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作者： Agrawal, Vikas Baier, Jorge Bekris, Kostas Chen, Yiling D'Avila Garcez, Artur S. Hitzler, Pascal Haslum, Patrik Jannach, Dietmar Law, Edith Lecue, Freddy Lamb, Luis C. Matuszek, Cynthia Palacios, Hector Srivastava, Biplav Shastri, Lokendra Sturtevant, Nathan Stern, Roni Tellex, Stefanie Vassos, Stavros Center for Knowledge Driven Intelligent Systems Enterprise Technology Research Labs Infosys Limited India Pontificia Universidad Católica de Chile Chile Rutgers University NJ United States Harvard University United States City University London United Kingdom Wright State University Dayton OH United States Australian National University Australia TU Dortmund Germany Machine Learning Department Carnegie Mellon University United States IBM Research - Smarter Cities Technology Centre Dublin Ireland Federal University of Rio Grande do Sul Porto Alegre Brazil Computer Science and Engineering Department University of Washington United States Universidad Carlos III de Madrid Spain IBM Research - India India University of Denver United States Ben Gurion University Negev Israel Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

The AAAI-12 Workshop program was held Sunday and Monday, July 22-23, 2012, at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included nine workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation Systems, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, and Semantic Cities. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence.

关键词： Problem solving

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Teachers' views on the use of empathic robotic tutors in the classroom

Teachers' views on the use of empathic robotic tutors in the...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sofia Serholt Wolmet Barendregt Iolanda Leite Helen Hastie Aidan Jones Ana Paiva Asimina Vasalou Ginevra Castellano Department of Applied Information Technology University of Gothenburg Gothenburg Sweden GAIPS Instituto Superior Tecnico Lisboa Portugal Social Robotics Lab Yale University New Haven CT USA School of Mathematical and Computer Science Heriot-Watt University Edinburgh UK School of Electronic Electrical and Computer Engineering University of Birmingham Birmingham UK London Knowledge Lab Institute of Education London UK

ISBN: (纸本)9781479967667

In this paper, we describe the results of an interview study conducted across several European countries on teachers' views on the use of empathic robotic tutors in the classroom. The main goals of the study were to elicit teachers' thoughts on the integration of the robotic tutors in the daily school practice, understanding the main roles that these robots could play and gather teachers' main concerns about this type of technology. Teachers' concerns were much related to the fairness of access to the technology, robustness of the robot in students' hands and disruption of other classroom activities. They saw a role for the tutor in acting as an engaging tool for all, preferably in groups, and gathering information about students' learning progress without taking over the teachers' responsibility for the actual assessment. The implications of these results are discussed in relation to teacher acceptance of ubiquitous technologies in general and robots in particular.

关键词： Educational institutions Interviews Robot sensing systems Service robots Europe

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Correction to: Network Functions Virtualization for Mobile Core and Heterogeneous Cellular Networks

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Wireless Personal Communications 2021年第3期122卷 2561-2561页

作者： Qureshi, Kashif Naseer Ahmad , Ejaz Anwar, Muhammad Ghafoor, Kayhan Zrar Jeon, Gwanggil Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer Research Faisalabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

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On the Stability and Generalization of Triplet Learning

arXiv

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arXiv 2023年

作者： Chen, Jun Chen, Hong Jiang, Xue Gu, Bin Li, Weifu Gong, Tieliang Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China College of Science Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Key Laboratory of Smart Farming for Agricultural Animals Wuhan430070 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates School of Computer Science and Technology Xi'an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China

Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and person re-identification. Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the theoretical understanding of their generalization performance. To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. Specifically, we establish the first general high-probability generalization bound for the triplet learning algorithm satisfying the uniform stability, and then 1 obtain the excess risk bounds of the order (Equation presented). for both stochastic gradient descent (SGD) and regularized risk minimization (RRM), where 2n is approximately equal to the number of training samples. Moreover, an optimistic generalization bound in expectation as fast as O(n−1) is derived for RRM in a low noise case via the on-average stability analysis. Finally, our results are applied to triplet metric learning to characterize its theoretical underpinning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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An empirical examination of balancing strategy for counterfactual estimation on time series 24

An empirical examination of balancing strategy for counterfa...

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Proceedings of the 41st International Conference on Machine Learning

作者： Qiang Huang Chuizheng Meng Defu Cao Biwei Huang Yi Chang Yan Liu School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China Department of Computer Science University of Southern California California Los Angeles Halicioğlu Data Science Institute University of California San Diego San Diego California School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Changchun Jilin China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

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Efficient Contact Tracing for pandemics using blockchain

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Informatics in Medicine Unlocked 2021年 26卷

作者： Bari, Nida Qamar, Usman Khalid, Ayesha Knowledge & Data Science Research Centre Computer & Software Engineering Department College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan The Centre for Secure Information Technologies (CSIT) ECIT Institute Queen's University of Belfast Belfast BT7 1NN United Kingdom

Blockchain technologies have been benefiting many industries by being decentralized, secure and confidential. They offer great potential in pandemic impacted scenarios as well. Contact tracing helps to mitigate the transmission of disease by alerting people who may have been exposed so they can act on time to protect themselves. Contact tracing systems face some challenges related to issues of medical privacy, data security and transparency. Multiple researches show concern that contact tracing discourages people to seek medication because of the fear of loss of data, subsequent stigma, discrimination, or abuse. In this paper, we discuss how contact tracing can be improved using blockchain technology and could be able to solve these issues. The aim of our proposed system would be to reduce the impact of pandemic, to implement this merger of the blockchains and the Contact tracing app and to ensure user privacy and avoid data misuse by incorporating a symmetric key cryptographic mechanism. To tackle the scalability related issues of Blockchain this framework uses IPFS, a distributed file storage system also known as Interplanetary File System. This blockchain based solution system will enhance contact tracing by making it more stable, secure, performant, highly useable and above all effective in the fight against any pandemic. © 2021 The Authors

关键词： And pandemic Blockchain Contact tracing COVID-19 Distributed system Interplanetary file system

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Social network analysis of the vulnerabilities of interdependent critical infrastructures

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International Journal of Critical Infrastructures 2008年第3期4卷 256-273页

作者： Chai, Choon-Lee Liu, X. Zhang, W.J. Deters, Ralph Liu, D. Dyachuk, Dmytro Tu, Y.L. Baber, Zaheer Department of Sociology University of Saskatchewan #1019-9 Campus Drive Saskatoon SK S7N 5A5 Canada Department of Mechanical Engineering University of Saskatchewan 57 Campus Dr. Saskatoon SK S7N 5A9 Canada Department of Computer Science University of Saskatchewan 176 Thorvaldson Building 110 Science Place Saskatoon SK S7N 5C9 Canada Department of Mechanical Engineering and Manufacturing Engineering University of Calgary 2500 University Drive NW Calgary AB T2N 1N4 Canada Department of Sociology University of Toronto 725 Spadina Avanue Toronto ON M5S 2J4 Canada Department of Sociology University of Saskatchewan Canada Department of Mechanical and Manufacturing Engineering University of Calgary Canada Department of Mechanical Engineering University of Saskatchewan Canada International Journal of CAD Computer Science Department University of Saskatchewan SME Department of Sociology University of Toronto Knowledge Media Design Institute Centre of Culture Communication and Information Technology

Interdependency among critical infrastructures contributes to vulnerabilities of those infrastructures. In this paper, the vulnerabilities of critical infrastructures are identified and analysed using social network analysis. In the analysis of network centrality, the importance of each critical infrastructure, in terms of its contribution to infrastructure interdependency, is ranked. The findings show that electricity and telecommunication are the two most important critical infrastructures that contribute to infrastructure interdependency, which further render the infrastructure network vulnerable to cascading damage. An example system is given to illustrate the ideas and results presented in this paper. Copyright © 2008 Inderscience Enterprises Ltd.

关键词： computer networks

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Repetitive nonoverlapping sequential pattern mining

arXiv

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arXiv 2023年

作者： Geng, Meng Wu, Youxi Li, Yan Liu, Jing Fournier-Viger, Philippe Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Shenzhen University Shenzhen China Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Research Center for Knowledge Engineering The Zhejiang Lab Hangzhou311121 China

Sequential pattern mining (SPM) is an important branch of knowledge discovery that aims to mine frequent sub-sequences (patterns) in a sequential database. Various SPM methods have been investigated, and most of them are classical SPM methods, since these methods only consider whether or not a given pattern occurs within a sequence. Classical SPM can only find the common features of sequences, but it ignores the number of occurrences of the pattern in each sequence, i.e., the degree of interest of specific users. To solve this problem, this paper addresses the issue of repetitive nonoverlapping sequential pattern (RNP) mining and proposes the RNP-Miner algorithm. To reduce the number of candidate patterns, RNP-Miner adopts an itemset pattern join strategy. To improve the efficiency of support calculation, RNP-Miner utilizes the candidate support calculation algorithm based on the position dictionary. To validate the performance of RNP-Miner, 10 competitive algorithms and 20 sequence databases were selected. The experimental results verify that RNP-Miner outperforms the other algorithms, and using RNPs can achieve a better clustering performance than raw data and classical frequent patterns. All the algorithms were developed using the PyCharm environment and can be downloaded from https://***/wuc567/Pattern-Mining/tree/master/RNPMiner. Copyright © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection

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Neurocomputing 2024年 563卷

作者： Li, Jiawen Li, Ronghui Ni, Shiwen Kao, Hung-Yu Naval Architecture and Shipping College Guangdong Ocean University Guangdong Zhanjiang524000 China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Guangdong Zhanjiang524000 China Intelligent Knowledge Management Lab National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the prevalence of social media platforms, rumors have been a serious social problem. Notably, existing rumor detection methods simply provide detection labels while ignoring their explanation. However, illustrating the reasons why a suspicious statement is a rumor is essential. To address this realistic scenario, we propose a novel Evidence-Providing Rumor Detection model called EPRD. EPRD incorporates a wide variety of information from both prior knowledge sources and current comments. It also learns bilaterally friendly representations for interpretable rumor detection. EPRD first retrieves evidence from prior knowledge sources and checks the relationship between the given statement and its evidence. Our model then constructs two heterogeneous graph objects to simulate the propagation layout of the current comments and evidence relationships. Finally, EPRD integrates the GraphSAGE component and attention mechanism to detect rumors. To the best of our knowledge, we propose the first model that incorporates prior knowledge to verify rumors and boost credibility. Experiments on two real-world Twitter datasets demonstrate that EPRD consistently exhibits the best rumor detection performance. Moreover, EPRD outperforms other baselines in the early rumor detection task. © 2023

关键词： Graph neural networks

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Hybrid optimal intrusion detection model for WSN with PCA-based selected feature set

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AIP Conference Proceedings 2024年第1期3072卷

作者： Madhavarapu Chandan S. G. Santhi T. Srinivasa Rao Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalainagar - 608002 Tamil Nadu India Department of Computer Science and Engineering Gudlavalleru Engineering College Seshadri Rao Knowledge Village Gudlavalleru 521356 Andhra Pradesh India

A complicated problem in Wireless Sensor Network environments is identifying the anomalies since the security risks become more diverse. WSNs have several obstacles, including a shortage of energy, inadequate memory, low computation capacity, and a brief contact range. As a result, improving the recognition precision and convergence speed of intrusion detection in such settings is critical. This paper proposes a hybrid optimal intrusion detection model for WSN to enhance detection accuracy. The data with class imbalance problem is handled with SMOTE method. Raw features, higher-order statistical, and entropy-based features are the set of features extracted from the balanced data. The extracted features are subjected to the PCA-based feature selection process for selecting the relevant data from the extracted features to make the process more accurate. A hybrid model (combining QNN and Deep Maxout) trains the selected features to decide on the presence of intrusion. In this model, the optimal training is done with the hybrid RUJSO algorithm that combines the algorithms like ROA and JFO, through which the optimal weights of the hybrid model are tuned. The attacker node is mitigated from the network once the model identifies the attack.

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