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arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

arXiv

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arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

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[Retracted] Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

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Security and Communication Networks 2023年第1期2023卷

作者： Mohammed Naif Alatawi Najah Alsubaie Habib Ullah Khan Tariq Sadad Hathal Salamah Alwageed Shaukat Ali Islam Zada Information Technology Department Faculty of Computing and Information Technology University of Tabuk Tabuk Saudi Arabiaut.edu.sa Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabiapnu.edu.sa Department of Accounting and Information Systems College of Business and Economics Qatar University Doha Qatarqu.edu.qa Department of Computer Science University Engineering and Technology Mardan Khyber Pakhtunkhwa Pakistan College of Computer and Information Sciences Jouf University Sakakah Saudi Arabiaju.edu.sa Department of Computer Science University of Peshawar Peshawar Khyber Pakhtunkhwa Pakistanuop.edu.pk Department of Software Engineering Faculty of Computing & Information Technology International Islamic University Islamabad Pakistaniiu.edu.pk

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.

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Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series

Optimization of Neural Network Parameters Based on a Genetic...

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2020 International Multi-Conference on Industrial engineering and Modern Technologies, FarEastCon 2020

作者： Tormozov, V.S. Zolkin, A.L. Vasilenko, K.A. Pacific National University Senior Lecturer of the Software Engineering for Computers and Computer-based Systems Sub-faculty Tikhookeanskaya St. 136 Khabarovsk680054 Russia Ph.D. in Engineering Science Povolzhskiy State University of Telecommunications and Informatics Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Samara443010 Russia Highest Category Lecturer Service and Design College of the Vladivostok State University of Economics and Service Service and Design College of the Vladivostok State University of Economics and Service Vladivostok690092 Russia

ISBN: (纸本)9781728169514

The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values. © 2020 IEEE.

关键词： Structural optimization

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Toward accurate link between code and software documentation

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science China(information sciences) 2018年第5期61卷 72-86页

作者： Yingkui CAO Yanzhen ZOU Yuxiang LUO Bing XIE Junfeng ZHAO Key Laboratory of High Confidence Software Technologies Ministry of Education School of Electronics Engineering and Computer Science Peking University Beida (Binhai) Information Research

Recovering traceability links between source code and software documentation is an important research topic in software maintenance and software reuse. There have been a lot of research efforts in recovering traceability between documentation and code elements(class, interface, method, etc.), mostly based on program analysis. However, there are still a lot of noise links being established in existing work. In this paper, we propose a novel approach to classifying code elements, occurring in a document, into contextual code elements and salient code elements. As a result, we can filter the noise traceability links between a software document and its contextual code elements and get a higher quality link set. Our classifier is trained based on open source project Lucene's source code and 1899 Stack Overflow answer documents about *** extract code elements from these documents and represent each of these code elements with a 7-dimension feature vector, then we use a decision-tree-based learning model to classify them as salient or not. In the experiments, we get a precision of 70.7% in recognizing the salient code elements of these documents and get12% improvement compared with Rigby's work. We can filter out 56.5%～69.3% noise traceability links with different thresholds in our classifier. It can improve the quality of traceability links between source code and their related software documents in application.

关键词： traceability link code element software document contextual code element salient codeelement

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Uplink Throughput Maximization for Low Latency in Wireless Powered Communication Networks

Uplink Throughput Maximization for Low Latency in Wireless P...

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International Conference on Communication Technology (ICCT)

作者： M.S. Syam Yue Ling Che Sheng Luo Kaishun Wu College of Information Engineering Shenzhen University College of Computer Science and Software Engineering Shenzhen University

With the coming of 5G era, wireless transmissions of high data rates in the uplink (UL) are intensely demanded, for supporting low-latency wireless transmissions in various applications. In this paper, a joint downlink (DL) and UL optimization to maximize the sum of UL throughput of devices for a given sum of DL throughput in a wireless powered communication network (WPCN) with two devices is considered. The time frame is divided into two phases: a DL phase followed by a UL phase. In the DL, a hybrid access point (H-AP) first conducts wireless energy transfer (WET) to both devices, and then conducts wireless information transfer (WIT) to each device respectively. In the UL, each device conducts WIT to the H-AP in its allocated time. Each device exploits the total harvested energy from the H-AP in the DL to support its DL information reception and decoding as well as WIT in the UL. We optimize the time allocation for WET and WIT in the DL, WIT for each device in the UL, as well as transmit power of H-AP and devices, so as to maximize total UL throughput of the network for a given DL throughput, subject to energy causality constraints for each device, H-AP's and each device's transmit power constraint and time allocation constraint. However, this problem is non-convex and thus is difficult to be solved. To address this difficulty, we transform this problem into a convex optimization problem, and solve it efficiently. Simulation results are also conducted to show the performance of the proposed system.

关键词： Throughput Resource management Wireless communication Uplink Convex functions Transforms Time factors

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Comprehensive information Extraction with Separable Representation Learning for Multi-View Clustering

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Penglei Wu, Danyang Xu, Jin Nie, Feiping South China University of Technology School of Software Engineering Guangzhou510641 China Northwest A. & F University College of Information Engineering Shaanxi China Northwest A&F University Shaanxi Eng. Research Center for Intelligent Perception and Analysis of Agricultural Information Shaanxi China South China University of Technology School of Future Technology Guangzhou510006 China Northwestern Polytechnical University School of Computer Science School of Artificial Intelligence OPtics and ElectroNics Xi'an710072 China Xi'an710072 China

Deep Multi-View Clustering (MVC) methods partition multi-view data into disjoint clusters in an unsupervised manner, showing significant promise across various domains. However, current MVC methods primarily focus on capturing the consistency information shared across all views and undervalue the specificity information inherent in each view that reflects its unique characteristics. Furthermore, the underexploration of the separability of learned representations limits the overall clustering performance of existing MVC methods and leads to undesirable clustering results. In this paper, we propose a fully differentiable and end-to-end deep MVC framework, named Comprehensive information Extraction with Separable Representation Learning (CIRSEL), to address these issues. CIRSEL recasts specificity information extraction as a high-order graph pooling process to capture the view-specific characteristics of individual views. Utilizing the cross-attention mechanism, CIRSEL adaptively fuses the consistent and view-specific representations to achieve comprehensive information extraction. Subsequently, CIRSEL maps representations into a unit hypersphere space with evenly distributed prototypes and maximizes the variational estimation of Mutual information, which enhances the inter-cluster separability and intra-cluster compactness in the embedding space and further benefits the following clustering learning. Finally, CIRSEL introduces a nuclear norm-based balance regularization, which ensures balanced clustering results can be directly retrieved by the cosine similarity between the representations and prototypes. Extensive experiments on ten benchmark datasets demonstrate the effectiveness of CIRSEL compared to sixteen current MVC methods. © 1991-2012 IEEE.

关键词： Image retrieval

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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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Implementation of Multiplicative Seasonal ARIMA Modeling and Flood Prediction Based on Long-Term Time Series Data in Indonesia 1

引用

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Supatmi, Sri Huo, Rongtao Sumitra, Irfan Dwiguna School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu China Computer Engineering Department Universitas Komputer Indonesia BandungWest Java Indonesia Postgraduate of Information System Department Universitas Komputer Indonesia BandungWest Java Indonesia

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

Time series modeling and prediction has fundamental importance in the various practical field. Thus, a lot of productive research works is working in this field for several years. Many essential methods have been proposed in publications to improve the accuracy and efficiency of time series modeling and prediction. This research aims to present the proposed prediction model namely Multiplicative seasonal ARIMA model (MSARIMA) based on non-stationary time series data to predicting the flood event. In this paper, we have described the performance of the ARMA model, the ARIMA model, and MSARIMA model to predict the flood event in the Region over Indonesia in 42 years (1976–2017). We have used the two performance measures respectively (MAPE, and RMSPE) to evaluate prediction accuracy as well as to compare different models fitted to a time series data. The result has shown the proposed predicting model has the best performance accuracy than the others model in this research. © 2019, Springer Nature Switzerland AG.

关键词： Time series

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Hybrid-attention guided network with multiple resolution features for person re-identification

arXiv

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arXiv 2020年

作者： Zhang, Guoqing Yang, Junchuan Zheng, Yuhui Wang, Ye Wu, Yi Chen, Shengyong School of Computer and Software Nanjing University of Information Science Technology Nanjing210044 China School of Marketing Logistics Management Nanjing University of Finance and Economics Nanjing China Wormpex AI Research School of Computer Science Engineering Tianjin University of Technology

Extracting effective and discriminative features is very important for addressing the challenges of person re-identification (re-ID). Prevailing deep convolutional neural networks usually use high-level features for identifying pedestrian. However, some essential spatial information resided in low-level features will be lost when learning the high-level features. Most existing person re-ID methods mainly rely on hand-craft bounding boxes, where person images are precisely aligned. It is unrealistic in practical applications due to the inaccuracy of automatic detection algorithms. To address these problems, we propose a hybrid-attention guided network with multiple resolution features for person re-ID. We first construct a multi-resolution fusion strategy to ensure that multi-resolution features can be spatially aligned during feature fusion and at the same time ensure the discriminability of features after fusion. Then, we introduce the attention mechanism and multi-granularity operation to reduce the impact of irregular bounding boxes by gently expanding the size of feature maps. In addition, a new multi-pool feature extractor is designed to obtain different types of information by using two different pools and the feature representation capability can be further improved. Extensive experiments display the superiority of our approach. Our code is available at https://***/libraflower/MutipleFeature-for-PRID. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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