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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Dandan Cheng Xingwei Wang Shuang Zhang Min Huang College of Computer Science and Engineering Shenyang China College of Software Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781538680988;9781538680971

Information-Centric Networking (ICN), as a promising networking paradigm, is facing some challenges with its routing, such as content retrieval, efficiency, etc. In this paper, a Cat Swarm Optimization (CSO)based Quality of Service (QoS)Routing mechanism (CSOQR)is proposed to solve the routing problems in ICN. Firstly, a network model is built to imitate biological behaviors including link model and intelligent node model. Secondly, a user's Quality of Experience (QoE)is mapped to the corresponding QoS level, thus the user's QoS satisfaction is calculated. Then, the forwarding probability of an interface is calculated according to user's QoS satisfaction, and two forwarding interfaces are selected respectively based on Tracing Mode Face Choosing (TMFC)and Seeking Mode Face Choosing (SMFC). In this way, multiple content sources can be found through multiple paths to improve routing success rate, and we devise a migration strategy to balance node load. Finally, the proposed mechanism is simulated on both CERNET and CERNET2. Simulation results show that the proposed CSOQR has good performance in terms of routing success rate, routing hop count and node load.

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Community-based Malware Immunization Strategy

Community-based Malware Immunization Strategy

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International Conference on Systems and Informatics (ICSAI)

作者： Wei Yang Li Zhang Teng Chen Yu Yao Software College Northeastern University Shenyang China Huawei Technologies Co. Ltd Beijing China School of Computer Science and Engineering Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

In recent years, security threats have occurred frequently, especially the large-scale spread of malware has brought huge losses to individuals and the country as a whole. This paper considers the nodes of the network have the feature of forming a community. A community-based malware immunization strategy is proposed according to the characteristics that information spread quickly within the community and spread slowly between communities. A concrete community detection method is proposed based on the K-shell and the label propagation algorithm, as well as key node selection algorithm is proposed in order to control the outbreak of malicious code with the fastest speed and at the lowest cost. The experiments demonstrate that the proposed approach can achieve maximum immunity with minimal cost comparing to classical immunization strategies.

关键词： Informatics

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A novel hybrid collaborative filtering approach to recommendation using reviews:the product attributes perspective 30

A novel hybrid collaborative filtering approach to recommend...

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30th International Conference on software engineering and Knowledge engineering, SEKE 2018

作者： Cao, Min Zhou, Sijing Gao, Honghao Li, Youhuizi School of Computer Engineering and Science Shanghai University Shanghai200444 China Computing Center Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China College of Computer Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)1891706446

The product recommendation research has been focusing on modelling users' reviews to construct the relation of users and products. Thus, the recommended performance can be improved by obtaining virtual ratings from corresponding reviews. However, these perspectives on reviews do not take into account the product field characteristic, which may impact the recommendation performance. To this point, this paper proposes a hybrid collaborative filtering approach to compute the correlation value considering product attributes. First, Product Attribute Weight and Product Attribute Score are introduced to formalize the product attributes for user and product respectively in a quantitative way. After that, the recommended ranking formula for the new model is presented. Finally, we carry out experimental analysis to show our method can effectively improve the performance of recommendation under a sparseness dataset. © 2018 Universitat zu Koln. All rights reserved.

关键词： Collaborative filtering

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Enhancing countries' fitness with recommender systems on the international trade network

arXiv

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arXiv 2019年

作者： Liao, Hao Wu, Xing-Tong Vidmer, Alexandre Huang, Xiao-Min Liu, Ming-Kai Zhou, Mingyang Zhang, Yi-Cheng National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Physics University of Fribourg Fribourg1700 Switzerland

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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Energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave communications with SWIPT

arXiv

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arXiv 2020年

作者： Hao, Wanming Sun, Gangcan Zhou, Fuhui Mi, De Shi, Jia Xiao, Pei Leung, Victor C.M. School of Information Engineering Zhengzhou University Zhengzhou450001 China 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China State Key Laboratory of Integrated Services Networks Xi'dian University Xian710071 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T1Z4 Canada

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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PNet: An Efficient Network for Pneumonia Detection 12

PNet: An Efficient Network for Pneumonia Detection

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12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Li, Zhongliang Yu, Juan Li, Xuechen Li, Yingqi Dai, Weicai Shen, Linlin Mou, Lisha Pu, Zuhui Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Institute of Translational Medicine Shenzhen University Health Science Center Shenzhen University School of Medicine Shenzhen China Imaging Department Shenzhen University Health Science Center Shenzhen University School of Medicine Shenzhen China Shenzhen Second People's Hospital First Affiliated Hospital Shenzhen University Shenzhen China Imaging Department Shenzhen Bao'an District Songgang People's Hospital Shenzhen China Imaging Department Fifth People's Hospital of Longgang District Shenzhen China

ISBN: (纸本)9781728148526

Pneumonia is a common lung disease and affects millions of people worldwide each year. The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosing lung diseases. Usually, chest X-ray images (CXRs) are taken at physical examination and evaluated by radiologists. The unbalance between tremendous numbers of CXRs and limited number of radiologists need to be solved by computer Assisted Diagnosis (CAD). As deep neural networks have shown promising results in CAD, we implemented a deep learning-based framework, PNet, for pneumonia detection. 10784 chest X-ray images collected at the Shenzhen No.2 People's Hospital were employed for training and evaluation. The experimental results showed that the proposed PNet (1,695,777 parameters) achieved higher accuracy and F1 score than classical networks like AlexNet (42,725,889 parameters) and VGG16 (27,560,769 parameters). © 2019 IEEE.

关键词： Deep neural networks

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Data poisoning attacks on neighborhood-based recommender systems

arXiv

引用

arXiv 2019年

作者： Chen, Liang Xu, Yangjun Xie, Fenfang Huang, Min Zheng, Zibin school of data and computer science Sun Yat-Sen University Guangzhou China school of data and computer science Sun Yat-Sen University Guangzhou China National Engineering Research Center of Digital Life Sun Yat-sen University Guangzhou China College of software South China University of Technology Guangzhou China

Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighbourhood-based recommender systems be easily fooled? To this end, we shed light on the robustness of neighbourhood-based recommender systems and propose a novel data poisoning attack framework encoding the purpose of attack and constraint against them. We firstly illustrate how to calculate the optimal data poisoning attack, namely UNAttack. We inject a few well-designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and the transferability of our proposed method. Besides, some interesting phenomenons can be found. For example, 1) neighbourhood-based recommender systems with Euclidean Distance-based similarity have strong robustness. 2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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Federated Diabetes Mellitus Analysis via Homomorphic Encryption

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Journal of Physics: Conference Series 2020年第1期1684卷

作者： Yun Xie Peng Li Xiang Zhu Qiuling Wu Nanjing University of Science and Technology ZiJin College Nanjing 210023 China College of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China School of Software Engineering Jinling Institute of Technology Nanjing 211169 China

Diabetes is one of the most extensive chronic diseases in the world. Most patients suffer from the disease and its complications for a long time due to the lack of accurate and standardized treatment at early stage. Therefore, by analyzing diabetes data and establishing relevant predictive models, it is very meaningful to give reasonable health advice to high-risk groups. The establishment of an accurate prediction model requires a large number of data sources as support, and multiple medical institutions participate in data contribution and collaborative learning. Federated learning provides a secure general architecture for distributed collaborative learning. However, in the process of federated learning, the data of participants is still subject to the risk of security attacks or indirect information leakage. For example, when a participant uploads the local model parameters to an honest but curious cloud server, the cloud server can obtain relevant information from the participant. In order to solve this problem, this paper proposes a federated forest algorithm based on homomorphic encryption to strengthen the protection of the data privacy of the participants while ensuring that the accuracy of data analysis does not decrease. Analysis proves that our algorithm has good performance in privacy protection and prediction accuracy.

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On security improvement of adaptive pixel pair matching with modified searching mechanism

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International Journal of Network Security 2019年第2期21卷 230-235页

作者： Hong, Wien Zheng, Shuozhen Zhou, Xiaoyu School of Electrical and Computer Engineering Nanfang College of Sun Yat-Sen University 882 Wenquan Rd. Conghua district Guangzhou510970 China School of Computer and Software Nanjing University of Information Science and Technology 219 Niliu Rd. Nanjing Jiangsu210044 China

This paper proposes a data hiding scheme that improves the adaptive pixel pair matching (APPM) method. Based on pixel pair matching, APPM employs a pixel pair as an embedding unit, and uses a specially designed reference table that minimizing the embedding distortion for data embedment. Although APPM has the capability to em bed secret digit in any notational system, it is vulnerable to the detection by the RS scheme if digits in 4-ary no tational system are embedded into images with large at area (pixels having the similar grayscale values) such car toon images. A modified version of APPM is proposed in this paper by using a revised pixel pair replacement mechanism (PPRM). With the proposed PPRM method, the stego image not only is totally undetectable by the RS scheme but also provides the equivalent image quality of the original APPM method. © 2019 Femto Technique Co., Ltd.

关键词： Pixels

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A Passenger Satisfaction Prediction Method Based on Driving Time

A Passenger Satisfaction Prediction Method Based on Driving ...

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IEEE International Conference on Computing, Communications and IoT Applications

作者： Zhao Huang Fan Li Qingquan Li Jizhe Xia Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen University College of Information Engineering Shenzhen University Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen University College of Computer Science and Software Engineering Shenzhen University Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen University

ISBN: (数字)9781728119731

ISBN: (纸本)9781728119748

Public transport is an important means of transportation for urban residents. In China, the government strongly advocates urban residents to travel by public transport. However, the unreasonable allocation of public transport resources and a large number of passengers result in in-bus congestion during the peak period, which will decrease the passenger satisfaction and increase the loss of passengers. Targeting on the issue, this paper provides a passenger satisfaction prediction method based on Driving time (PSP-DT) using Long Short-Term Memory (LSTM) network. First, the bus arrival and departure time are extracted by matching the bus GPS trajectory with the bus station data using the map matching method. Second, the historical driving time is captured from arrival time and departure time, and the future driving time is predicted by LSTM considering weather. Third, passenger satisfaction based on driving time is estimated by the proportion of overload and standard load. By using the 18 working days data of bus No. 14 and a total of 162,628 bus driving data records in Guangzhou city, China, our experimental results show that 1) the prediction model performs well and the average MAPE and RMSE is 5.57% and 11.40. 2) During the morning and evening peak period, the lowest passenger satisfaction of twenty stations is 45.78% and 71.86% separately.

关键词： Public transport Passenger satisfaction Driving time prediction LSTM GPS trajectory Passenger satisfaction Public Transit driving time transportation mean Departure time passengers standard load

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