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Room Allocation With Capacity Diversity and Budget Constraints

IEEE ACCESS 2019年 7卷 42968-42986页

作者： Li, Yunpeng Jiang, Yichuan Wu, Weiwei Jiang, Jiuchuan Fan, Hui Xian Univ Sci & Technol Coll Comp Sci & Technol Innovat Grp Interdisciplinary Comp Technol Xian Shaanxi Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Shandong Technol & Business Univ Shandong Coll & Univ Future Intelligent Comp Coinnovat Ctr Yantai Peoples R China

With the development of the room rental market, many room rental websites have been created, e.g., SpareRoom and EasyRoommate. On these websites, people find not only rooms for rent but also suitable roommates. Inspired by the rental mode in practice, a benchmark room allocation model was introduced by Chan et al., in which 2n agents must be allocated to n rooms that have the same capacity and each agent can be allocated to any room. However, in practice, rooms may differ in terms of capacity, e.g., college dorms or apartments may contain both two-bed rooms and four-bed rooms. Moreover, an agent can only be allocated to a room of which the rent does not exceed the agent's budget. In this scenario, we must consider not only the agents' preferences but also the capacity diversity of the rooms and the budget constraints while allocating the rooms. Therefore, this paper investigates the room allocation problem with capacity diversity and budget constraints. We mainly focus on finding an allocation that maximizes social welfare. First, this paper demonstrates that finding an allocation that maximizes the social welfare is NP-hard (i.e., non-deterministic polynomial-time hard), even if only one room's capacity is larger than 1 and the other rooms' capacities are all 1. Second, this paper presents a (c* + 2)/2 + e-factor approximation algorithm (with epsilon > 0) for the case in which the capacity of each room does not exceed a constant c*. Third, this paper proposes a heuristic algorithm based on the local search for the general case in which the capacity of each room is not bounded by a constant. The experimental results demonstrate that the proposed algorithm can produce near-optimal solutions. Finally, this paper investigates how to find a roommate stable or room envyfree allocation with a social welfare guarantee.

关键词： Room allocation capacity diversity budget constraints algorithm design

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Energy-Efficient Analytics for Geographically Distributed Big Data

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IEEE INTERNET COMPUTING 2019年第3期23卷 18-29页

作者： Zhao, Peng Yang, Xinyu Lin, Jie Yang, Shusen Yu, Wei Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Informat & Syst Sci Xian Shaanxi Peoples R China Imperial Coll London London England Towson Univ Dept Comp & Informat Sci Towson MD USA

Big data analytics on geographically distributed datasets (across data centers or clusters) has been attracting increased interest in both academia and industry, posing significant complications for system and algorithm design. In this paper, we systematically investigate the geodistributed big data analytics framework by analyzing the fine-grained paradigm and key design principles. We present a dynamic global manager selection algorithm to minimize energy consumption cost by fully exploiting the system diversities in geography and variation over time. The algorithm makes real-time decisions based on measurable system parameters through stochastic optimization methods, while achieving performance balance between energy cost and latency. Extensive trace-driven simulations verify the effectiveness and efficiency of the proposed algorithm. We also highlight several potential research directions that remain open and require future elaborations in analyzing geodistributed big data.

关键词： design theory geography Energy efficiency Optimization methods Energy costs system parameter Distribute algorithm design data center big data energy consumption

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Stable and efficient collision detection scheme for hip-surgery training system

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8769-S8781页

作者： Wang, Monan Mao, Zhiyong Ma, Yuzheng Cao, Jiaqi Harbin Univ Sci & Technol Mech & Power Engn Coll Harbin Heilongjiang Peoples R China

To satisfy real-time requirements, collision detection algorithms should be stable and efficient, and should be capable of accurate detection. This paper presents a collision detection algorithm that is based on the concept of dimension reduction of imitating latitude and longitude space. We achieve a rough and accurate detection function of the algorithm after four steps including latitude detection, bounding box detection, crossing-algorithm detection, and an affine coordinate system for accurate detection. The efficiency of the proposed collision detection algorithm is higher than that of the aligned axis bounding box (AABB) collision detection algorithm in terms of both rough detection and accurate detection. The efficiencies of coarse detection and accurate detection are basically the same, and the efficiencies of both types of detection are stable. The proposed collision detection algorithm has good stability, high efficiency, and accurate detection, and it can be used as the core algorithm of a collision detection module in virtual surgery training systems.

关键词： Virtual surgery training Collision detection algorithm design Imitating latitude and longitude Spatial dimensional reduction

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An online target tracking protocol for vehicular Ad Hoc networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2019年第4期12卷 969-988页

作者： Derder, Abdessamed Moussaoui, Samira Doukha, Zouina Boualouache, Abdelwahab USTHB Univ Dept Comp Sci RIIMA Lab BP 32 El Alia Bab Ezzouar Algiers Algeria

Target tracking in vehicular ad hoc networks (VANETs) contributes to the design of many types of applications, namely: traffic management, security, car recovery and apprehension of an illegal runaway target. Inter-vehicular communication allows vehicles to participate and collaborate in the tracking process. For such applications, a large volume of data can be required to be transferred between the participating vehicles and a control center, which can easily congest the wireless network in a VANET and decrease the tracking efficiency if not managed properly. Therefore, one important challenge in this context is optimizing bandwidth usage to avoid collisions, delays and accelerate the overall tracking process. Thus, we propose a collaborative tracking protocol for VANETs based on a new strategy that we named virtual RSUs, which aims essentially to ensure the network communication coverage during the tracking process on the one hand, and on the other hand, to optimize bandwidth usage during the overall tracking process. In addition, in order to deal with uncertainties and enhance the tracking precision and further decrease the network load, we propose a theoretical pertinence level assignment strategy based on the Transferable Belief Model (TBM), that takes the target detection notifications as inputs. We believe this protocol holds potentials to serve as a basic algorithm to implement vehicle tracking applications for VANETs. Simulative study demonstrates clearly that the proposed protocol provides better performance in terms of network load for target tracking in a VANET as compared to a previous approach.

关键词： Vehicular Ad Hoc networks (VANETs) Intelligent transportation system (ITS) Wireless Ad Hod network Mobile Ad Hoc network (MANET) Tracking Virtual RSUs algorithm design Network protocol

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Hybrid evolutionary programming using adaptive Levy mutation and modified Nelder-Mead method

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SOFT COMPUTING 2019年第17期23卷 7913-7939页

作者： Pang, Jinwei He, Jun Dong, Hongbin Harbin Engn Univ Dept Comp Sci & Technol Harbin Heilongjiang Peoples R China Nottingham Trent Univ Sch Sci & Technol Clifton Campus Nottingham NG11 8NS England

Evolutionary programming has been widely applied to solve global optimization problems. Its performance is related to both mutation operators and fitness landscapes. In order to make evolutionary programming more efficient, its mutation operator should adapt to fitness landscapes. The paper presents novel hybrid evolutionary programming with adaptive Levy mutation, in which the shape parameter of Levy probability distribution adapts to the roughness of local fitness landscapes. Furthermore, a modified Nelder-Mead method is added to evolutionary programming for enhancing its exploitation ability. The proposed algorithm is tested on 39 selected benchmark functions and also benchmark functions in CEC2005 and CEC2017. The experimental results demonstrate that the overall performance of the proposed algorithm is better than other algorithms in terms of the solution accuracy.

关键词： Global optimization Evolutionary programming Fitness landscape Levy distribution Nelder-Mead method algorithm design

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Stability in barter exchange markets

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2019年第5期33卷 518-539页

作者： Gupta, Sushmita Panolan, Fahad Saurabh, Saket Zehavi, Meirav HBNI Natl Inst Sci Educ & Res Jatni India Univ Bergen Dept Informat Bergen Norway HBNI Inst Math Sci Chennai Tamil Nadu India Ben Gurion Univ Negev Beer Sheva Israel

The notion of stability is the foundation of several classic problems in economics and computer science that arise in a wide-variety of real-world situations, including Stable Marriage, Stable Roommate, Hospital Resident and Group Activity Selection. We study this notion in the context of barter exchange markets. The input of our problem of interest consists of a set of people offering goods/services, with each person subjectively assigning values to a subset of goods/services offered by other people. The goal is to find a stable transaction, a set of cycles that is stable in the following sense: there does not exist a cycle such that every person participating in that cycle prefers to his current "status". For example, consider a market where families are seeking vacation rentals and offering their own homes for the same. Each family wishes to acquire a vacation home in exchange of its own home without any monetary exchange. We study such a market by analyzing a stable transaction of houses involving cycles of fixed length. The underlying rationale is that an entire trade/exchange fails if any of the participating agents cancels the agreement;as a result, shorter (trading) cycles are desirable. We show that given a transaction, it can be verified whether or not it is stable in polynomial time, and that the problem of finding a stable transaction is NP-hard even if each person desires only a small number of other goods/services. Having established these results, we study the problem of finding a stable transaction in the framework of parameterized algorithms.

关键词： algorithm design Stability Barter exchange FPT W[1]-hard

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A novel CNN based security guaranteed image watermarking generation scenario for smart city applications

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INFORMATION SCIENCES 2019年 479卷 432-447页

作者： Li, Daming Deng, Lianbing Gupta, Brij Bhooshan Wang, Haoxiang Choi, Chang Zhuhai Da Hengqin Sci & Technol Dev Co Ltd Postdoctoral Res Ctr Zhuhai Peoples R China City Univ Macau Macau Peoples R China Int Postdoctoral Sci & Technol Res Inst Co Ltd Zhuhai Peoples R China Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Zhuhai Da Hengqin Sci & Technol Dev Co Ltd Zhuhai Peoples R China Natl Inst Technol Kurukshetra Kurukshetra Haryana India Cornell Univ Ithaca NY 14853 USA Chosun Univ Dept Comp Engn Gwangju South Korea Chosun Univ IT Res Inst Gwangju South Korea

The rise of machine learning increases the current computing capabilities and paves the way to novel disruptive applications. In the current era of big data, the application of image retrieval technology for large-scale data is a popular research area. To ensure the robustness and security of digital image watermarking, we propose a novel algorithm using synergetic neural networks. The algorithm first processes a meaningful gray watermark image, then embeds it as a watermark signal into the block Discrete Cosine Transform (DCT) component. The companion algorithm for detection and extraction of the watermark uses a cooperative neural network, where the suspected watermark signal is used as the input while the output consists in the result of the recognition process. The simulation experiments show that the algorithm can complete certain image processing operations with improved performance, not only simultaneously completing watermark detection and extraction, but also efficiently determining the watermark attribution. Compared with other state-of-the-art models, the proposed model obtains an optimal Peak Signal-to-noise ratio (PSNR). (C) 2018 Elsevier Inc. All rights reserved.

关键词： Convolutional neural network Image watermark Generation scenario algorithm design Smart cities

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Study of multiple moving targets' detection in fisheye video based on the moving blob model

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第1期78卷 877-896页

作者： Wu, Jianhui Huang, Feng Hu, Wenjing He, Wei Tu, Bing Guo, Longyuan Ou, Xianfeng Zhang, Guoyun Coll Informat & Commun Engn Key Lab Optimizat & Control Complex Syst Shanghai Peoples R China Hunan Inst Sci & Technol Res Ctr Heterogeneous Comp & Its Applicat Yueyang 414006 Hunan Peoples R China

This paper discussed some improved algorithms for multiple moving targets detection and tracking in fisheye video sequences which based on the moving blob model. The view field of fisheye lens achieved 183 degree which used in our system, so it has more effective in the no blind surveillance system. However, the fisheye image has a big distortion that makes it difficult to achieve an intelligent function. In this paper we try to establish a moving blob model to detect and track multiple moving targets in the fisheye video sequences, in order to achieve the automation and intelligent ability for no blind surveillance system. It is divided into three steps. Firstly, the distortion model of fisheye lens was established, we are discussing the character of the imaging principle of fisheye lens, and calculate the distortion coefficient which can be used in the moving blob model. Secondly, the principle of the moving blob model was analyzed in detail which based on the fisheye distortion model. It was included four main algorithms, which the first is the traditional algorithm of background extraction;and the background updating algorithm;the algorithm of the fisheye video sequence with the background subtracted in order to get the moving blobs;the algorithm of removing the shadow of blobs in RGB space. Thirdly, we determined that every extracted blob is a real moving target by calculating the pixels with a threshold, which can discard the faulty moving targets. Lastly, we designed the algorithm for tracking the moving targets based on the moving blobs selected through calculating the geometry center. The experiment indicated that every algorithm has a better processing efficiency of multiple moving targets in fisheye video sequences. Compared the traditional algorithm, the improved algorithm can be detected the moving target in a circular fisheye image effectively and stably.

关键词： Multiple moving targets fisheye system moving blob model algorithm design

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A review on the self and dual interactions between machine learning and optimisation

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2019年第2期8卷 143-165页

作者： Song, Heda Triguero, Isaac Ozcan, Ender Univ Nottingham Sch Comp Sci Automated Scheduling Optimisat & Planning Res Grp Nottingham NG8 1BB England

Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from the latter search for the best option or solution to a given problem. To employ these techniques automatically and effectively aligning with the real aim of artificial intelligence, both sets of techniques are frequently hybridised, interacting with each other and themselves. This study focuses on such interactions aiming at (1) presenting a broad overview of the studies on self and dual interactions between machine learning and optimisation;(2) providing a useful tutorial for researchers and practitioners in both fields in support of collaborative work through investigation of the recent advances and analyses of the advantages and disadvantages of different techniques to tackle the same or similar problems;(3) clarifying the overlapping terminologies having different meanings used in both fields;(4) identifying research gaps and potential research directions.

关键词： Machine learning Optimisation Artificial intelligence Meta-learning algorithm design Metaheuristic Hyper-heuristic

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Video image target monitoring based on RNN-LSTM

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第4期78卷 4527-4544页

作者： Liu, Feng Chen, Zhigang Wang, Jie Cent S Univ Sch Software Changsha 410075 Hunan Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan Shandong Peoples R China Hengyang Normal Univ Coll Comp Sci & Technol Hengyang Peoples R China

Traditional image object classification and detection algorithms and strategies cannot meet the problem of video image acquisition and processing. Deep learning deliberately simulates the hierarchical structure of human brain, and establishes the mapping from low-level signals to high-level semantics, so as to achieve hierarchical feature representation of data. Deep learning technology has powerful visual information processing ability, which has become the forefront technology and domestic and international research hotspots to deal with this challenge. In order to solve the problem of target space location in video surveillance system, time-consuming and other problems, in this paper, we propose the algorithm based on RNN-LSTM deep learning. At the same time, according to the principle of OpenGL perspective imaging and photogrammetry consistency, we use 3D scene simulation imaging technology, relying on the corresponding relationship between video images and simulation images we locate the target object. In the 3D virtual scene, we set up the virtual camera to simulate the imaging processing of the actual camera, and the pixel coordinates in the video image of the surveillance target are substituted into the simulation image, next, the spatial coordinates of the target are inverted by the inverse process of the virtual imaging. The experimental results show that the detection of target objects has high accuracy, which has an important reference value for outdoor target localization through video surveillance images.

关键词： Recurrent neural network Long short-term memory Deep learning network Video image Target monitoring algorithm design

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