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IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Tingting Jiang Yi Zhu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Information Engineering Yangzhou University Yangzhou China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical roots, and explore the origins of a family more easily. However, the multi-type, multisource dynamic changes and specialized nature of genealogical data bring challenges to the development of contemporary knowledge graph models. Applying existing methods to genealogical data can result in problems of overlooking certain specialized vocabulary and dynamic properties such as personal experiences. In this paper, we propose a genealogical knowledge graph model GKGM that combines HAO intelligence (h uman intelligence + a rtificial intelligence + o rganizational intelligence) and ontology granularity division technology to address the above problems. Furthermore, a method of applying the model to construct genealogical knowledge graphs is demonstrated, and an experiment conducted on a real-world genealogical dataset verifies the feasibility and effectiveness of the model.

关键词： Ontologies data models Blood data mining Biological system modeling History

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The Design of an Open Hybrid Recommendation System for Mobile Commerce

The Design of an Open Hybrid Recommendation System for Mobil...

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International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： Chengzhi Liu Caihong Sun Jia Yu Information School and Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

To meet more and more complex recommendation needs, it is quite important to implement hybrid recommendations for mobile commerce. In this paper, we propose a design for open hybrid recommendation systems in mobile commerce, which could integrate multiple recommendation algorithms together to improve recommendation performance. First, three solutions for an open hybrid recommendation approach are discussed in detail, which are generic customer profile, weighted hybrid recommendation algorithm, and mobile device profile creation. After that, we give out a multi-agent architecture design to make the three solutions work together. Finally a prototype system based on our proposed architecture is implemented to demonstrate the feasibility of our design and evaluate the performance of the proposed open hybrid recommendation system.

关键词： Business Customer profiles Context awareness Laboratories data engineering knowledge engineering Prototypes XML Sun Australia

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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IEEE International Conference on Cloud Computing and Big data Analysis (ICCCBDA)

作者： Tao Hu Zan Zhang Chenyang Bu Lei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency.

关键词： Proteins Location awareness Social networking (online) Social groups Heuristic algorithms knowledge graphs Hardware

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Access control of blockchain based on dual-policy attribute-based encryption

Access control of blockchain based on dual-policy attribute-...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Xueheng Wang Yihua Gao Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China

In scenarios where multiple parties such as the Internet of Things and Supply Chains participate in data sharing and computing, when accessing data, users not only need to accept the forward access control of the data owner, but also needs to perform reverse access control on the data, so as to realize two-way access control. In the traditional blockchain, all users can participate in accounting and view transaction data, and only protect user privacy through “pseudo-anonymity”. The access rights of different users cannot be distinguished, which cannot meet the user's two-way access control needs. However, most of the existing blockchain-based access control schemes are one-way access control, which cannot meet the needs of users for two-way access control in scenarios such as the Internet of Things and Supply Chains. Therefore, it is particularly important to design a two-way access control mechanism suitable for application in the blockchain. On this basis, this paper proposes a dual strategy attribute-based encryption (ABE) scheme for distributed outsourcing. This scheme combines two existing schemes, ciphertext-policy ABE and key-policy ABE, and proposes two access structures and a structure of attribute sets. The primary access structure and the secondary attributes are stored in the ciphertext, and the secondary access structure and the primary attributes are stored in the user's private key. Only when the primary attribute set satisfies the primary access structure and the secondary attribute set satisfies the secondary access structure can the user unlock the ciphertext. This scheme has no single authorization center; instead, blockchain nodes jointly participate in authorization. In addition, the proposed scheme outsources the encryption and decryption of the ciphertext to blockchain nodes to reduce the computing pressure on users and can adapt to the decentralized environment of the blockchain and provide users with two-way access control services. Finally

关键词： Authorization data privacy High performance computing Conferences Supply chains Blockchain Permission

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Managing Electronic Records in E-government:Current Trends and Future Directions Internationally

Managing Electronic Records in E-government:Current Trends a...

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International Conference on Management and Service Science(2011年第五届管理与服务科学国际会议 MASS 2011)

作者： Xiaomi An Wei Zhang Shuyang Sun Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of School of Information Resources Management Renmin University of China Beijing China

More and more literature has been written on electronic records management (ERM) in e-government in recent three years, however, much is limited to experiences of one country with more focus on electronic records management systems (ERMS). This paper aims to investigate current trends and future directions of ERM including ERMS in e-government with more focus on comprehensive approaches internationally. Representative shows current ERMS have limitations either from IT perspectives or records and archives management perspectives managing records as data. Future research is called for more concerns of e-government and more involvement in efficient civil service. Case studies of representative policies, regulations, best practices guidelines from Australia, Canada, New Zealand, UK and U.S show challenges of ERM in e-government are getting more and more recognized in information risk, data governance and efficient civil service. Current trends of ERM in e-government are toward multidisciplinary and collaborative approaches to ERM managing records as information resources and business asset. Findings indicate that future directions of ERM in e-government would be toward meta-synthesis management at both organizational and national level, with integration concerns of collaboration, optimization and innovation of both ERM and e-government for civil service. The findings may have implications to be of use to both ERM and e-government professions internationally.

关键词： Electronic government Law Information services Collaboration

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Feature Enhancement for Multi-modal Entity Alignment Transformer

Feature Enhancement for Multi-modal Entity Alignment Transfo...

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IEEE International Conference on Big knowledge (ICBK)

作者： Zewen Deng Zan Zhang Bingbing Dong Xiulin Zheng Xindong Wu Key Laboratory of Knowledge Engineering With Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Multi-modal entity alignment (MMEA) aims to identify equivalent entities across different multi-modal knowledge graphs. In these graphs, entities are enriched with information from various modalities, such as text, images, and numerical data, making the alignment task both challenging and crucial for improving knowledge graph quality. Current MMEA algorithms typically encode entity information separately for each modality using corresponding encoders, and then integrate these representations through various modal fusion strategies. However, these methods often fail to fully exploit the multi-modal information of entities. To address this issue, we propose a feature-enhanced multi-modal entity alignment transformer (FEMEAT). FEMEAT enhances entity attribute information by incorporating modal distribution data, which captures the inherent distribution of different modalities for each entity. This inclusion allows the model to have a richer understanding of entity characteristics across modalities. Additionally, FEMEAT utilizes an Optical Character Recognition (OCR) model to extract and incorporate textual information from images. By integrating this text extracted from images, the model can better utilize the visual modality, enhancing its ability to understand and process multi-modal information. Furthermore, FEMEAT employs a multi-head cross-modal attention (MHCA) mechanism for modal fusion to achieve comprehensive multi-modal entity representation. This mechanism enables the model to attend to different modalities simultaneously and learn a detailed representation of entities by considering the interactions between modalities. The multi-head cross-modal attention mechanism facilitates a nuanced understanding and integration of multi-modal data. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance across various training scenarios. The code and datasets used in this study can be accessed at https://***/zewenD/FEMEAT.

关键词： Training Visualization Attention mechanisms Optical character recognition Noise knowledge graphs Transformers Robustness Numerical models data mining

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Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network 20

Search Result Diversification Framework Based on Dual Star-s...

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Qin, Xubo Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong School of Information Renmin University Gaoling School of Artificial Intelligence Renmin University of China Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Research shows that most queries issued by users are short queries. Those queries may be ambiguous or broad for specifying users' actual information need. The same query issued by different users may lead to different information needs. In order to satisfy different users' needs, the search engines need to rank the search result document diversely. This leads to the search result diversification technology. For those previous diversification approaches based on global document interactions, fully-connected self-attention networks are used for capturing the interactions among all the candidate documents, leading to well performance. However, those models only take the document interactions into consideration, ignoring the relevance signals of candidate documents. As the annotated datasets are not enough for model training, the effectiveness of the model will be limited. In this paper we prove a new implicit diversification framework based on self-attention network in the shape of dual-star. A star-shaped topological structure is deployed with an external query node, in order to effectively extract the global interaction signals relevant to the query. Using self-attention to learn the similarity between each document, this model can be trained easily, and perform significantly better than the state-of-the-art implicit approach indicating the potential of implicit methods. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Search engines

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Switchable Online knowledge Distillation

arXiv

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arXiv 2022年

作者： Qian, Biao Wang, Yang Yin, Hongzhi Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China The University of Queensland

Online knowledge Distillation (OKD) improves the involved models by reciprocally exploiting the difference between teacher and student. Several crucial bottlenecks over the gap between them — e.g., Why and when does a large gap harm the performance, especially for student? How to quantify the gap between teacher and student? — have received limited formal study. In this paper, we propose Switchable Online knowledge Distillation (SwitOKD), to answer these questions. Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes — expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher). To possess an appropriate distillation gap, we further devise an adaptive switching threshold, which provides a formal criterion as to when to switch to learning mode or expert mode, and thus improves the student’s performance. Meanwhile, the teacher benefits from our adaptive switching threshold and keeps basically on a par with other online arts. We further extend SwitOKD to multiple networks with two basis topologies. Finally, extensive experiments and analysis validate the merits of SwitOKD for classification over the state-of-the-arts. Our code is available at https://***/hfutqian/SwitOKD. Copyright © 2022, The Authors. All rights reserved.

关键词： Students

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Extricating from GroundTruth: An Unpaired Learning Based Evaluation Metric for Image Captioning

Extricating from GroundTruth: An Unpaired Learning Based Eva...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhong-Qiu Zhao Yue-Lin Sun Nan-Xun Wang Wei-Dong Tian Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Recently, instead of pursuing high performance on classical evaluation metrics, the research focus of image captioning has shifted to generating sentences which are more vivid and stylized than human-written ones. However, there are still no applicable metrics which can judge how close the generated captions are to the human-written ones. In this paper, we propose a novel learning-based evaluation metric, namely Unpaired Image Captioning Evaluation (UICE), which can be trained to distinguish between human-written and generated captions. Unlike existing metrics, our UICE consists of two parts: the semantic alignment module measuring the semantic distance between extracted image features and caption meanings, and the syntactic discriminating module syntactically judging how human-like the candidate caption is. The semantic alignment module is implemented by mapping the image features and the word embedding into a unified tensor space. And the syntactic discriminating module is designed to be learning-based, and thereby can be trained to be stylized by users' own, fed with additional personalized corpus during the training process. Extensive experiments indicate that our metric can correctly judge the grammatical correctness of generated captions and the semantic consistency between captions and corresponding images.

关键词： Measurement Semantics Syntactics Feature extraction Training Pathology SPICE

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TREC: transient redundancy elimination-based convolution 22

TREC: transient redundancy elimination-based convolution

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiawei Guan Feng Zhang Jiesong Liu Hsin-Hsuan Sung Ruofan Wu Xiaoyong Du Xipeng Shen Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Computer Science Department North Carolina State University

ISBN: (纸本)9781713871088

The intensive computations in convolutional neural networks (CNNs) pose challenges for resource-constrained devices; eliminating redundant computations from convolution is essential. This paper gives a principled method to detect and avoid transient redundancy, a type of redundancy existing in input data or activation maps and hence changing across inferences. By introducing a new form of convolution (TREC), this new method makes transient redundancy detection and avoidance an inherent part of the CNN architecture, and the determination of the best configurations for redundancy elimination part of CNN backward propagation. We provide a rigorous proof of the robustness and convergence of TREC-equipped CNNs. TREC removes over 96% computations and achieves 3.51× average speedups on microcontrollers with minimal (about 0.7%) accuracy loss.

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