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9th Edition of IEEE Region 10 Humanitarian technology Conference, R10-HTC 2021

作者： Senevirathne, Gayathri Manathunga, Kalpani Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665432405

Interface design is a mandatory aspect influencing the success of an e-Learning system implementation. User interface (UI) design of e-learning is a point of interaction between user and computer software. Users prefer more attractive and simpler interface designs rather than dull or complex designs. This study aims to outline the impact of UI design on the satisfaction of learners. Specifically, this study will be evaluating different user interaction design strategies such as ease of navigation, ease of resource discoverability, ease of configuring integrated tools etc. in e-learning platforms such as learning management systems, and massive open online courses (MOOCs). Further, this research aims to find answers for the challenges and issues faced by students and teachers when using e-learning platforms. A comprehensive questionnaire was distributed among teachers and students. Collected data was analyzed to get an idea about main interface design problems that frustrate the learners and teachers and distract them from educational tasks. Using this statistical analysis results, a model is proposed indicating success factors and failure factors that may affect to e-learning system interface designing. Moreover, this research also results in a set of guidelines or suggestions that can be followed to improve UI designing in e-learning platforms. Finally, an initial prototype implementation capable of recommending suggestions intelligently for e-learning platform designers and users is proposed after modelling the user satisfaction factors. © 2021 IEEE.

关键词： User interfaces

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information geometry of bosonic Gaussian thermal states

arXiv

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arXiv 2024年

作者： Huang, Zixin Wilde, Mark M. School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

Bosonic Gaussian thermal states form a fundamental class of states in quantum information science. This paper explores the information geometry of these states, focusing on characterizing the distance between two nearby states and the geometry induced by a parameterization in terms of their mean vectors and Hamiltonian matrices. In particular, for the family of bosonic Gaussian thermal states, we derive expressions for their Fisher–Bures and Kubo–Mori information matrices with respect to their mean vectors and Hamiltonian matrices. An important application of our formulas consists of fundamental limits on how well one can estimate these parameters. We additionally establish formulas for the derivatives and the symmetric logarithmic derivatives of bosonic Gaussian thermal states. The former could have applications in gradient descent algorithms for quantum machine learning when using bosonic Gaussian thermal states as an ansatz, and the latter in formulating optimal strategies for single parameter estimation of bosonic Gaussian thermal states. Finally, the expressions for the aforementioned information matrices could have additional applications in natural gradient descent algorithms when using bosonic Gaussian thermal states as an ansatz. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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Semi-definite optimization of the measured relative entropies of quantum states and channels

arXiv

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arXiv 2024年

The measured relative entropies of quantum states and channels find operational significance in quantum information theory as achievable error rates in hypothesis testing tasks. They are of interest in the near term, as they correspond to hybrid quantum-classical strategies with technological requirements far less challenging to implement than required by the most general strategies allowed by quantum mechanics. In this paper, we prove that these measured relative entropies can be calculated efficiently by means of semi-definite programming, by making use of variational formulas for the measured relative entropies of states and semi-definite representations of the weighted geometric mean and the operator connection of the logarithm. Not only do the semi-definite programs output the optimal values of the measured relative entropies of states and channels, but they also provide numerical characterizations of optimal strategies for achieving them, which is of significant practical interest for designing hypothesis testing protocols. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimal systems

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Streamlining software Release Process and Resource Management for Microservice-based Architecture on multi-cloud

Streamlining Software Release Process and Resource Managemen...

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IEEE International Conference on Multi Topic

作者： Isuru Pathum Herath Samuditha Jayawardena Ahamed Fadhil Nuwan Kodagoda Udara Srimath S.Samaratunge Arachchillage Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Science Sri Lanka Institute of Information Technology Sri Lanka

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry’s automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components.

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Leveraging Deep Learning and Knowledge Distillation for Enhanced Traffic Anomaly Detection in Transportation Systems 6

Leveraging Deep Learning and Knowledge Distillation for Enha...

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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Tran, Mai-Khiem Huynh, Viet-Tham Tran, Minh-Triet University of Science VNU-HCM Software Engineering Laboratory and Faculty of Information Technology Ho Chi Minh City Viet Nam John von Neumann Institute VNU-HCM Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

This paper introduces an innovative approach to enhance traffic anomaly detection in transportation systems using deep learning and knowledge distillation. We create a robust dataset from 427 videos containing 1,415 accident-related events, spanning various anomalies like accidents, car crashes, and pedestrian violations. To address real-time anomaly detection challenges, we propose a novel lightweight neural network architecture inspired by EfficientNet-B0, designed for efficient video anomaly detection. Through knowledge distillation, a student model learns from a teacher model's predictions, resulting in heightened anomaly detection accuracy. Experimental results highlight the approach's efficacy, with the knowledge-distilled student model consistently outperforming the standalone lightweight network, achieving an accuracy of 94.83% compared to 94.16%. This research offers a practical solution for real-time traffic anomaly detection, which is especially valuable in resource-constrained environments. Fusing a unique dataset, EfficientNet-B0-like structure, lightweight architecture, and knowledge distillation holds significant potential for fostering safer and more efficient transportation systems. © 2023 IEEE.

关键词： Distillation

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Human Resource Application Development using Person-Job Matching for Placement of Prospective Employees

Human Resource Application Development using Person-Job Matc...

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Advancement in Data Science, E-learning and information Systems (ICADEIS), International Conference on

作者： Alifiah Putri Sinung Suakanto Joko Siswanto Farah Fitriavida Arifin Irfan Darmawan Dina Angela Department of Information System Faculty of Software Engineering Telkom University Bandung Indonesia Industrial Engineering Research Group Bandung of Institute Technology Bandung Indonesia Information Techology Research Group Faculty of Industrial Engineering Institut Teknologi Harapan Bangsa Bandung Indonesia

The recruitment process presents a significant challenge for Human Resource (HR) staff, particularly in their ability to objectively assess candidate competencies and align them with job requirements. While qualitative comparisons between resumes and position requirements may yield candidates who appear suitable, this approach proves ineffective in terms of time and cost. Furthermore, many recruiters lack sufficient knowledge of the positions they are hiring for, making it difficult to justify their selection decisions. This research aims to address this challenge by developing an HR person-job matching application for the recruitment of prospective employees. The study focuses on analyzing and designing applications that can quantitatively measure the competencies of candidates and compare them with job requirements using a person-job matching method. The effectiveness of this approach was evaluated by testing it on a sample of employees from small and medium-sized enterprises (SMEs). The study found that out of the 16 employees across three job positions, the person-job matching application achieved an accuracy rate of 75%. This accuracy percentage was determined by comparing the fit value results with the actual conditions of the employees in their respective workplaces. The findings highlight the potential of the HR person-job matching application to enhance the efficiency and effectiveness of recruitment efforts.

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Studies on the Quality Assessment of PRNG for Q-Trits Quantum Cryptography Protocols 8

Studies on the Quality Assessment of PRNG for Q-Trits Quantu...

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8th IEEE International Conference on Problems of Infocommunications, Science and technology, PIC S and T 2021

作者： Gnatyuk, Sergiy Okhrimenko, Tetiana Fesenko, Andrii Dorozhynskyi, Sergiy Computer And Software Engineering National Aviation University Faculty of Cybersecurity Kyiv Ukraine Taras Shevchenko National University of Kyiv Faculty of Information Technology Kyiv Ukraine

ISBN: (纸本)9781665406826

Cybersecurity contains three basic features such as confidentiality, integrity and availability. Data confidentiality ensuring by encryption has faced quantum computing threat, but this problem can be solved by using post-quantum cryptography and/or quantum cryptography (quantum key distribution). For cryptographic applications statistical properties of the generated pseudorandom sequences should not be differ from random sequences. To determine such properties a number of statistical testing methods are used, but they all are focused on the evaluation of binary sequences. In quantum cryptography there are some protocols with Q-trits (qutrits), but these sequences cannot be assessed effectively by binary statistic methods. From this position in the paper quality assessment approach for Q-trits pseudo random number generators and formed sequences was proposed. Also the experimental study of proposed approach (in comparison with NIST STS) was carried out. Given results show that NIST STS technique cannot be used to evaluate the quality of the Q-trit sequences and generators, but developed method is suitable to use in practice for evaluating Q-trit sequences (generators) quality. © 2021 IEEE.

关键词： Random number generation

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Dynamic Path Planning for Mobile Robots with Deep Reinforcement Learning

引用

IFAC-PapersOnLine 2022年第11期55卷 19-24页

作者： Laiyi Yang Jing Bi Haitao Yuan School of Software Engineering in Faculty of Information Technology Beijing University of Technology Beijing 100124 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

Traditional path planning algorithms for mobile robots are not effective to solve high-dimensional problems, and suffer from slow convergence and complex modelling. Therefore, it is highly essential to design a more efficient algorithm to realize intelligent path planning of mobile robots. This work proposes an improved path planning algorithm, which is based on the algorithm of Soft Actor-Critic (SAC). It attempts to solve a problem of poor robot performance in complicated environments with static and dynamic obstacles. This work designs an improved reward function to enable mobile robots to quickly avoid obstacles and reach targets by using state dynamic normalization and priority replay buffer techniques. To evaluate its performance, a Pygame-based simulation environment is constructed. The proposed method is compared with a Proximal Policy Optimization (PPO) algorithm in the simulation environment. Experimental results demonstrate that the cumulative reward of the proposed method is much higher than that of PPO, and it is also more robust than PPO.

关键词： Deep reinforcement learning path planning Soft Actor-Critic algorithm continuous reward functions mobile robots

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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