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arXiv 2023年

作者： Xiong, Zong-Xing Zhang, Yongli Li, Mao-Sheng Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou510665 China School of Mathematics South China University of Technology Guangzhou510641 China

A set of orthogonal multipartite quantum states are called (distinguishability-based) genuinely nonlocal if they are locally indistinguishable across any bipartition of the subsystems. In this work, we consider the problem of constructing small genuinely nonlocal sets consisting of generalized GHZ states in multipartite systems. For system (C2) ⊗ N where N is large, using the language of group theory, we show that a tiny proportion Θ(1/√2N) of states among the N-qubit GHZ basis suffice to exhibit genuine nonlocality. Similar arguments also hold for the canonical generalized GHZ bases in systems (Cd) ⊗ N, wherever d is even and N is large. What is more, moving to the condition that any fixed N is given, we show that d + 1 genuinely nonlocal generalized GHZ states exist in (Cd) ⊗ N, provided the local dimension d is sufficiently large. As an additional merit, within and beyond an asymptotic sense, the latter result also indicates some evident limitations of the "trivial othogonality-preserving local measurements" (TOPLM) technique that has been utilized frequently for detecting genuine nonlocality. © 2023, CC BY.

关键词： Group theory

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Efficient Specialisation in Prolog Using the Hand-Written Compiler Generator LOGEN

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Electronic Notes in Theoretical computer science 2000年第2期30卷 157-162页

作者： Michael Leuschel Jesper Jørgensen Declarative Systems and Software Engineering Dept. of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Dept. of Mathematics and Physics Royal Veterinary and Agricultural University Thorvaldsensvej 40 DK-1871 Frederiksberg C Denmark

The so called “ cogen approach” to program specialisation, writing a compiler generator instead of a specialiser, has been used with considerable success in partial evaluation of both functional and imperative languages. In earlier work we have shown that this approach is also applicable to partial evaluation of logic programming languages, also called partial deduction. In this paper we extend upon this by allowing partially instantiated datastructures (via binding types), which are especially important in the context of logic programming. We also extend cogen to directly support a large part of Prolog's declarative and non-declarative features and how semi-online specialisation can be efficiently integrated. Benchmarks show that the resulting cogen is very efficient, generates very efficient generating extensions (executing up to several orders of magnitude faster than current online systems) which in turn perform very good and non-trivial specialisation, even rivalling existing online systems.

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Adaptive and impulsive cluster synchronization of a general complex dynamical network

Adaptive and impulsive cluster synchronization of a general ...

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IEEE International Conference on Networking, Sensing and Control

作者： Bingxiong Shi Junan Lu Jinhu Lu Xinghuo Yu College of Mathematics and Statistics and State Key Laboratory of Software Engineering Wuhan University of China Wuhan China School of Electrical and Computer Engineering RMIT University Melbourne Australia Key Laboratory of Systems and Control Institute of Systems Science Chinese Academy and Sciences Beijing China School of Electrical and Computer Engineering RMIT University Melbourne VIC Australia

ISBN: (纸本)9781424464500;9781424464531

Recently, cluster synchronization has attracted an increasing interest in various disciplines, including mathematics, physics, and engineering sciences. This paper further investigates the cluster synchronization of a complex dynamical network by using adaptive and impulsive control techniques, respectively. Based on a complex dynamical network with two different clusters, several adaptive and impulsive cluster synchronization criteria are then proposed. It should be especially pointed out that the dynamics and its dimensions of the individual dynamical systems are different in the two different clusters. Also, some numerical simulations are given to verify the effectiveness of the above theoretical results.

关键词： Complex networks mathematics Numerical simulation Physics Adaptive systems Programmable control Adaptive control Biology History Technological innovation

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Graph Partitioning Technique for Separating Nets in Single-row Networks

Graph Partitioning Technique for Separating Nets in Single-r...

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2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会

作者： Norazaliza Mohd.Jamil Noraziah Ahmad Shaharuddin Salleh Faculty of Computer Systems & Software Engineering Universiti Malaysia PahangLocked Bag 1225000KuantanPahangMalaysia Department of Mathematics Faculty of ScienceUniversiti Teknologi Malaysia81310Johor BahruMalaysia

Single-row routing is fundamentally a routing technique for pairs of nodes arranged in a single-row *** contributes in the printed circuit hoard(PCB)design. The main objective in single-row routing is to achieve minimum congestion arising from the number of horizontal tracks in the *** results for a single layer network have been achieved through our previous model called ***,a single layer model suffers from non-tolerable lower bound values with high congestion depending on the network size. These results may further be improved by partitioning the network into two or more *** this paper,we propose a technique based on the graph partitioning concept for partitioning the nodes from a single-row network into several layers of planar *** experiment result shows that the proposed technique is able to decrease the network congestions.

关键词： Single-row routing ESSR graph partitioning maximum clique Kernighan-Lin algorithm

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Training Differentially Private Models with Secure Multiparty Computation

arXiv

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arXiv 2022年

作者： Pentyala, Sikha Railsback, Davis Maia, Ricardo J.M. Melanson, David Dowsley, Rafael Nascimento, Anderson De Cock, Martine School of Engineering and Technology University of Washington Tacoma United States Department of Computer Science University of Brasilia Brazil Department of Software Systems and Cybersecurity Monash University Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Mila - Quebec Ai Institute Canada

We address the problem of learning a machine learning model from training data that originates at multiple data owners, while providing formal privacy guarantees regarding the protection of each owner's data. Existing solutions based on Differential Privacy (DP) achieve this at the cost of a drop in accuracy. Solutions based on Secure Multiparty Computation (MPC) do not incur such accuracy loss but leak information when the trained model is made publicly available. We propose an MPC solution for training DP models. Our solution relies on an MPC protocol for model training, and an MPC protocol for perturbing the trained model coefficients with Laplace noise in a privacy-preserving manner. The resulting MPC+DP approach achieves higher accuracy than a pure DP approach, while providing the same formal privacy guarantees. Our work obtained first place in the iDASH2021 Track III competition on confidential computing for secure genome analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Enhancing e-KYC Security and Privacy: Harnessing Quantum Computing and Blockchain in Web 3.0

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Distributed Ledger Technologies: Research and Practice 2024年第4期3卷 1-23页

作者： G. M. Faruk Ahmed Imran Hasan Md Soumike Hassan Rahul Khan Abu Jor Al Gefari Zain Buksh Joseph Liu A B M Shawkat Ali Cybersecurity Cell Rupali Bank Limited Dhaka Bangladesh Department of Computer Science and Engineering Islamic University Kushtia Bangladesh Department of Statistics Jahangirnagar University Dhaka Bangladesh Department of Pharmacy Bangladesh University Dhaka Bangladesh Rajshahi Science and Technology University Natore Bangladesh Department of Computer Science and Mathematics The University of Fiji Lautoka Fiji Department of Software Systems and Cybersecurity Monash University Melbourne Australia

By implementing a successful and effective Electronic-Know Your Customer (e-KYC) infrastructure, conventional defects and expenses may be greatly reduced. Strong authentication procedures are used by existing e-KYC solutions to guarantee safety and confidentiality. However, these technologies have shortcomings like as key administration expenses and dependency on encryption. We suggest a revolutionary strategy that integrates blockchain technology, Web 3.0, and quantum computing to get around these restrictions. In this research, we show how current e-KYC processes could possibly be improved, making them faster and more reliable, by integrating Web 3.0 with quantum computing. Our solution makes it simple to quickly verify several consumers, increasing productivity. We provide improved safety for online requests, transcending the weaknesses inherent in traditional encryption techniques by utilizing quantum computing technology and applying the concept of quantum encryption key methodology. We contribute to the development of safe and private solutions by addressing the shortcomings of the present e-KYC systems. The results of our research show how Web 3.0 and quantum computer technology have the power to completely alter the e-KYC environment.

关键词： e-KYC Quantum Computing Blockchain Web 3.0 Privacy Security

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Energy Efficient Decentralized Authentication in Internet of Underwater Things Using Blockchain

Energy Efficient Decentralized Authentication in Internet of...

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IEEE Globecom Workshops

作者： Abbas Yazdinejad Reza M. Parizi Gautam Srivastava Ali Dehghantanha Kim-Kwang Raymond Choo Cyber Science Lab School of Computer Science University of Guelph Ontario Canada College of Computing and Software Engineering Kennesaw State University GA USA Department of Mathematics and Computer Science Brandon University Manitoba Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX USA

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

In recent years, there has been rapid growth in developing smart cities. Nearly 70% of the Earthâ€™s surface is covered by water and a large proportion of underwater environments are still unknown and have not been explored. In this context, Internet of things (IoT) is one of the most important technologies used in smart cities. Due to the growth of IoT and its influence in all areas of human life, including the underwater environment, a new class of IoT, called Internet of underwater things (IoUT) has emerged. IoUT includes a network of underwater smart devices that are connected to each other and has applications in environmental monitoring, underwater exploration, disaster prevention and military. In autonomous interactions of underwater devices, objects must be authenticated and securely interconnected to avoid security risks by malicious nodes. Most authentication methods and security mechanisms are centralized and often require a trustful third party in communications, which may well increase the computation cost and energy consumption due to the subsequent overhead, especially for underwater communications. On the other hand, there are restrictions on devices in the underwater environment, the most important of which are energy constraints. In this paper, we propose a robust, transparent, and energy-efficient decentralized authentication mechanism for IoUT using blockchain technology. We show through results that the proposed method is suitable for underwater devices with limited memory, energy, and computational power. The proposed modelâ€™s decentralized authentication in a cluster network has a significant effect on reducing the energy consumption of the devices by 74.63% compared to classic authentication methods. Moreover, using the proposed method allows a savings of more than 41.9% in end-to-end delay and increases delivery rate by 21.6%.

关键词： Authentication Internet of Things Smart cities Energy consumption Cryptography

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COVID-19 pneumonia level detection using deep learning algorithm

TechRxiv

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TechRxiv 2020年

作者： Ghafoor, Kayhan Zrar Ali, Abbas M. Mulahuwaish, Aos Maghdid, Halgurd S. Hussein, Safwan Mawlood Mohammed, Marwan A. Department of Software Engineering Salahaddin University-Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom Department of Computer Science and Information Systems Saginaw Valley State University University Center 7400 Bay Rd Science East 174 MI48710 United States Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq Computer Engineering Department Tishk International University Iraq

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases;in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65% and 91.304 % of CNN and kNN respectively. © 2020, CC BY.

关键词： COVID-19

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Synchronization-based scalability of rings and chains of dense lumps

Synchronization-based scalability of rings and chains of den...

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作者： Lu, Jun-An Zhang, Yong Chen, Juan Lü, Jinhu Wu, Xiaoqun School of Mathematics and Statistics Wuhan University Wuhan 430072 China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Electrical and Computer Engineering RMIT University Melbourne VIC 3001 Australia

ISBN: (纸本)9789881725592

This paper aims to investigate the scalability of synchronizability for the complex clustering networks of dense rings or chains with the network scale increasing. Our results reveal that the synchronizability of the above complex clustering networks decreases with the network scale increasing for any clustering structures. When the network size is increasing, the increase of the number of clusters decreases the synchronizability much faster than the increase of the size of individual cluster. Moreover, it is found that the ring or the chain structure has much greater influence on weakening the network synchronizability than that of the clustering property. © 2011 Chinese Assoc of Automati.

关键词： Scalability

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software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm

arXiv

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arXiv 2019年

作者： Zamli, Kamal Z. Din, Fakhrud Ramli, Nazirah Ahmed, Bestoun S. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Kuantan Pahang26300 Malaysia Department of Computer Science and IT Faculty of Information Technology University of Malakand KPK Pakistan Department of Mathematics and Statistics Faculty of Information Technology and Quantitative Science Universiti Teknologi MARA Pahang Malaysia Faculty of Electrical Engineering Department of Computer Science Czech Technical University Czech Republic

Although showing competitive performances in many real world optimization problems, Teaching Learning based Optimization Algorithm (TLBO) has been criticized for having poor control on exploration and exploitation. Addressing these issues, a new variant of TLBO called Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) has been developed in the literature. This paper describes the adoption of Fuzzy Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) for software module clustering problem. Comparative studies with the original Teaching Learning based Optimization (TLBO) and other Fuzzy TLBO variant demonstrate that ATLBO gives superior performance owing to its adaptive selection of search operators based on the need of the current search. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

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