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arXiv 2019年

作者： Zhang, Chunkai Chen, Yingyang Yin, Ao Qin, Zhen Zhang, Xing Zhang, Keli Jiang, Zoe L. Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China Engineering Laboratory for Big Data Collaborative Security Technology

Piecewise Aggregate Approximation (PAA) is a competitive basic dimension reduction method for high-dimensional time series mining. When deployed, however, the limitations are obvious that some important information will be missed, especially the trend. In this paper, we propose two new approaches for time series that utilize approximate trend feature information. Our first method is based on relative mean value of each segment to record the trend, which divide each segment into two parts and use the numerical average respectively to represent the trend. We proved that this method satisfies lower bound which guarantee no false dismissals. Our second method uses a binary string to record the trend which is also relative to mean in each segment. Our methods are applied on similarity measurement in classification and anomaly detection, the experimental results show the improvement of accuracy and effectiveness by extracting the trend feature suitably. Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

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Dynamic data fusion using multi-input models for malware classification

arXiv

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arXiv 2019年

作者： Zenkov, Viktor Laska, Jason Department of Computer Science and Electrical Engineering University of Tennessee Knoxville United States Cyber and Information Security Research Group Oak Ridge National Laboratory

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Floor Based Sensors Walk Identification System Using Dynamic Time Warping with Cloudlet Support

Floor Based Sensors Walk Identification System Using Dynamic...

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International Conference on Semantic Computing

作者： Robert Hughes Fadi Muheidat Mason Lee Lo'ai A. Tawalbeh Electrical and Computer Engineering Department Univeristy of the Pacific Stockton CA USA Department of Computing and Cyber Security Texas A&M University San Antonio TX USA

Current advances in sensor design technologies and computing power (computational and artificial intelligence) have made it possible to build smart assistive living systems that can improve the lives of people. Because older adults want to age in place at home, there is a need to monitor their health status, detect emergency situations, and notify health care providers. We have improved a floor based monitoring system, which we call the smart carpet, originally to detect falls, but we can take advantage of the continual 24/7 monitoring capability to get important information on gait, fall detection, counting the number of people traversing the carpet and studied the waveform for useful information. Recently, we studied the characteristics of the waveform of the scavenged signal from the sensors and used computational intelligence and feature extractions and classifications to separate people. In this paper, we used Dynamic Time Warping (DTW) to help improve on walk identification, compared with the MFCC feature extraction methods. Results showed that our system identifies walks using a dynamic time warping algorithm and KNN classifier with 86% precision, 76% recall, 81% accuracy. We also present a cooperative cloudlet mobile computing model for eldercare and medical applications where the decisions are very time sensitive. The sensors data will be sent to the nearest cloudlet for analysis and extracting real-time decisions in minimal delay. Users can obtain these results and make decisions by accessing the cloud through their mobile devices and in a real time manner.

关键词： Cloud computing Heuristic algorithms Intelligent sensors Time series analysis Sensor systems Task analysis

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Detecting Bogus Information Attack in Vehicular Ad Hoc Network: A Context-Aware Approach 16

Detecting Bogus Information Attack in Vehicular Ad Hoc Netwo...

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16th International Learning and technology Conference, L and T 2019

作者： Ghaleb, Fuad A. Zainal, Anazida Maroof, Mohd Aizaini Rassam, Murad A. Saeed, Faisal Cybersecurity X Lap Information Assurance and Security Research Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor81310 Malaysia Department of Computer and Electronic Engineering Sana'a Community College Sana'a5695 Yemen Faculty of Engineering and Information Technology Taiz University Taiz6803 Yemen College of Computer Science and Engineering University of Taibah Medina Saudi Arabia

Most of Vehicular Ad Hoc Network's (VANET) and Fly Ad Hoc Network (FANET) applications, protocols, and service rely on cooperativeness among network participants. Vehicles, could be unmanned aerial vehicles (e.g. drones), share their sensors' information to improve network performance and provide safety, traffic efficiency, and entertainments. The accuracy and reliability of this information are vital for VANET performance. However, misbehaving (or faulty) vehicles, which exploit the cooperativeness feature by sharing bogus information, can disrupt the fundamental operations of any potential application and cause loss of people lives and properties. Unfortunately, existing detection approaches cannot effectively thwart such attacks. The highly dynamic vehicular context has been vastly neglected as well as they rely on predefined and static security perimeters to differentiate between false and correct information. In this paper, a context-aware detection approach is proposed to improve the detection accuracy and reduce the false alarm rate. Firstly, the features that well represent the vehicular context has been extracted. Then, an online unsupervised learning method namely the hierarchical clustering algorithm is used to classify the received messages into either genuine or spurious. Finally, Bayesian-based hypothesis testing has been used to confirm the validity of the classification. Results show that the proposed detection approach is promising in effectively detect bogus information attack and improve applications performance. © 2019 The Authors. Published by Elsevier B.V.

关键词： Vehicular ad hoc networks

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Unextendible product operator basis

arXiv

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arXiv 2021年

作者： Hu, Mengyao Chen, Lin Shi, Fei Zhang, Xiande Tura, Jordi School of Mathematical Sciences Beihang University Beijing100191 China Instituut-Lorentz Universiteit Leiden P.O. Box 9506 Leiden2300 RA Netherlands LMIB Beihang University Ministry of Education China International Research Institute for Multidisciplinary Science Beihang University Beijing100191 China Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong School of Cyber Security University of Science and Technology of China Hefei230026 China School of Mathematical Sciences University of Science and Technology of China Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China

Quantum nonlocality is associated with the local indistinguishability of orthogonal states. Unextendible product basis (UPB), a widely used tool in quantum information, exhibits nonlocality which is the powerful resource for quantum information processing. In this work we extend the definitions of nonlocality and genuine nonlocality from states to operators. We also extend UPB to the notions of unextendible product operator basis, unextendible product unitary operator basis (UPUOB) and strongly UPUOB. We construct their examples, and show the nonlocality of some strongly UPUOBs under local operations and classical communications. We study the phenomenon of these operators acting on quantum states. As an application, we distinguish the two-dimensional strongly UPUOB which only consumes three ebits of entanglement. Our results imply that such UPUOBs exhibit nonlocality as UPBs and the distinguishability of them requires entanglement resources. © 2021, CC BY-NC-SA.

关键词： Quantum entanglement

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Dynamical Interaction Between Information and Disease Spreading in Populations of Moving Agents

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computers, Materials & Continua 2018年第10期57卷 123-144页

作者： Lingling Xia Bo Song Zhengjun Jing Yurong Song Liang Zhang Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China Global Big Data Technologies Centre University of Technology SydneySydney2007Australia College of Computer Engineering Jiangsu University of TechnologyChangzhou213001China College of Automation Nanjing University of Posts and TelecommunicationsNanjing210003China

Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading *** the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each *** Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading *** results show that the increasing of information transmission rate can reduce the scale of disease spreading in some *** infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information *** addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.

关键词： Complex networks Markov chains theory interaction process spreading dynamics double-layer network

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Dense attention fluid network for salient object detection in optical remote sensing images

arXiv

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arXiv 2020年

作者： Zhang, Qijian Cong, Runmin Li, Chongyi Cheng, Ming-Ming Fang, Yuming Cao, Xiaochun Zhao, Yao Kwong, Sam The Department of Computer Science City University of Hong Kong Hong Kong The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Computer Science Nankai University Tianjin300071 China School of Information Technology Jiangxi University of Finance and Economics NanchangJiangxi330032 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps. Specifically, the GCA module is composed of two key components, where the global feature aggregation module achieves mutual reinforcement of salient feature embeddings from any two spatial locations, and the cascaded pyramid attention module tackles the scale variation issue by building up a cascaded pyramid framework to progressively refine the attention map in a coarse-to-fine manner. In addition, we construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations, which is currently the largest publicly available benchmark. Extensive experiments demonstrate that our proposed DAFNet significantly outperforms the existing state-of-the-art SOD competitors. https://***/ rmcong/DAFNet_TIP20 © 2020, CC BY-NC-SA.

关键词： Object detection

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A manifold-based adversarial autoencoder with Fourier convolution for hyperspectral unmixing

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Applied Soft Computing 2025年 177卷

作者： Guo, Ziyang Xiao, Meixia Zhu, Fa Chen, Xingchi Shankar, Achyut Zamani, Mazdak Singh, Sushil Kumar Department of Artificial Intelligence Nanjing University of Information Science and Technology No. 129 of Ningliu Road Nanjing210044 China Research Center of Nonlinear Science School of Mathematical and Physical Sciences Wuhan Textile University Wuhan China College of Information Science and Technology & College of Artificial Intelligence Nanjing Forestry University Nanjing210037 China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen518000 China Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom State University of New York - Empire State University New York10016 United States Department of Computer Engineering Marwadi University Gujarat Rajkot India

Hyperspectral unmixing aims to decompose each subpixel into their pure endmembers and the corresponding proportions. But existing deep autoencoder-based hyperspectral unmixing methods often suffer from obstacles like endmember variability, local respective fields and insufficient use of inner structure. In the manuscript, we build a manifold-based Fourier adversarial autoencoder which regards generative adversarial mechanism as a utilization of prior information. This method combines manifold learning with adversarial autoencoder in order to promote the performance of hyperspectral unmixing. Specifically, firstly, in order to preserve local manifold structure, we add a discriminator to the autoencoder which uses the covariance matrices of a superpixel as real samples while covariance matrices of the abundance as fake samples;secondly, we add a regularization term of Laplacian eigenmap at the loss of autoencoder to in-depth abbreviate autoencoder solution space;thirdly, Fast Fourier Convolution modules are used to enhance multi-scale information fusion. At last, comparative experiments are conducted on three popular datasets, including Jasper, Urban4 and Samson, to validate the effectiveness of the proposed method. © 2025 Elsevier B.V.

关键词： Eigenvalues and eigenfunctions

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Efficient Context-aware File System Approach

Efficient Context-aware File System Approach

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International Conference on Software Defined Systems (SDS)

作者： Abdul Razaque Fathi Amsaad Muder Almiani Wan Xiaoya Li Shiyu Raouf Ganda Majdi Rawashdeh Amer AlRahayfeh Dept. of Computer Engineering and Telecommunication School of Information Security Eastern Michigan University US College of Information Technology Al-Hussein Bin Talal University Jordan Dept. of Computer Science NYIT Nanjing China Yale School of Management Yale University USA Department of Business Information Technology Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9781728172194

ISBN: (纸本)9781728172200

With the rapid development of science and technology, wireless networks are now widely used throughout the world. Given the requirement to improve the usability of computers, a context-aware file system has been rendered as an important research topic. Enhanced methods must be proposed to improve the accuracy of user authority in such systems. In this study, we introduce a combined approach involving the historical and physical user contexts to gain the contextual information for the file system. We proposed a role-based access control (RBAC) based on context awareness model, which is an extension of the traditional RBAC model, to implement context awareness in the users' permission for the file system. Using the context constraints, the file system can control the access of the user to protect the file system from damages caused by hackers. Results showed that the proposed method is more accurate than traditional ones because it can control the user access through context awareness.

关键词： File systems Context modeling Context-aware services Heuristic algorithms Object recognition Error analysis

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HideMe: Privacy-Preserving Photo Sharing on Social Networks

HideMe: Privacy-Preserving Photo Sharing on Social Networks

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Fenghua Li Zhe Sun Ang Li Ben Niu Hui Li Guohong Cao School of Cyber Security University of Chinese Academy of Sciences China Department of Computer Science and Computer Engineering University of Arkansas USA Chinese Academy of Sciences Institute of Information Engineering China State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University China Department of Computer Science and Engineering The Pennsylvania State University PA USA

Photo sharing on Online Social Networks (OSNs) has become one of the most popular social activities in our daily life. However, some associated friends or bystanders in the photos may not want to be viewed due to privacy concerns. In this paper, we propose the design, implementation and evaluation of HideMe, a framework to preserve the associated users' privacy for online photo sharing. HideMe acts as a plugin to existing photo sharing OSNs, and it enables the following: a) extraction of factors when users upload their photos, b) associated friends in the uploaded photos are able to set their own privacy policies based on scenarios, instead of a photo-by-photo setting, c) any user in other friend's uploaded photos could be hidden away from unwanted viewers based on one time policy generation. We also design a distance-based algorithm to identify and protect the privacy of bystanders. Moreover, HideMe not only protects users' privacy but also reduces the system overhead by a carefully designed face matching algorithm. We have implemented a prototype of HideMe, and evaluation results have demonstrated its effectiveness and efficiency.

关键词： Privacy Face Access control Data privacy Facebook Databases

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