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21st International Conference on Mobile and Ubiquitous Multimedia, MUM 2022

作者： Ferreira-Brito, Filipa Gjoreski, Hristijan Mayora, Oscar Luštrek, Mitja Kizhevska, Emilija Guerreiro, João Gerling, Kathrin Bermúdez I Badia, Sergi Guerreiro, Tiago LASIGE Faculdade de Ciências da Universidade de Lisboa Portugal Ss. Cyril and Methodius University in Skopje Skopje Macedonia Center for Health and Wellbeing Fondazione Bruno Kessler Trento Italy Department of Intelligent Systems Jožef Stefan Institute Slovenia Slovenia Ss. Cyril and Methodius University in Skopje Macedonia KU Leuven Department of Computer Science Human-Computer Interaction Belgium Departamento de Engenharia Informatica and NOVA Laboratory for Computer Science and Informatics Universidade da Madeira Portugal and ARDITI Portugal

ISBN: (纸本)9781450398213

Virtual Reality (VR) has become a mainstream opportunity to develop immersive environments where people are exposed to relevant stimuli in a systematic but safe way. Besides the controlled exposure to stimuli, VR systems enable a more efficient and unbiased gathering of information with practical applications in diagnosis, intervention, and health monitoring processes. Nonetheless, scientific literature has demonstrated that interacting with VR technology and its peripherals can be challenging, especially in populations less familiar with technology (e.g., older adults) or with clinical conditions that are not adequately accommodated by the technology, such as cognitive (e.g., dementia) or physical impairments (e.g., blindness). This workshop will focus on systems, technological approaches, and theoretical frameworks to develop user/patient-centred VR tools for diagnosis, intervention, and monitoring of health and wellbeing outcomes. Moreover, we welcome work focused on accessibility issues, aiming to promote equal access and opportunity to use VR tools by people with diverse abilities. © 2022 ACM.

关键词： Virtual reality

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Weakly-supervised disentanglementwithout compromises 37

Weakly-supervised disentanglementwithout compromises

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37th International Conference on Machine Learning, ICML 2020

作者： Locatello, Francesco Poole, Ben Rätsch, Gunnar Schölkopf, Bernhard Bachem, Olivier Tschannen, Michael Department of Computer Science ETH Zurich Max Planck Institute for Intelligent Systems Google Research Brain Team

ISBN: (纸本)9781713821120

intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at least one of the underlying factors of variation. First, we theoretically show that only knowing how many factors have changed, but not which ones, is sufficient to learn disentangled representations. Second, we provide practical algorithms that learn disentangled representations from pairs of images without requiring annotation of groups, individual factors, or the number of factors that have changed. Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets. Finally, we evaluate our learned representations and find that they are simultaneously useful on a diverse suite of tasks, including generalization under covariate shifts, fairness, and abstract reasoning. Overall, our results demonstrate that weak supervision enables learning of useful disentangled representations in realistic scenarios. © 2020 37th International Conference on Machine Learning, ICML 2020. All rights reserved.

关键词： intelligent agents

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles 27

Motion Planning Integrated with Vehicle-Terrain Interactions...

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27th IEEE International Conference on intelligent Transportation systems, ITSC 2024

作者： Yin, Jianhua Zhan, Xianyuan Ji, Tianying Xu, Bingrong He, Yi Zhang, Daqing Li, Lingxi Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan430063 China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for Ai Industry Research Tsinghua University Beijing100084 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Tsinghua University Department of Computer Science and Technology Beijing100084 China School of Automation Wuhan University of Technology Wuhan430070 China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha410128 China Sunward Intelligent Equipment Co. Ltd. Changsha410100 China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN46202 United States

ISBN: (纸本)9798331505929

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains. © 2024 IEEE.

关键词： Off road vehicles

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Accelerated First-Order Optimization under Nonlinear Constraints

arXiv

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arXiv 2023年

作者： Muehlebach, Michael Jordan, Michael I. Learning and Dynamical Systems Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Baden-Wuerttemberg Tübingen72076 Germany Department of Electrical Engineering and Computer Science University of California Berkeley 387 Soda Hall BerkeleyCA94720 United States

We exploit analogies between first-order algorithms for constrained optimization and non-smooth dynamical systems to design a new class of accelerated first-order algorithms for constrained optimization. Unlike Frank-Wolfe or projected gradients, these algorithms avoid optimization over the entire feasible set at each iteration. We prove convergence to stationary points even in a nonconvex setting and we derive accelerated rates for the convex setting both in continuous time, as well as in discrete time. An important property of these algorithms is that constraints are expressed in terms of velocities instead of positions, which naturally leads to sparse, local and convex approximations of the feasible set (even if the feasible set is nonconvex). Thus, the complexity tends to grow mildly in the number of decision variables and in the number of constraints, which makes the algorithms suitable for machine learning applications. We apply our algorithms to a compressed sensing and a sparse regression problem, showing that we can treat nonconvex p constraints (p © 2023, CC BY.

关键词： Constrained optimization

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A Generic Load Flow Solution for Droop-Controlled AC Microgrids Considering Electric Vehicle Loads 11

A Generic Load Flow Solution for Droop-Controlled AC Microgr...

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11th IEEE International Conference on Power Electronics, Drives and Energy systems, PEDES 2024

作者： Meena, Ghanshyam Mathur, Akhilesh Singh, V.P. Murari, Krishna Kamalasadan, Sukumar Malaviya National Institute of Technology Department of Electrical Engineering Rajasthan Jaipur302017 India The University of Toledo Electrical Engineering and Computer Science Department ToledoOH43606 United States University of North Carolina at Charlotte Power Energy and Intelligent Systems Laboratory Epic Department of Electrical Engineering CharlotteNC28223 United States

Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs due to the absence of slack bus and dependency of active power generation of droop-controlled DGs on frequency. In this paper, a Modified Newton Raphson (MNR) approach is used to perform load flow analysis of isolated MGs considering various types of electric vehicle (EV) loads. The presented method has been validated on 6-bus and 38-bus test systems of the islanded microgrid to give an accurate and simple approach for computing the load flow solutions. © 2024 IEEE.

关键词： Islanding

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Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks

arXiv

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arXiv 2022年

作者： Li, Xiao Wang, Ziqi Zhang, Bo Sun, Fuchun Hu, Xiaolin The Department of Computer Science and Technology Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems BN-Rist Tsinghua University Beijing100084 China The Department of Computer Science University of Illinois Urban ChampaignIL United States

Adversarial attacks can easily fool object recognition systems based on deep neural networks (DNNs). Although many defense methods have been proposed in recent years, most of them can still be adaptively evaded. One reason for the weak adversarial robustness may be that DNNs are only supervised by category labels and do not have part-based inductive bias like the recognition process of humans. Inspired by a well-known theory in cognitive psychology – recognition-by-components, we propose a novel object recognition model ROCK (Recognizing Object by Components with human prior Knowledge). It first segments parts of objects from images, then scores part segmentation results with predefined human prior knowledge, and finally outputs prediction based on the scores. The first stage of ROCK corresponds to the process of decomposing objects into parts in human vision. The second stage corresponds to the decision process of the human brain. ROCK shows better robustness than classical recognition models across various attack settings. These results encourage researchers to rethink the rationality of currently widely-used DNN-based object recognition models and explore the potential of part-based models, once important but recently ignored, for improving robustness. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

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Research 2022年第4期2022卷 137-146页

作者： Xiong Ying Si-Yang Leng Huan-Fei MaD Qing Nie Ying-Cheng Lai Wei Lin School of Mathematical Sciences SCMSand SCAMFudan UniversityShanghai 200433China Research Institute for Inteligent Complex Systems CCSBand LCNBIFudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain ScienceFudan UniversityShanghai 200032China Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mathematical Sciences Soochow UniversitySuzhou 215006China Department of Mathematics Department of Developmental and Cll BiologyAnd NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCA 92697-3875USA School of Electrical ComputerAnd Energy EngineeringArizona State UniversityTempeArizona 85287-5706USA Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Data-based detection and quantification of causation in complex,nonlinear dynamical systems is of paramount importance to science,engineering,and *** by the widely used methodology in recent years,the cross-map-based techniques,we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms,which is consistent with the natural interpretation of *** particular,instead of measuring the smoothness of the cross-map as conventionally implemented,we define causation through measuring the scaling law for the continuity of the investigated dynamical system *** uncovered scaling law enables accurate,reliable,and eficient detection of causation and assessment of its strength in general complex dynamical systems,outperforming those existing representative *** continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

关键词： dynamical scaling continuity

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Named Entity Recognition Using a Semi-supervised Model Based on BERT and Bootstrapping 5th

Named Entity Recognition Using a Semi-supervised Model Based...

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5th China Conference on Knowledge Graph, and Semantic Computing, CCKS 2020

作者： Liu, Yuanyuan Li, Xiang Shi, Jiaxin Zhang, Lei Li, Juanzi Department of Computer Science and Technology BNRist Beijing China KIRC Institute for Artificial Intelligence Tsinghua University Beijing100084 China State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing100084 China

ISBN: (纸本)9789811619632

Named Entity Recognition (NER) aims to recognize entities from texts, which is important for lots of natural language processing tasks, including knowledge graph and so on. Supervised NER method requires expensive datasets. In order to reduce expensive labor costs, we propose a semi-supervised model based on BERT and Bootstrapping to recognize more entities with a small amount of labeled data and less human labor. The method works in the following steps. Firstly, we build a small entity dictionary, and tag the collected texts using this dictionary. Secondly, we learn a model based on the small dataset. Thirdly, recognize entities that are not in dictionary and add the high-quality entities to the dictionary. Then, we repeat these steps until no new high-quality entities are found. Experiments show that the proposed model performs better than baselines on the Financial Entity Recognition task in 2019 CCF BDCI. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

A Trustable LSTM-Autoencoder Network for Cyberbullying Detec...

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IEEE International Conference on Big Data

作者： Mst Shapna Akter Hossain Shahriar Alfredo Cuzzocrea Fan Wu Juanjose Rodriguez-Cardenas Department of Intelligent Systems and Robotics University of West Florida USA Center of Cybersecurity University of West Florida USA iDEA Lab University of Calabria Rende Italy Dept. of Computer Science Tuskegee University Tuskegee USA Dept. of Information Technology Kennesaw State University Georgia USA

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models’ performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper.

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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