondly, parameter noise is introduced into the whole connection layer and output layer, which adds parameter noise to its weight and causes network output changes. According to the changes, a large number of new states are explored, more abundant samples are provided, and effective decision information is provided. Finally, the experimental results show that the effectiveness of the model is validated in multiple game environments of Atari 2600.",
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"affiliation": "Changchun University of Science and Technology",
"fullName": "Xiaming Wu",
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"affiliation": "Changchun University of Science and Technology",
"fullName": "Enzhi Chen",
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{
"affiliation": "Changchun University of Science and Technology",
"fullName": "Mingqiu Li",
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"affiliation": "Changchun University of Science and Technology",
"fullName": "Chunyang Wang",
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"abstract": "Multiple adverse health conditions co-occurring in a patient are typically associated with poor prognosis and increased office or hospital visits. Developing methods to identify patterns of co-occurring conditions can assist in diagnosis. Thus, identifying patterns of association among co-occurring conditions is of growing interest. In this paper, we report preliminary results from a data-driven study, in which we apply a machine learning method, namely, topic modeling, to Electronic Medical Records (EMRs), aiming to identify patterns of associated conditions. Specifically, we use the well-established Latent Dirichlet Allocation (LDA), a method based on the idea that documents can be modeled as a mixture of latent topics, where each topic is a distribution over words. In our study, we adapt the LDA model to identify latent topics in patients' EMRs. We evaluate the performance of our method both qualitatively and quantitatively, and show that the obtained topics indeed align well with distinct medical phenomena characterized by co-occurring conditions.",
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"content": "Multiple adverse health conditions co-occurring in a patient are typically associated with poor prognosis and increased office or hospital visits. Developing methods to identify patterns of co-occurring conditions can assist in diagnosis. Thus, identifying patterns of association among co-occurring conditions is of growing interest. In this paper, we report preliminary results from a data-driven study, in which we apply a machine learning method, namely, topic modeling, to Electronic Medical Records (EMRs), aiming to identify patterns of associated conditions. Specifically, we use the well-established Latent Dirichlet Allocation (LDA), a method based on the idea that documents can be modeled as a mixture of latent topics, where each topic is a distribution over words. In our study, we adapt the LDA model to identify latent topics in patients' EMRs. We evaluate the performance of our method both qualitatively and quantitatively, and show that the obtained topics indeed align well with distinct medical phenomena characterized by co-occurring conditions.",
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"proceeding": {
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"abstract": "Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.",
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"content": "Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.",
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"affiliation": "Hong Kong University of Science and Technology",
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"abstract": "Datasets from Electronic Health Records (EHRs) are increasingly large and complex, creating challenges in their use for predictive modeling. The two major challenges are large-scale and high-dimensionality. One of the common way to address the large-scale challenge is through use of data phenotypes: clinically relevant characteristic groupings that can be expressed as logical queries (e.g., “senior patients with diabetes”). With the increasing use of machine learning across the continuum of care, phenotypes play an important role in modeling for population management, clinical trials, observational and interventional research, and quality measures. Yet, phenotype interpretation can often be difficult and require post-hoc clarifications from experienced clinicians. For example, detailed analysis may be needed to find that all patients in a a phenotype are diabetic seniors with complications from previous surgery. Moreover, the high-dimensionality problem is often addressed either separately or simultaneously with phenotyping by dimension reduction methods that may further hinder interpretability. In this paper, we introduce the notion of interpretable data phenotypes generated by an unsupervised learning technique. Methods are designed to disambiguate relative feature memberships, thus facilitating general clinical validation, and alleviating the problem of high-dimensionality. The empirical study applies the proposed unsupervised interpretable phenotyping method to a real world healthcare dataset (MIMIC), then uses hospital length of stay as a reference prediction task. The results demonstrate that the proposed method produces phenotypes with improved interpretability and without diminishing the quality of prediction results.",
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"abstract": "The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision applications, like image editing, synthesizing high resolution images, generating videos, etc. These networks and the corresponding learning scheme can handle various visual space mappings. We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discovery in object detection pipelines. A crucial advantage of our method is that it learns a new deep similarity metric, to distinguish multiple objects in one image. We demonstrate that the network can act as an encoder-decoder generating parts of an image which contain an object, or as a modified deep CNN to represent images for object detection in supervised and weakly supervised scheme. Our ranking GAN offers a novel way to search through images for object specific patterns. We have conducted experiments for different scenarios and demonstrate the method performance for object synthesizing and weakly supervised object detection and classification using the MS-COCO and PASCAL VOC datasets.",
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"title": "Maximal Balanced Signed Biclique Enumeration in Signed Bipartite Graphs",
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"abstract": "Maximal biclique enumeration is a fundamental problem in bipartite graph analysis, and can find numerous applications. However, previous studies only focus on unsigned bipartite graphs. Signed information, such as friend and enemy, naturally exists in real-world networks. It is critical to leverage signed information to better characterize biclique. To fill this gap, in this paper, we propose a novel biclique model, named balanced signed biclique, by leveraging the property of balance theory. Specifically, given a signed bipartite graph <tex>Z_$G$_Z</tex>, two positive integers <tex>Z_$\\tau_{U}, \\tau_{V}$_Z</tex>, a subgraph <tex>Z_$S=(U_{S},\\ V_{S},\\ E_{S})$_Z</tex> of <tex>Z_$G$_Z</tex> is a balanced signed biclique if <tex>Z_$i$_Z</tex>) <tex>Z_$S$_Z</tex> is a biclique without any unstable motif, i.e., unbalanced butterfly, and ii) <tex>Z_$\\vert U_{S}\\vert \\geq\\tau_{U}$_Z</tex> and <tex>Z_$\\vert V_{S}\\vert \\geq\\tau_{V}$_Z</tex>. In this paper, we aim to enumerate all the maximal balanced signed bicliques, which is proved to be NP-hard. Moreover, due to the unique features of signed bipartite graphs, the previous works cannot be applied to our problem directly. To construct a reasonable baseline, we extend the existing biclique enumeration framework for unsigned bipartite graphs and integrate the developed balanced bipartite graph property. To scale for larger networks, novel optimized strategies are proposed to overcome the three limitations in the baseline method. Extensive experi-ments are conducted on 8 real-world datasets to demonstrate the efficiency and effectiveness of proposed techniques and model. Compared with the baseline approach, the optimized algorithm can achieve up to 3 orders of magnitude speedup.",
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"content": "Maximal biclique enumeration is a fundamental problem in bipartite graph analysis, and can find numerous applications. However, previous studies only focus on unsigned bipartite graphs. Signed information, such as friend and enemy, naturally exists in real-world networks. It is critical to leverage signed information to better characterize biclique. To fill this gap, in this paper, we propose a novel biclique model, named balanced signed biclique, by leveraging the property of balance theory. Specifically, given a signed bipartite graph <tex>$G$</tex>, two positive integers <tex>$\\tau_{U}, \\tau_{V}$</tex>, a subgraph <tex>$S=(U_{S},\\ V_{S},\\ E_{S})$</tex> of <tex>$G$</tex> is a balanced signed biclique if <tex>$i$</tex>) <tex>$S$</tex> is a biclique without any unstable motif, i.e., unbalanced butterfly, and ii) <tex>$\\vert U_{S}\\vert \\geq\\tau_{U}$</tex> and <tex>$\\vert V_{S}\\vert \\geq\\tau_{V}$</tex>. In this paper, we aim to enumerate all the maximal balanced signed bicliques, which is proved to be NP-hard. Moreover, due to the unique features of signed bipartite graphs, the previous works cannot be applied to our problem directly. To construct a reasonable baseline, we extend the existing biclique enumeration framework for unsigned bipartite graphs and integrate the developed balanced bipartite graph property. To scale for larger networks, novel optimized strategies are proposed to overcome the three limitations in the baseline method. Extensive experi-ments are conducted on 8 real-world datasets to demonstrate the efficiency and effectiveness of proposed techniques and model. Compared with the baseline approach, the optimized algorithm can achieve up to 3 orders of magnitude speedup.",
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"normalizedAbstract": "Maximal biclique enumeration is a fundamental problem in bipartite graph analysis, and can find numerous applications. However, previous studies only focus on unsigned bipartite graphs. Signed information, such as friend and enemy, naturally exists in real-world networks. It is critical to leverage signed information to better characterize biclique. To fill this gap, in this paper, we propose a novel biclique model, named balanced signed biclique, by leveraging the property of balance theory. Specifically, given a signed bipartite graph -, two positive integers -, a subgraph - of - is a balanced signed biclique if -) - is a biclique without any unstable motif, i.e., unbalanced butterfly, and ii) - and -. In this paper, we aim to enumerate all the maximal balanced signed bicliques, which is proved to be NP-hard. Moreover, due to the unique features of signed bipartite graphs, the previous works cannot be applied to our problem directly. To construct a reasonable baseline, we extend the existing biclique enumeration framework for unsigned bipartite graphs and integrate the developed balanced bipartite graph property. To scale for larger networks, novel optimized strategies are proposed to overcome the three limitations in the baseline method. Extensive experi-ments are conducted on 8 real-world datasets to demonstrate the efficiency and effectiveness of proposed techniques and model. Compared with the baseline approach, the optimized algorithm can achieve up to 3 orders of magnitude speedup.",
"fno": "088300b887",
"keywords": [
"Computational Complexity",
"Graph Theory",
"Maximal Balanced Signed Biclique Enumeration",
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"NP Hard Problem",
"Conferences",
"Data Engineering",
"Bipartite Graph",
"Biclique",
"Signed Graph",
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"affiliation": "East China Normal University,Shanghai,China",
"fullName": "Renjie Sun",
"givenName": "Renjie",
"surname": "Sun",
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{
"affiliation": "Zhejiang Gongshang University,Hangzhou,China",
"fullName": "Yanping Wu",
"givenName": "Yanping",
"surname": "Wu",
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{
"affiliation": "East China Normal University,Shanghai,China",
"fullName": "Chen Chen",
"givenName": "Chen",
"surname": "Chen",
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{
"affiliation": "Zhejiang Gongshang University,Hangzhou,China",
"fullName": "Xiaoyang Wang",
"givenName": "Xiaoyang",
"surname": "Wang",
"__typename": "ArticleAuthorType"
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{
"affiliation": "The University of New South Wales,Sydney,Australia",
"fullName": "Wenjie Zhang",
"givenName": "Wenjie",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
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{
"affiliation": "The University of New South Wales,Sydney,Australia",
"fullName": "Xuemin Lin",
"givenName": "Xuemin",
"surname": "Lin",
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"abstract": "We demonstrate nonstochastic Kronecker graph generators produce massive-scale bipartite graphs with ground truth global and local properties and discuss their use for validation of graph analytics. Given two small connected scale-free graphs with adjacency matrices A and B, their Kronecker product graph [1] has adjacency matrix C = A ⊗ B. We first demonstrate that having one factor A non-bipartite (alternatively, adding all self loops to a bipartite A) with other factor B bipartite ensures G<sub>C</sub> is bipartite and connected. Formulas for ground truth of many graph properties (including degree, diameter, and eccentricity) carry over directly from the general case presented in previous work [2], [3]. However, the analysis of higher-order structure and dense structure is different in bipartite graphs, as no odd-length cycles exist (including triangles) and the densest possible structures are bicliques. We derive formulas to give ground truth for 4-cycles (a.k.a. squares or butterflies) at every vertex and edge in G<sub>C</sub>. Additionally, we demonstrate that bipartite communities (dense vertex subsets) in the factors A, B yield dense bipartite communities in the Kronecker product C. We additionally discuss interesting properties of Kronecker product graphs revealed by the formulas an their impact on using them as benchmarks with ground truth for various complex analytics. For example, for connected A and B of nontrivial size, G<sub>C</sub> has 4-cycles at vertices/edges associated with vertices/edges in A and B that have none, making it difficult to generate graphs with ground truth bipartite generalizations of truss decomposition (e.g. the k-wing decomposition of [4]).",
"abstracts": [
{
"abstractType": "Regular",
"content": "We demonstrate nonstochastic Kronecker graph generators produce massive-scale bipartite graphs with ground truth global and local properties and discuss their use for validation of graph analytics. Given two small connected scale-free graphs with adjacency matrices A and B, their Kronecker product graph [1] has adjacency matrix C = A ⊗ B. We first demonstrate that having one factor A non-bipartite (alternatively, adding all self loops to a bipartite A) with other factor B bipartite ensures G<sub>C</sub> is bipartite and connected. Formulas for ground truth of many graph properties (including degree, diameter, and eccentricity) carry over directly from the general case presented in previous work [2], [3]. However, the analysis of higher-order structure and dense structure is different in bipartite graphs, as no odd-length cycles exist (including triangles) and the densest possible structures are bicliques. We derive formulas to give ground truth for 4-cycles (a.k.a. squares or butterflies) at every vertex and edge in G<sub>C</sub>. Additionally, we demonstrate that bipartite communities (dense vertex subsets) in the factors A, B yield dense bipartite communities in the Kronecker product C. We additionally discuss interesting properties of Kronecker product graphs revealed by the formulas an their impact on using them as benchmarks with ground truth for various complex analytics. For example, for connected A and B of nontrivial size, G<sub>C</sub> has 4-cycles at vertices/edges associated with vertices/edges in A and B that have none, making it difficult to generate graphs with ground truth bipartite generalizations of truss decomposition (e.g. the k-wing decomposition of [4]).",
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"affiliation": "University of Minnesota,School of Mathematics,Minneapolis,MN,USA",
"fullName": "Trevor Steil",
"givenName": "Trevor",
"surname": "Steil",
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"affiliation": "Carnegie Mellon University,Software Engineering Institute,Pittsburgh,PA,USA",
"fullName": "Scott McMillan",
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"affiliation": "Lawrence Livermore National Laboratory (LLNL),Center for Applied Scientific Computing (CASC),Livermore,CA,USA",
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"title": "2020 IEEE International Conference on Knowledge Graph (ICKG)",
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"doi": "10.1109/ICBK50248.2020.00072",
"title": "Collaborative Adversarial Learning for Relational Learning on Multiple Bipartite Graphs",
"normalizedTitle": "Collaborative Adversarial Learning for Relational Learning on Multiple Bipartite Graphs",
"abstract": "Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. How to make efficient relation inference with few observed links is the main problem on multiple bipartite graphs. Most existing approaches attempt to solve the sparsity problem via learning shared representations to integrate knowledge from multi-source data for shared entities. However, they merely model the correlations from one aspect (e.g. distribution, representation), and cannot impose sufficient constraints on different relations of the shared entities. One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains. In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly models the joint distribution of the shared entities across multiple bipartite graphs. The objective of CAL is formulated from a variational lower bound that maximizes the joint log-likelihoods of the observations. In particular, CAL consists of distribution-level and feature-level alignments for knowledge from multiple bipartite graphs. The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs. Extensive experiments on two real-world datasets have shown that the proposed model outperforms the existing methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. How to make efficient relation inference with few observed links is the main problem on multiple bipartite graphs. Most existing approaches attempt to solve the sparsity problem via learning shared representations to integrate knowledge from multi-source data for shared entities. However, they merely model the correlations from one aspect (e.g. distribution, representation), and cannot impose sufficient constraints on different relations of the shared entities. One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains. In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly models the joint distribution of the shared entities across multiple bipartite graphs. The objective of CAL is formulated from a variational lower bound that maximizes the joint log-likelihoods of the observations. In particular, CAL consists of distribution-level and feature-level alignments for knowledge from multiple bipartite graphs. The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs. Extensive experiments on two real-world datasets have shown that the proposed model outperforms the existing methods.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. How to make efficient relation inference with few observed links is the main problem on multiple bipartite graphs. Most existing approaches attempt to solve the sparsity problem via learning shared representations to integrate knowledge from multi-source data for shared entities. However, they merely model the correlations from one aspect (e.g. distribution, representation), and cannot impose sufficient constraints on different relations of the shared entities. One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains. In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly models the joint distribution of the shared entities across multiple bipartite graphs. The objective of CAL is formulated from a variational lower bound that maximizes the joint log-likelihoods of the observations. In particular, CAL consists of distribution-level and feature-level alignments for knowledge from multiple bipartite graphs. The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs. Extensive experiments on two real-world datasets have shown that the proposed model outperforms the existing methods.",
"fno": "09194523",
"keywords": [
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"Graph Theory",
"Learning Artificial Intelligence",
"Multiple Bipartite Graphs",
"Shared Entities",
"Relational Learning",
"Collaborative Adversarial Learning",
"CAL",
"Real World Datasets",
"Bipartite Graph",
"Correlation",
"Mathematical Model",
"Collaboration",
"Knowledge Transfer",
"Task Analysis",
"Fuses",
"Relational Learning",
"Bipartite Graph",
"Joint Distribution Matching",
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],
"authors": [
{
"affiliation": "Shanghai Jiao Tong University,Cooperative Medianet Innovation Center,Shanghai,China",
"fullName": "Jingchao Su",
"givenName": "Jingchao",
"surname": "Su",
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"affiliation": "Shanghai Jiao Tong University,Cooperative Medianet Innovation Center,Shanghai,China",
"fullName": "Xu Chen",
"givenName": "Xu",
"surname": "Chen",
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"normalizedAbstract": "The increasing use of machine-learning (ML) enabled systems in critical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles. In traditional system development, model-based techniques have been widely adopted, where the central premise is that abstract models of the required system provide a sound basis for judging its implementation. We posit an analogous approach for ML systems using an ML technique that extracts from the high-dimensional training data implicitly describing the required system, a low-dimensional underlying structure-a manifold. It is then harnessed for a range of quality assurance tasks such as test adequacy measurement, test input generation, and runtime monitoring of the target ML system. The approach is built on variational autoencoder, an unsupervised method for learning a pair of mutually near-inverse functions between a given high-dimensional dataset and a low-dimensional representation. Preliminary experiments establish that the proposed manifold-based approach, for test adequacy drives diversity in test data, for test generation yields fault-revealing yet realistic test cases, and for run-time monitoring provides an independent means to assess trustability of the target system's output.",
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"affiliation": "University of Minnesota,Minneapolis,Minnesota",
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"abstract": "Data clustering is a fundamental topic in machine learning and data mining areas. In recent years, researchers have proposed a series of effective methods based on Low Rank Representation (LRR) which could explore low-dimension subspace structure embedded in original data effectively. The traditional LRR methods usually are designed for vectorial data from linear spaces with Euclidean distance. However, high-dimension data (such as video clip or imageset) are always considered as non-linear manifold data such as Grassmann manifold with non-linear metric. In addition, traditional LRR clustering method always adopt single nuclear norm as low rank constraint which would lead to suboptimal solution and decrease the clustering accuracy. In this paper, we proposed a new low rank method on Grassmann manifold for video or imageset data clustering task. In the proposed method, video or imageset data are formulated as sample data on Grassmann manifold first. And then a double low rank constraint is proposed by combining the nuclear norm and bilinear representation for better construct the representation matrix. The experimental results on several public datasets show that the proposed method outperforms the state-of-the-art clustering methods.",
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"content": "Data clustering is a fundamental topic in machine learning and data mining areas. In recent years, researchers have proposed a series of effective methods based on Low Rank Representation (LRR) which could explore low-dimension subspace structure embedded in original data effectively. The traditional LRR methods usually are designed for vectorial data from linear spaces with Euclidean distance. However, high-dimension data (such as video clip or imageset) are always considered as non-linear manifold data such as Grassmann manifold with non-linear metric. In addition, traditional LRR clustering method always adopt single nuclear norm as low rank constraint which would lead to suboptimal solution and decrease the clustering accuracy. In this paper, we proposed a new low rank method on Grassmann manifold for video or imageset data clustering task. In the proposed method, video or imageset data are formulated as sample data on Grassmann manifold first. And then a double low rank constraint is proposed by combining the nuclear norm and bilinear representation for better construct the representation matrix. The experimental results on several public datasets show that the proposed method outperforms the state-of-the-art clustering methods.",
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"normalizedAbstract": "Data clustering is a fundamental topic in machine learning and data mining areas. In recent years, researchers have proposed a series of effective methods based on Low Rank Representation (LRR) which could explore low-dimension subspace structure embedded in original data effectively. The traditional LRR methods usually are designed for vectorial data from linear spaces with Euclidean distance. However, high-dimension data (such as video clip or imageset) are always considered as non-linear manifold data such as Grassmann manifold with non-linear metric. In addition, traditional LRR clustering method always adopt single nuclear norm as low rank constraint which would lead to suboptimal solution and decrease the clustering accuracy. In this paper, we proposed a new low rank method on Grassmann manifold for video or imageset data clustering task. In the proposed method, video or imageset data are formulated as sample data on Grassmann manifold first. And then a double low rank constraint is proposed by combining the nuclear norm and bilinear representation for better construct the representation matrix. The experimental results on several public datasets show that the proposed method outperforms the state-of-the-art clustering methods.",
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"affiliation": "Dalian University of Technology,Faculty of Electronic Information and Electrical Engineering,Dalian,China,116024",
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"title": "Learning low bending and low distortion manifold embeddings",
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"abstract": "Autoencoders are a widespread tool in machine learning to transform high-dimensional data into a lower-dimensional representation which still exhibits the essential characteristics of the input. The encoder provides an embedding from the input data manifold into a latent space which may then be used for further processing. For instance, learning interpolation on the manifold may be simplified via the new manifold representation in latent space. The efficiency of such further processing heavily depends on the regularity and structure of the embedding. In this article, the embedding into latent space is regularized via a loss function that promotes an as isometric and as flat embedding as possible. The required training data comprises pairs of nearby points on the input manifold together with their local distance and their local Fréchet average. This regularity loss functional even allows to train the encoder on its own. The loss functional is computed via a Monte Carlo integration which is shown to be consistent with a geometric loss functional defined directly on the embedding map. Numerical tests are performed using image data that encodes different data manifolds. The results show that smooth manifold embeddings in latent space are obtained. These embeddings are regular enough such that interpolation between not too distant points on the manifold is well approximated by linear interpolation in latent space.",
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"content": "Autoencoders are a widespread tool in machine learning to transform high-dimensional data into a lower-dimensional representation which still exhibits the essential characteristics of the input. The encoder provides an embedding from the input data manifold into a latent space which may then be used for further processing. For instance, learning interpolation on the manifold may be simplified via the new manifold representation in latent space. The efficiency of such further processing heavily depends on the regularity and structure of the embedding. In this article, the embedding into latent space is regularized via a loss function that promotes an as isometric and as flat embedding as possible. The required training data comprises pairs of nearby points on the input manifold together with their local distance and their local Fréchet average. This regularity loss functional even allows to train the encoder on its own. The loss functional is computed via a Monte Carlo integration which is shown to be consistent with a geometric loss functional defined directly on the embedding map. Numerical tests are performed using image data that encodes different data manifolds. The results show that smooth manifold embeddings in latent space are obtained. These embeddings are regular enough such that interpolation between not too distant points on the manifold is well approximated by linear interpolation in latent space.",
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"affiliation": "University of Münster,Münster,Germany",
"fullName": "Juliane Braunsmann",
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"affiliation": "University of Bonn,Bonn,Germany",
"fullName": "Marko Rajković",
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"affiliation": "University of Bonn,Bonn,Germany",
"fullName": "Martin Rumpf",
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"affiliation": "University of Münster,Münster,Germany",
"fullName": "Benedikt Wirth",
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