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IEEE Conference on Wireless Communications and Networking

作者： Jingyuan Tang Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Semantic segmentation is regarded as an important technology for future communication and sensing networks due to its promising ability to extract features of transmit data. It integrates the functionality of computer vision and can realize high-fidelity transmission in the channel with lower bandwidth. Knowledge Base (KB) is a key component for the semantic segmentation framework. In this paper, we consider the scenario of intelligent transportation and propose a heterogeneous KB to extract the to-be-transmitted features and information at multiple levels, i.e., the raw level, symbol level, feature level and image level. The compressed features are obtained by a deep-learning-based framework. The proposed KB is shared with the transmitter and the receiver, by a service-oriented multi-stream transmission algorithm to meet various requirements of services. Experiments results demonstrate significant performance gain in terms of quality of service and encoding efficiency.

关键词： Semantic segmentation Semantics Knowledge based systems Transportation Symbols Object detection Streaming media

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Enhancing stock market prediction with extended coupled Hidden Markov Model over multi-sourced data

arXiv

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arXiv 2018年

作者： Zhang, Xi Li, Yixuan Wang, Senzhang Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Institute of Electronic and Information Engineering UESTC Guangdong China Department of Computer Science University of Illinois ChicagoIL United States Institute for Data Science Tsinghua University Beijing China

Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market uctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an Extended Coupled Hidden Markov Model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the uctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Hidden Markov models

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

引用

Annual Conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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RESTful web service composition: Extracting a process model from Linear Logic theorem proving

RESTful web service composition: Extracting a process model ...

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International Conference on Next Generation Web services Practices (NWeSP)

作者： Xia Zhao Enjie Liu Gordon J. Clapworthy Na Ye Yueming Lu Department of Computer Science and Technology University of Bedfordshire Luton UK School of Electronic and Information Engineering Xi'an Jiaotong University Xian China School of Information and Control Engineering Xi'an University of architecture and technology Xian China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

This paper introduces a two-stage Linear Logic based program synthesis approach to automatic RESTful web service composition. The Linear Logic theorem proof is applied at both resource and service invocation method levels, which greatly improves the searching efficiency and guarantees the correctness and completeness of the service composition. Furthermore, the process calculus is used as formalism for the composition process, which enables the approach to be executable at the business management level. The process calculus is attached to the Linear Logic inference rules in the style of type theory, so the process model is extracted directly from the complete proof. An example is given to show the extraction of a process model from a Linear Logic proof search.

关键词： Web services Business Calculus Educational institutions Next generation networking Marine vehicles Electronic mail

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PA-GAN: Graph Attention Network for Preference-Aware Social Recommendation

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Journal of Physics: Conference Series 2021年第1期1848卷

作者： Liyang Hou Wenping Kong Yali Gao Yang Chen Xiaoyong Li Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing 100876 China

Social recommendation has been recently proposed by incorporating trust relationship to alleviate data-sparsity and cold-start problems. However, most of existing works only focus on friends different contribution to model user representation. They ignore users have different preference on items, and share different preference with friends. To address these problems, in this paper, we propose a novel Preference-Aware Graph Attention Network (PA-GAN) for trust recommendation. And we design three modules: item aggregation for user, friend-preference aggregation for user and user aggregation for item to model users' local and global preference. Experiments on two publicly available datasets shows the proposed model PA-GAN outperforms the state-of-the-art recommendation models, and improves performance greatly.

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Joint resource management for MC-NOMA: A deep reinforcement learning approach

arXiv

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arXiv 2021年

作者： Wang, Shaoyang Lv, Tiejun Ni, Wei Beaulieu, Norman C. Guo, Y. Jay Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Sydney2122 Australia Beijing Key Laboratory for Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Global Big Data Technologies Centre University of Technology Sydney UltimoNSW2007 Australia

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users. © 2021, CC BY.

关键词： Resource allocation

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X-Prcaf : Xposed Based Protecting Cache File from Leaks in Android Social Applications

X-Prcaf : Xposed Based Protecting Cache File from Leaks in A...

引用

International Conference on trustworthy Systems and their Applications (TSA)

作者： Qi Tang Wen Zhang Xiaoyong Li Bin Wang Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing P.R. China Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing P.R. China School of Computer Science Beijing University of Posts and Telecommunications Beijing P.R. China

ISBN: (纸本)9781509035403

With the high frequent use of social applications on Android platform, the cache file privacy disclosure issues have become increasingly serious. To our best knowledge, there is no effective privacy protection solution for social applications cache files. In this paper, we analyze the present situation of social applications cache file leaks on Android platform, and provide a privacy disclosure assessment criterion based on file storage directories and security state machines. And a cache file privacy protection framework, X-Prcaf (Xposed-based-Protecting-Cache-File), is proposed, which can make social applications avoid privacy data leaks in running process. This framework mainly uses taint tracking technology, operating system hook technology, and cryptographic technology. It aims to protect the entire life cycle of the social applications cache files, by strategy pre-generation, real-time monitoring and security reinforcement. Experiments demonstrate that X-Prcaf has a good effect on the cache file leaks of social software.

关键词： Data privacy Software Cryptography Smart phones Privacy Telecommunications

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Review of Semi-Structured Document Information Extraction Techniques Based on Deep Learning

Review of Semi-Structured Document Information Extraction Te...

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Machine Learning, Cloud computing and Intelligent Mining (MLCCIM), International Conference on

作者： Yangchun Li Wei Jiang Shouyou Song Chinese Academy of Cybersapce Studies School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Cybersapce Studies Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Chinese Academy of Cybersapce Studies Ministry of Education Beijing University of Posts and Telecommunications Beijing China

With the advent of global digital transformation, using an intelligent method based on deep learning to extract crucial information from semi-structured documents, as represented by various types of receipts and invoices, has emerged as an imperative measure to ensure business stability, data security, and improved work efficiency. This paper provides a detailed review on deep learning-based techniques for information extraction, with systematic introduction, hierarchical analysis, method comparison, and summary with expectations for future development. The review begins with a comprehensive explication of the defining characteristics of semi-structured documents, along with a detailed introduction to the research background, application areas, and technical challenges related to information extraction from semi-structured documents. Then the review extends to an overview of two developmental stages, i.e. the shift from traditional information extraction to deep learning-based information extraction, followed by discussion about technical architecture and method classification, which elaborates on key technologies in terms of typical datasets, detection and recognition, and information reduction. Lastly, paper summarizes the prospects and development in the field. Future research will focus on strengthening algorithm universal and lightweight, as well as improving information protection capabilities and the diversity of datasets.

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CPCED: a container escape detection system based on CNI plugin

CPCED: a container escape detection system based on CNI plug...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yu Hao Xu Zhang Dongbin Wang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications National Engineering Research Center for Mobile Internet Security Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Key Laboratory of Ministry of Education and Trustworthy Distributed Computing and Service Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Container escape detection is a critical research topic in the field of cloud security. Among the challenges faced in modern cloud security, the issue of container escape poses a significant threat due to its direct impact on the security of the host machine. Although recent research has proposed various methods to address such issue, they have shortcomings in terms of real-time capabilities and deployment in multinode environments. Meanwhile, container network interface (CNI) plugins provide network functionality for container management systems such as Kubernetes, offering ease of use and scalability. Therefore, we propose CPCED, a real-time container escape detection system running on a generic CNI plugin, and implement the prototype. The system defines the container namespace event that is used to detect insecure interactions by monitoring its changes in the permissions of the process namespace. To detect the events, an algorithm is proposed to extract suspicious process paths and command context in Kubernetes and Docker environments. The experimental results show that this system detects nine container escape vulnerabilities successfully. Compared to PACED, one of the real-time container escape attack detection systems, the memory usage of CPCED is reduced by 7.6% on average, and the average detection time of single vulnerability is 18.6% of PACED’s.

关键词： Privacy Runtime Cloud computing security Scalability Prototypes Computer architecture Containers Real-time systems Security Network interfaces

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Discount rate hurts group buying? An empirical study from catering industry

Discount rate hurts group buying? An empirical study from ca...

引用

International Conference on services Systems and services Management, ICSSSM

作者： Rujin Zhang Xiangling Fu Jiayin Qi Sam Seongmin Jeon School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China School of Management Shanghai University of International Business and Economics Shanghai China School of Software Beijing University of Posts and Telecommunications Beijing China Business School Gachon University Seongnam

ISBN: (纸本)9781509028436

With the development of online group buying, more and more experts are beginning to focus on this emerging e-commerce model. Based on the data from ***, our study examined the impact of discount rate on the consumers' group buying behavior, considering the star, the score, the location and other factors as control variables. Our results suggest that the discount rate has a significant negative impact on the consumers' group buying behavior, and selling price, the effective period, the number of restaurant and the locations will reduce this effect, and the score and the star will increase this effect. The findings of our research can help online group buying restaurants to make a reasonable price decision.

关键词： Economics Indexes

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