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Cooperative Cache Transfer-based On-demand Network Coded Broadcast in Vehicular Networks

ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2019年第4期18卷 38-38页

作者： Ali, G. G. Md Nawaz Noor-A-Rahim, Md Rahman, Md Ashiqur Ayalew, Beshah Chong, Peter H. J. Guan, Yong Liang Clemson Univ Dept Automot Engn Greenville SC 29607 USA Univ Coll Cork Sch Comp Sci & IT Cork Ireland Univ Arizona Dept Comp Sci Tucson AZ 85721 USA Auckland Univ Technol Dept Elect & Elect Engn Auckland New Zealand Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

Real-time traffic updates, safety and comfort driving, infotainment, and so on, are some envisioned applications in vehicular networks. Unlike traditional broadcast, network-coding-assisted broadcast can satisfy multiple vehicles with different data items in a coded form. However, server side encoding requires the prior knowledge about vehicles' cache information for the successful decoding at the vehicles' sides. The explicit cache upload from vehicles to Road Side Unit (RSU) wastes upload bandwidth. In multi-RSU vehicular networks, we propose a Cooperative Cache Transfer-based On-demand Network Coded Broadcast called CCTCB. In the proposed CCTCB approach, vehicles do not need to upload their cache information to the server, rather the RSU server learns the vehicles' cache intrinsically. We derive a probabilistic model to analyze the coding opportunity in the proposed cooperative cache transfer mechanism incorporating vehicle mobility. The comprehensive simulation results validate the superiority of the proposed approach.

关键词： Vehicular communication network coding on-demand broadcast cooperative cache transfer algorithm design

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Competitiveness Maximization on Complex Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第7期48卷 1054-1064页

作者： Zhao, Jiuhua Liu, Qipeng Wang, Lin Wang, Xiaofan Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China

We consider a model of competition on complex networks, in which two competitors are fixed to opposite states while other agents, called normal agents, adjust their states according to a distributed consensus protocol. Suppose that one of the competitors could enhance its influence by creating new links. A natural question is, when the number of new links is limited due to the limited resource, how to add these links so as to maximize the influence of the given competitor over the other one (called competitiveness). We consider two competitiveness maximization problems: Problem 1 tries to maximize the number of supporters of the competitor, while Problem 2 tries to maximize the total supporting degree of normal agents toward the competitor. We prove that Problem I is NP-hard. We also show that the objective function of Problem 2 is monotonous and submodular, and hence there exists a polynomial-time greedy algorithm (GA) approximately solving Problem 2. Several centrality-based heuristic algorithms of less computational burden are also designed to provide approximate solutions to these two problems. We carry out extensive simulations to check the performances of these algorithms in six real networks. We find that GA always provides the best approximate solution to Problem 2, while for Problem I, GA only has the best performance in directed networks. Furthermore, among those heuristic algorithms, an algorithm based on centrality in descending order is better than its counterpart in ascending order in solving Problem 2 in statistical sense. But for Problem 1, the performance of the centrality-based heuristic algorithms is more sensitive to the network structure and the locations of competitors.

关键词： algorithm design competitive dynamics NP-hard optimization submodular

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A new cooperative framework for parallel trajectory-based metaheuristics

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APPLIED SOFT COMPUTING 2018年 65卷 374-386页

作者： Shi, Jialong Zhang, Qingfu Xi An Jiao Tong Univ Sch Math & Stat Xian Shaanxi Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

In this paper, we propose the Parallel Elite Biased framework (PEB framework) for parallel trajectory-based metaheuristics. In the PEB framework, multiple search processes are executed concurrently. During the search, each process sends its best found solutions to its neighboring processes and uses the received solutions to guide its search. Using the PEB framework, we design a parallel variant of Guided Local Search (GLS) called PEBGLS. Extensive experiments have been conducted on the Tianhe-2 supercomputer to study the performance of PEBGLS on the Traveling Salesman Problem (TSP). The experimental results show that PEBGLS is a competitive parallel metaheuristic for the TSP, which confirms that the PEB framework is useful for designing parallel trajectory-based metaheuristics. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Parallel metaheuristics algorithm design Guided Local Search

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On Automatic Formation of Effective Therapy Groups in Social Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2018年第3期5卷 713-726页

作者： Hsu, Bay-Yuan Lan, Yi-Feng Shen, Chih-Ya Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Tunghai Univ Ctr Gen Educ Taichung 40704 Taiwan Tunghai Univ Dept Social Work Taichung 40704 Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30013 Taiwan

Group therapy is one of the major clinical interventions to help patients with mental disorders. However, finding an effective therapy group is very challenging, since there are three crucial criteria that should be carefully considered simultaneously: 1) avoidance of isolation and loneliness;2) unfamiliarity of patients;and 3) size of the therapy group. Manual selection of therapy group members, which is the common approach for most psychiatrists and mental health professionals today, may incur human bias and is very time-consuming. Therefore, we propose a new research problem, namely, Unfamiliarity-Aware Therapy Group Selection with Noah's Ark Principle (UTNA) for automatic selections of therapy group members from the social network while addressing the three crucial criteria mentioned above. In this paper, we first analyze the NP-hardness and inapproximability of the UTNA problem. Then, we consider a special case of UTNA on trees, namely, Tree-based UTNA, and propose a linear-time dynamic programming algorithm to find the optimal solution. We then design an efficient algorithm, Effective Therapy Group Discovery, to select suitable members efficiently and effectively for the general UTNA problem. We invite 10 psychiatrists and clinical psychologists to conduct an expert study for validating our problem formulation, where all experts agree that our problem formulation is helpful for them, and the groups selected by our proposed approach are better than or similar to their manual configurations. In addition, we also conduct extensive experiments on three real data sets. Experimental results show that our proposed algorithms outperform other baselines in both efficiency and solution quality.

关键词： algorithm design group formation Noah's ark principle social networks

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Adaptive Learning algorithm Convergence in Passive and Reactive Environments

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NEURAL COMPUTATION 2018年第10期30卷 2805-2832页

作者： Golden, Richard M. Univ Texas Dallas Richardson TX 75080 USA

Although the number of artificial neural network and machine learning architectures is growing at an exponential pace, more attention needs to be paid to theoretical guarantees of asymptotic convergence for novel, nonlinear, high-dimensional adaptive learning algorithms. When properly understood, such guarantees can guide the algorithm development and evaluation process and provide theoretical validation for a particular algorithm design. For many decades, the machine learning community has widely recognized the importance of stochastic approximation theory as a powerful tool for identifying explicit convergence conditions for adaptive learning machines. However, the verification of such conditions is challenging for multidisciplinary researchers not working in the area of stochastic approximation theory. For this reason, this letter presents a new stochastic approximation theorem for both passive and reactive learning environments with assumptions that are easily verifiable. The theorem is widely applicable to the analysis and design of important machine learning algorithms including deep learning algorithms with multiple strict local minimizers, Monte Carlo expectation-maximization algorithms, contrastive divergence learning in Markov fields, and policy gradient reinforcement learning.

关键词： Environments stochastic approximation letter Artificial neural networks Converge learning algorithms Machine Learning Artificial Intelligence Approximation Theory algorithm design Theorem REACTIVE Self tuning

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Smart Buildings IoT Networks Accuracy Evolution Prediction to Improve Their Reliability Using a Lotka-Volterra Ecosystem Model

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SENSORS 2019年第21期19卷 4642-4642页

作者： Casado-Vara, Roberto Canal-Alonso, Angel Martin-del Rey, Angel De la Prieta, Fernando Prieto, Javier Univ Salamanca BISITE Res Grp Edificio Multiusos I D I Salamanca 37007 Spain Univ Salamanca Inst Fundamental Phys & Math Dept Appl Math Calle Parque 2 E-37008 Salamanca Spain

Internet of Things (IoT) is the paradigm that has largely contributed to the development of smart buildings in our society. This technology makes it possible to monitor all aspects of the smart building and to improve its operation. One of the main challenges encountered by IoT networks is that the the data they collect may be unreliable since IoT devices can lose accuracy for several reasons (sensor wear, sensor aging, poorly constructed buildings, etc.). The aim of our work is to study the evolution of IoT networks over time in smart buildings. The hypothesis we have tested is that, by amplifying the Lotka-Volterra equations as a community of living organisms (an ecosystem model), the reliability of the system and its components can be predicted. This model comprises a set of differential equations that describe the relationship between an IoT network and multiple IoT devices. Based on the Lotka-Volterra model, in this article, we propose a model in which the predators are the non-precision IoT devices and the prey are the precision IoT devices. Furthermore, a third species is introduced, the maintenance staff, which will impact the interaction between both species, helping the prey to survive within the ecosystem. This is the first Lotka-Volterra model that is applied in the field of IoT. Our work establishes a proof of concept in the field and opens a wide spectrum of applications for biology models to be applied in IoT.

关键词： Internet of Things Lotka-Volterra model predator-prey system bio-inspired system evolution algorithm design

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New Bipartite Graph Techniques for Irregular Data Redistribution Scheduling

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algorithmS 2019年第7期12卷 142-142页

作者： Li, Qinghai Yu, Chang Wu Zhejiang Ind & Trade Vocat Coll Dept Elect Engn East Rd 717 Wenzhou 325003 Peoples R China Chung Hua Univ Dept Comp Sci & Informat Engn 707Sec 2WuFu Rd Hsinchu 30012 Taiwan

For many parallel and distributed systems, automatic data redistribution improves its locality and increases system performance for various computer problems and applications. In general, an array can be distributed to multiple processing systems by using regular or irregular distributions. Some data distribution adopts BLOCK, CYCLIC, or BLOCK-CYCLIC to specify data array decomposition and distribution. On the other hand, irregular distributions specify a different-size data array distribution according to user-defined commands or procedures. In this work, we propose three bipartite graph problems, including the "maximum edge coloring problem", the "maximum degree edge coloring problem", and the "cost-sharing maximum edge coloring problem" to formulate these kinds of distribution problems. Next, we propose an approximation algorithm with a ratio bound of two for the maximum edge coloring problem when the input graph is biplanar. Moreover, we also prove that the "cost-sharing maximum edge coloring problem" is an NP-complete problem even when the input graph is biplanar.

关键词： data redistribution scheduling edge coloring approximation algorithms graph techniques bipartite graphs algorithm design

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Fast constant-time gcd computation and modular inversion

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2019年第3期2019卷 340-398页

作者： Bernstein, Daniel J. Yang, Bo-Yin Department of Computer Science University of Illinois at Chicago ChicagoIL60607–7045 United States Horst Görtz Institute for IT Security Ruhr University Bochum Germany Institute of Information Science and Research Center of Information Technology and Innovation Academia Sinica 128 Section 2 Academia Road Taipei115-29 Taiwan

This paper introduces streamlined constant-time variants of Euclid’s algorithm, both for polynomial inputs and for integer inputs. As concrete applications, this paper saves time in (1) modular inversion for Curve25519, which was previously believed to be handled much more efficiently by Fermat’s method, and (2) key generation for the ntruhrss701 and sntrup4591761 lattice-based cryptosystems. © 2019, Ruhr-University of Bochum. All rights reserved.

关键词： algorithm design Branchless algorithms Constant-time computations Curve25519 Euclid’s algorithm Gcd Greatest common divisor Modular inversion Modular reciprocal NTRU

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Concept of Control System for Optimal Switching of Phase Shifting Transformers 12

Concept of Control System for Optimal Switching of Phase Shi...

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12th International Conference on Elektro

作者： Szablicki, Mateusz Rzepka, Piotr Halinka, Adrian Sowa, Pawel Silesian Tech Univ Inst Power Syst & Control Gliwice Poland

ISBN: (纸本)9781538647592

paper presents an innovative concept for automatic selection of an optimal tap of the phase shifting transformer (PST) before its switching-on. The purpose of the system is to enable the switching-on of the PST without sudden changes in power network operating conditions. For the investigation of the underlying phenomena during switching, a test power network containing the accurate dynamic model of the PST was developed. The investigations were performed for different scenarios of changes in the power network parameters and control options of the PST. This allowed determining the desired functionality and structure of the automation system for selecting an optimal tap of the PST before its switching-on. The developed automation system is based on synchronous measurements, and this represents a novel implementation of such measurements. Verification simulation studies indicate that the proposed concept achieves the stated objectives. It is expected that the practical application of this automation system can resolve the transient overcurrent in the adjoining network components arising from PST switching. Overall this will enable significant minimization of negative effects of PST switching-on the power network operating conditions.

关键词： algorithm design control systems phasor measurement unit phase shifters power system

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A Practical Application of a Dataset Analysis in an Intrusion Detection System 17

A Practical Application of a Dataset Analysis in an Intrusio...

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17th IEEE International Symposium on Network Computing and Applications (NCA)

作者： Fernandez, Diego Vigoya, Laura Cacheda, Fidel Novoa, Francisco J. Lopez-Vizcaino, Manuel F. Carneiro, Victor Univ A Coruna CITIC Campus Elvina S-N La Coruna 15071 Spain

ISBN: (纸本)9781538676592

In this paper a systematic analysis of a public intrusion detection dataset has been developed in order to understand how the traffic behaves in this particular context. This analysis is used for avoiding common pitfalls introduced because of a misunderstanding of data peculiarities and for selecting and tailoring correctly the algorithms. Specifically, we have employed machine learning algorithms to classify the traffic into normal and attack flows. In addition, it is important to decide what features should be evaluated. Typically, standard metrics do not take into account time spent in the classification, what is essential in an intrusion detection system. This is the reason why we introduce a metric that considers both the accuracy and the delay to make the decision and that is employed for evaluating machine learning algorithms in other domains. The conclusions obtained from our dataset analysis can be used to develop new models that could fit the data better than existing approaches.

关键词： intrusion detection systems analysis metric algorithm design computer network management

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