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Advanced information Systems Engineering Workshops 1

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丛书名： Lecture Notes in Business information Processing

1000年

作者： Marko Bajec Johann Eder

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On the Move to Meaningful Internet Systems: OTM 2012 1

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丛书名： Lecture Notes in computer science

1000年

作者： Robert Meersman Hervé Panetto Tharam Dillon Stefanie Rinderle-Ma Peter Dadam Xiaofang Zhou Siani Pearson Alois Ferscha Sonia Bergamaschi Isabel F. Cruz

ISBN: (数字)9783642336065

ISBN: (纸本)9783642336058

The two-volume set LNCS 7565 and 7566 constitutes the refereed proceedings of three confederated international conferences: Cooperative information Systems (CoopIS 2012), Distributed Objects and Applications - Secure Virtual Infrastructures (DOA-SVI 2012), and Ontologies, DataBases and Applications of SEmantics (ODBASE 2012) held as part of OTM 2012 in September 2012 in Rome, Italy. The 53 revised full papers presented were carefully reviewed and selected from a total of 169 submissions. The 22 full papers included in the first volume constitute the proceedings of CoopIS 2012 and are organized in topical sections on business process design; process verification and analysis; service-oriented architectures and cloud; security, risk, and prediction; discovery and detection; collaboration; and 5 short papers.

关键词： Software Engineering information Storage and Retrieval information Systems Applications (incl. Internet) Artificial Intelligence Systems and Data Security computer Appl. in Administrative Data Processing

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A Relation-Constraint Link Prediction Model for Dynamic Knowledge Graphs with Entity Drift

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ACM Transactions on Knowledge Discovery from Data 1000年

作者： Xiulin Zheng Peipei Li Zan Zhang Jia Wu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China and School of Computer Science and Information Engineering Hefei University of Technology China School of Computing Faculty of Science and Engineering Macquarie University Australia

Knowledge Graphs (KGs) often suffer from incompleteness and this issue motivates the task of Knowledge Graph Completion (KGC). Traditional KGC models mainly concentrate on static KGs with a fixed set of entities and relations, or dynamic KGs with temporal characteristics, faltering in their generalization to constantly evolving KGs with possible irregular entity drift. Thus, in this paper, we propose a novel link prediction model based on the embedding representation to handle the incompleteness of KGs with entity drift, termed as DCEL. Unlike traditional link prediction, DCEL could generate precise embeddings for drifted entity without imposing any regular temporal characteristic. The drifted entity is added into the KG with its links to the existing entity predicted in an incremental fashion with no requirement to retrain the whole KG for computational efficiency. In terms of DCEL model, it fully takes advantages of unstructured textual description, and is composed of four modules, namely MRC (Machine Reading Comprehension), RCAA (Relation Constraint Attentive Aggregator), RSA (Relation Specific Alignment) and RCEO (Relation Constraint Embedding Optimization). Specifically, the MRC module is first employed to extract short texts from long and redundant descriptions. Then, RCAA is used to aggregate the embeddings of textual description of drifted entity and the pre-trained word embeddings learned from corpus to a single text-based entity embedding while shielding the impact of noise and irrelevant information. After that, RSA is applied to align the text-based entity embedding to graph-based space to obtain the corresponding graph-based entity embedding, and then the learned embeddings are fed into the gate structure to be optimized based on the RCEO to improve the accuracy of representation learning. Finally, the graph-based model TransE is used to perform link prediction for drifted entity. Extensive experiments conducted on benchmark datasets in terms of evaluat

关键词： dynamic knowledge graph entity drift link prediction relation constraint

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Advances in Multimedia information Processing - PCM 2010 1

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丛书名： Lecture Notes in computer science

1000年

作者： Guoping Qiu Kin Man Lam Hitoshi Kiya Xiang-Yang Xue C.-C. Jay Kuo Michael S. Lew

ISBN: (数字)9783642156960

ISBN: (纸本)9783642156953

The 2010 Pacific-Rim Conference on Multimedia (PCM 2010) was held in Shanghai at Fudan University, during September 21–24, 2010. Since its inauguration in 2000, PCM has been held in various places around the Pacific Rim, namely Sydney (PCM 2000), Beijing (PCM 2001), Hsinchu (PCM 2002), Singapore (PCM 2003), Tokyo (PCM 2004), Jeju (PCM 2005), Zhejiang (PCM 2006), Hong Kong (PCM 2007), Tainan (PCM 2008), and Bangkok (PCM 2009). PCM is a major annual international conference organized as a forum for the dissemination of state-of-the-art technological advances and research results in the fields of theoretical, experimental, and applied multimedia analysis and processing. PCM 2010 featured a comprehensive technical program which included 75 oral and 56 poster presentations selected from 261 submissions from Australia, Canada, China, France, Germany, Hong Kong, India, Iran, Italy, Japan, Korea, Myanmar, Norway, Singapore, Taiwan, Thailand, the UK, and the USA. Three distinguished researchers, Prof. Zhi-Hua Zhou from Nanjing University, Dr. Yong Rui from Microsoft, and Dr. Tie-Yan Liu from Microsoft Research Asia delivered three keynote talks to the conference. We are very grateful to the many people who helped to make this conference a s- cess. We would like to especially thank Hong Lu for local organization, Qi Zhang for handling the publication of the proceedings, and Cheng Jin for looking after the c- ference website and publicity. We thank Fei Wu for organizing the special session on large-scale multimedia search in the social network settings.

关键词： Multimedia information Systems computer Communication Networks information Systems Applications (incl. Internet) information Storage and Retrieval User Interfaces and Human computer Interaction Software Engineering

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Advances in Intelligent Informatics, Smart Technology and Natural Language Processing 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Thanaruk Theeramunkong Rachada Kongkachandra Mahasak Ketcham Narit Hnoohom Pokpong Songmuang Thepchai Supnithi Kiyota Hashimoto

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Vehicle Dynamics and Interaction for Trajectory Prediction and Traffic Control

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Jian Chen Shaorui Zhou Wei Wang Yuzhu Hu Jianqing Li Ben-Guo He Junxin Chen Marwan Omar Ali Kashif Bashir Xiping Hu School of Intelligent Systems Engineering Sun Yat-Sen University China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University China the School of Software Dalian University of Technology China Information Technology and Management Illinois Institute of Technology USA Department of Computing and Mathematics Manchester Metropolitan University UK Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.

关键词： Trajectory Prediction Vehicle Dynamics Interaction Perception Graph Convolution Attention Mechanism

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Audio-Visual Event Localization using Multi-task Hybrid Attention Networks for Smart Healthcare Systems

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ACM Transactions on Internet Technology 1000年

作者： Han Liang Jincai Chen Fazlullah Khan Gautam Srivastava Jiangfeng Zeng Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China and Institute of Natural and Mathematical Sciences Massey University Auckland New Zealand Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Ningbo Zhejiang 315104 China Department of Mathematics and Computer Science Brandon University Brandon Canada and Research Centre for Interneural Computing China Medical University Taichung Taiwan and Department of Computer Science and Math Lebanese American University Beirut Lebanon The School of Information Management Central China Normal University Wuhan China

Human perception heavily relies on two primary senses: vision and hearing, which are closely inter-connected and capable of complementing each other. Consequently, various multimodal learning tasks have emerged, with audio-visual event localization (AVEL) being a prominent example. AVEL is a popular task within the realm of multimodal learning, with the primary objective of identifying the presence of events within each video segment and predicting their respective categories. This task holds significant utility in domains such as healthcare monitoring and surveillance, among others. Generally speaking, audio-visual co-learning offers a more comprehensive information landscape compared to single-modal learning, as it allows for a more holistic perception of ambient information, aligning with real-world applications. Nevertheless, the inherent heterogeneity of audio and visual data can introduce challenges related to event semantics inconsistency, potentially leading to incorrect predictions. To track these challenges, we propose a multi-task hybrid attention network (MHAN) to acquire high-quality representation for multimodal data. Specifically, our network incorporates hybrid attention of uni- and parallel cross-modal (HAUC) modules, which consists of a uni-modal attention block and a parallel cross-modal attention block, leveraging multimodal complementary and hidden information for better representation. Furthermore, we advocate for the use of a uni-modal visual task as auxiliary supervision to enhance the performance of multimodal tasks employing a multi-task learning strategy. Our proposed model has been proven to outperform the state-of-the-art results based on extensive experiments conducted on the AVE dataset.

关键词： hybrid attention parallel attention multi-task learning healthcare monitoring

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Adaptive Target Oriented Tracking

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ACM Transactions on Intelligent Systems and Technology 1000年

作者： Sixian Chan Xianpeng Zeng Zhoujian Wu Yu Wang Xiaolong Zhou Tinglong Tang Jie Hu the College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering the College of Computer and Information at China Three Gorges University China the College of Computer Science and Technology Zhejiang University of Technology China Hangzhou Xsuan Technology Co. Ltd China The College of Electrical and Information Engineering Quzhou University China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering the College of Computer and Information China Three Gorges University China Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University China

The current one-stream tracking pipelines are early relation modeling in feature extraction. However, insufficient discrimination may result in ambiguous relation modeling during early feature extraction. Moreover, the non-target information occupies most of the search image, rendering most relation modeling futile. To tackle the above issues, we propose tracking via learning adaptive target-oriented representation, named ATOTrack. We design an Untied positional encoding to mark the template token and the search region token separately, which reduces the confused relationship between the template and the search region. Besides, we introduce an Auto-Mask Learner to decouple the target and non-target information in the search region. Interestingly, the Auto-Mask Learner can self-learn and mask the ineffective information to interpret adaptive target oriented representation. Extensive experiments demonstrate that ATOTrack is superior to existing methods, which achieves state-of-the-art performance on six tracking benchmarks. In Particular, ATOTrack establishes a new record on AViST with 57% AO. The code and models will be released as soon.

关键词： Visual tracking Adaptive target oriented Untied positional encoding Auto-Mask Learner

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On the Move to Meaningful Internet Systems: OTM 2012 1

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丛书名： Lecture Notes in computer science

1000年

作者： Robert Meersman Hervé Panetto Tharam Dillon Stefanie Rinderle-Ma Peter Dadam Xiaofang Zhou Siani Pearson Alois Ferscha Sonia Bergamaschi Isabel F. Cruz

ISBN: (数字)9783642336157

ISBN: (纸本)9783642336140

The two-volume set LNCS 7565 and 7566 constitutes the refereed proceedings of three confederated international conferences: Cooperative information Systems (CoopIS 2012), Distributed Objects and Applications - Secure Virtual Infrastructures (DOA-SVI 2012), and Ontologies, DataBases and Applications of SEmantics (ODBASE 2012) held as part of OTM 2012 in September 2012 in Rome, Italy. The 53 revised full papers presented were carefully reviewed and selected from a total of 169 submissions. The 31 full papers included in the second volume constitute the proceedings of DOA-SVI 2012 with 10 full papers organized in topical sections on privacy in the cloud; resource management and assurance; context, compliance and attack; and ODBASE 2012 with 21 full papers organized in topical sections on using ontologies and semantics; applying probalistic techniques to semantic information; exploiting and querying semantic information; and managing and storing semantic information.

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Simulated Evolution and Learning 1

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丛书名： Lecture Notes in computer science

1000年

作者： Xiaodong Li Michael Kirley Mengjie Zhang David Green Vic Ciesielski Hussein Abbass Zbigniew Michalewicz Tim Hendtlass Kalyanmoy Deb Kay Chen Tan Jürgen Branke Yuhui Shi

ISBN: (数字)9783540896944

ISBN: (纸本)9783540896937

This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning,held December 7–10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world.

关键词： Programming Techniques Computation by Abstract Devices Artificial Intelligence Simulation and Modeling User Interfaces and Human computer Interaction Discrete Mathematics in computer science

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