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23rd International Conference on Web-Based Learning, ICWL 2024 was held in conjunction with the 9th International Symposium on Emerging Technologies for Education, SETE 2024

作者： Lei, Philip I. S. Mendes, António José Lam, Chan-Tong CISUC Department of Informatics Engineering University of Coimbra Coimbra Portugal Computing Program Macao Polytechnic University Macao SAR China

ISBN: (纸本)9789819644063

After learning the basic features of a programming language, such as expressions and control statements, novice programmers need to combine these features to solve programming questions. Some of these combined code snippets are reusable constructs useful to solve future questions. They are referred to as plans, schemas, or patterns in the literature. An example of such a pattern is determining the equality of three values using the ‘equal and logical and operators. Previous research has investigated the acquisition of these patterns by observing and interviewing novice programmers as they solve programming problems. In this paper, we test the hypothesis that students tend to reuse acquired patterns to solve subsequent problems by analyzing a computer science education dataset called CodeWorkout, which contains students’ code submissions for introductory-level programming questions. We employed directed labeled tree to represent the Abstract Syntax Tree (AST) of the submitted code and the patterns. To search for a pattern, we first applied the subgraph isomorphism algorithm VF2 with wildcard node labels to identify candidate matches, followed by enforcing the proper ordering of descendant nodes between the pattern and the candidate matches. Experimental results demonstrate that our approach effectively identifies common usage of patterns. Moreover, the findings indicate that if a student employs a pattern in one question, they are likely to reuse the same pattern in a subsequent question. This paper also reveals that when multiple alternative patterns exist for the same function, students tend to maintain their preference for a particular alternative while solving different questions. These insights can inform future curriculum design and instructional strategies in programming education. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Innovative Byzantine Fault Tolerance in Hyperledger Fabric with BDLS 6

Innovative Byzantine Fault Tolerance in Hyperledger Fabric w...

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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Al Salih, Ahmed Wang, Yongge University of North Carolina at Charlotte College of computing and informatics Software and Information Systems Department CharlotteNC United States

ISBN: (纸本)9798350351538

Decentralized systems are integral to various sectors, including public and private organizations. A key component of these systems is the dissemination protocol. Hyperledger Fabric, a prominent production-ready distribution network, allows for a pluggable consensus protocol, but has historically lacked detailed technical guidance for implementing such modules. Initially, Fabric employed Kafka and later switched to Raft, both Crash Fault Tolerant (CFT) protocols that do not support Byzantine Fault Tolerance (BFT). Recently, Hyperledger Fabric introduced SmartBFT in its 3.0 release, a new BFT solution. However, SmartBFT, based on the PBFT family, suffers from high message complexity that scales poorly with network growth, undermining efficiency for large-scale implementations in industries such as healthcare, finance, and education. Our research demonstrates that the message complexity of SmartBFT grows quadratically with the number of orderer nodes, leading to significant communication overhead. In contrast, our proposed implementation of BDLS maintains linear message complexity, making it more scalable and efficient for large networks. This stark contrast highlights BDLS’s superior scalability and efficiency. Additionally, BDLS consistently outperforms SmartBFT in throughput analysis of Fabric 3.0, achieving significantly higher TPS-40% and 20% of Raft’s TPS in LAN and WAN environments, respectively. All code is available in the GitHub repository: https://***/hyperledger-labs/bdls. © 2024 IEEE.

关键词： Decentralized systems

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CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks 21

CE-QArg: Counterfactual Explanations for Quantitative Bipola...

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21st International Conference on Principles of Knowledge Representation and Reasoning, KR 2024

作者： Yin, Xiang Potyka, Nico Toni, Francesca Department of Computing Imperial College London United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9781956792058

There is a growing interest in understanding arguments’ strength in Quantitative Bipolar Argumentation Frameworks (QBAFs). Most existing studies focus on attribution-based methods that explain an argument’s strength by assigning importance scores to other arguments but fail to explain how to change the current strength to a desired one. To solve this issue, we introduce counterfactual explanations for QBAFs. We discuss problem variants and propose an iterative algorithm named Counterfactual Explanations for Quantitative bipolar Argumentation frameworks (CE-QArg). CE-QArg can identify valid and cost-effective counterfactual explanations based on two core modules, polarity and priority, which help determine the updating direction and magnitude for each argument, respectively. We discuss some formal properties of our counterfactual explanations and empirically evaluate CEQArg on randomly generated QBAFs. © 2024 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved.

关键词： Knowledge representation

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Preface for the 10th International conference on sociotechnical perspectives in Information Systems (STPIS 2024)

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CEUR Workshop Proceedings 2024年 3857卷 1-7页

作者： Kävrestad, Joakim Bednar, Peter Rajanen, Mikko School of Engineering Jönköping University Sweden Department of Informatics University of Lund Sweden School of Computing University of Portsmouth United Kingdom INTERACT Research Unit University of Oulu Finland

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Evaluation of an agent-based reinforcement learning framework for model-based testing 7

Evaluation of an agent-based reinforcement learning framewor...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Ramírez-Méndez, Jose Quesada-López, Christian University of Costa Rica Graduate Program in Computing and Informatics San Pedro Costa Rica University of Costa Rica Department of Computer Science and Informatics San Pedro Costa Rica

ISBN: (纸本)9798350364538

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework's performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review of Human Vulnerabilities in Cyber Security: Challenges and Solutions for Microfinance Institutions

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Journal of Information Security 2025年第1期16卷 114-130页

作者： Evaline Waweru Simon Maina Karume Alex Kibet Department of Computing and Informatics The Cooperative University Nairobi Kenya Department of Computer Science and Information Technology Kabarak University Nakuru Kenya Department of Computing and Informatics Laikipia University Laikipia Kenya

This review examines human vulnerabilities in cybersecurity within Microfinance Institutions, analyzing their impact on organizational resilience. Focusing on social engineering, inadequate security training, and weak internal protocols, the study identifies key vulnerabilities exacerbating cyber threats to MFIs. A literature review using databases like IEEE Xplore and Google Scholar focused on studies from 2019 to 2023 addressing human factors in cybersecurity specific to MFIs. Analysis of 57 studies reveals that phishing and insider threats are predominant, with a 20% annual increase in phishing attempts. Employee susceptibility to these attacks is heightened by insufficient training, with entry-level employees showing the highest vulnerability rates. Further, only 35% of MFIs offer regular cybersecurity training, significantly impacting incident reduction. This paper recommends enhanced training frequency, robust internal controls, and a cybersecurity-aware culture to mitigate human-induced cyber risks in MFIs.

关键词： Human Vulnerabilities Cybersecurity Microfinance Institutions Cyber Threats Cybersecurity Awareness Risk Mitigation

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Implementation of OpenMP for Solving Linear Shallow Water Equations using Staggered Grid and MacCormack Scheme 3

Implementation of OpenMP for Solving Linear Shallow Water Eq...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Iryanto Gunawan, Putu Harry Palupi, Irma Ikhsan, Nurul Politeknik Indramayu Informatics Department Indramayu Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350394559

This study outlines the implementation of OpenMP parallelization techniques to solve the Linear Shallow Water Equations (LSWE) using the MacCormack scheme and a staggered grid. These simulations are essential in various scientific disciplines, including but not limited to environmental modeling and fluid dynamics. The distribution of computational resources over several threads is the key to this attempt, resulting in significant improvements in computational performance. This highlights the possibility of developing scalable and highly efficient approaches for simulating the complicated dynamics of shallow water, addressing a significant demand in various research and applications. We discover insights within our experimental grid size trial. In general, the staggered grid outperforms the MacCormack scheme regarding computational speed and root mean square error. However, compared to the MacCormack scheme, the staggered grid technique achieves an error cap of 2 × +10-4, still higher than the MacCormack schemes for the Cauchy or initial value problem in early time of simulation. Our research also delves into the actual application of parallel processing, which is a significant tool for increasing the computing efficacy of LSWE simulations. Compared to typical serial computation, our approach achieves a significant speed-up score improvement of up to 3.721 times via the distribution of computational work among four threads. This practical advancement highlights the viability of scalable and robust systems when confronted with the complicated simulation of shallow water dynamics, highlighting a promising trajectory for future developments in this subject area. © 2023 IEEE.

关键词： Initial value problems

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Genetic Algorithm Augmented Inception-Net based Image Classifier Accelerated on FPGA

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Multimedia Tools and Applications 2023年第29期82卷 45097-45125页

作者： Kaziha, Omar Bonny, Talal Jarndal, Anwar Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Deep learning models for computer vision applications specifically and for machine learning generally are now the state of the art. The growth of size and complexity of neural networks has made them more and more reliable, yet in greater need of computational power and memory as is evident from the heavy reliance on graphical processing units and cloud computing for training them. As the complexity of deep neural networks increases, the need for fast processing neural networks in real-time embedded applications at the edge also increases and accelerating them using reconfigurable hardware suggests a solution. In this work, a convolutional neural network based on the inception net architecture is first optimized in software and then accelerated by taking advantage of field programmable gate array (FPGA) parallelism. Genetic algorithm augmented training is proposed and used on the neural network to produce an optimum model from the first training run without re-training iterations. Quantization of the network parameters is performed according to the weights of the network. The resulting neural network is then transformed into hardware by writing the register transfer level (RTL) code for FPGAs with exploitation of layer parallelism and a simple trial-and-error allocation of resources with the help of the roofline model. The approach is simple and easy to use as compared to many complex existing methods in literature and relies on trial and error to customize the FPGA design to the model needed to work on any computer vision or multimedia application deep learning model. Simulation and synthesis are performed. The results prove that the genetic algorithm reduces the number of back-propagation epochs in software and brings the network closer to the global optimum in terms of performance. Quantization to 16 bits also shows a reduction in network size by almost half with no performance drop. The synthesis of our design also shows that the Inception-based classifier is cap

关键词： Genetic algorithms

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Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

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Computers, Materials & Continua 2023年第7期76卷 347-366页

作者： Niharika Gupta Baijnath Kaushik Mohammad Khalid Imam Rahmani Saima Anwar Lashari Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatraJ&KIndia College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer *** prediction of diabetes is crucial to taking precautionary steps to avoid or delay its *** this study,we proposed a Deep Dense Layer Neural Network(DDLNN)for diabetes prediction using a dataset with 768 instances and nine *** also applied a combination of classical machine learning(ML)algorithms and ensemble learning algorithms for the effective prediction of the *** classical ML algorithms used were Support Vector Machine(SVM),Logistic Regression(LR),Decision Tree(DT),K-Nearest Neighbor(KNN),and Naïve Bayes(NB).We also constructed ensemble models such as bagging(Random Forest)and boosting like AdaBoost and Extreme Gradient Boosting(XGBoost)to evaluate the performance of prediction *** proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the *** combined ML models used majority voting to select the best outcomes among the *** efficacy of the proposed and other models was evaluated for effective diabetes *** investigation concluded that the proposed model,after hyperparameter tuning,outperformed other learning models with an accuracy of 84.42%,a precision of 85.12%,a recall rate of 65.40%,and a specificity of 94.11%.

关键词： Diabetes prediction hyperparameter tuning k-fold validation machine learning neural network

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OpenMP Performance for 1D Shallow Water Equations with High Order of Time Discretization 3

OpenMP Performance for 1D Shallow Water Equations with High ...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Gunawan, Putu Harry Iryanto, Iryanto Palupi, Irma Ikhsan, Nurul School of Computing Telkom University Bandung Indonesia Politeknik Indramayu Informatics Department Indramayu Indonesia

ISBN: (纸本)9798350394559

OpenMP is pivotal in simulating shallow water equations (SWE), a foundational model with applications in diverse fields like hydrodynamics and environmental engineering. These equations describe fluid motion in scenarios where the depth is significantly smaller than the horizontal length scale. Efficient numerical simulations of SWE require parallel processing to handle computational complexity and achieve prompt results. OpenMP facilitates this parallelization by enabling developers to identify regions in the code for concurrent execution by multiple threads. This approach is particularly advantageous in solving discretized SWE, where the computational domain is partitioned, with each thread managing a portion of the workload. By employing OpenMP directives, programmers can harness modern multicore processors' parallel capabilities, significantly accelerating shallow water dynamics simulations. This acceleration contributes to flood modeling, coastal engineering, and environmental risk assessment advancements. The research uses the Runge-Kutta method to analyze OpenMP performance for high-order time discretization of SWE. Results indicate that the 4th-order Runge-Kutta method outperforms Euler and the 2nd-order Runge-Kutta method in RMSE evaluation. Moreover, employing four threads in OpenMP reduces computational time by 3.5 times compared to serial execution, with an efficiency rate exceeding 80%. © 2023 IEEE.

关键词： Runge Kutta methods

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