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Gaze Analysis and Modeling of Cognitive Process During Debugging for Novice programmers’ Learning

Gaze Analysis and Modeling of Cognitive Process During Debug...

Thematic area Human Interface and the Management of information, HIMI 2022 Part of the 24th HCI International Conference, HCII 2022

作者： Yoshimori, Kohei Kano, Toru Akakura, Takako Graduate School of Engineering Department of Information and Computer Technology Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan Faculty of Engineering Department of Information and Computer Technology Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan

ISBN: (纸本)9783031064234

Recently, the demand for IT human resources has increased. Therefore, the expansion of programming education should be considered to strengthen the supply of it. This study aimed to propose debugging learning support for novice programmers using experts’ gaze behavior. First, we gave feedback to proficient users on their gazing behavior during program debugging and interviewed them about their cognitive processes. Based on the results, we developed a cognitive process model of expert debugging. However, most of the visualized information was based on gaze transitions, and it was difficult to see the characteristics of the cognitive process. This study helped to improve the previously proposed model and developed one that focuses more on the cognitive process during program debugging. Data from students with little programming experience were collected and compared with the cognitive process model of programming experts. This enabled the identification of the characteristics of the program debugging process of non-experts. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： program debugging

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A Novel Resource Allocation in Software-Defined Networks for IoT Application 31

A Novel Resource Allocation in Software-Defined Networks for...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： De Sousa, Alexandre Ladeira Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Santo Andre Graduate Program in Information Engineering Sao Paulo Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

The rapid proliferation of Internet of Things (IoT) devices has posed significant challenges for network resource allocation and management. In this paper, we propose a novel methodology for efficient resource allocation in Software-Defined Networks (SDNs) to meet the unique requirements of IoT applications. Our approach leverages the flexibility and programmability of SDN to dynamically allocate network resources based on the specific needs of IoT devices. Our proposal combines reinforcement learning with dynamic resource allocation, demonstrating its effectiveness in optimizing resource allocation and enhancing network performance. The results highlight the potential of our approach to addressing the resource challenges in SDN-based IoT environments. The proposed method obtains results superior to 30% of throughput in comparison to a traditional resource allocation in SDN to IoT applications. © 2023 University of Split, FESB.

关键词： Internet of things

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis 6

Learning Adaptation and Generalization from Human-Inspired M...

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6th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023

作者： Ho, Joshua Wang, Chien-Min King, Chung-Ta You, Yi-Hsin Feng, Chi-Wei Chen, Yen-Min Kuo, Bo-Yi Institute of Information Science Academia Sinica Taipei11529 Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Taiwan University Taipei10631 Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei10022 Taiwan

ISBN: (纸本)9798350331288

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a humaninspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future. © 2023 IEEE.

关键词： Reinforcement learning

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QoE Enhancement of Multi-View Video Transmission Over ICN/CCN by Means of Cache Control Method

QoE Enhancement of Multi-View Video Transmission Over ICN/CC...

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IEEE Games Media Entertainment (GEM)

作者： Yoshiyuki Takada Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

This paper considers multi-view video and audio transmission on ICN (information-Centric Networking)/CCN (Content-Centric Networking). Routers in ICN/CCN can cache content. Besides, the capacity of routers' caches is finite, so various cache control schemes have been proposed to improve cache efficiency. This paper presents and evaluates a new and suitable control scheme for multi-view video and audio transmission. For this purpose, we construct a network environment with Cefore. We assess application-level QoS (Quality of Service) and QoE (Quality of Experience). We then show the effectiveness of the proposed control scheme.

关键词： information-centric networking Entertainment industry Quality of service Media Quality of experience Frequency control

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Usefulness of Honeypots Towards Data Security: A Systematic Literature Review

Usefulness of Honeypots Towards Data Security: A Systematic ...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Kalpin Erlangga Silaen Meyliana Meyliana Harco Leslie Hendric Spits Warnars Harjanto Prabowo Achmad Nizar Hidayanto Maria Susan Anggreainy Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Information System Faculty of Computer Science Universitas Indonesia Depok Indonesia

The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity.

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Towards Energy-Efficient Scheduling of UAV-Enabled Mobile Edge Computing Systems

Towards Energy-Efficient Scheduling of UAV-Enabled Mobile Ed...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yuan, Haitao Wang, Meijia Bi, Jing Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing University of Technology Faculty of Information Technology Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Current mobile edge computing (MEC) owns cloud resources at the network edge, which enables low-latency mobile services. In addition to fixed MEC servers, MEC proxy servers with certain mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs), and vehicles, have emerged as competitors in providing services. In this work, aiming at a task offloading problem of a UAV-assisted MEC system, a hybrid network environment with multiple mobile devices (MDs) and multiple UAVs is established. A constrained mixed integer nonlinear program of the UAV-assisted hybrid cloud-edge system is formulated. A novel hybrid metaheuristic algorithm called Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) is presented to solve the program. Then, a task offloading and resource scheduling method is designed to intelligently minimize the total energy consumption of the hybrid system. Simulation results verify superiority of GSPSO over its three benchmark algorithms, thus demonstrating the proposed method significantly improves the energy efficiency of the UAV-enabled hybrid system. © 2023 IEEE.

关键词： computation offloading mobile edge computing particle swarm optimization simulated annealing UAVs

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Random Forest Based QSAR Model Analysis for Predicting Drug Effectiveness Against Mycobacterium Tuberculosis Bacteria

Random Forest Based QSAR Model Analysis for Predicting Drug ...

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International Conference on ICT For Smart Society (ICISS)

作者： Alicia Josephine Ekosputri Amanda Ardianti Maria Susan Anggreainy Brychan Artanto Nora Fitriawati Fanny Angelia Valentina Vincent Kartamulya Santoso Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This research focuses on the use of machine learning methods, especially Random Forest, in designing tuberculosis drugs more effectively and efficiently. The research process begins with the creation of a QSAR (Quantitative Structure-Activity Relationship) model using a dataset from ChEMBL that includes compounds with activity against Mycobacterium tuberculosis. This model aims to predict the pIC50 value, which is an indicator of a compound's biological activity as a tuberculosis drug. The results of the Random Forest model showed high accuracy, which is 92%. This accuracy indicates that the model has a good ability in predicting the effectiveness of compounds against Mycobacterium tuberculosis. With this result, the developed model can be used as an auxiliary tool in the discovery of tuberculosis drugs and can be further developed to increase the effectiveness in drug research.

关键词： Drugs Accuracy Tuberculosis Biological system modeling Linear regression Predictive models Compounds Reliability Random forests Overfitting

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Recent Trends for Text Summarization in Scientific Documents

Recent Trends for Text Summarization in Scientific Documents

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Arief Agus Sukmandhani Yulyani Arifin Muhammad Zarlis Widodo Budiharto Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required; therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization.

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Light Cone Cancellation for Variational Quantum Eigensolver Ansatz

arXiv

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arXiv 2024年

作者： Yan, Xinjian Lee, Xinwei Xie, Ningyi Saito, Yoshiyuki Kurosawa, Leo Asai, Nobuyoshi Cai, Dongsheng Lau, Hoong Chuin Graduate School of Science and Technology University of Tsukuba Japan School of Computing and Information Systems Singapore Management University Singapore Graduate School of Computer Science and Engineering University of Aizu Japan School of Computer Science and Engineering University of Aizu Japan Faculty of Engineering Information and Systems University of Tsukuba Japan

Variational Quantum Algorithms (VQAs) represent a class of algorithms that utilize a hybrid approach, combining classical and quantum computing techniques. In this approach, classical computers serve as optimizers that update circuit parameters to find approximate solutions to complex problems. In this study, we apply a method known as Light Cone Cancellation (LCC) to optimize variational circuits, effectively reducing the required number of qubits and gates for circuit simulation. We then evaluate the performance of LCC one of the VQAs—the Variational Quantum Eigensolver (VQE)—to address the Max-Cut problem. Compared with the Quantum Approximate Optimization Algorithm (QAOA), VQE offers greater degrees of freedom and promising results at lower circuit depths. By applying LCC to VQE, we can shift the complexity of circuit simulation from the number of qubits to the number of edges in the graph, i.e., from exponential time to polynomial time. This enables us to solve large problems up to 100 vertices, without actually simulating the entire circuit. From our simulation in a 7-qubit and a 27-qubit fake backends, we show that LCC yields higher approximation ratios than those cases without LCC, implying that the effect of noise is reduced when LCC is applied. © 2024, CC BY.

关键词： Qubits

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A Quantum Q-Learning Model for the Capacitated Vehicle Routing Problem

A Quantum Q-Learning Model for the Capacitated Vehicle Routi...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Jiahua Xu Ningyi Xie Xinwei Lee Yoshiyuki Saito Nobuyoshi Asai Dongsheng Cai Graduate School of Science and Technology University of Tsukuba Japan School of Computing and Information Systems Singapore Management University Singapore Graduate School of Computer Science and Engineering University of Aizu Japan School of Computer Science and Engineering University of Aizu Japan Faculty of Engineering Information and Systems University of Tsukuba Japan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart.

关键词： Training Q-learning Quantum algorithm Multicore processing Vehicle routing Buildings Noise measurement Integrated circuit modeling Vehicle dynamics Quantum circuit

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