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Active Learning for Name Entity Recognition with External Knowledge

ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Ying Ma Yu Zhang Arun Kumar Sangaiah Ming Yan Guoqi Li Tian Wang Harbin Institute of Technology Xiamen University of Technology China Xiamen University of Technology China National Yunlin University of Science and Technology China Institute of High Performance Computing Agency for Science Technology and Research Singapore Institute of Automation China Academy of Sciences China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College (UIC) China

Named Entity Recognition (NER) is an important task in knowledge extraction, which targets extracting structural information from unstructured text. To fully employ the prior-knowledge of the pre-trained language models, some research works formulate the NER task into the machine reading comprehension form (MRC-form) to enhance their model generalization capability of commonsense knowledge. However, this transformation still faces the data-hungry issue with limited training data for the specific NER tasks. To address the low-resource issue in NER, we introduce a method named active multi-task-based NER (AMT-NER), which is a two-stage multi-task active learning training model. Specifically, A multi-task learning module is first introduced into AMT-NER to improve its representation capability in low-resource NER tasks. Then, a two-stage training strategy is proposed to optimize AMT-NER multi-task learning. An associated task of Natural Language Inference (NLI) is also employed to enhance its commonsense knowledge further. More importantly, AMT-NER introduces an active learning module, uncertainty selective, to actively filter training data to help the NER model learn efficiently. Besides, we also find different external supportive data under different pipelines improves model performance differently in the NER tasks. Extensive experiments are performed to show the superiority of our method, which also proves our findings that the introduction of external knowledge is significant and effective in the MRC-form NER tasks.

关键词： Named Entity Recognition Machine Reading Comprehension Multi-task Learning Active Learning

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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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Understanding Natural Language Understanding 1

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1000年

作者： Erik Cambria

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Mathematical Research for Blockchain Economy 1

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丛书名： Springer Proceedings in Business and Economics

1000年

作者： Panos Pardalos Ilias Kotsireas Yike Guo William Knottenbelt

ISBN: (数字)9783030533564

ISBN: (纸本)9783030533557;9783030533588

Blockchain Technology has been considered as the most fundamental and revolutionising invention since the Internet. Every year, thousands of blockchain projects are launched and circulated in the market, and there is a tremendous wealth of blockchain applications, from finance to healthcare, education, media, logistics and more. However, due to theoretical and technical barriers, most of these applications are impractical for use in a real-world business context. The papers in this book reveal the challenges and limitations, such as scalability, latency, privacy andsecurity, and showcase solutions and developments to overcome them.

关键词： Financial Services Optimization Economic Theory/Quantitative Economics/Mathematical Methods Mathematical Applications in Computer science Business Finance

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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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Mathematical Research for Blockchain Economy 1

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丛书名： Springer Proceedings in Business and Economics

1000年

作者： Panos Pardalos Ilias Kotsireas Yike Guo William Knottenbelt

ISBN: (数字)9783030371104

ISBN: (纸本)9783030371098;9783030371128

This book presents the best papers from the 1st International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2019, held in Santorini, Greece. While most blockchain conferences and forums are dedicated to business applications, product development or Initial Coin Offering (ICO) launches, this conference focused on the mathematics behind blockchain to bridge the gap between practice and theory.;Every year, thousands of blockchain projects are launched and circulated in the market, and there is a tremendous wealth of blockchain applications, from finance to healthcare, education, media, logistics and more. However, due to theoretical and technical barriers, most of these applications are impractical for use in a real-world business context. The papers in this book reveal the challenges and limitations, such as scalability, latency, privacy and security, and showcase solutions and developments to overcome them.

关键词： Financial Engineering Game Theory, Economics, Social and Behav. sciences Math Applications in Computer science Economic Theory/Quantitative Economics/Mathematical Methods Business Finance

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Coherence-guided Preference Disentanglement for Cross-domain Recommendations

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ACM Transactions on Information Systems 1000年

作者： Zongyi Xiang Yan Zhang Lixin Duan Hongzhi Yin W. Tsang Ivor University of Electronic Science and Technology of China Chengdu China Faculty of Science and Technology Charles Darwin University Darwin Australia Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China The University of Queensland Brisbane Australia CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

Discovering user preferences across different domains is pivotal in cross-domain recommendation systems, particularly when platforms lack comprehensive user-item interactive data. The limited presence of shared users often hampers the effective modeling of common preferences. While leveraging shared items’ attributes, such as category and popularity, can enhance cross-domain recommendation performance, the scarcity of shared items between domains has limited research in this area. To address this, we propose a Coherence-guided Preference Disentanglement (CoPD) method aimed at improving cross-domain recommendation by i) explicitly extracting shared item attributes to guide the learning of shared user preferences and ii) disentangling these preferences to identify specific user interests transferred between domains. CoPD introduces coherence constraints on item embeddings of shared and specific domains, aiding in extracting shared attributes. Moreover, it utilizes these attributes to guide the disentanglement of user preferences into separate embeddings for interest and conformity through a popularity-weighted loss. Experiments conducted on real-world datasets demonstrate the superior performance of our proposed CoPD over existing competitive baselines, highlighting its effectiveness in enhancing cross-domain recommendation performance. The code is available at https://***/XiangZongyi/CoPD.

关键词： Cross-domain recommendation preference disentanglement graph collaborative filtering user popularity

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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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Privacy Meets Performance: Enhancing Distributed Simulation-based Federated Multi-agent Learning with Privacy-preserving Surrogate Model

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ACM Transactions on Modeling and Computer Simulation 1000年

作者： Bo Zhang Wen Jun Tan Wentong Cai Allan Zhang College of Computing and Data Science Nanyang Technological University Singapore Singapore Nanyang Technological University Singapore Singapore Singapore Institute of Manufacturing Technology SIMTech) Agency for Science Technology and Research A*STAR) Singapore Singapore

In recent years, model-free multi-agent reinforcement learning (MaRL) has become a powerful tool for learning effective policies to solve optimization problems. However, individual agents may raise concerns about sharing their internal data, simulation models, and decision models in collaborative optimization. Distributed simulation (DS) and federated learning have been widely used as privacy-preserving methods to hide simulation details and maintain data and model privacy. Despite their benefits, these methods often require large amounts of interaction and data to converge, which leads to a high communication time, especially if the agents are distributed around the world. To address this issue, we propose a distributed surrogate model for DS-based federated MaRL to utilize the surrogate model instead of DS during the training. This can enhance data efficiency and effectiveness to accelerate agent learning while maintaining data and model privacy. An aerospace supply chain (SC) is used as the experimental scenario to evaluate the performance of our proposed approach, in terms of SC profits, training convergence, and execution time. Experimental results show that our proposed approach can achieve higher SC profits with the same number of simulation runs, converge faster, and reduce execution time to gain the same level of SC profits.

关键词： Distributed Surrogate Model Multi-agent Reinforcement Learning data-driven Decision Making Privacy Preserving Supply Chain Inventory Management

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Mathematical Research for Blockchain Economy 1

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丛书名： Lecture Notes in Operations Research

1000年

作者： Panos Pardalos Ilias Kotsireas Yike Guo William Knottenbelt

ISBN: (数字)9783031186790

ISBN: (纸本)9783031186783;9783031186813

This book presents the best papers from the 3rd International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2022, held in Vilamoura, Portugal. While most blockchain conferences and forums are dedicated to business applications, product development or Initial Coin Offering (ICO) launches, this conference focuses on the mathematics behind blockchain to bridge the gap between practice and theory.;Blockchain Technology has been considered as the most fundamental and revolutionising invention since the Internet. Every year, thousands of blockchain projects are launched and circulated in the market, and there is a tremendous wealth of blockchain applications, from finance to healthcare, education, media, logistics and more. However, due to theoretical and technical barriers, most of these applications are impractical for use in a real-world business context. The papers in this book reveal the challenges and limitations, such as scalability, latency, privacy and security, and showcase solutions and developments to overcome them.

关键词： Financial Services Optimization Economic Theory/Quantitative Economics/Mathematical Methods Mathematical Applications in Computer science Business Finance

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