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IEEE International Conference on Web Services (ICWS)

作者： Zijie Fang Xiaolong Xu Fei Dai Lianyong Qi Xuyun Zhang Wanchun Dou 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 School of Information Science and Engineering Qufu Normal University China Macquarie University Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.

关键词： Energy consumption Intelligent vehicles Web services Optimization methods Bandwidth Servers Edge computing

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Task Offloading for Automatic Speech Recognition in Edge-Cloud Computing Based Mobile Networks

Task Offloading for Automatic Speech Recognition in Edge-Clo...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Shitong Cheng Zhenghui Xu Xiuhua Li Xiongwei Wu Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Dept. Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

Explosively increasing multimedia services and applications, e.g., automatic speech recognition (ASR), have aggravated the burden on the cloud server in mobile networks. To address the challenge, mobile edge computing has emerged for partially alleviating the workload of the cloud server and enhancing the quality of service of mobile users. In this paper, we aim to employ the technique of edge-cloud computing to accelerate the processing of ASR tasks generated by users in mobile networks. Particularly, we deploy a convolutional neural network based encoder in each edge server to extract features of the audio data. Based on certain network constraints (i.e., user association and edge servers' storage/computing capacity), we propose a low-complexity and distributed iterative greedy method to address the formulated nonlinear mixed-integer nonconvex optimization problem. Simulation results demonstrate the effectiveness of the proposed scheme on reducing the total delay in the network.

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Deep Reinforcement Learning for loT Networks: Age of Information and Energy Cost Tradeoff

Deep Reinforcement Learning for loT Networks: Age of Informa...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Xiongwei Wu Xiuhua Li Jun Li P. C. Ching H. Vincent Poor Princeton University Princeton USA School of Big Data & Software Engineering Chongqing University Chongqing China School of Electronic & Optical Engineering Nanjing University of Science and Technology Nanjing China The Chinese University of Hong Kong Shatin Hong Kong SAR China

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

In most Internet of Things (IoT) networks, edge nodes are commonly used as to relays to cache sensing data generated by IoT sensors as well as provide communication services for data consumers. However, a critical issue of IoT sensing is that data are usually transient, which necessitates temporal updates of caching content items while frequent cache updates could lead to considerable energy cost and challenge the lifetime of IoT sensors. To address this issue, we adopt the Age of Information (AoI) to quantity data freshness and propose an online cache update scheme to obtain an effective tradeoff between the average AoI and energy cost. Specifically, we first develop a characterization of transmission energy consumption at IoT sensors by incorporating a successful transmission condition. Then, we model cache updating as a Markov decision process to minimize average weighted cost with judicious definitions of state, action, and reward. Since user preference towards content items is usually unknown and often temporally evolving, we therefore develop a deep reinforcement learning (DRL) algorithm to enable intelligent cache updates. Through trial-and-error explorations, an effective caching policy can be learned without requiring exact knowledge of content popularity. Simulation results demonstrate the superiority of the proposed framework.

关键词： Sensors Internet of Things Energy consumption Transient analysis Wireless sensor networks Signal to noise ratio Intelligent sensors

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Dynamic Task Offloading with Minority Game for Internet of Vehicles in Cloud-Edge Computing

Dynamic Task Offloading with Minority Game for Internet of V...

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IEEE International Conference on Web Services (ICWS)

作者： Bowen Shen Xiaolong Xu Fei Dai Lianyong Qi Xuyun Zhang Wanchun Dou 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 School of Information Science and Engineering Qufu Normal University China Macquaire University Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

With the advent of the Internet of Vehicles (IoV), drivers are now provided with diverse time-sensitive vehicular services that usually require a large scale of computation. As civilian vehicles are generally insufficient in computational resources, their service requests are offloaded to cloud data centers and edge computing devices (ECDs) with ample computational resources to enhance the quality of service (QoS). However, ECDs are often overloaded with excessive service requests. In addition, as the network conditions and service compositions are complicated and dynamic, the centralized control of ECDs is hard to achieve. To tackle these challenges, a dynamic task offloading method with minority game (MG) in cloud-edge computing, named DOM, is proposed in this paper. Technically, MG is an effective tool with a distributed mechanism which can minimize the dependency on centralized control in resource allocation. In the MG, reinforcement learning (RL) is applied to optimize the distributed decision-making of participants. Finally, with a real-world dataset of IoV services, the effectiveness and adaptability of DOM are evaluated.

关键词： Cloud computing Games Quality of service Reinforcement learning Space exploration Task analysis Vehicle dynamics

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SR2CNN: Zero-shot learning for signal recognition

arXiv

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arXiv 2020年

作者： Dong, Yihong Jiang, Xiaohan Zhou, Huaji Lin, Yun Shi, Qingjiang School of Software Engineering Tongji University Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China School of Artificial Intelligence Xidian University Xi'an710071 China School of College of Information and Communication Engineering Harbin Engineering University Harbin150001 China

Signal recognition is one of the significant and challenging tasks in the signal processing and communications field. It is often a common situation that there's no training data accessible for some signal classes to perform a recognition task. Hence, as widely-used in image processing field, zero-shot learning (ZSL) is also very important for signal recognition. Unfortunately, ZSL regarding this field has hardly been studied due to inexplicable signal semantics. This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation. The key idea behind SR2CNN is to learn the representation of signal semantic feature space by introducing a proper combination of cross entropy loss, center loss and reconstruction loss, as well as adopting a suitable distance metric space such that semantic features have greater minimal inter-class distance than maximal intra-class distance. The proposed SR2CNN can discriminate signals even if no training data is available for some signal class. Moreover, SR2CNN can gradually improve itself in the aid of signal detection, because of constantly refined class center vectors in semantic feature space. These merits are all verified by extensive experiments with ablation studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Vector spaces

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Study of Dynamic Group Evolution for Health Prediction of Mangrove Ecosystem

Study of Dynamic Group Evolution for Health Prediction of Ma...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Qinghong Zou Qingyu Xiong Hualing Yi Qiude Li Rui Xu Kai Wang School of Big Data & Software Engineering Chongqing University China School of Automation Chongqing University China

ISBN: (纸本)9781728126289

Many researchers have applied neural networks to mangrove ecosystem health research. However, it is challenging to find efficient training algorithms for neural networks, so the prediction results are often difficult to meet the needs. The dynamic group evolution (DGE) algorithm is a recently proposed metaheuristic algorithm, which exhibits a rapid convergence rate and good performance in searching and avoiding local optima. In the present study, we use DGE and back propagation (BP) for training feed-forward neural network and build a DGBPNN algorithm model, which takes into account both global exploration and local exploitation. In this paper, we use the model for man-grove ecosystem health prediction, and the performance of the proposed algorithm is determined by comparing its performance with those of other optimization training algorithms. The experimental results show that our proposed training algorithm exhibits promising performance in training neural networks and is expected to apply to more real-world problems.

关键词： backpropagation convergence ecology feedforward neural nets learning (artificial intelligence) medical computing optimisation search problems feedforward neural nets medical computing back propagation tabu search Mangroves Converge ecology Neural network algorithms Training Functional training

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Business optimization in logistics and performance through technology implementation in developing countries

Business optimization in logistics and performance through t...

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2019第二届信息科学与系统国际会议

作者： Kevin Michel Rabefaritra Feno Heriniaina Rabevohitra Faradina Haingonirina School of Big Data and Software Engineering Chongqing University Andrianarimanana School of Public Administration Chongqing University

While fast-growing countries in terms of technology like Japan, USA, South Korea, China, Finland, etc. are taking advantage of information and communication technology(ICT) to launch mobile apps, set up online payments and improve public security, public and private businesses also use it to improve economic development and problem-solving efficiency. That is not the case in developing countries where IT use is not considered when it comes to solving a problem because of its higher investment cost. This paper intends to highlight technology's role in business economic development, how it can solve problems, and the difference it can bring. We introduce GreenApp: a set of a mobile and a web application developed for a social waste management company in Madagascar called Greentsika, which has solved many of its monitoring issues and improved financial control and health. This technology improved the business by a factor of 7.14 as proven by computing the economic value created before and after application use.

关键词： Greentsika IT management Web services IT Business Enterprise Application

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Crowd Counting for Static Images: A Survey of Methodology

Crowd Counting for Static Images: A Survey of Methodology

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第三十九届中国控制会议

作者： Ying Luo Jinhu Lu Baochang Zhang School of Automation Science and Electrical Engineering Beihang University Shenyuan Honors College Beihang University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environmentand Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang University

Crowd counting for static images is one of the typical application fields of image processing. it is essential to direct crowd analysis for public security purposes under the background of a rapidly growing social environment. Crowd counting for static images involves crucial challenges such as occlusions, scale variations, scene perspective distortions and diverse crowd distributions. It has now become an independent research hotspot. This paper investigates the literature of crowd counting tasks, summarizes the evolution of counting approaches, compares and analyzes traditional methods and current trends of CNN estimation-based counting, and then evaluates common datasets for crowd counting tasks. Conclusions concerning summarizations and prospects are made.

关键词： Crowd counting crowd analysis pattern recognition computer vision

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Multi-Level Chaotic Maps for 3D Textured Model Encryption 2nd

Multi-Level Chaotic Maps for 3D Textured Model Encryption

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Jin, Xin Zhu, Shuyun Wu, Le Zhao, Geng Li, Xiaodong Zhou, Quan Lu, Huimin Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan

ISBN: (纸本)9783030177621

With the rapid progress of virtual reality and augmented reality technologies, 3D contents are the next widespread media in many applications. Thus, the protection of 3D models is primarily important. Encryption of 3D models is essential to maintain confidentiality. Previous work on encryption of 3D surface model often considers the point clouds, the meshes, and the textures individually. In this work, a multi-level chaotic maps model for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons, and textures. For vertices which make main contribution for recognizing, we use high-level 3D Lu chaotic map to encrypt them. For polygons and textures which make relatively smaller contributions for recognizing, we use 2D Arnold’s cat map and 1D logistic map to encrypt them, respectively. The experimental results show that our method can get similar performance with the other method and use the same high-level chaotic map for point cloud, polygons, and textures, while we use less time. Besides, our method can resist more method of attacks such as statistic attack, brute-force attack, and correlation attack. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Textures

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Application of an Improved Watershed Algorithm Based on Distance Map Reconstruction in Bean Image Segmentation

SSRN

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SSRN 2022年

作者： Liu, Hongquan Zhang, Weijin Wang, Fushun Sun, Xiaohua Wang, Junhao Wang, Chen Wang, Xinxin College of Urban and Rural Construction Hebei Agricultural University Baoding071000 China College of Information Science and Technology Hebei Agricultural University Baoding071000 China Hebei Key Laboratory of Agricultural Big Data Baoding071000 China Department of Digital Media Hebei Software Institute Baoding071000 China Agricultural Technology Innovation Center in Mountainous Areas of Hebei Province Baoding071000 China Agricultural Engineering Technology Research Center of National North Mountainous Area Baoding071000 China Hebei Agricultural University Hebei Mountain Research Institute Baoding071000 China

As an important step in image processing, image segmentation can be used to determine the accuracy of object counts, and area and contour data. In addition, image segmentation is indispensable in seed testing research. Due to the uneven grey level of the original image, traditional watershed algorithms generate many incorrect edges, resulting in oversegmentation and undersegmentation, which affects the accuracy of obtaining seed phenotype information. The DMR-watershed algorithm, an improved watershed algorithm based on distance map reconstruction, is proposed in this paper. According to the grey distribution characteristics of the image, the grey reduction amplitude h was selected to generate the mask image with the same grey distribution trend as that of the original image. The original greyscale map was reconstructed with corresponding thresholds selected according to the false minima of different regions that are to be segmented, which generates an accurate distance map that eliminates the wrong edges. An adzuki bean image was selected as the experimental material and the residual rate of the segmentation counting results of each algorithm was investigated in two cases of two-particle adhesion and multiparticle adhesion. The results of the proposed algorithm were compared with those of the traditional watershed algorithm, edge detection algorithm and concave point analysis algorithm which are commonly used for seed segmentation. In the case of two-particle adhesion, the residual rates of the watershed algorithm and edge detection algorithm were 0.233 and 0.275, respectively, while the residual rate of the concave point analysis algorithm was 0 which proved to be suitable for two-particle adhesion. In the case of multiparticle adhesion, the concave point analysis algorithm was not applicable because it would destroy the seed image. The residual rates of the watershed algorithm and edge detection algorithm were 0.063 and 0.188, respectively, while the residual rat

关键词： Image segmentation

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