arXiv ID string | arXiv URL string | PDF URL string | DOI string | Publication Date timestamp[ns] | Updated Date string | Title string | Authors string | Author Affiliations string | Abstract string | Categories string | Primary Category string | Comment string | Journal Reference string | Matched Conferences string | label int64 | source string |
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2301.01743v1 | http://arxiv.org/abs/2301.01743v1 | http://arxiv.org/pdf/2301.01743v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Chatbots as Problem Solvers: Playing Twenty Questions with Role Reversals | David Noever; Forrest McKee | null | New chat AI applications like ChatGPT offer an advanced understanding of question context and memory across multi-step tasks, such that experiments can test its deductive reasoning. This paper proposes a multi-role and multi-step challenge, where ChatGPT plays the classic twenty-questions game but innovatively switches... | cs.AI; cs.CL | cs.AI | null | null | null | 0 | ArXiv |
2301.00330v2 | http://arxiv.org/abs/2301.00330v2 | http://arxiv.org/pdf/2301.00330v2 | null | 2023-01-01T00:00:00 | 2023-03-25 | Efficient On-device Training via Gradient Filtering | Yuedong Yang; Guihong Li; Radu Marculescu | null | Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-pr... | cs.CV; cs.AI; cs.LG | cs.CV | CVPR2023, 19 pages, 13 figures | null | CVPR | 1 | CVPR |
2301.00409v1 | http://arxiv.org/abs/2301.00409v1 | http://arxiv.org/pdf/2301.00409v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification | Shizhan Gong; Cheng Chen; Yuqi Gong; Nga Yan Chan; Wenao Ma; Calvin Hoi-Kwan Mak; Jill Abrigo; Qi Dou | null | Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance ... | cs.CV; cs.AI | cs.CV | 12 pages, 5 figures | null | null | 0 | ArXiv |
2301.00383v2 | http://arxiv.org/abs/2301.00383v2 | http://arxiv.org/pdf/2301.00383v2 | 10.1109/TIP.2023.3235583 | 2023-01-01T00:00:00 | 2023-02-13 | Discriminative Radial Domain Adaptation | Zenan Huang; Jun Wen; Siheng Chen; Linchao Zhu; Nenggan Zheng | null | Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDA) which br... | cs.LG; cs.CV | cs.LG | 13 pages, 14 figures | null | null | 0 | ArXiv |
2301.00406v4 | http://arxiv.org/abs/2301.00406v4 | http://arxiv.org/pdf/2301.00406v4 | null | 2023-01-01T00:00:00 | 2024-03-06 | Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data | Rui Ding; Juntian Ye; Qifeng Gao; Feihu Xu; Yuping Duan | null | Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reco... | cs.CV; eess.IV | cs.CV | null | null | null | 0 | ArXiv |
2301.00452v2 | http://arxiv.org/abs/2301.00452v2 | http://arxiv.org/pdf/2301.00452v2 | null | 2023-01-01T00:00:00 | 2023-06-06 | Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning | Yonghao Long; Wang Wei; Tao Huang; Yuehao Wang; Qi Dou | null | Surgical robot automation has attracted increasing research interest over the past decade, expecting its potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied intelligence has demonstrated promising ability to learn good control policies for various complex tasks, where embodie... | cs.RO; cs.AI; cs.CV; cs.LG | cs.RO | null | null | null | 0 | ArXiv |
2301.00399v1 | http://arxiv.org/abs/2301.00399v1 | http://arxiv.org/pdf/2301.00399v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Semantic Operator Prediction and Applications | Farshad Noravesh | null | In the present paper, semantic parsing challenges are briefly introduced and QDMR formalism in semantic parsing is implemented using sequence to sequence model with attention but uses only part of speech(POS) as a representation of words of a sentence to make the training as simple and as fast as possible and also avoi... | cs.CL | cs.CL | null | null | null | 0 | ArXiv |
2301.00447v1 | http://arxiv.org/abs/2301.00447v1 | http://arxiv.org/pdf/2301.00447v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Image To Tree with Recursive Prompting | James Batten; Matthew Sinclair; Ben Glocker; Michiel Schaap | null | Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodolo... | cs.CV; cs.LG | cs.CV | 12 pages, 5 figures | null | null | 0 | ArXiv |
2301.00411v2 | http://arxiv.org/abs/2301.00411v2 | http://arxiv.org/pdf/2301.00411v2 | null | 2023-01-01T00:00:00 | 2023-01-11 | Detachable Novel Views Synthesis of Dynamic Scenes Using Distribution-Driven Neural Radiance Fields | Boyu Zhang; Wenbo Xu; Zheng Zhu; Guan Huang | null | Representing and synthesizing novel views in real-world dynamic scenes from casual monocular videos is a long-standing problem. Existing solutions typically approach dynamic scenes by applying geometry techniques or utilizing temporal information between several adjacent frames without considering the underlying backgr... | cs.CV | cs.CV | null | null | null | 0 | ArXiv |
2301.00364v1 | http://arxiv.org/abs/2301.00364v1 | http://arxiv.org/pdf/2301.00364v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Generalizable Black-Box Adversarial Attack with Meta Learning | Fei Yin; Yong Zhang; Baoyuan Wu; Yan Feng; Jingyi Zhang; Yanbo Fan; Yujiu Yang | null | In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries ... | cs.LG; cs.CR; cs.CV | cs.LG | T-PAMI 2022. Project Page is at https://github.com/SCLBD/MCG-Blackbox | null | null | 0 | ArXiv |
2301.00442v1 | http://arxiv.org/abs/2301.00442v1 | http://arxiv.org/pdf/2301.00442v1 | 10.1145/3407023.3407026 | 2023-01-01T00:00:00 | 2023-01-01 | An Overview of Limitations and Approaches in Identity Management | Daniela Pöhn; Wolfgang Hommel | null | Identity and access management (I&AM) is the umbrella term for managing users and their permissions. It is required for users to access different services. These services can either be provided from their home organization, like a company or university, or from external service providers, e.g., cooperation partners. I&... | cs.CR; cs.SE; cs.SY; eess.SY | cs.CR | The 15th International Conference on Availability, Reliability and Security (ARES 2020), August 25--28, 2020, Virtual Event, Ireland | null | null | 0 | ArXiv |
2301.00395v1 | http://arxiv.org/abs/2301.00395v1 | http://arxiv.org/pdf/2301.00395v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation | Ge Zhang; Yizhi Li; Yaoyao Wu; Linyuan Zhang; Chenghua Lin; Jiayi Geng; Shi Wang; Jie Fu | null | As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chin... | cs.CL; cs.AI; cs.CY; cs.LG | cs.CL | null | null | null | 0 | ArXiv |
2301.00479v2 | http://arxiv.org/abs/2301.00479v2 | http://arxiv.org/pdf/2301.00479v2 | null | 2023-01-01T00:00:00 | 2023-01-14 | The Design Principle of Blockchain: An Initiative for the SoK of SoKs | Luyao Sunshine Zhang | null | Blockchain, also coined as decentralized AI, has the potential to empower AI to be more trustworthy by creating a decentralized trust of privacy, security, and audibility. However, systematic studies on the design principle of blockchain as a trust engine for an integrated society of cyber-physical-social-system (CPSS)... | cs.CR; cs.AI; cs.CL; stat.ML | cs.CR | null | null | null | 0 | ArXiv |
2301.00345v1 | http://arxiv.org/abs/2301.00345v1 | http://arxiv.org/pdf/2301.00345v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction | Jorge Quesada; Lakshmi Sathidevi; Ran Liu; Nauman Ahad; Joy M. Jackson; Mehdi Azabou; Jingyun Xiao; Christopher Liding; Matthew Jin; Carolina Urzay; William Gray-Roncal; Erik C. Johnson; Eva L. Dyer | null | There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of ... | cs.CV; cs.LG | cs.CV | 10 pages, 4 figures, Accepted at NeurIPS 2022 | null | NEURIPS | 1 | NEURIPS |
2301.01223v1 | http://arxiv.org/abs/2301.01223v1 | http://arxiv.org/pdf/2301.01223v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | ExploreADV: Towards exploratory attack for Neural Networks | Tianzuo Luo; Yuyi Zhong; Siaucheng Khoo | null | Although deep learning has made remarkable progress in processing various types of data such as images, text and speech, they are known to be susceptible to adversarial perturbations: perturbations specifically designed and added to the input to make the target model produce erroneous output. Most of the existing studi... | cs.CR; cs.LG | cs.CR | null | null | null | 0 | ArXiv |
2301.00377v1 | http://arxiv.org/abs/2301.00377v1 | http://arxiv.org/pdf/2301.00377v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Ithaca. A Tool for Integrating Fuzzy Logic in Unity | Alfonso Tejedor Moreno; Jose A. Piedra-Fernandez; Juan Jesus Ojeda-Castelo; Luis Iribarne | null | Ithaca is a Fuzzy Logic (FL) plugin for developing artificial intelligence systems within the Unity game engine. Its goal is to provide an intuitive and natural way to build advanced artificial intelligence systems, making the implementation of such a system faster and more affordable. The software is made up by a C\# ... | cs.AI; cs.HC | cs.AI | null | null | null | 0 | ArXiv |
2301.00422v1 | http://arxiv.org/abs/2301.00422v1 | http://arxiv.org/pdf/2301.00422v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Leveraging Semantic Representations Combined with Contextual Word Representations for Recognizing Textual Entailment in Vietnamese | Quoc-Loc Duong; Duc-Vu Nguyen; Ngan Luu-Thuy Nguyen | null | RTE is a significant problem and is a reasonably active research community. The proposed research works on the approach to this problem are pretty diverse with many different directions. For Vietnamese, the RTE problem is moderately new, but this problem plays a vital role in natural language understanding systems. Cur... | cs.CL | cs.CL | In Proceedings of the 9th NAFOSTED Conference on Information and Computer Science (NICS 2022) | null | null | 0 | ArXiv |
2301.01221v2 | http://arxiv.org/abs/2301.01221v2 | http://arxiv.org/pdf/2301.01221v2 | null | 2023-01-01T00:00:00 | 2023-01-11 | Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain | Vesal Ahsani; Ali Rahimi; Mehdi Letafati; Babak Hossein Khalaj | null | In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy... | cs.CR | cs.CR | 21 pages, 4 figures, added references for section 3-A | null | null | 0 | ArXiv |
2301.00337v2 | http://arxiv.org/abs/2301.00337v2 | http://arxiv.org/pdf/2301.00337v2 | null | 2023-01-01T00:00:00 | 2023-04-18 | Separable Tendon-Driven Robotic Manipulator with a Long, Flexible, Passive Proximal Section | Christian DeBuys; Florin C. Ghesu; Jagadeesan Jayender; Reza Langari; Young-Ho Kim | null | This work tackles practical issues which arise when using a tendon-driven robotic manipulator (TDRM) with a long, flexible, passive proximal section in medical applications. Tendon-driven devices are preferred in medicine for their improved outcomes via minimally invasive procedures, but TDRMs come with unique challeng... | cs.RO | cs.RO | null | null | null | 0 | ArXiv |
2301.00436v3 | http://arxiv.org/abs/2301.00436v3 | http://arxiv.org/pdf/2301.00436v3 | null | 2023-01-01T00:00:00 | 2023-04-03 | Hierarchical Explanations for Video Action Recognition | Sadaf Gulshad; Teng Long; Nanne van Noord | null | To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for the classification. However, existing methods often ignore the hierarchical relationship between these prototypes, and thus can not explain semantic concepts at both higher level (e.g., wa... | cs.CV; cs.AI; cs.LG | cs.CV | null | null | null | 0 | ArXiv |
2301.00433v1 | http://arxiv.org/abs/2301.00433v1 | http://arxiv.org/pdf/2301.00433v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Optimization of Image Transmission in a Cooperative Semantic Communication Networks | Wenjing Zhang; Yining Wang; Mingzhe Chen; Tao Luo; Dusit Niyato | null | In this paper, a semantic communication framework for image transmission is developed. In the investigated framework, a set of servers cooperatively transmit images to a set of users utilizing semantic communication techniques. To evaluate the performance of studied semantic communication system, a multimodal metric is... | cs.AI; cs.CV; cs.IT; math.IT | cs.AI | 29 pages, 10 figures | null | null | 0 | ArXiv |
2301.00418v1 | http://arxiv.org/abs/2301.00418v1 | http://arxiv.org/pdf/2301.00418v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Is word segmentation necessary for Vietnamese sentiment classification? | Duc-Vu Nguyen; Ngan Luu-Thuy Nguyen | null | To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4- based language models for Vietnamese, including one model without word segmentation, and four models using R... | cs.CL | cs.CL | In Proceedings of the 16th International Conference on Computing and Communication Technologies (RIVF 2022) | null | null | 0 | ArXiv |
2301.00371v2 | http://arxiv.org/abs/2301.00371v2 | http://arxiv.org/pdf/2301.00371v2 | null | 2023-01-01T00:00:00 | 2024-03-18 | Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment | Libo Zhang; Wenzhang Zhou; Heng Fan; Tiejian Luo; Haibin Ling | null | Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship ... | cs.CV | cs.CV | null | null | null | 0 | ArXiv |
2301.00429v1 | http://arxiv.org/abs/2301.00429v1 | http://arxiv.org/pdf/2301.00429v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension | Hang Thi-Thu Le; Viet-Duc Ho; Duc-Vu Nguyen; Ngan Luu-Thuy Nguyen | null | Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro... | cs.CL | cs.CL | In Proceedings of the 9th NAFOSTED Conference on Information and Computer Science (NICS 2022) | null | null | 0 | ArXiv |
2301.00355v2 | http://arxiv.org/abs/2301.00355v2 | http://arxiv.org/pdf/2301.00355v2 | null | 2023-01-01T00:00:00 | 2023-01-05 | Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits | Ruibo Liu; Chenyan Jia; Ge Zhang; Ziyu Zhuang; Tony X Liu; Soroush Vosoughi | null | We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through reinforcement learning, Second Thought not only achieves superior perf... | cs.CL; cs.AI; cs.CY | cs.CL | In proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) | null | NEURIPS | 1 | NEURIPS |
2301.00321v1 | http://arxiv.org/abs/2301.00321v1 | http://arxiv.org/pdf/2301.00321v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques | Muhammad Suleman; Muhammad Asif; Tayyab Zamir; Ayaz Mehmood; Jebran Khan; Nasir Ahmad; Kashif Ahmad | null | This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts whi... | cs.CL | cs.CL | 5 pages, 1 figure, and 4 tables | null | null | 0 | ArXiv |
2301.00424v1 | http://arxiv.org/abs/2301.00424v1 | http://arxiv.org/pdf/2301.00424v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | GoogLe2Net: Going Transverse with Convolutions | Yuanpeng He | null | Capturing feature information effectively is of great importance in vision tasks. With the development of convolutional neural networks (CNNs), concepts like residual connection and multiple scales promote continual performance gains on diverse deep learning vision tasks. However, the existing methods do not organicall... | cs.CV | cs.CV | 33 pages, 7 figures | null | null | 0 | ArXiv |
2301.00397v1 | http://arxiv.org/abs/2301.00397v1 | http://arxiv.org/pdf/2301.00397v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Inflected Forms Are Redundant in Question Generation Models | Xingwu Sun; Hongyin Tang; chengzhong Xu | null | Neural models with an encoder-decoder framework provide a feasible solution to Question Generation (QG). However, after analyzing the model vocabulary we find that current models (both RNN-based and pre-training based) have more than 23\% inflected forms. As a result, the encoder will generate separate embeddings for t... | cs.CL | cs.CL | null | null | null | 0 | ArXiv |
2301.00320v1 | http://arxiv.org/abs/2301.00320v1 | http://arxiv.org/pdf/2301.00320v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Relevance Classification of Flood-related Twitter Posts via Multiple Transformers | Wisal Mukhtiar; Waliiya Rizwan; Aneela Habib; Yasir Saleem Afridi; Laiq Hasan; Kashif Ahmad | null | In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potenti... | cs.CL | cs.CL | 5 pages, 1 figure, 2 tables | null | null | 0 | ArXiv |
2301.00443v1 | http://arxiv.org/abs/2301.00443v1 | http://arxiv.org/pdf/2301.00443v1 | 10.1145/3538969.3544430 | 2023-01-01T00:00:00 | 2023-01-01 | TaxIdMA: Towards a Taxonomy for Attacks related to Identities | Daniela Pöhn und Wolfgang Hommel | null | Identity management refers to the technology and policies for the identification, authentication, and authorization of users in computer networks. Identity management is therefore fundamental to today's IT ecosystem. At the same time, identity management systems, where digital identities are managed, pose an attractive... | cs.CR | cs.CR | The 17th International Conference on Availability, Reliability and Security (ARES 2022), August 23-26, 2022, Vienna, Austria | null | null | 0 | ArXiv |
2301.00362v2 | http://arxiv.org/abs/2301.00362v2 | http://arxiv.org/pdf/2301.00362v2 | 10.1109/TITS.2023.3312453 | 2023-01-01T00:00:00 | 2023-09-24 | Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation | Wenhui Huang; Yanxin Zhou; Xiangkun He; Chen Lv | null | Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-maki... | cs.RO; cs.AI; cs.LG | cs.RO | null | null | null | 0 | ArXiv |
2301.00374v1 | http://arxiv.org/abs/2301.00374v1 | http://arxiv.org/pdf/2301.00374v1 | 10.1016/j.knosys.2023.111273 | 2023-01-01T00:00:00 | 2023-01-01 | Optimizing Readability Using Genetic Algorithms | Jorge Martinez-Gil | null | This research presents ORUGA, a method that tries to automatically optimize the readability of any text in English. The core idea behind the method is that certain factors affect the readability of a text, some of which are quantifiable (number of words, syllables, presence or absence of adverbs, and so on). The nature... | cs.CL; cs.AI | cs.CL | 32 pages | null | null | 0 | ArXiv |
2301.00366v3 | http://arxiv.org/abs/2301.00366v3 | http://arxiv.org/pdf/2301.00366v3 | 10.3390/s23073649 | 2023-01-01T00:00:00 | 2023-04-04 | SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation | Kunal Chaturvedi; Ali Braytee; Jun Li; Mukesh Prasad | null | This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net based discriminator. The proposed method ext... | cs.CV; cs.LG | cs.CV | null | null | null | 0 | ArXiv |
2301.01143v1 | http://arxiv.org/abs/2301.01143v1 | http://arxiv.org/pdf/2301.01143v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning | Fengbei Liu; Yuanhong Chen; Chong Wang; Yu Tain; Gustavo Carneiro | null | Learning with noisy-labels has become an important research topic in computer vision where state-of-the-art (SOTA) methods explore: 1) prediction disagreement with co-teaching strategy that updates two models when they disagree on the prediction of training samples; and 2) sample selection to divide the training set in... | cs.CV | cs.CV | null | null | null | 0 | ArXiv |
2301.00379v1 | http://arxiv.org/abs/2301.00379v1 | http://arxiv.org/pdf/2301.00379v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | A review of Implementation and Challenges of Unmanned Aerial Vehicles for Spraying Applications and Crop Monitoring in Indonesia | Muhamad Rausyan Fikri; Taufiq Candra; Kushendarsyah Saptaji; Ajeng Nindi Noviarini; Dilla Ayu Wardani | null | The rapid development of technology has brought unmanned aerial vehicles (UAVs) to become widely known in the current era. The market of UAVs is also predicted to continue growing with related technologies in the future. UAVs have been used in various sectors, including livestock, forestry, and agriculture. In agricult... | cs.RO | cs.RO | null | null | null | 0 | ArXiv |
2301.00328v1 | http://arxiv.org/abs/2301.00328v1 | http://arxiv.org/pdf/2301.00328v1 | 10.1063/5.0111335 | 2023-01-01T00:00:00 | 2023-01-01 | Internet of Things: Digital Footprints Carry A Device Identity | Rajarshi Roy Chowdhury; Azam Che Idris; Pg Emeroylariffion Abas | null | The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet connectivity. To secure a network, device identification plays key role. In this paper, a device fingerprinting (DFP) model, which is able to disti... | cs.LG; cs.CR | cs.LG | 8th Brunei International Conference on Engineering and Technology (BICET 2021), Universiti Teknologi Brunei | null | null | 0 | ArXiv |
2301.00346v1 | http://arxiv.org/abs/2301.00346v1 | http://arxiv.org/pdf/2301.00346v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | Thanh Vinh Vo; Arnab Bhattacharyya; Young Lee; Tze-Yun Leong | null | We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple compon... | cs.LG; cs.AI; stat.ME; stat.ML | cs.LG | NeurIPS 2022 | null | NEURIPS | 1 | NEURIPS |
2301.01234v1 | http://arxiv.org/abs/2301.01234v1 | http://arxiv.org/pdf/2301.01234v1 | null | 2023-01-01T00:00:00 | 2023-01-01 | AmbieGen: A Search-based Framework for Autonomous Systems Testing | Dmytro Humeniuk; Foutse Khomh; Giuliano Antoniol | null | Thorough testing of safety-critical autonomous systems, such as self-driving cars, autonomous robots, and drones, is essential for detecting potential failures before deployment. One crucial testing stage is model-in-the-loop testing, where the system model is evaluated by executing various scenarios in a simulator. Ho... | cs.RO; cs.NE | cs.RO | 17 pages, 10 figures | null | null | 0 | ArXiv |
2301.00555v2 | http://arxiv.org/abs/2301.00555v2 | http://arxiv.org/pdf/2301.00555v2 | null | 2023-01-02T00:00:00 | 2025-06-03 | Scene Structure Guidance Network: Unfolding Graph Partitioning into Pixel-Wise Feature Learning | Jisu Shin; Seunghyun Shin; Hae-Gon Jeon | null | Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific. In this paper, we propose a single general neural network architecture ... | cs.CV; cs.AI | cs.CV | 35 pages, 14 figures, journal extension version of SSGNet (https://ojs.aaai.org/index.php/AAAI/article/view/25322) | null | null | 0 | ArXiv |
2301.00521v2 | http://arxiv.org/abs/2301.00521v2 | http://arxiv.org/pdf/2301.00521v2 | null | 2023-01-02T00:00:00 | 2023-10-13 | A Policy Optimization Method Towards Optimal-time Stability | Shengjie Wang; Fengbo Lan; Xiang Zheng; Yuxue Cao; Oluwatosin Oseni; Haotian Xu; Tao Zhang; Yang Gao | null | In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization. However, these criteria only guarantee the infinite-time convergence of the system's state to an equilibrium point, which leads to sub-optimality of the policy. ... | cs.RO; cs.LG | cs.RO | 27 pages, 11 figues. 7th Annual Conference on Robot Learning. 2023 | null | null | 0 | ArXiv |
2301.01170v1 | http://arxiv.org/abs/2301.01170v1 | http://arxiv.org/pdf/2301.01170v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Transformer Based Geocoding | Yuval Solaz; Vitaly Shalumov | null | In this paper, we formulate the problem of predicting a geolocation from free text as a sequence-to-sequence problem. Using this formulation, we obtain a geocoding model by training a T5 encoder-decoder transformer model using free text as an input and geolocation as an output. The geocoding model was trained on geo-ta... | cs.CL; cs.AI | cs.CL | null | null | null | 0 | ArXiv |
2301.00629v1 | http://arxiv.org/abs/2301.00629v1 | http://arxiv.org/pdf/2301.00629v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear | Manuele Leonelli; Gherardo Varando | null | Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they are only capable of formally encoding symmetric conditional independence, which in practice is often too strict to hold. Asymmetry-labeled DAGs have been recently proposed to both extend the class o... | cs.AI; stat.ML | cs.AI | null | null | null | 0 | ArXiv |
2302.05286v1 | http://arxiv.org/abs/2302.05286v1 | http://arxiv.org/pdf/2302.05286v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Archaeological Sites Detection with a Human-AI Collaboration Workflow | Luca Casini; Valentina Orrù; Andrea Montanucci; Nicolò Marchetti; Marco Roccetti | null | This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly available satellite imagery and vector shapes coming from a large corpus of annot... | cs.CV; cs.LG; I.2.6 | cs.CV | 15 pages, 5 figures, 2 tables | null | null | 0 | ArXiv |
2301.00628v2 | http://arxiv.org/abs/2301.00628v2 | http://arxiv.org/pdf/2301.00628v2 | 10.1111/emip.12537 | 2023-01-02T00:00:00 | 2023-04-13 | Using Active Learning Methods to Strategically Select Essays for Automated Scoring | Tahereh Firoozi; Hamid Mohammadi; Mark J. Gierl | null | Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-... | cs.CL | cs.CL | null | null | null | 0 | ArXiv |
2301.00620v1 | http://arxiv.org/abs/2301.00620v1 | http://arxiv.org/pdf/2301.00620v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Dynamically Modular and Sparse General Continual Learning | Arnav Varma; Elahe Arani; Bahram Zonooz | null | Real-world applications often require learning continuously from a stream of data under ever-changing conditions. When trying to learn from such non-stationary data, deep neural networks (DNNs) undergo catastrophic forgetting of previously learned information. Among the common approaches to avoid catastrophic forgettin... | cs.CV; cs.AI; cs.LG; cs.NE | cs.CV | Camera ready version - 18th International Conference on Computer Vision Theory and Applications (VISAPP 2023) | null | null | 0 | ArXiv |
2301.00514v1 | http://arxiv.org/abs/2301.00514v1 | http://arxiv.org/pdf/2301.00514v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding | Jiahao Zhu; Daizong Liu; Pan Zhou; Xing Di; Yu Cheng; Song Yang; Wenzheng Xu; Zichuan Xu; Yao Wan; Lichao Sun; Zeyu Xiong | null | Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. H... | cs.CV | cs.CV | Accepted by EMNLP Findings, 2022 | null | EMNLP | 1 | EMNLP |
2301.00794v3 | http://arxiv.org/abs/2301.00794v3 | http://arxiv.org/pdf/2301.00794v3 | null | 2023-01-02T00:00:00 | 2023-09-09 | STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural Videos | Anshul Shah; Benjamin Lundell; Harpreet Sawhney; Rama Chellappa | null | We address the problem of extracting key steps from unlabeled procedural videos, motivated by the potential of Augmented Reality (AR) headsets to revolutionize job training and performance. We decompose the problem into two steps: representation learning and key steps extraction. We propose a training objective, Bootst... | cs.CV | cs.CV | Accepted at ICCV 2023 | null | ICCV | 1 | ICCV |
2301.00604v1 | http://arxiv.org/abs/2301.00604v1 | http://arxiv.org/pdf/2301.00604v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Russia-Ukraine war: Modeling and Clustering the Sentiments Trends of Various Countries | Hamed Vahdat-Nejad; Mohammad Ghasem Akbari; Fatemeh Salmani; Faezeh Azizi; Hamid-Reza Nili-Sani | null | With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sent... | cs.CL | cs.CL | null | null | null | 0 | ArXiv |
2301.00596v1 | http://arxiv.org/abs/2301.00596v1 | http://arxiv.org/pdf/2301.00596v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | A contrastive learning approach for individual re-identification in a wild fish population | Ørjan Langøy Olsen; Tonje Knutsen Sørdalen; Morten Goodwin; Ketil Malde; Kristian Muri Knausgård; Kim Tallaksen Halvorsen | null | In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifyin... | cs.CV; cs.AI; cs.LG; I.2.6; I.4.9; I.5.4; J.3 | cs.CV | null | null | null | 0 | ArXiv |
2301.00771v2 | http://arxiv.org/abs/2301.00771v2 | http://arxiv.org/pdf/2301.00771v2 | 10.55417/fr.2023004 | 2023-01-02T00:00:00 | 2023-04-11 | Flexible Supervised Autonomy for Exploration in Subterranean Environments | Harel Biggie; Eugene R. Rush; Danny G. Riley; Shakeeb Ahmad; Michael T. Ohradzansky; Kyle Harlow; Michael J. Miles; Daniel Torres; Steve McGuire; Eric W. Frew; Christoffer Heckman; J. Sean Humbert | null | While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration sys... | cs.RO | cs.RO | Field Robotics special issue: DARPA Subterranean Challenge, Advancement and Lessons Learned from the Finals | null | null | 0 | ArXiv |
2301.00714v2 | http://arxiv.org/abs/2301.00714v2 | http://arxiv.org/pdf/2301.00714v2 | null | 2023-01-02T00:00:00 | 2023-02-27 | Learning Road Scene-level Representations via Semantic Region Prediction | Zihao Xiao; Alan Yuille; Yi-Ting Chen | null | In this work, we tackle two vital tasks in automated driving systems, i.e., driver intent prediction and risk object identification from egocentric images. Mainly, we investigate the question: what would be good road scene-level representations for these two tasks? We contend that a scene-level representation must capt... | cs.CV; cs.RO | cs.CV | 18 pages | null | null | 0 | ArXiv |
2301.00716v1 | http://arxiv.org/abs/2301.00716v1 | http://arxiv.org/pdf/2301.00716v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale | Felix Hamann; Adrian Ulges; Maurice Falk | null | We address the challenge of building domain-specific knowledge models for industrial use cases, where labelled data and taxonomic information is initially scarce. Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discover... | cs.LG; cs.AI; cs.CL | cs.LG | null | null | null | 0 | ArXiv |
2301.00866v1 | http://arxiv.org/abs/2301.00866v1 | http://arxiv.org/pdf/2301.00866v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | 3DSGrasp: 3D Shape-Completion for Robotic Grasp | Seyed S. Mohammadi; Nuno F. Duarte; Dimitris Dimou; Yiming Wang; Matteo Taiana; Pietro Morerio; Atabak Dehban; Plinio Moreno; Alexandre Bernardino; Alessio Del Bue; Jose Santos-Victor | null | Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose ... | cs.RO; cs.AI | cs.RO | null | null | null | 0 | ArXiv |
2301.00709v1 | http://arxiv.org/abs/2301.00709v1 | http://arxiv.org/pdf/2301.00709v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Tsetlin Machine Embedding: Representing Words Using Logical Expressions | Bimal Bhattarai; Ole-Christoffer Granmo; Lei Jiao; Rohan Yadav; Jivitesh Sharma | null | Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that cap... | cs.CL; cs.AI; cs.LG | cs.CL | 9 pages, 7 figures | null | null | 0 | ArXiv |
2301.00888v1 | http://arxiv.org/abs/2301.00888v1 | http://arxiv.org/pdf/2301.00888v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | SAFEMYRIDES: Application of Decentralized Control Edge-Computing to Ridesharing Monitoring Services | Samaa Elnagar; Manoj A. Thomas; Kweku-Muata Osei-Bryson | null | Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Internet of Things (IoTs) to take decisions and run cognitive tasks locally. This research introduces a d... | cs.CR; cs.AI | cs.CR | 18 pages, eight figures and it is under review in Information System Frontiers | null | null | 0 | ArXiv |
2301.00858v2 | http://arxiv.org/abs/2301.00858v2 | http://arxiv.org/pdf/2301.00858v2 | null | 2023-01-02T00:00:00 | 2023-03-01 | Robust Average-Reward Markov Decision Processes | Yue Wang; Alvaro Velasquez; George Atia; Ashley Prater-Bennette; Shaofeng Zou | null | In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this pape... | cs.LG; cs.AI | cs.LG | AAAI 2023 | null | AAAI | 1 | AAAI |
2301.00887v1 | http://arxiv.org/abs/2301.00887v1 | http://arxiv.org/pdf/2301.00887v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Towards Computer-Vision Based Vineyard Navigation for Quadruped Robots | Lee Milburn; Juan Gamba; Claudio Semini | null | There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadrup... | cs.RO | cs.RO | null | null | null | 0 | ArXiv |
2301.00561v1 | http://arxiv.org/abs/2301.00561v1 | http://arxiv.org/pdf/2301.00561v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Local Differential Privacy for Sequential Decision Making in a Changing Environment | Pratik Gajane | null | We study the problem of preserving privacy while still providing high utility in sequential decision making scenarios in a changing environment. We consider abruptly changing environment: the environment remains constant during periods and it changes at unknown time instants. To formulate this problem, we propose a var... | cs.LG; cs.CR | cs.LG | Accepted at AAAI Privacy Preserving Artificial Intelligence (PPAI), 2023. arXiv admin note: text overlap with arXiv:1708.05033 | null | AAAI | 1 | AAAI |
2301.00802v3 | http://arxiv.org/abs/2301.00802v3 | http://arxiv.org/pdf/2301.00802v3 | null | 2023-01-02T00:00:00 | 2024-05-17 | Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning | Shourav B. Rabbani; Ivan V. Medri; Manar D. Samad | null | Deep learning methods are primarily proposed for supervised learning of images or text with limited applications to clustering problems. In contrast, tabular data with heterogeneous features pose unique challenges in representation learning, where deep learning has yet to replace traditional machine learning. This pape... | cs.LG; cs.AI | cs.LG | null | null | null | 0 | ArXiv |
2301.00618v4 | http://arxiv.org/abs/2301.00618v4 | http://arxiv.org/pdf/2301.00618v4 | null | 2023-01-02T00:00:00 | 2025-07-17 | An Event-based Algorithm for Simultaneous 6-DOF Camera Pose Tracking and Mapping | Masoud Dayani Najafabadi; Mohammad Reza Ahmadzadeh | null | Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively ... | cs.CV | cs.CV | null | null | null | 0 | ArXiv |
2301.00891v1 | http://arxiv.org/abs/2301.00891v1 | http://arxiv.org/pdf/2301.00891v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Understanding Political Polarisation using Language Models: A dataset and method | Samiran Gode; Supreeth Bare; Bhiksha Raj; Hyungon Yoo | null | Our paper aims to analyze political polarization in US political system using Language Models, and thereby help candidates make an informed decision. The availability of this information will help voters understand their candidates views on the economy, healthcare, education and other social issues. Our main contributi... | cs.CL | cs.CL | null | null | null | 0 | ArXiv |
2301.00704v1 | http://arxiv.org/abs/2301.00704v1 | http://arxiv.org/pdf/2301.00704v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Muse: Text-To-Image Generation via Masked Generative Transformers | Huiwen Chang; Han Zhang; Jarred Barber; AJ Maschinot; Jose Lezama; Lu Jiang; Ming-Hsuan Yang; Kevin Murphy; William T. Freeman; Michael Rubinstein; Yuanzhen Li; Dilip Krishnan | null | We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large ... | cs.CV; cs.AI; cs.LG | cs.CV | null | null | null | 0 | ArXiv |
2301.00592v1 | http://arxiv.org/abs/2301.00592v1 | http://arxiv.org/pdf/2301.00592v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Edge Enhanced Image Style Transfer via Transformers | Chiyu Zhang; Jun Yang; Zaiyan Dai; Peng Cao | null | In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the ... | cs.CV; eess.IV | cs.CV | null | null | null | 0 | ArXiv |
2301.00580v2 | http://arxiv.org/abs/2301.00580v2 | http://arxiv.org/pdf/2301.00580v2 | null | 2023-01-02T00:00:00 | 2023-06-17 | Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery | Fan Zhang; Arianna Salazar Miranda; Fábio Duarte; Lawrence Vale; Gary Hack; Min Chen; Yu Liu; Michael Batty; Carlo Ratti | null | The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the l... | cs.CV; cs.CY | cs.CV | null | null | null | 0 | ArXiv |
2301.00493v1 | http://arxiv.org/abs/2301.00493v1 | http://arxiv.org/pdf/2301.00493v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting | Benjamin Wilson; William Qi; Tanmay Agarwal; John Lambert; Jagjeet Singh; Siddhesh Khandelwal; Bowen Pan; Ratnesh Kumar; Andrew Hartnett; Jhony Kaesemodel Pontes; Deva Ramanan; Peter Carr; James Hays | null | We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point cl... | cs.CV; cs.AI; cs.LG; cs.RO | cs.CV | Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks | null | null | 0 | ArXiv |
2301.00876v3 | http://arxiv.org/abs/2301.00876v3 | http://arxiv.org/pdf/2301.00876v3 | null | 2023-01-02T00:00:00 | 2023-11-24 | MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding | Steven H. Wang; Antoine Scardigli; Leonard Tang; Wei Chen; Dimitry Levkin; Anya Chen; Spencer Ball; Thomas Woodside; Oliver Zhang; Dan Hendrycks | null | Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on ... | cs.CL | cs.CL | EMNLP 2023. 5 pages + appendix. Code and dataset are available at https://github.com/TheAtticusProject/maud | null | EMNLP | 1 | EMNLP |
2301.00823v2 | http://arxiv.org/abs/2301.00823v2 | http://arxiv.org/pdf/2301.00823v2 | null | 2023-01-02T00:00:00 | 2023-11-03 | Bringing data minimization to digital wallets at scale with general-purpose zero-knowledge proofs | Matthias Babel; Johannes Sedlmeir | null | Today, digital identity management for individuals is either inconvenient and error-prone or creates undesirable lock-in effects and violates privacy and security expectations. These shortcomings inhibit the digital transformation in general and seem particularly concerning in the context of novel applications such as ... | cs.CR; 68P27; H.0; J.0 | cs.CR | null | null | null | 0 | ArXiv |
2301.00622v1 | http://arxiv.org/abs/2301.00622v1 | http://arxiv.org/pdf/2301.00622v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images | Kun Zhao; Qian Gao; Siyuan Hao; Jie Sun; Lijian Zhou | null | Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the pot... | cs.CV; cs.AI | cs.CV | 16 pages, 16 figures | null | null | 0 | ArXiv |
2301.00710v2 | http://arxiv.org/abs/2301.00710v2 | http://arxiv.org/pdf/2301.00710v2 | null | 2023-01-02T00:00:00 | 2024-07-17 | Honeypot Implementation in a Cloud Environment | Stefan Machmeier | null | In this age of digitalization, Internet services face more attacks than ever. An attacker's objective is to exploit systems and use them for malicious purposes. Such efforts are rising as vulnerable systems can be discovered and compromised through Internet-wide scanning. One known methodology besides traditional secur... | cs.CR | cs.CR | null | null | null | 0 | ArXiv |
2301.00792v1 | http://arxiv.org/abs/2301.00792v1 | http://arxiv.org/pdf/2301.00792v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings | Francisco Valentini; Germán Rosati; Diego Fernandez Slezak; Edgar Altszyler | null | Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effect of frequency when measuring female vs. male gender bias with word e... | cs.CL; cs.AI | cs.CL | Camera Ready for EMNLP 2022 (Findings) | null | EMNLP | 1 | EMNLP |
2301.00527v1 | http://arxiv.org/abs/2301.00527v1 | http://arxiv.org/pdf/2301.00527v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data | Jumin Lee; Woobin Im; Sebin Lee; Sung-Eui Yoon | null | In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion research has focused on a single object. To realize our goal, we represent a scene with discrete class labels, i.e., categorical distribution, ... | cs.CV | cs.CV | null | null | null | 0 | ArXiv |
2301.00808v1 | http://arxiv.org/abs/2301.00808v1 | http://arxiv.org/pdf/2301.00808v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders | Sanghyun Woo; Shoubhik Debnath; Ronghang Hu; Xinlei Chen; Zhuang Liu; In So Kweon; Saining Xie | null | Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models w... | cs.CV | cs.CV | Code and models available at https://github.com/facebookresearch/ConvNeXt-V2 | null | null | 0 | ArXiv |
2301.00565v1 | http://arxiv.org/abs/2301.00565v1 | http://arxiv.org/pdf/2301.00565v1 | 10.1007/978-3-031-08473-7_42 | 2023-01-02T00:00:00 | 2023-01-02 | Using meaning instead of words to track topics | Judicael Poumay; Ashwin Ittoo | null | The ability to monitor the evolution of topics over time is extremely valuable for businesses. Currently, all existing topic tracking methods use lexical information by matching word usage. However, no studies has ever experimented with the use of semantic information for tracking topics. Hence, we explore a novel sema... | cs.CL; cs.AI | cs.CL | null | null | null | 0 | ArXiv |
2301.00524v3 | http://arxiv.org/abs/2301.00524v3 | http://arxiv.org/pdf/2301.00524v3 | null | 2023-01-02T00:00:00 | 2025-02-11 | Learning Confident Classifiers in the Presence of Label Noise | Asma Ahmed Hashmi; Aigerim Zhumabayeva; Nikita Kotelevskii; Artem Agafonov; Mohammad Yaqub; Maxim Panov; Martin Takáč | null | The success of Deep Neural Network (DNN) models significantly depends on the quality of provided annotations. In medical image segmentation, for example, having multiple expert annotations for each data point is common to minimize subjective annotation bias. Then, the goal of estimation is to filter out the label noise... | cs.CV; cs.HC; cs.LG | cs.CV | null | null | null | 0 | ArXiv |
2301.00901v1 | http://arxiv.org/abs/2301.00901v1 | http://arxiv.org/pdf/2301.00901v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Towards Modeling and Influencing the Dynamics of Human Learning | Ran Tian; Masayoshi Tomizuka; Anca Dragan; Andrea Bajcsy | null | Humans have internal models of robots (like their physical capabilities), the world (like what will happen next), and their tasks (like a preferred goal). However, human internal models are not always perfect: for example, it is easy to underestimate a robot's inertia. Nevertheless, these models change and improve over... | cs.RO; cs.AI | cs.RO | 18th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2023 | null | null | 0 | ArXiv |
2301.00805v2 | http://arxiv.org/abs/2301.00805v2 | http://arxiv.org/pdf/2301.00805v2 | null | 2023-01-02T00:00:00 | 2023-07-23 | Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation | Jianzong Wu; Xiangtai Li; Henghui Ding; Xia Li; Guangliang Cheng; Yunhai Tong; Chen Change Loy | null | In this work, we focus on open vocabulary instance segmentation to expand a segmentation model to classify and segment instance-level novel categories. Previous approaches have relied on massive caption datasets and complex pipelines to establish one-to-one mappings between image regions and words in captions. However,... | cs.CV | cs.CV | ICCV-2023 | null | ICCV | 1 | ICCV |
2301.00740v1 | http://arxiv.org/abs/2301.00740v1 | http://arxiv.org/pdf/2301.00740v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification | Shuangmei Wang; Rui Ma; Tieru Wu; Yang Cao | null | Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may... | cs.CV | cs.CV | null | null | null | 0 | ArXiv |
2301.00730v1 | http://arxiv.org/abs/2301.00730v1 | http://arxiv.org/pdf/2301.00730v1 | null | 2023-01-02T00:00:00 | 2023-01-02 | Lifting-wing Quadcopter Modeling and Unified Control | Quan Quan; Wang Shuai; Gao Wenhan | null | Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orient... | cs.RO | cs.RO | null | null | null | 0 | ArXiv |
2301.00810v3 | http://arxiv.org/abs/2301.00810v3 | http://arxiv.org/pdf/2301.00810v3 | 10.1145/3568162.3576989 | 2023-01-02T00:00:00 | 2023-03-17 | SIRL: Similarity-based Implicit Representation Learning | Andreea Bobu; Yi Liu; Rohin Shah; Daniel S. Brown; Anca D. Dragan | null | When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to te... | cs.RO; cs.AI; cs.HC; cs.LG | cs.RO | 12 pages, 6 figures, HRI 2023 | null | null | 0 | ArXiv |
2301.00746v2 | http://arxiv.org/abs/2301.00746v2 | http://arxiv.org/pdf/2301.00746v2 | null | 2023-01-02T00:00:00 | 2023-03-25 | NaQ: Leveraging Narrations as Queries to Supervise Episodic Memory | Santhosh Kumar Ramakrishnan; Ziad Al-Halah; Kristen Grauman | null | Searching long egocentric videos with natural language queries (NLQ) has compelling applications in augmented reality and robotics, where a fluid index into everything that a person (agent) has seen before could augment human memory and surface relevant information on demand. However, the structured nature of the learn... | cs.CV | cs.CV | 13 pages, 7 figures, appearing in CVPR 2023 | null | CVPR | 1 | CVPR |
2301.01036v2 | http://arxiv.org/abs/2301.01036v2 | http://arxiv.org/pdf/2301.01036v2 | null | 2023-01-03T00:00:00 | 2023-06-25 | High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction | Boyu Zhang; Hongliang Yuan; Mingyan Zhu; Ligang Liu; Jue Wang | null | Generating high-quality, realistic rendering images for real-time applications generally requires tracing a few samples-per-pixel (spp) and using deep learning-based approaches to denoise the resulting low-spp images. Existing denoising methods have yet to achieve real-time performance at high resolutions due to the ph... | cs.CV; eess.IV | cs.CV | null | null | null | 0 | ArXiv |
2301.01350v1 | http://arxiv.org/abs/2301.01350v1 | http://arxiv.org/pdf/2301.01350v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | LunarNav: Crater-based Localization for Long-range Autonomous Lunar Rover Navigation | Shreyansh Daftry; Zhanlin Chen; Yang Cheng; Scott Tepsuporn; Brian Coltin; Ussama Naam; Lanssie Mingyue Ma; Shehryar Khattak; Matthew Deans; Larry Matthies | null | The Artemis program requires robotic and crewed lunar rovers for resource prospecting and exploitation, construction and maintenance of facilities, and human exploration. These rovers must support navigation for 10s of kilometers (km) from base camps. A lunar science rover mission concept - Endurance-A, has been recomm... | cs.RO; cs.AI; cs.CV | cs.RO | IEEE Aerospace Conference 2023. arXiv admin note: text overlap with arXiv:2203.10073 | null | null | 0 | ArXiv |
2301.01088v1 | http://arxiv.org/abs/2301.01088v1 | http://arxiv.org/pdf/2301.01088v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | Explaining Imitation Learning through Frames | Boyuan Zheng; Jianlong Zhou; Chunjie Liu; Yiqiao Li; Fang Chen | null | As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent ... | cs.LG; cs.CV | cs.LG | null | null | null | 0 | ArXiv |
2301.01064v1 | http://arxiv.org/abs/2301.01064v1 | http://arxiv.org/pdf/2301.01064v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora | Dinesh Nagumothu; Bahadorreza Ofoghi; Guangyan Huang; Peter W. Eklund | null | Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenI... | cs.CL; cs.AI | cs.CL | 10 pages, 3 figures, Published to Conference on Computational Natural Language Learning | In Proceedings of the 26th Conference on Computational Natural Language Learning, CoNLL, Dec 2022,Abu Dhabi, United Arab Emirates. Association for Computational Linguistics | ACL | 1 | ACL |
2301.01100v1 | http://arxiv.org/abs/2301.01100v1 | http://arxiv.org/pdf/2301.01100v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | Understanding Imbalanced Semantic Segmentation Through Neural Collapse | Zhisheng Zhong; Jiequan Cui; Yibo Yang; Xiaoyang Wu; Xiaojuan Qi; Xiangyu Zhang; Jiaya Jia | null | A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last... | cs.CV; cs.LG | cs.CV | Technical Report | null | null | 0 | ArXiv |
2301.01033v1 | http://arxiv.org/abs/2301.01033v1 | http://arxiv.org/pdf/2301.01033v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | Dissecting Continual Learning a Structural and Data Analysis | Francesco Pelosin | null | Continual Learning (CL) is a field dedicated to devise algorithms able to achieve lifelong learning. Overcoming the knowledge disruption of previously acquired concepts, a drawback affecting deep learning models and that goes by the name of catastrophic forgetting, is a hard challenge. Currently, deep learning methods ... | cs.CV; cs.LG | cs.CV | null | null | null | 0 | ArXiv |
2301.01296v1 | http://arxiv.org/abs/2301.01296v1 | http://arxiv.org/pdf/2301.01296v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models | Sucheng Ren; Fangyun Wei; Zheng Zhang; Han Hu | null | Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-ba... | cs.CV | cs.CV | Code is available at https://github.com/OliverRensu/TinyMIM | null | null | 0 | ArXiv |
2301.01298v3 | http://arxiv.org/abs/2301.01298v3 | http://arxiv.org/pdf/2301.01298v3 | null | 2023-01-03T00:00:00 | 2023-01-16 | Contextual Conservative Q-Learning for Offline Reinforcement Learning | Ke Jiang; Jiayu Yao; Xiaoyang Tan | null | Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of practical application. However, extrapolation error generated by distribution shift will still lead to the overestimation for those actions that... | cs.LG; cs.AI; cs.RO | cs.LG | the work is not finished | null | null | 0 | ArXiv |
2301.00912v1 | http://arxiv.org/abs/2301.00912v1 | http://arxiv.org/pdf/2301.00912v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics | Yahao Ding; Zhaohui Yang; Quoc-Viet Pham; Zhaoyang Zhang; Mohammad Shikh-Bahaei | null | Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication ... | cs.LG; cs.AI | cs.LG | null | null | null | 0 | ArXiv |
2301.00936v1 | http://arxiv.org/abs/2301.00936v1 | http://arxiv.org/pdf/2301.00936v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | Control and Dynamic Motion Planning for a Hybrid Air-Underwater Quadrotor: Minimizing Energy Use in a Flooded Cave Environment | Ilya Semenov; Robert Brown; Michael Otte | null | We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics ... | cs.RO; cs.SY; eess.SY | cs.RO | 8 pages, 9 figures, written in 2020 | null | null | 0 | ArXiv |
2301.00998v2 | http://arxiv.org/abs/2301.00998v2 | http://arxiv.org/pdf/2301.00998v2 | 10.1109/TPAMI.2019.2922175 | 2023-01-03T00:00:00 | 2023-01-04 | Vocabulary-informed Zero-shot and Open-set Learning | Yanwei Fu; Xiaomei Wang; Hanze Dong; Yu-Gang Jiang; Meng Wang; Xiangyang Xue; Leonid Sigal | null | Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has ... | cs.CV; cs.LG | cs.CV | 17 pages, 8 figures. TPAMI 2019 extended from CVPR 2016 (arXiv:1604.07093) | IEEE Transactions on Pattern Analysis and Machine Intelligence (2019) | CVPR | 1 | CVPR |
2301.01237v1 | http://arxiv.org/abs/2301.01237v1 | http://arxiv.org/pdf/2301.01237v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | Safe Path following for Middle Ear Surgery | Bassem Dahroug; Brahim Tamadazte; Nicolas Andreff | null | This article formulates a generic representation of a path-following controller operating under contained motion, which was developed in the context of surgical robotics. It reports two types of constrained motion: i) Bilateral Constrained Motion, also called Remote Center Motion (RCM), and ii) Unilaterally Constrained... | cs.RO | cs.RO | 40 pages, 26 figures | null | null | 0 | ArXiv |
2301.01058v1 | http://arxiv.org/abs/2301.01058v1 | http://arxiv.org/pdf/2301.01058v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC Networks | Shao-Di Wang; Hui-Ming Wang; Zhetao Li; Victor C. M. Leung | null | For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the low-cost and low-complexity machine-type devices, active jamming attack in the uplink access is a serious threat. Uplink ... | cs.CR | cs.CR | null | null | null | 0 | ArXiv |
2301.10293v1 | http://arxiv.org/abs/2301.10293v1 | http://arxiv.org/pdf/2301.10293v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | A Fast Feature Point Matching Algorithm Based on IMU Sensor | Lu Cao | null | In simultaneous localization and mapping (SLAM), image feature point matching process consume a lot of time. The capacity of low-power systems such as embedded systems is almost limited. It is difficult to ensure the timely processing of each image information. To reduce time consuming when matching feature points in S... | cs.CV | cs.CV | 6 pages, 4 figures, 2 tables | null | null | 0 | ArXiv |
2301.01352v1 | http://arxiv.org/abs/2301.01352v1 | http://arxiv.org/pdf/2301.01352v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | WLD-Reg: A Data-dependent Within-layer Diversity Regularizer | Firas Laakom; Jenni Raitoharju; Alexandros Iosifidis; Moncef Gabbouj | null | Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher l... | cs.LG; cs.CV | cs.LG | accepted at AAAI 2023. arXiv admin note: substantial text overlap with arXiv:2106.06012 | null | AAAI | 1 | AAAI |
2301.00964v1 | http://arxiv.org/abs/2301.00964v1 | http://arxiv.org/pdf/2301.00964v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | e-Inu: Simulating A Quadruped Robot With Emotional Sentience | Abhiruph Chakravarty; Jatin Karthik Tripathy; Sibi Chakkaravarthy S; Aswani Kumar Cherukuri; S. Anitha; Firuz Kamalov; Annapurna Jonnalagadda | null | Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detectin... | cs.RO; cs.HC; cs.LG | cs.RO | null | null | null | 0 | ArXiv |
2301.00969v1 | http://arxiv.org/abs/2301.00969v1 | http://arxiv.org/pdf/2301.00969v1 | 10.1145/3564625.3567998 | 2023-01-03T00:00:00 | 2023-01-03 | Boosting Neural Networks to Decompile Optimized Binaries | Ying Cao; Ruigang Liang; Kai Chen; Peiwei Hu | null | Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful applicati... | cs.LG; cs.CR | cs.LG | null | null | null | 0 | ArXiv |
2301.01006v2 | http://arxiv.org/abs/2301.01006v2 | http://arxiv.org/pdf/2301.01006v2 | null | 2023-01-03T00:00:00 | 2023-03-15 | Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling | Penghao Wu; Li Chen; Hongyang Li; Xiaosong Jia; Junchi Yan; Yu Qiao | null | Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and v... | cs.CV | cs.CV | ICLR2023 | null | ICLR | 1 | ICLR |
2301.00975v1 | http://arxiv.org/abs/2301.00975v1 | http://arxiv.org/pdf/2301.00975v1 | null | 2023-01-03T00:00:00 | 2023-01-03 | Surveillance Face Anti-spoofing | Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Chenxu Zhao; Xu Zhang; Stan Z. Li; Zhen Lei | null | Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant... | cs.CV | cs.CV | 15 pages, 9 figures | null | null | 0 | ArXiv |
2301.01380v2 | http://arxiv.org/abs/2301.01380v2 | http://arxiv.org/pdf/2301.01380v2 | null | 2023-01-03T00:00:00 | 2023-05-19 | Ego-Only: Egocentric Action Detection without Exocentric Transferring | Huiyu Wang; Mitesh Kumar Singh; Lorenzo Torresani | null | We present Ego-Only, the first approach that enables state-of-the-art action detection on egocentric (first-person) videos without any form of exocentric (third-person) transferring. Despite the content and appearance gap separating the two domains, large-scale exocentric transferring has been the default choice for eg... | cs.CV | cs.CV | null | null | null | 0 | ArXiv |
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