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Magnifying computing gaps Establishing encrypted communication over unidirectional channels

THEORETICAL COMPUTER SCIENCE 2016年 636卷 17-26页

作者： Dolev, Shlomi Korach, Ephraim Li, Ximing Li, Yin Uzan, Galit Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel Ben Gurion Univ Negev Dept Ind Engn IL-84105 Beer Sheva Israel South China Agr Univ Modern Educ Technol Ctr Guangzhou 510642 Guangdong Peoples R China

In the field of information security, the best way of protecting the privacy of the participants is to keep silent. Consider two, not necessarily identical, powerful computers or computer-grids connected by a unidirectional communication link that should transfer a long stream of information in the presence of a listening adversary that is slightly weaker. We present schemes that enhance the computation strength gap between the powerful computers and the adversary. The gap between the amount of information decrypted by the adversary and the information decrypted by the receiver grows with time. Based on a new defined shortest vector problem, we also propose a simplified version scheme in which only the receivers are computationally powerful. The scheme is self stabilizing in the sense that it can establish a security level without relying on (previously distributed private keys that are part of) the state. The iterative nested approach can be used for enhancing the security of the classical protocol of Ralph Merkle [19]. Several applications for sensor networks and for secure communication with survivors are suggested. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Unidirectional encryption combinatorial optimization problems

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A novel chaotic search for quadratic assignment problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2002年第3期139卷 543-556页

作者： Hasegawa, M Ikeguchi, T Aihara, K Itoh, K Yokosuka Radio Commun Res Ctr Commun Res Lab Yokosuka Kanagawa 2390847 Japan Saitama Univ Grad Sch Sci & Technol Dept Informat & Math Sci Saitama 3388570 Japan Saitama Univ Grad Sch Sci & Technol Dept Informat & Comp Sci Fac Engn Saitama 3388570 Japan Univ Tokyo Grad Sch Frontier Sci Dept Complex Sci & Engn Bunkyo Ku Tokyo 1138656 Japan Japan Sci & Technol Corp CREST Kawaguchi Saitama 3320012 Japan Tokyo Univ Sci Fac Ind Sci & Technol Dept Appl Elect Noda Chiba 2788510 Japan

We propose a novel method for solving the quadratic assignment problems. First, we realize the conventional tabu search on a neural network, and modify it to a chaotic version. Our novel method includes both effects of chaotic dynamics and tabu search. We compare the performance of the novel chaotic search with the conventional tabu search and an exponential tabu search whose memory effect for tabu (forbidding previous moves) decays exponentially. We show that the exponential tabu search has higher performance than the conventional tabu search, and further that the novel method with a chaotic neural network exhibits the best performance. We also propose a controlling method of the chaotic neural network for realizing easy and robust applications of our method. Then, better performance can be realized without manual parameter setting for various problems. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： neural networks chaos combinatorial optimization problems quadratic assignment problems tabu search

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Local Search with Probabilistic Modeling for Learning Multiple-Valued Logic Networks

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2011年第2期E94A卷 795-805页

作者： Gao, Shangce Cao, Qiping Ishii, Masahiro Tang, Zheng Toyama Univ Grad Sch Innovat Life Sci Toyama 9308555 Japan Tateyama Inst Syst Toyama 9300016 Japan

This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).

关键词： multiple-valued logic network learning local search probabilistic modeling combinatorial optimization problems

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On approximability of the independent/connected edge dominating set problems

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INFORMATION PROCESSING LETTERS 2001年第6期79卷 261-266页

作者： Fujito, T Nagoya Univ Dept Elect Nagoya Aichi 4648603 Japan

We investigate polynomial-time approximability of the problems related to edge dominating sets of graphs. When edges are unit-weighted, the edge dominating set problem is polynomially equivalent to the minimum maximal matching problem, in either exact or approximate computation, and the former problem was recently found to be approximable within a factor of 2 even with arbitrary weights. It will be shown, in contrast with this, that the minimum weight maximal matching problem cannot be approximated within any polynomially computable factor unless P = NP. The connected edge dominating set problem and the connected vertex cover problem also have the same approximability when edges/vertices are unit-weighted. The former problem is already known to be approximable, even with general edge weights, within a factor of 3.55. We will show that, when general weights are allowed, (1) the connected edge dominating set problem can be approximated within a factor of 3 + epsilon, and (2) the connected vertex cover problem is approximable within a factor of lnn + 3 but cannot be within (1 - epsilon) lnn for any epsilon > 0 unless NP subset of DTIME(n(O(loglogn))). (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： combinatorial optimization problems approximation algorithms edge dominating set

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Optimizing parameter settings for hopfield neural networks using reinforcement learning

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EVOLVING SYSTEMS 2024年第6期15卷 2419-2440页

作者： Rbihou, Safae Joudar, Nour-Eddine Haddouch, Khalid Sidi Mohamed Ben Abdellah Univ Engn Syst & Applicat Fes Morocco Sidi Mohamed Ben Abdellah Univ Dept Math Fes Morocco

Hopfield neural network stands out from artificial neural network models for solving optimization problems due to its unique ability to rapidly converge on global solutions without requiring complex supervised learning steps, although the performance of this network depends mainly on the tuning of its hyperparameters. This study aims to address the complexity of hyperparameter optimization in Hopfield neural networks by framing it as a sequential decision problem. Unlike traditional optimization techniques, which often struggle with the dynamic and iterative nature of hyperparameter tuning, the proposed approach uses reinforcement learning (RL) principles to adapt and optimize in real time. By using RL, we can dynamically adjust hyperparameters based on feedback from the network's performance, resulting in a more efficient and effective optimization process. The proposed approach significantly improves both the performance of the network and the execution time, thereby increasing the overall efficiency and effectiveness of the system. To demonstrate the effectiveness of the proposed approach, we applied it to the optimization of the tourist visit planning problem. The application of Hopfield neural networks, combined with reinforcement learning to optimize the hyperparameters of this network, has enabled the creation of a powerful model for optimizing tourist itineraries. This approach shows a clear improvement over traditional methods, maximizing the visitor experience and ensuring more efficient and enjoyable visits.

关键词： Hopfield neural networks Hyperparameter optimization Reinforcement learning combinatorial optimization problems Tourist planning problems

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Using a bank of binary Hopfield networks as constraints solver in hybrid algorithms

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NEUROCOMPUTING 2008年第4-6期71卷 1061-1068页

作者： Salcedo-Sanz, Sancho Ortiz-Garcia, Emilio G. Perez-Bellido, Angel M. Portilla-Figueras, Jose A. Univ Alcala de Henares Escuela Politecn Super Dept Signal Theory & Commun Madrid 28871 Spain

This paper proposes the use of a bank of Hopfield networks to solve a class of constraints which appear in combinatorial optimization problems. Specifically, we deal with problems which constraints' structure can be represented by a binary matrix C, and can be separated in independent substructures. We show that a bank of Hopfield networks can solve these constraints, and also can be easily hybridized with a global search algorithm, such as simulated annealing, to obtain a final solution to the problem. We apply our approach to the solution of a famous logic-type puzzle, the light-up puzzle, where we report improvements over a branch and bound algorithm, and to an important problem which arises in electronic control: the so-called Crossbar Switch Problem. (c) 2007 Elsevier B.V. All rights reserved.

关键词： bank of Hopfield networks hybrid algorithms combinatorial optimization problems light-up puzzle crossbar switch problem

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Improving charging capacity for wireless sensor networks by deploying one mobile vehicle with multiple removable chargers

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AD HOC NETWORKS 2017年 63卷 79-90页

作者： Zou, Tao Xu, Wenzheng Liang, Weifa Peng, Jian Cai, Yiqiao Wang, Tian Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia Huaqiao Univ Dept Comp Sci & Technol Xiamen 361021 Peoples R China

Wireless energy transfer is a promising technology to prolong the lifetime of wireless sensor networks (WSNs), by employing charging vehicles to replenish energy to lifetime-critical sensors. Existing studies on sensor charging assumed that one or multiple charging vehicles being deployed. Such an assumption may have its limitation for a real sensor network. On one hand, it usually is insufficient to employ just one vehicle to charge many sensors in a large-scale sensor network due to the limited charging capacity of the vehicle or energy expirations of some sensors prior to the arrival of the charging vehicle. On the other hand, although the employment of multiple vehicles can significantly improve the charging capability, it is too costly in terms of the initial investment and maintenance costs on these vehicles. In this paper, we propose a novel charging model that a charging vehicle can carry multiple low-cost removable chargers and each charger is powered by a portable high-volume battery. When there are energy-critical sensors to be charged, the vehicle can carry the chargers to charge multiple sensors simultaneously, by placing one portable charger in the vicinity of one sensor. Under this novel charging model, we study the scheduling problem of the charging vehicle so that both the dead duration of sensors and the total travel distance of the mobile vehicle per tour are minimized. Since this problem is NP-hard, we instead propose a (3 + epsilon)-approximation algorithm if the residual lifetime of each sensor can be ignored;otherwise, we devise a novel heuristic algorithm, where epsilon is a given constant with 0 < epsilon <= 1. Finally, we evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the performance of the proposed algorithms are very promising. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Rechargeable sensor networks One charging vehicle with multiple removable chargers Charging vehicle scheduling Approximation algorithms combinatorial optimization problems

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Utilizing Multi-Body Interactions in a CMOS-Based Ising Machine for LDPC Decoding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第1期71卷 40-50页

作者： Elmitwalli, Eslam Ignjatovic, Zeljko Kose, Selcuk Univ Rochester Dept Elect & Comp Engn Rochester NY 14620 USA

Ising machines have shown great promise in solving combinatorial optimization problems (COPs) using nature-inspired computation with higher speed and efficiency over traditional von Neumann computing systems. CMOS-based implementations combine the maturity and scaling ability of CMOS with the efficacy of Ising machines. In this paper, a low-density parity-check (LDPC) decoding solution is implemented with a CMOS-based resistively-coupled Ising machine known as(QuBRIM), using multi-body interactions among CMOS-based Ising machine nodes for the first time. State-of-the-art CMOS-based Ising implementations currently utilize order reduction to solve problems with higher-than-quadratic terms. In this paper, a new mechanism is proposed to implement higher-than-quadratic terms on Ising machines without the need for order reduction. The proposed methodology is implemented and verified with CMOS technology using 45 nm Generic PDK (GPDK).High accuracy rates are reported for the LDPC decoder based on the proposed methodology, comparable to Normalized Min-Sum, Offset Min-Sum, and Layered Belief-Propagation decoders, with a bit error rate (BER) as low as 4x10(-8) at a signal-to-noise ratio(SNR) of 4dB. Furthermore, the proposed LDPC decoder attains a normalized energy efficiency (NEE) of 1.29 pJ/bit/iteration, surpassing the state-of-the-art decoders by a minimum factor of2.4 and as much as 7.6 times.

关键词： combinatorial optimization problems Ising machine LDPC decoding multi-body interaction

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Ant Colony optimization with Memory and Its Application to Traveling Salesman Problem

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2012年第3期E95A卷 639-645页

作者： Wang, Rong-Long Zhao, Li-Qing Zhou, Xiao-Fan Univ Fukui Grad Sch Engn Fukui 9108507 Japan

Ant Colony optimization (ACO) is one of the most recent techniques for solving combinatorial optimization problems, and has been unexpectedly successful. Therefore, many improvements have been proposed to improve the performance of the ACO algorithm. In this paper an ant colony optimization with memory is proposed, which is applied to the classical traveling salesman problem (TSP). In the proposed algorithm, each ant searches the solution not only according to the pheromone and heuristic information but also based on the memory which is from the solution of the last iteration. A large number of simulation runs are performed, and simulation results illustrate that the proposed algorithm performs better than the compared algorithms.

关键词： ant colony optimization memory combinatorial optimization problems traveling salesman problem

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Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2020年第11期19卷 2699-2713页

作者： Huang, Meitian Liang, Weifa Shen, Xiaojun Ma, Yu Kan, Haibin Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia Univ Missouri Sch Comp & Engn Kansas City MO 64110 USA Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Inst Adv Commun & Data Sci Fudan Zhongan Joint Lab Blockchain & Informat Sec Shanghai Peoples R China

Along with Network Function Virtualization (NFV), Mobile Edge Computing (MEC) is becoming a new computing paradigm that enables accommodating innovative applications and services with stringent response delay and resource requirements, including autonomous vehicles and augmented reality. Provisioning reliable network services for users is the top priority of most network service providers, as unreliable services or severe service failures can result in tremendous losses of users, particularly for their mission-critical applications. In this paper, we study reliability-aware VNF instances provisioning in an MEC, where different users request different network services with different reliability requirements through paying their requested services with the aim to maximize the network throughput. To this end, we first formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request. We then show that the problem is NP-hard and formulate an Integer Linear Programming (ILP) solution. Due to the NP-hardness of the problem, we instead devise an approximation algorithm with a logarithmic approximation ratio for the problem. Moreover, we also consider two special cases of the problem. For one special case where each request only requests one primary and one secondary VNF instances, the problem is still NP-hard, and we devise a constant approximation algorithm for it. For another special case where different VNFs have the same amounts of computing resource demands, we show that it is polynomial-time solvable by developing a dynamic programming solution for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and the empirical results of the algorithms outperform their analytical counter

关键词： Reliability Cloud computing Approximation algorithms Heuristic algorithms Mobile computing Edge computing Network function virtualization Reliability-aware VNF instances provisioning VNF instance placements virtualized network function implementation approximation algorithms generalized assignment problems dynamic programming mobile edge computing combinatorial optimization problems

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