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Applications of Evolutionary computing 1

丛书名： Lecture Notes in Computer Science

1000年

作者： Stefano Cagnoni Colin G. Johnson Juan J. Romero Cardalda Elena Marchiori David W. Corne Jean-Arcady Meyer Agnès Guillot Jens Gottlieb Martin Middendorf Günther R. Raidl Emma Hart

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Advances in Web Mining and Web Usage Analysis 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Bamshad Mobasher Olfa Nasraoui Bing Liu Brij Masand

ISBN: (数字)9783540471288

ISBN: (纸本)9783540471271

TheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage,and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications.

关键词： Artificial Intelligence Computer Communication Networks database Management Information Storage and Retrieval Information Systems Applications (incl. Internet) Computers and Society

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data Mining 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Rafiqul Islam Yun Sing Koh Yanchang Zhao Graco Warwick David Stirling Chang-Tsun Li Zahidul Islam

This book constitutes the refereed proceedings of the 16th Australasian Conference on data Mining, AusDM 2018, held in Bathurst, NSW, Australia, in November 2018.

ISBN: (数字)9789811366611

ISBN: (纸本)9789811366604

This book constitutes the refereed proceedings of the 16th Australasian Conference on data Mining, AusDM 2018, held in Bathurst, NSW, Australia, in November 2018.

关键词： data Mining and Knowledge Discovery Machine Learning Information Systems Applications (incl. Internet) Systems and data Security

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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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database Systems for Advanced Applications 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Bonghee Hong Xiaofeng Meng Lei Chen Werner Winiwarter Wei Song

ISBN: (数字)9783642402708

ISBN: (纸本)9783642402692

This book constitutes the workshop proceedings of the 18th International Conference on database Systems for Advanced Applications, DASFAA 2013, held in Wuhan, China, in April 2013. The volume contains three workshops, each focusing on specific area that contributes to the main themes of the DASFAA conference: The First International Workshop on Big data Management and Analytics (BDMA 2013), the Third International Workshop on Social Networks and Social Web (SNSM 2013) and the International Workshop on Semantic computing and Personalization (SeCoP 2013).

关键词： Information Systems Applications (incl. Internet) Artificial Intelligence Information Storage and Retrieval database Management Computer Communication Networks data Mining and Knowledge Discovery

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Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaotong Wu Yuwen Liu Xiaoxiao Chi Rong Jiang Xiaokang Zhou Wajid Rafique Maqbool Khan Alibaba Business School Hangzhou Normal University China College of Computer Science and Technology China University of Petroleum (East China) China Department of Computing Macquarie University Australia Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Faculty of Business Data Science Kansai University Japan and RIKEN Center for Advanced Intelligence Project Japan Department of Electrical and Software Engineering University of Calgary Canada Software Competence Center Hagenberg Austria

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).

关键词： collaborative learning optimal adaption vehicle application data security self-adaptive system

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MORepair: Teaching LLMs to Repair Code via Multi-Objective Fine-Tuning

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ACM Transactions on Software engineering and Methodology 1000年

作者： Boyang Yang Haoye Tian Jiadong Ren Hongyu Zhang Jacques Klein Tegawende Bissyande Claire Le Goues Shunfu Jin School of Information Science and Engineering Yanshan University China School of Computing and Information Systems University of Melbourne Australia School of Big Data and Software Engineering Chongqing University China SnT University of Luxembourg Luxembourg School of Computer Science Carnegie Mellon University USA

Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning Large language models (LLMs) to unlock state-of-the-art performance. Fine-tuning approaches proposed in the literature for LLMs on program repair tasks generally overlook the need to reason about the logic behind code changes, beyond syntactic patterns in the data. High-performing fine-tuning experiments also usually come at very high computational costs. With MORepair, we propose a novel perspective on the learning focus of LLM fine-tuning for program repair: we not only adapt the LLM parameters to the syntactic nuances of the task of code transformation (objective ➊), but we also specifically fine-tune the LLM with respect to the logical reason behind the code change in the training data (objective ➋). Such a multi-objective fine-tuning will instruct LLMs to generate high-quality *** apply MORepair to fine-tune four open-source LLMs with different sizes and architectures. Experimental results on function-level and repository-level repair benchmarks show that the implemented fine-tuning effectively boosts LLM repair performance by 11.4% to 56.0%. We further show that our fine-tuning strategy yields superior performance compared to the state-of-the-art approaches, including standard fine-tuning, Fine-tune-CoT, and RepairLLaMA.

关键词： Program Repair Fine-tuning Large Language Model Open Source

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AMSPM: Adaptive Model Selection and Partition Mechanism for Edge Intelligence-driven 5G Smart City with Dynamic computing Resources

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ACM Transactions on Sensor Networks 1000年

作者： Xin Niu Xuejiao Cao Chen Yu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the sustainability of intelligent services in smart city, we propose the Adaptive Model Selection and Partition Mechanism (AMSPM) in 5G smart city where EI provides services, which mainly consists of Adaptive Model Selection (AMS) and Adaptive Model Partition (AMP). In AMSPM, the model selection and partition of deep neural network (DNN) are formulated as an optimization problem. Firstly, we propose a recursive-based algorithm named AMS based on the computing resources of edge devices to derive an appropriate DNN model that satisfies the latency demand of intelligent services. Then, we adaptively partition the selected DNN model according to the computing resources of edge devices. The experimental results demonstrate that, when compared with state-of-the-art model selection and partition mechanisms, AMSPM not only reduces latency but also enhances computing resource utilization.

关键词： Edge intelligence changing available computing resources adaptive model selection and partition smart city sustainable intelligent services

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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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Health Information Science 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Jing He Xiaohui Liu Elizabeth A. Krupinski Guandong Xu

ISBN: (数字)9783642293610

ISBN: (纸本)9783642293603

This book constitutes the refereed proceedings of the First International Conference on Health Information Science, held in Beijing, China, in April 2012. The 15 full papers presented together with 1 invited paper and 3 industry/panel statements in this volume were carefully reviewed and selected from 38 submissions. The papers cover all aspects of the health information sciences and the systems that support this health information management and health service delivery. The scope includes 1) medical/health/biomedicine information resources, such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, 2) data management, data mining, and knowledge discovery (in health domain), all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, and 3) development of new architectures and applications for health information systems.

关键词： Information Systems Applications (incl. Internet) Information Storage and Retrieval database Management Artificial Intelligence Computer Communication Networks Health Informatics

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