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Entertainment for Education. Digital Techniques and Systems 1

丛书名： Lecture Notes in Computer Science

1000年

作者： Xiaopeng Zhang Shaochun Zhong Zhigeng Pan Kevin Wong Ruwei Yun

ISBN: (数字)9783642145339

ISBN: (纸本)9783642145322

With the technical advancement of digital media and the medium of communication in recent years, there is a widespread interest in digital entertainment. An emerging te- nical research area edutainment, or educational entertainment, has been accepted as education using digital entertainment. Edutainment has been recognized as an eff- tive way of learning using modern digital media tools, like computers, games, mobile phones, televisions, or other virtual reality applications, which emphasizes the use of entertainment with application to the education domain. The Edutainment conference series was established in 2006 and subsequently - ganized as a special event for researchers working in this new interest area of e-learning and digital entertainment. The main purpose of Edutainment conferences is to facilitate the discussion, presentation, and information exchange of the scientific and technological development in the new community. The Edutainment conference series becomes a valuable opportunity for researchers, engineers, and graduate s- dents to communicate at these international annual events. The conference series - cludes plenary invited talks, workshops, tutorials, paper presentation tracks, and panel discussions. The Edutainment conference series was initiated in Hangzhou, China in 2006. Following the success of the first event, the second (Edutainment 2007 in Hong Kong, China), third (Edutainment 2008 in Nanjing, China), and fourth editions (Edutainment 2009 in Banff, Canada) were organized. Edutainment 2010 was held during August 16–18, 2010 in Changchun, China. Two workshops were jointly org- ized together with Edutainment 2010.

关键词： Computers and Education

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C2lRec: Causal Contrastive Learning for User Cold-start Recommendation with Social Variable

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ACM Transactions on Information Systems 1000年

作者： Xiaolong Xu Hongsheng Dong Haolong Xiang Xiyuan Hu Xiaoyong Li Xiaoyu Xia Xuyun Zhang Lianyong Qi Wanchun Dou School of Software Nanjing University of Information Science and Technology China Faculty of Information Technology Beijing University of Technology China School of Meteorology and Oceanography National University of Defense Technology China School of Computing Technologies RMIT University Australia School of Computing Macquarie University Australia Faculty of Science and Engineering China University of Petroleum (East China) China State Key Laboratory for Novel Software Technology Nanjing University China

Embedding-based recommender systems rely on historical interactions to model users, which poses challenges for recommending to new users, known as the user cold-start problem. Some approaches incorporate social networks to deduce preferences based on the social circles of cold-start users to solve the problem of sparse features. However, such methods have difficulty distinguishing between superficial correlations and causal relationships in social behaviors, leading to inaccuracies in predicting user preferences. To address the aforementioned issues, we propose the Causal Contrastive Learning Recommendation (C2lRec) framework. Specifically, we causally model the inference of hidden preferences from the feature and historical behavior of warm users and predict user interactions based on such preferences. The counterfactual inference is subsequently performed to intervene and extract interactions from historical behaviors of warm users that influence their preferences, designating as primary causal variables. Additionally, we utilize the primary causal variables from users within the social circle of cold-start users to substitute the missing historical interactions of cold-start users and employ a similar causal modeling approach to uncover hidden preferences as we do with warm users. Finally, we realize causal contrastive learning to enhance the distribution of cold-start users. Extensive experiments conducted on three public datasets demonstrate that the recommendation performance of C2lRec exceeds that of state-of-the-art methods.

关键词： Recommender System Cold-start Causality Representation Learning Social Networks

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Conceptual Modeling for Advanced Application Domains 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Shan Wang Katsumi Tanaka Shuigeng Zhou Tok-Wang Ling Jihong Guan Dong-qing Yang Fabio Grandi Eleni E. Mangina Il-Yeol Song Heinrich C. Mayr

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An X Language-Driven Framework for Systematic Development of Digital Twin Healthcare Systems

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Kunyu Xie Lin Zhang Yuan Yang Xiaohe Li Ridha Khedri Zhen Chen M. Jamal Deen Simulation Technology Innovation Center Hangzhou International Innovation Institute of Beihang University Hangzhou 311115 Zhejiang China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Intelligent Manufacturing Systems Technology Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Shenzhen Third People’s Hospital & Second Hospital Affiliated to Southern University of Science and Technology Shenzhen 518112 Guangdong China Dept. of Computing and Software McMaster University Hamilton Ontario Canada L8S 4K1 Department of Electrical and Computer Engineering McMaster University Hamilton ON L8S 4L8 Canada

The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH). The Cloud DTH Platform (CDTH) serves as a cloud-based framework that integrates DTH models, healthcare resources, patient data, and medical services. By leveraging real-time data from medical devices, the CDTH platform enables intelligent healthcare services such as disease prediction and medical resource optimization. However, the platform functions as a system of systems (SoS), comprising interconnected yet independent healthcare services. This complexity is further compounded by the integration of both black-box AI models and domain-specific mechanistic models, which pose challenges in ensuring the interpretability and trustworthiness of DTH models. To address these challenges, we propose a Model-Based Systems engineering (MBSE)-driven DTH modeling methodology derived from systematic requirement and functional analyses. To implement this methodology effectively, we introduce a DTH model development approach using the X language, along with a comprehensive toolchain designed to streamline the development process. Together, this methodology and toolchain form a robust framework that enables engineers to efficiently develop interpretable and trustworthy DTH models for the CDTH platform. By integrating domain-specific mechanistic models with AI algorithms, the framework enhances model transparency and reliability. Finally, we validate our approach through a case study involving elderly patient care, demonstrating its effectiveness in supporting the development of DTH models that meet healthcare and interpretability requirements.

关键词： Digital Twin Model-based system engineering X Language Medical Cyber-Physical Systems Digital Twin Healthcare Design

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Feature Disentanglement Based Heterogeneous Defect Prediction

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ACM Transactions on software engineering and Methodology 1000年

作者： Xu Yu Jiaqi Yan Qinqin Gao Qinglong Peng Bin Yu Junwei Du Ying Xing Dunwei Gong Qingdao Institute of Software China University of Petroleum East China) Qingdao China College of Computer Science and Technology China University of Petroleum East China) Qingdao China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China University of Petroleum East China) Qingdao China School of Data Science Qingdao University of Science and Technology Qingdao China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun China School of Data Science Qingdao University of Science and Technology Qingdao China School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Data Science Qingdao University of Science and TechnologyQingdao China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Yunnan Key Laboratory of Software Engineering Kunming China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China

Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous cross-project defect prediction and heterogeneous cross-project defect prediction (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this paper proposes a heterogeneous defect prediction method based on feature disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this paper, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.

关键词： Heterogeneous Defect Prediction Feature Disentanglement Domain-related Features Domain-independent Features

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DeFeed: Secure Decentralized Cross-Contract Data Feed in Web 3.0 for Connected Autonomous Vehicles

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xingchen Sun Runhua Xu Wei Ni Li Duan Chao Li Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University China School of Computer Science and Engineering Beihang University China School of Electrical and Data Engineering University of Technology Sydney Australia

Smart contracts have been a topic of interest in blockchain research and are a key enabling technology for Connected Autonomous Vehicles (CAVs) in the era of Web 3.0. These contracts enable trustless interactions without the need for intermediaries, as they operate based on predefined rules encoded on the blockchain. However, smart contacts face significant challenges in cross-contract communication and information sharing, making it difficult to establish seamless connectivity and collaboration among CAVs with Web 3.0. In this paper, we propose DeFeed, a novel secure protocol that incorporates various gas-saving functions for CAVs, originated from in-depth research into the interaction among smart contracts for decentralized cross-contract data feed in Web 3.0. DeFeed allows smart contracts to obtain information from other contracts efficiently in a single click, without complicated operations. We judiciously design and complete various functions with DeFeed, including a pool function and a cache function for gas optimization, a subscribe function for facilitating data access, and an update function for the future iteration of our protocol. Tailored for CAVs with Web 3.0 use cases, DeFeed enables efficient data feed between smart contracts underpinning decentralized applications and vehicle coordination. Implemented and tested on the Ethereum official test network, DeFeed demonstrates significant improvements in contract interaction efficiency, reducing computational complexity and gas costs. Our solution represents a critical step towards seamless, decentralized communication in Web 3.0 ecosystems.

关键词： Blockchain Ethereum Smart Contract Data Feed Web3 CAVs

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Information security and Cryptology 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Xuejia Lai Moti Yung Dongdai Lin

ISBN: (数字)9783642215186

ISBN: (纸本)9783642215179

This book constitutes the thoroughly refereed post-conference proceedings of the 6th International Conference on Information security and Cryptology, Inscrypt 2010, held in Shanghai, China, in October 2010.;The 35 revised full papers presented were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on encryption schemes, stream ciphers, sequences and elliptic curves, secure computing, hash functions, key management, digital signatures, privacy and algebraic cryptanalysis, hashing and authentication, and hardware and software issues.

关键词： Computer Communication Networks Cryptology Management of Computing and Information Systems Algorithm Analysis and Problem Complexity Systems and Data security Computers and Society

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Tensor Computation for Seismic Data Processing 1

Tensor Computation for Seismic Data Processing

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丛书名： Earth Systems Data and Models

1000年

作者： Feng Qian Shengli Pan Gulan Zhang

ISBN: (数字)9783031789007

ISBN: (纸本)9783031788994;9783031789021

This book aims to provide a comprehensive understanding of tensor computation and its applications in seismic data analysis, exclusively catering to seasoned researchers, graduate students, and industrial engineers alike. Tensor emerges as a natural representation of multi-dimensional modern seismic data, and tensor computation can help prevent possible harm to the multi-dimensional geological structure of the subsurface that occurred in classical seismic data analysis. It delivers a wealth of theoretical, computational, technical, and experimental details, presenting an engineer's perspective on tensor computation and an extensive investigation of tensor-based seismic data analysis techniques. Embark on a transformative exploration of seismic data processing—unlock the potential of tensor computation and reshape your approach to high-dimensional geological structures. The discussion begins with foundational chapters, providing a solid background in both seismic data processing and tensor computation. The heart of the book lies in its seven chapters on tensor-based seismic data analysis methods. From structured low-tubal-rank tensor completion to cutting-edge techniques like tensor deep learning and tensor convolutional neural networks, each method is meticulously detailed. The superiority of tensor-based data analysis methods over traditional matrix-based data analysis approaches is substantiated through synthetic and real field examples, showcasing their prowess in handling high-dimensional modern seismic data. Notable chapters delve into seismic noise suppression, seismic data interpolation, and seismic data super-resolution using advanced tensor models. The final chapter provides a cohesive summary of the conclusion and future research directions, ensuring readers facilitate a thorough understanding of tensor computation applications in seismic data processing. The appendix includes a hatful of information on existing tensor computation software, enhancing the b

关键词： Earth Sciences, general Geophysics/Geodesy Environmental Physics

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SEED: Speculative security Metadata Updates for Low-Latency Secure Memory

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ACM Transactions on Architecture and Code Optimization 1000年

作者： Xueliang Wei Dan Feng Wei Tong Bing Wu Xu Jiang Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Techonolgy Wuhan China

Securing systems’ main memory is important for building trusted data centers. To ensure memory security, encryption and integrity verification techniques update the security metadata (e.g., encryption counters and integrity trees) during memory data writes. Existing studies are optimistic about the effect of data writes on system performance since they regard all data writes as background operations. However, we show that security metadata updates significantly increase data write latency. High-latency data writes frequently fill up write buffers in the system, forcing the system to perform the writes in the critical path. As a result, performance-critical data reads need to wait for the execution of these writes, which increases data read latency and degrades system *** this paper, we propose SEED that improves the performance of secure memory systems by speculatively updating security metadata in the background before data writes arrive. To enable speculative updates, SEED predicts which dirty cache lines will be written to memory through natural evictions. We find that cache evictions depend on multiple factors. To decouple the dependencies for accurate predictions, we devise a two-step eviction prediction method based on our observation that the next eviction victim rarely changes in a set. The first step predicts which cache sets will evict cache lines, while the second step predicts which cache lines will be evicted by finding the next eviction victims in the sets. For predicted evictions, we develop a speculative updater to perform speculative updates. We analyze the invariants that must be followed by the updater to ensure the correctness of speculative updates. The updater rolls back the speculatively updated security metadata of inaccurate predictions. To reduce the rollback overhead, we devise a rollback batching and an update pausing optimization for the updater. Experimental results show that SEED reduces data write latency by 39.8%, data rea

关键词： Memory security encryption integrity verification eviction prediction

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Requirements engineering 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Didar Zowghi Zhi Jin

ISBN: (数字)9783662436103

ISBN: (纸本)9783662436097

This book constitutes the proceedings of the first Asia Pacific Requirements engineering Symposium, APRES 2014, held in Auckland, New Zealand, in April 2014. The 16 papers presented were carefully reviewed and selected from 30 submissions. The focus of the papers is on the following topics: novel ideas, methods, tools, and techniques for improving and enhancing Requirement engineering products and processes.

关键词： software engineering System Performance and Evaluation Algorithm Analysis and Problem Complexity software engineering/Programming and Operating Systems

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