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AN EMPIRICAL FEASIBILITY STUDY OF SOCIETAL RISK CLASSIFICATION TOWARD BBS POSTS

Journal of Systems science and Systems Engineering 2018年第6期27卷 709-726页

作者： Jindong Chen Xiaoji Zhou Xijin Tang School of Economics and Management Beifing Information Science &Technology University Beifing 100192 China China Academy of Aerospace Systems Science and Engineering Beijing 100048 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Beijing Key Lab of Green Development Decision Based on Big Data Beijing 100192 China University of Chinese Academy of Sciences Beijing 100049 China

Societal risk classification is the fundamental issue for online societal risk monitoring. To show the challenge and feasibility of societal risk classification toward BBS posts, an empirical analysis is implemented in this paper. Through effectiveness analysis, Support Vector Machine based on Bag-Of-Words （BOW-SVM） is adopted for challenge validation, and the distributed document embeddings of BBS posts generated by Paragraph Vector are applied to feasibility study. Based on BOW-SVM, cross-validations of BBS posts labeled by different groups and annotators are conducted. The big fluctuation of cross-validation results indicates the differences of individual risk perceptions, which brings more challenges to societal risk classification. Furthermore, based on the distributed document embeddings of BBS posts, the pairwise similarities of more than 300 thousands BBS posts from different societal risk categories are compared. The higher similarities of BBS posts in the same societal risk category reveal that BBS posts in the same societal risk category share more features than BBS posts in different categories, which manifests the feasibility of societal risk classification of BBS posts, and also reflects the possibility to improve the performance of societal risk monitoring.

关键词： Societal risk classification Tianya Forum cross validation pairwise similarity individual risk perception

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Multi-view graph learning by joint modeling of consistency and inconsistency

arXiv

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arXiv 2020年

作者： Liang, Youwei Huang, Dong Wang, Chang-Dong Yu, Philip S. College of Mathematics and Informatics South China Agricultural University Guangzhou China Tencent Shenzhen China College of Mathematics and Informatics South China Agricultural University Guangzhou China Pazhou Lab Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

—Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimization

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Delay Optimization Based on Improved Differential Evolutionary Algorithm for Task Offloading in Fog Computing networks

Delay Optimization Based on Improved Differential Evolutiona...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xujie Li Guangzhao Zhang Xuedong Zheng Siyang Hua College of Computer and Information Hohai University Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Lab for Wireless Sensor network and Communication Chinese Academy of Sciences Shanghai China Nanjing China-Spacenet Satellite Telecom Co. Ltd Nanjing China Talent Science and Technology Co. Ltd Nanjing China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

With the rapid development of communication technology, more and more devices tend to be intelligent. Intelligent devices with computing functions can provide computing services to reduce total task completion time in fog computing networks. This paper aims at minimizing the total task completion time to study multi-user multi-node multi-task offloading problem. Firstly, the task offloading process of task node and fog node is modeled. Then, the system function of the total task completion time is constructed and an optimization problem with constraints is generated. The problem is NP-hard and difficult to be solved by traditional methods. Finally, the improved differential evolution (IDE) algorithm is used to solve the task offloading problem. Simulation results show that it has better performance and can significantly shorten the total task completion time compared with other algorithms.

关键词： Task analysis Delays Computational modeling Edge computing Optimization Signal to noise ratio Wireless communication

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One secure IoT scheme for protection of true nodes 1

引用

3rd International Conference on Smart Computing and Communications, SmartCom 2018

作者： Fan, Yongkai Zhao, Guanqun Lin, Xiaodong Sun, Xiaofeng Zhu, Dandan Lei, Jing Beijing Key Lab of Petroleum Data Mining China University of Petroleum Beijing China Department of Computer Science and Technology China University of Petroleum Beijing China

ISBN: (数字)9783030057558

ISBN: (纸本)9783030057541

As a new generation of information technologies, IoT has been applied into many industrial fields and made great contributions to our everyday life. However, vulnerability of IoT constrains the application of IoT, especially, when the node used in IoT systems is malicious one which may break the system and leakage vital data (for example, nodes used by patient to transfer condition data). To tackle the security concerning, we propose one secure IoT framework used to protect true nodes and ensure the secure operation. Firstly, we introduce two types of IoT classic architectures and summarize the security challenges, then we give an introduction and comparison of several IoT security frameworks. At last, we propose our scheme for protecting the safety of IoT nodes in the perception layer. © 2018, Springer Nature Switzerland AG.

关键词： Internet of things

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A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history (vol 29, pg 761, 2022)

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2022年第9期29卷 1657-1657页

作者： Maurits, Marc P. Korsunsky, Ilya Raychaudhuri, Soumya Murphy, Shawn N. Smoller, JordanW Weiss, Scott T. Petukhova, Lynn M. Weng, Chunhua Wei, Wei-Qi Huizinga, Thomas W. J. Reinders, Marcel J. T. Karlson, Elizabeth W. van den Akker, Erik B. Knevel, Rachel Department of Rheumatology Leiden University Medical Center Leiden The Netherlands. Leiden Computational Biology Center Leiden University Medical Center Leiden The Netherlands. Center for Data Sciences Brigham and Women's Hospital Harvard Medical School Boston MA USA. Research Information Science and Computing Mass General Brigham Boston MA USA. Center for Precision Psychiatry Department of Psychiatry Massachusetts General Hospital Boston MA USA. Psychiatric and Neurodevelopmental Genetics Unit Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. The Delft Bioinformatics Lab Delft University of Technology Delft The Netherlands. Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Harvard Medical School Boston MA USA. Section of Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands.

OBJECTIVE: To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects. MATERIAL AND METHODS: We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features. RESULTS: We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache" clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles. DISCUSSION: Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data. CONCLUSION: We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.

关键词： ICD PhenoGraph clustering eMERGE electronic health records electronic medical records

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Aligning cross-lingual entities with multi-aspect information

arXiv

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arXiv 2019年

作者： Yang, Hsiu-Wei Zou, Yanyan Shi, Peng Lu, Wei Lin, Jimmy Sun, Xu David R. Cheriton School of Computer Science University of Waterloo StatNLP Research Group Singapore University of Technology and Design MOE Key Lab of Computational Linguistics School of EECS Peking University Center for Data Science Beijing Institute of Big Data Research Peking University

Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a source language with their counterparts in target languages. In this work, we investigate embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other. Specifically, we apply graph convolutional networks (GCNs) to combine multi-aspect information of entities, including topological connections, relations, and attributes of entities, to learn entity embeddings. To exploit the literal descriptions of entities expressed in different languages, we propose two uses of a pretrained multilingual BERT model to bridge cross-lingual gaps. We further propose two strategies to integrate GCN-based and BERT-based modules to boost performance. Extensive experiments on two benchmark datasets demonstrate that our method significantly outperforms existing systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge graph

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Correction to: An empirical study on the joint impact of feature selection and data resampling on imbalance classification

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Applied Intelligence 2022年第7期53卷 8506-8506页

作者： Zhang, Chongsheng Soda, Paolo Bi, Jingjun Fan, Gaojuan Almpanidis, George García, Salvador Ding, Weiping Henan Key Lab of Big Data Analysis and Processing Henan University Henan China Department of Engineering University Campus Bio-Medico of Rome Rome Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umea University Umea Sweden DaSCI Andalusian Research Institute University of Granada Granada Spain School of Information Science and Technology Nantong University Nantong China

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Deep Convolutional Neural network Based Object Detector for X-Ray Baggage Security Imagery

Deep Convolutional Neural Network Based Object Detector for ...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jinyi Liu Xiaxu Leng Ying Liu School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Beijing China

Detection and classification of X-ray baggage security imagery are of great significance not only in daily life. However, up to now, it is still difficult for the traditional image processing methods to detect and distinguish objects which we want to detect in X-Ray baggage security imagery. Moreover, it is even more challenging to classify different types of objects. Although the current state-of-the-art detectors have achieved impressive performance on various public datasets with visible images, these detectors fail to deal with objects in X-ray images. This paper proposes an object detection algorithm for X-Ray baggage security screening images. Firstly, to outline the detected object from X-Ray baggage security imagery, we propose a fore-ground-background segmentation method which based on color information. Then, to classify and outline different type of object in X-Ray im-age, a deep convolutional neural networks (DCNNs) based object detection framework Faster R-CNN are proposed. In this stage we also use transfer learning method to speed up Faster R-CNN. The proposed method is proved to achieve 77% mAP by in a real-word dataset which contains 32,253 sub-way X-Ray baggage security screening images.

关键词：

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Error Recovery of RDMA Packets in data Center networks

Error Recovery of RDMA Packets in Data Center Networks

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International Conference on Computer Communications and networks (ICCCN)

作者： Yi Wang Kexin Liu Chen Tian Bo Bai Gong Zhang School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Future Network Theory Lab Huawei Hong Kong China

Modern data center applications need high throughput (40Gbps) and ultra-low latency (

关键词： Packet loss Receivers Throughput data centers Switches Ethernet

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RDMA Load Balancing via data Partition

RDMA Load Balancing via Data Partition

引用

International Conference on Computer Communications and networks (ICCCN)

作者： Yi Wang Ya-nan Jiang Qiufang Ma Chen Tian Bo Bai Gong Zhang School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Future Network Theory Lab Huawei Hong Kong China

With the development of data center networks, traditional TCP/IP cannot support the demand in data centers. Remote Direct Memory Access (RDMA) technology could improve the performance of DCN significantly because of high throughput and low latency. However, load balancing is a key issue in RDMA which has not been solved distribute. This paper will propose an algorithm to solve the load balance problem in RDMA on application layer without hardware changing. The main idea is to divide data to chunks and data chunks on multiple reachable paths for transmission. However, it is no trivial to find the optimal chunk size and the path number, some empirical values are found by varieties of experiments and tests. Moreover, the chunk allocation scheme also needs to consider the traffic condition in DCNs to find more free paths to transmit. We evaluate the algorithm in application layer with ns3 simulator. The experiment results show that with our algorithm the completion time can decrease 81.03% at most.

关键词： Task analysis Load management data centers Servers Software Routing Receivers

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