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IEEE International Conference on Services Computing (SCC)

作者： Yi Pan Xiaoning Sun Yunni Xia Wanbo Zheng Xin Luo College of Computer Science Chongqing University Chongqing China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.

关键词： Cloud computing Processor scheduling Computational modeling Quality of service Predictive models Dynamic scheduling Real-time systems

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Attribute-Based Weighted Keyword Search Scheme Supporting Multi-Search Mechanism in Fog Computing

Attribute-Based Weighted Keyword Search Scheme Supporting Mu...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Shulan Wang Yuan Li Haiyan Wang Xi Zhang Jianyong Chen College of Big Data and Internet Shenzhen University Shenzhen P.R. China College of Computer Science and Software Engineering Shenzhen University Shenzhen P.R. China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen P.R. China

ISBN: (数字)9781728165509

ISBN: (纸本)9781728165516

With the popularity of intelligent terminal, fog computing as a new generation of internet technology can integrate the computing power of terminals and improve the effective utilization of social resources. At the same time, more and more data owners store ciphertext in the network. Hence, the advent of searchable encryption (SE) scheme enables searchers to search ciphertext without downloading all of it. However, the existing SE scheme cannot identify the matching degree of keywords and ciphertext. Besides, these schemes can only provide one or two search mechanism. In this paper, we propose a ciphertext-policy attribute-based weighted keyword searchable encryption scheme (ABWKS-MSM) in fog computing in order to solve the matching degree issue and provide multi-search mechanism, so that it becomes more friendly to fog computing applications. Furthermore, the proposed weighted keyword model can reduce the computational and storage cost. Finally, the performance analysis is also given, which proves that our scheme is efficient and feasible in fog computing.

关键词： data security Searchable encryption Multisearch mechanism Ciphertext-policy attribute-based encryption Fog computing Ciphertext data Security computer information security Scheme Key Search searcher Downloading computational power effective utilization KEYWORD cryptography

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Forecasting of Credit Card Default Based on Incremental Random Forest

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Xiang Gao Junhao Wen Cheng Zhang College of Computer Science Chongqing University Chongqing China Chongqing Branch of China Minsheng Bank Chongqing China College of Big Data & Software Engineering Chongqing University Chongqing China

In this paper, the incremental random forest algorithm is proposed for the classification and prediction problem of dynamically increasing data. Traditional batch machine learning algorithms perform modeling at one time and cannot allow newly generated samples to participate in learning, which leads to too much model deviation. This paper combines incremental learning with random forest and proposes incremental random forest. Applying this algorithm to the problem of predicting credit card customer default behavior can help banks control risks and reduce losses. It is important to conduct card issuance audits on card issuers and early warning of risks to cardholders. The algorithm performed better in the experiment of predicting the default behavior of credit card customers based on a batch of credit card holder data of a bank in Taiwan. Compared with random forest, decision tree, logistic regression, naive bayes, BP neural network, and support vector machine, it has relatively better performance in our experiment.

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Machine learning for drug repositioning: Recent advances and challenges

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Current Research in Chemical Biology 2023年 3卷

作者： Lijun Cai Jiaxin Chu Junlin Xu Yajie Meng Changcheng Lu Xianfang Tang Guanfang Wang Geng Tian Jialiang Yang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Geneis Beijing Co. Ltd. Beijing 100102 China Qingdao Genesis Institute of Big Data Mining and Precision Medicine Qingdao 266000 China Chifeng Municipal Hospital Chifeng Inner Mongolia 024000 China

Because translating the growing body of knowledge about human disease into treatments has been slower than expected, the application of machine learning techniques to drug repositioning has become attractive. An effective and comprehensive understanding of the current state of drug repositioning can help researchers to investigate more efficient and accurate algorithms. In this study, we first present the theoretical rationale for drug repositioning analysis. Then, we conduct a comprehensive review on machine learning algorithms for drug discovery, which include (1) traditional machine learning-based models using linear and logistic regression, support vector machines, random forest, and decision tree, (2) network transmission-based models using drug–disease similarity and network similarity-based reasoning, (3) matrix completion and matrix factorization-based methods using matrix completion, logistic matrix factorization, collaborative matrix factorization, and regularized matrix factorization, and (4) deep learning-based methods using deep neural networks, convolutional neural networks, recurrent neural networks, and graph convolutional networks. This is followed by a review of commonly used data sources for drug repositioning, as well as an introduction to particular data sources that can be employed by researchers. To conclude, we discuss the future developments and challenges of drug repositioning methods.

关键词： Machine learning Drug repositioning Interactions Neural networks Drug-target interactions

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Transferring knowledge distillation for multilingual social event detection

arXiv

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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A study on the uncertainty of convolutional layers in deep neural networks

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University ShenzhenGuangdong518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution. © 2020, CC-BY.

关键词： Convolution

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Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

Weighted Linear Multiple Kernel Learning for Saliency Detect...

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： Center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

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Incorporating Hidden Layer representation into Adversarial Attacks and Defences

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

In this paper, we propose a defence strategy to improves adversarial robustness incorporating hidden layer representation. The key of this defence strategy aims to compress or filter input’s information including adversarial perturbation. And this defence strategy can be regarded as an activation function which can be applied any kind of neural networks. We also prove theoretically the effectiveness of this defense strategy under certain conditions. Besides, incorporating hidden layer representation we propose three types of adversarial attacks to generate three types of adversarial examples, respectively. The experiments show that our defence method can significantly improve the adversarial robustness of deep neural networks which achieves the state-of-the-art performance even though we do not adopt adversarial training. © 2020, CC BY.

关键词： Deep neural networks

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A Framework of Blockchain-Based Security for WBANs

A Framework of Blockchain-Based Security for WBANs

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Smart BlockChain (SmartBlock), International Conference on

作者： Han Deng Xiangwei Meng Jun Guo Erhui Xi Hui Zhao Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou China School of Computer science and Engineering Hunan University of Science and Technology Xiangtan China School of Software Engineering Quanzhou University of Information Engineering Quanzhou China Guangzhou College of Technology and Business Guangzhou China Educational Information Technology Laboratory Henan University Kaifeng China

Purposed to collect and store human physiological data, WBANs as a telemedicine system play a significant role in sensor networks. The complex network environment presents a formidable challenge to the stability of the system and the security of the database. The traditional centralized architecture is to store the registration data of the sensor node and the patient's physiological data in the hub node. When the hub node is subjected to Denial of Service attacks (DoS) and Distributed Denial of Service attacks (DDoS), there will be a single point of failure, so that the sensor node cannot establish a communication connection with the hub node. In this paper, a cloud server layer architecture based on blockchain technology is proposed for WBNAs to ensure system stability and patient data security. Meanwhile, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme is used to ensure patient physiological data safety.

关键词： Wireless communication Cloud computing Architecture Distributed databases Blockchain Medical services Permission

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An advanced operating environment for mathematics education resources

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science China(Information sciences) 2018年第9期61卷 270-281页

作者： Yongsheng RAO Jingzhong ZHANG Yu ZOU Yanchun SUN Xiangping CHEN Songhua XU School of Data and Computer Science Sun Yat-Sen University Academy for Intelligent Software Guangzhou University Institute of Mathematics and Computer Science Guizhou Normal College School of Electronics Engineering and Computer Science Peking University Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing College of Computer Sciences New Jersey Institute of Technology

The potential of information technology(IT)in education modernization has been widely *** the broad efforts for advancing education IT,digital education resources continue to receive a high level of attention,with the... 详细信息

关键词： SSP OA An advanced operating environment for mathematics education resources OER Figure

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