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MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

IEEE transactions on cybernetics 2023年第12期53卷 7749-7759页

作者： Tao Chen Richang Hong Yanrong Guo Shijie Hao Bin Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and the School of Computer Science and Information Engineering Hefei University of Technology Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

关键词： Task analysis Feature extraction Semantics Depression Graph neural networks Electroencephalography data mining Mental disorders Multisensory integration

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EdgeNN: Efficient Neural Network Inference for CPU-GPU Integrated Edge Devices

EdgeNN: Efficient Neural Network Inference for CPU-GPU Integ...

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International Conference on data engineering

作者： Chenyang Zhang Feng Zhang Kuangyu Chen Mingjun Chen Bingsheng He Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China School of Computing National University of Singapore

With the development of the architectures and the growth of AIoT application requirements, data processing on edge has become popular. Neural network inference is widely employed for data analytics on edge devices. This paper extensively explores neural network inference on integrated edge devices and proposes EdgeNN, the first neural network inference solution on CPU-GPU integrated edge devices. EdgeNN has three novel characteristics. First, EdgeNN can adaptively utilize the unified physical memory and conduct the zero-copy optimization. Second, EdgeNN involves a novel inference-targeted inter- and intra-kernel CPU-GPU hybrid execution approach, which co-runs the CPU with the GPU to fully utilize the edge device’s computing resources. Third, EdgeNN adopts a fine-grained adaptive inference tuning approach, which can divide the complicated inference structure into sub-tasks mapped to the CPU and the GPU. Experiments show that on six popular neural network inference tasks, EdgeNN brings an average of 3.97×, 3.12×, and 8.80× speedups to inference on the CPU of the integrated device, inference on a mobile phone CPU, and inference on an edge CPU device. Additionally, it achieves 22.02% time benefits to the direct execution of the original programs. Specifically, 9.93% comes from better utilization of unified memory, and 10.76% comes from CPU-GPU hybrid execution. Besides, EdgeNN can deliver 29.14× and 5.70× higher energy efficiency than the edge CPU and the discrete GPU, respectively. We have made EdgeNN available at https://***/ChenyangZhang-cs/EdgeNN.

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Notice of Retraction: System Formulation of knowledge Sharing and Exchange in the Organization

Notice of Retraction: System Formulation of Knowledge Sharin...

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International Conference on Management and Service Science, MASS

作者： Jing Zhang Liuqi Ye Business School Hohai University HHU Nanjing Jiangsu China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing China

This article has been retracted by the publisher.

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An ontology automation construction scheme for Chinese e-government thesaurus optimizing

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Proceedings of the Association for Information Science and Technology 2020年第1期57卷

作者： Wang, Hao Zhang, Wei Deng, Sanhong Zhang, Baolong School of Information Management Nanjing University Nanjing China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing China

To optimize the term hierarchy in the manual e-government thesaurus, we combine the mainstream knowledge organization technology to form a complete set of ontology automation construction scheme. We build an e-government knowledge base by using subject words in the Comprehensive E-government Thesaurus as the term set and encyclopedia text as the corpus. The specific work includes the extraction of semantic features from the bag-of-words model, determination of the number of clusters by linear and nonlinear dimensionality reduction, division of terms by spectral clustering, social network analysis to determine the class label, and storing knowledge ontology via OWL. The recall rate of term hierarchy in the ontology is excellent, indicating the ontology has good knowledge extensibility, and also proving the efficiency of the scheme proposed in this work. Besides, the application model of a term hierarchy in information retrieval can show a richer semantic relation than the original thesaurus to guide the retrieval extension of government information resources. 83rd Annual Meeting of the Association for Information Science & Technology October 25-29, 2020. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Ontology

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Overview of the balancing pervasive information systems project and a call for research collaborations 10

Overview of the balancing pervasive information systems proj...

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10th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2018

作者： Xie, Sherry L. School of Information Resource Management Renmin University of China 59 Zhongguancun Ave. Beijing China Center for Digital Records Management Research Beijing China Key Laboratory of Knowledge and Data Engineering Ministry of Education of China China

ISBN: (纸本)9789897583308

The Research on Balancing Theories and Mechanisms to Pervasive Information Systems (B2P) is one of the component studies of the Records-centered Digital Information Management Theory and Mechanisms (DI{R}Mtm) Project, which aims pointedly at synchronizing records/information related research interests with the evolution of information technologies and their joint impact on society. Due to the breakthroughs in cloud computing, big data, the Internet of Things and artificial intelligence, ubiquitous/pervasive computing, conceived in 1988, has now been rapidly advanced. As its materialization in organizations, pervasive information systems (PISs) converges information technologies, data, digital operations and human actors and functions as the main channel for digital decisions. Digital decisions, therefore, are autonomous, algorithmic and penetrating, possessing influential abilities that are much greater than any single technology in human history, including the Internet. Such abilities make it a strong force for societal advancement, yet, at the same time, a potential hurdle – even harm – for decision recipients who are unaware of the decision-making methods. The B2P study is conceived as a reaction to this phenomenon. This paper gives an overview of the B2P study and calls for collaborations among researchers from the various fields relevant to the aims of the study, i.e., archival science, records management, information science, pubic administration, law, social sciences, and computer sciences. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Information management

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Records systems and information systems: Connecting in organizations 8th

Records systems and information systems: Connecting in organ...

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8th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2016

作者： Xie, Sherry L. Fan, Guanyan Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing100872 China Center for Electronic Records Management Research Beijing100872 China School of Information Resource Management Renmin University of China 59 Zhongguancun Ave Haidian District Beijing100872 China

ISBN: (纸本)9783319997001

The field of Information Systems (ISs) has long been recognized, so has Enterprise Information Systems (EISs), a field close to it. Long existing also in organizations or enterprises is the field of records management, now predominantly digital records management (DRM), which shares the many goals of ISs and EISs in supporting the operation and success of organizations. While the DRM field recognized rather early in its battle to digital records challenges that the need to establish formal collaborations with the ISs and EISs professions, it is still rare to spot today discussions regarding such collaborations in the general information and communication technology (ICT) literature. To help bridge the gap, this chapter introduces to the ISs and EISs professions one of the major developments of the international DRM field, that is, the records systems elaborated by the InterPARES (International Research on Permanent and Authentic Records in Electronic Systems) project, for the purpose of invoking further discussions. © Springer Nature Switzerland AG 2019.

关键词： Information systems

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Investigating different syntactic context types and context representations for learning word embeddings

Investigating different syntactic context types and context ...

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2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Li, Bofang Liu, Tao Zhao, Zhe Tang, Buzhou Drozd, Aleksandr Rogers, Anna Du, Xiaoyong School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Shenzhen Graduate School Harbin Institute of Technology China Global Scientific Information and Computing Center Tokyo Institute of Technology China Department of Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781945626838

The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task. © 2017 Association for Computational Linguistics.

关键词： Embeddings

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Using Ontology to Enhance Collaborative Recommendation Based on Community

Using Ontology to Enhance Collaborative Recommendation Based...

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International Conference on Web-Age Information Management Workshops (WAIM)

作者： Li Yu School of Information Key Laboratory of Data Engineering and Knowledge Engineerin Renmin University of China Beijing China

ISBN: (纸本)9781424432004;9780769531854

Collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. It is not adapted to multi-interest or title recommendation for the 'general neighbourhood' problem which is analyzed in this paper. Based on it, collaborative filtering recommendation based on community is presented by introducing the concept 'community neighbourhood' in the paper. Unfortunately, it results into severer sparsity problem which makes heavy effect on its performance. In order to overcome it, an ontological A-priori score is used to infer user preference and to pre-fill null rating first. After pre-filling using the ontology method, then collaborative filtering based on community is executed based on a dense rating matrix. The experiment shows that collaborative filtering based on community makes generally better performance than traditional method when data is not very sparse, and ontology method can truly enhance collaborative filtering based on community since the sparsity is overcame.

关键词： Communities Ontologies Filtering Collaboration Motion pictures Filling Argon

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Co-design of information systems with digital records management: A proposal for research 7

Co-design of information systems with digital records manage...

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7th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2015

作者： Xie, Sherry L. Key Laboratory of Data Enginerring and Knowledge Engineering Ministry of Education Beijing China Center for Electronic Records Management Research Beijing China School of Information Resource Management Renmin Univeristy of China 59 Zhonguancun Ave Beijing China

ISBN: (纸本)9789897581588

This position paper, via rationalizing the potentials of carrying out a carefully crafted research project, argues that the three fields of enterprise information system, business process management, and digital records management should join forces in serving the needs of organizations, regardless of their types. By preliminary findings uncovered during the conceiving process of the project, it advocates that the best point of convergence is the design phase, a basic step for all of the three fields and an increasingly critical one in today's digital environment. © 2015 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Information systems

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Deep Text Classification Can be Fooled

arXiv

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arXiv 2017年

作者： Liang, Bin Li, Hongcheng Su, Miaoqiang Bian, Pan Li, Xirong Shi, Wenchang School of Information Renmin University of China Beijing China Key laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (black-box attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and word-level DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived. Copyright © 2017, The Authors. All rights reserved.

关键词： Classification (of information)

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