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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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Multi-objective Cross-layer Resource Scheduling for Internet of Things in Edge-Cloud Computing

Multi-objective Cross-layer Resource Scheduling for Internet...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Ruichao Mo Fei Dai Qi Liu Wanchun Dou Xiaolong Xu School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Nowadays, Edge computing is being introduced as a powerful paradigm to collaborate with the cloud to provide sufficient computing resources for IoT to implement intelligent analysis and data mining. Generally, due to the edge nodes are closer to mobile users, the access latency and the cost of using cloud services are effectively reduced. However, the implementation of cross-layer resource scheduling between edge nodes and servers deployed in the cloud to meet service requirements (i.e., shortest completion time, maximum resource utilization, lower energy consumption, etc.) still faces great challenges. To address this challenge, a cross-layer resource scheduling method, named CRSM, for the IoT applications is proposed in this paper. Technically, the Pareto archived evolution strategy (PAES) is employed to optimize the time cost of IoT applications, resource utilization and energy consumption of edge node. Then, the technique for order preference by similarity to ideal solution (TOPSIS) and the multiple criteria decision making (MCDM) are leveraged to acquired the optimal cross-layer resource scheduling strategy. Finally, the comprehensive analysis of CRSM is introduced in detail.

关键词： Cloud computing Energy consumption Processor scheduling Internet of Things Resource management Servers Faces

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Traceable Multiauthority Attribute-Based Encryption with Outsourced Decryption and Hidden Policy for CIoT

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Wireless Communications and Mobile Computing 2021年第1期2021卷

作者： Liu, Suhui Yu, Jiguo Hu, Chunqiang Li, Mengmeng School of Computer Science Qufu Normal University Rizhao Shandong276826 China Jinan Shandong250353 China Jinan Shandong250014 China Shandong Laboratory of Computer Networks Jinan250014 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University China

Cloud-assisted Internet of Things (IoT) significantly facilitate IoT devices to outsource their data for high efficient management. Unfortunately, some unsettled security issues dramatically impact the popularity of IoT, such as illegal access and key escrow problem. Traditional public-key encryption can be used to guarantees data confidentiality, while it cannot achieve efficient data sharing. The attribute-based encryption (ABE) is the most promising way to ensure data security and to realize one-to-many fine-grained data sharing simultaneously. However, it cannot be well applied in the cloud-assisted IoT due to the complexity of its decryption and the decryption key leakage problem. To prevent the abuse of decryption rights, we propose a multiauthority ABE scheme with white-box traceability in this paper. Moreover, our scheme greatly lightens the overhead on devices by outsourcing the most decryption work to the cloud server. Besides, fully hidden policy is implemented to protect the privacy of the access policy. Our scheme is proved to be selectively secure against replayable chosen ciphertext attack (RCCA) under the random oracle model. Some theory analysis and simulation are described in the end. © 2021 Suhui Liu et al.

关键词： Internet of things

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Study on the Changes of Plant Ecological Communities and Environmental Factors in Cold Cities

Study on the Changes of Plant Ecological Communities and Env...

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2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control, ICBTE 2019

作者： Li, Shengju Wang, Xue Zhang, Yaosheng Zhang, Yuehua Li, Lan Jiamusi University Jiamusi City Heilongjiang Province154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province543000 China

There are certain geographical differences in the design process of green areas along the streets of the cold city. The cold climate brings special geographical features to the city. The snow and ice add new vitality and characteristics to the city, and at the same time public demand new challenges for the excavation and creation of cold garden resources. The definition of standards for cold cities and the contribution of cold cities are movements to cold cities. Green space next to the city streets are systematically defined, and the role and influence of public life generated by the concept of greenbelt are inspected. This paper analyzes the design principles and theoretical basis of the green space design along the street in the cold city. An example is given to analyze the problems and optimization suggestions that should be paid attention to during the design process of the green space next to the city. From the solution of the problematic elements in the greening landscape design along the cold city street, the methods and strategies of the greening landscape design along the cold city street are analyzed from the aspects of plant. © 2019 The Authors, published by EDP sciences.

关键词： Ecosystems

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Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction

arXiv

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arXiv 2020年

作者： Pian, Weiguo Wu, Yingbo Qu, Xiangmou Cai, Junpeng Kou, Ziyi Automotive Collaborative Innovation Center Chongqing University China School of Big Data & Software Engineering Chongqing University China Department of Computer Science and Engineering University of Notre Dame United States

Ride-hailing demand prediction is an essential task in spatial-temporal data mining. Accurate Ride-hailing demand prediction can help to pre-allocate resources, improve vehicle utilization and user experiences. Graph Convolutional Networks (GCN) is commonly used to model the complicated irregular non-Euclidean spatial correlations. However, existing GCN-based ride-hailing demand prediction methods only assign the same importance to different neighbor regions, and maintain a fixed graph structure with static spatial relationships throughout the timeline when extracting the irregular non-Euclidean spatial correlations. In this paper, we propose the Spatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailing demand prediction method. Based on the attention mechanism of GAT, STDGAT extracts different pair-wise correlations to achieve the adaptive importance allocation for different neighbor regions. Moreover, in STDGAT, we design a novel time-specific commuting-based graph attention mode to construct a dynamic graph structure for capturing the dynamic time-specific spatial relationships throughout the timeline. Extensive experiments are conducted on a real-world ride-hailing demand dataset, and the experimental results demonstrate the significant improvement of our method on three evaluation metrics RMSE, MAPE and MAE over state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Convolutional Neural Network Pruning: A Survey

Convolutional Neural Network Pruning: A Survey

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第三十九届中国控制会议

作者： Sheng Xu Anran Huang Lei Chen Baochang Zhang School of Automatic Science and Eletrical Engineering Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing

Deep convolutional neural networks have enabled remarkable progress over the last years on a variety of visual tasks,such as image recognition, speech recognition, and machine translation. These tasks contribute many to machine ***, developments of deep convolutional neural networks to a machine terminal remains challenging due to massive number of parameters and float operations that a typical model contains. Therefore, there is growing interest in convolutional neural network pruning. Existing work in this field of research can be categorized according to three dimensions: pruning method,training strategy, estimation criterion.

关键词： convolutional neural networks machine intelligence pruning method training strategy estimation criterion

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Study on the Influence of Seasonal Diversity Deduction of Landscape Plants on Landscape Vision

Study on the Influence of Seasonal Diversity Deduction of La...

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2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control, ICBTE 2019

作者： Li, Shengju Wang, Xue Zhang, Yaosheng Zhang, Yuehua Lan, Li Jiamusi University Jiamusi City Heilongjiang Province154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province543000 China

Urban garden landscape is a topic of great concern in the process of modern urban construction. It belongs to the landscape with vitality in the construction process. The plant landscape is also the product of the combination of economy, politics, culture and technology. The cold land plants belong to the only life-changing and cyclical elements in the garden design process. They use the seasonal changes of their distinctive features to realize the planning and design, which can effectively enrich the overall landscape effect. The seasonal changes of garden plants in cold regions mainly include space and color. The seasonal changes of color include leaves, flowers, fruits and branches, and the spatial characteristics are different periods of plants. In the process of realizing the garden configuration, it is important to comprehensively study the seasonal changes of plants, which can effectively improve the role of gardens. The study analyzes the influence of seasonal changes of garden plants on the landscape construction of garden space from three aspects: function, season and beauty. Therefore, the characteristics of seasonal changes of garden plants in cold regions are analyzed, so as to improve the rationality of landscape plant configuration in different seasons, and improve the level of garden landscape construction. © 2019 The Authors, published by EDP sciences.

关键词： Energy conservation

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Multi-Scale Generative Transformer-Based Primal-Dual PPO Framework for AAV-Aided Intelligent Transportation Networks

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Adam, Abuzar B. M. Kamal, Tahir Elhassan, Mohammed A. M. Alshahrani, Abdullah Alsamhi, Saeed Hamood Aziz, Ahmed Luxembourg City1855 Luxembourg University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Zhejiang Normal University School of Mathematics and Computer Science Jinhua321017 China University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah21493 Saudi Arabia University of Galway Insight Centre for Data Analytics GalwayH91 TK33 Ireland Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Bahrain Polytechnic School of ICT Isa Town00973 Bahrain IBB University Faculty of Engineering Electrical Engineering Department Ibb Yemen Benha University Faculty of Computer and Artificial Intelligence Department of Computer Science Banha13511 Egypt Central Asian University Engineering School Tashkent111221 Uzbekistan

Intelligent transportation networks are increasingly integrating autonomous aerial vehicles (AAVs) to enable services essential for modernizing industries like transportation, logistics, and search and rescue. A significant beneficiary is the Internet of Connected Vehicles (IoCVs), where AAVs play a transformative role in supporting next-generation cellular networks. This paper addresses the challenge of minimizing the cost of delivering content to vehicles on road segments with congested traffic or insufficient infrastructure. Vehicles entering these segments request content from a AAV-hosted library that updates dynamically based on item popularity. Each vehicle submits a request, requiring the AAV to compute an optimal trajectory to maximize operational utility. Given the AAV’s limited energy, we aim to develop an energy-efficient solution. The problem is framed as a joint optimization of caching decisions, AAV trajectory planning, and radio resource allocation, formulated as a mixed integer non-linear programming (MINLP) problem. The environment’s complexity, with random vehicle arrivals and fluctuating content, makes traditional optimization techniques inadequate. To address this, we reformulate the problem as a constrained Markov decision process (CMDP) and employ a primal-dual proximal policy optimization (PPO) algorithm, enhanced by a multi-scale generative transformer (MGFormer) for improved speed and accuracy over traditional deep neural networks (DNNs). Simulation results demonstrate that the proposed framework reduces service costs by up to 30% and achieves robust performance even under high-density traffic and Doppler shifts, validating its superiority over traditional Primal-dual PPO. © 2000-2011 IEEE.

关键词： Markov processes

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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LCL-AKA: Lightweight Authentication and Key Agreement Protocol for Power IoT

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IEEE Transactions on Smart Grid 2025年

作者： Liu, Zewei Hu, Chunqiang Ruan, Conghao Pu, Yuwen Hu, Pengfei Yu, Jiguo Cyber Physical Society Ministry of Education Key Laboratory of Dependable Service Computing Chongqing University China Chongqing University School of Big Data and Software Engineering Chongqing China China Southern Power Grid Joint Laboratory on Cyberspace Security China Shandong University School of Computer Science and Technology Shandong China Qilu University of Technology Shandong Academy of Sciences School of Computer Science and Technology Jinan250353 China Shandong Laboratory of Computer Networks Jinan250014 China

The rapid adoption of power Internet of Things (PIoT) systems has made security a critical concern, particularly as existing certificateless authentication and key agreement (CL-AKA) protocols face three fundamental limitations that hinder their practical deployment: excessive computational overhead from multi-round interactions, inadequate protection against advanced cryptanalytic attacks, and inflexible architectures unsuitable for dynamic grid environments. To tackle the previously mentioned challenges, this paper proposes lightweight authentication and key agreement protocol (LCL-AKA) for PIoT. Firstly, this protocol efficiently completes the process of identity authentication and session key agreement within a single round of communication interaction, significantly reducing computational costs and improving communication efficiency. Security is rigorously validated through formal proofs under the eCK model and comprehensive analysis showing resistance to key compromise impersonation, unknown key-share attack. Ultimately, comparative experiments are executed to delve into the security and performance characteristics of the proposed protocol. Compared to benchmark protocols deployed in PIoT, the authentication delay of LCL-AKA is decreased by at least 14.88%;in addition, the energy consumption of LCL-AKA is decreased by at least 11.28%. © 2010-2012 IEEE.

关键词： certificateless authentication and key agreement communication efficiency PIoT security proofs

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