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28th International Conference on Principles of Distributed Systems, OPODIS 2024

作者： Cichoń, Jacek Dworzański, Dawid Gotfryd, Karol Department of Fundamentals of Computer Science Faculty of Information and Communication Technology Wroclaw University of Science and Technology Poland

ISBN: (纸本)9783959773607

We study the reliability of the following simple mechanism for spreading information in a communication network in the presence of random message loss. Initially, some nodes have information that they want to distribute throughout the network. Each node that has received the information tries to broadcast it to all its neighbors. However, due to interference or communication failures, each transmission between two nodes is broken independently with some fixed probability. This transmission mechanism is the basis for the extrema propagation technique, proposed and analyzed in [2, 3, 10]. Extrema propagation is a simple but powerful method of spreading the extreme values stored by the nodes. In a fully reliable environment, only the number of rounds equal to the network diameter is required for all nodes to be informed. It was shown empirically in [2] that it also performs well in the presence of link failures and message loss. Extrema propagation is an algorithmic framework that was adopted for designing efficient method for distributed data aggregation, such as estimation of cardinalities, sums, and averages, estimation of data distribution via histograms and random sampling (cf. [3, 19]). In this paper, we propose a formal network model in which random transmission failures occur and analyze the operation time of the extrema propagation technique for any connected network graph. We provide new general probabilistic upper bounds on the number of rounds needed to spread the extreme values that depend only on the number of nodes, the diameter of the network, and the probability of successful transmission. For some special families of graphs, we also derive specific, more accurate estimates. Our theoretical and experimental results confirm the reliability and efficiency of the extrema propagation technique in faulty or noisy environments. © Jacek Cichoń, Dawid Dworzański, and Karol Gotfryd.

关键词： Mobile ad hoc networks

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Automatic Task Classification of Software Projects for Planning and Simulation 25th

Automatic Task Classification of Software Projects for Pla...

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Topical Area: Software, System and Service Engineering, S3E 2024, Held as Part of FedCSIS 2024, and 25th Conference on Practical Aspects of and Solutions for Software Engineering, KKIO 2024, held as part of SEAA 2024

作者： Wysocki, Wlodzimierz Ochodek, Miroslaw Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin Szczecin Poland Faculty of Computer Science and Telecommunications Poznan University of Technology Poznan Poland

ISBN: (纸本)9783031849121

Background: information about project tasks stored in Issue tracking systems (ITS) can be used for project analytics or process simulation. However, such issues must be classified beforehand. Considering the number of tasks stored in ITS, this task shall be done automatically. Aims: Our research aims to build an automatic recurring Jira issue classification model based on types and textual descriptions to enable the practical application of the model for software project planning and management. Method: We study a dataset from six industrial projects containing 9.6K tasks and augment it with an additional dataset of 91K task descriptions from other industrial projects to up-sample minority classes during training. We labeled the data using a semi-manual, active-learning-based method. We perform ten runs of 10-fold cross-validation for each project and evaluate classifiers using a set of state-of-the-art prediction quality metrics, i.e., Accuracy, Precision, Recall, F1-score, and MCC. Our machine-learning pipeline includes a Transformer-based sentence embedder (‘mxbai-embed-large-v1’) and an XGBoost classifier. We also study the impact of task-classification errors on project staffing issues. Results: The model automatically classifies software process tasks into 14 classes with MCCs ranging from 0.69 to 0.88. We built a confusion matrix that showed the most frequently confused task classes. We analyzed the consequences of classification errors. Conclusions: The study’s results enable the practical application of the software process model to analyze, plan, and manage software development projects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Application programs

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A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and Systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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Rüppell’s fox optimizer: A novel meta-heuristic approach for solving global optimization problems

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Cluster Computing 2025年第5期28卷 1-77页

作者： Braik, Malik Al-Hiary, Heba Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

A new swarm-based, nature-inspired meta-heuristic method, called Rüppell’s Fox Optimizer (RFO), is designed and examined in this study to address global optimization problems. RFO takes inspiration from the natural and intelligent communal foraging practices of Rüppell’s foxes, both during the day and at night. The optimizer mathematically simulates a variety of chief foraging activities of Rüppell’s foxes using their acute eyesight, hearing, and scent to hunt, to accommodate both exploration and exploitation aspects throughout the optimization action. The RFO algorithm simulates the rotating eyes’ feature of Rüppell’s foxes to a field of view of about 260°, as well as the feature of rotating their ears to a field of hearing of about 150°. Further, a cognitive component, ρ, is designed to oversee the evolution from global search to local search as well as the equilibrium between exploration and exploitation within the search domain. Based on Rüppell’s foxes’ foraging qualities, RFO reveals a variety of foraging habits. The dynamic patterns and activities of Rüppell’s foxes were mathematically mimicked in this study to create an efficient global optimizer, which when applied to optimization problems, ultimately yields feasible solutions. The performance level of RFO was thoroughly examined through a comparison with 12 other algorithms using three challenging test sets: Congress on Evolutionary Computation 2017 (CEC 2017) group with 29 test functions of various dimensions, the Congress on Evolutionary Computation 2019 (CEC 2019) with 10 test functions, and CEC 2020 set with 10 test functions. Using mean rank of Friedman’s test, RFO outperformed other comparative algorithms by 31.25%, 34.04%, 32.36%, and 55.13% on the 10-, 30-, 50-, and 100-dimensional CEC 2017 test set, respectively, 57.11% on the CEC 2019 test suite with 10 dimensions, 48.59%,and 51.68% on the 10- and 20-dimensional CEC 2020 set, respectively. The applicability of RFO is thoroughly examined on 6 clas

关键词： Heuristic methods

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and Systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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An Improved Hybrid Deep Learning Approach for Security Requirements Classification

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computers, Materials & Continua 2025年第3期82卷 4041-4067页

作者： Shoaib Hassan QianmuLi Muhammad Zubair Rakan AAlsowail Muhammad Umair School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Faculty of Information Technology and Computer Science University of Central PunjabLahore5400Pakistan Computer Skills Self-Development Skills DevelopmentDeanship of Common First YearKing Saud UniversityRiyadh11362Saudia Arabia

As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering *** literature studies have proposed numerousmodels for the classification of security ***,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning ***,most of the researchers focus only on the classification of requirements with security *** did not consider other nonfunctional requirements(NFR)directly or indirectly related to *** has been identified as a significant research gap in security requirements *** major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security *** use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering *** proposed methodology consists of two *** the first step,we analyze all the nonfunctional requirements and their relation with security *** found 10 NFRs that have a strong relationship with security *** the second step,we categorize those NFRs in the security requirements *** proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)***,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security *** performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,*** proposed study shows an enhancement in metrics

关键词： Requirements engineering security requirements deep learning CNN XGBoost classification

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EduGym Post-Mortem: Open Educational Resource for Educational Game

EduGym Post-Mortem: Open Educational Resource for Educationa...

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2025 International Conference on Advancement in Data science, E-learning and information System, ICADEIS 2025

作者： Tarigan, Jos Timanta Zamzami, Elviawaty Muisa Zendrato, Niskarto Nababan, Anandhini Medianty Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331513320

This paper aims to delve into the development of EduGym, an educational microlearning game specifically designed for mobile platforms. The discussion highlights the implementation and integration of Open Educational Resources (OER) and examines their significant impact on the development process. The project strategically used OER as the primary content source to ensure seamless future content expansion without requiring changes to the core application. This approach also empowers other developers to repurpose and adapt the content for various educational applications. Furthermore, the paper addresses key challenges encountered during the development process from a software engineering standpoint, including maintaining flexibility, ensuring scalability, and managing resource allocation. The study outlines the overall architecture of EduGym, detailing the development workflow and the ways in which OER integration influenced both development and resource management. The aim is to offer valuable insights and practical guidance for developers and educators who are exploring similar methodologies, informed by the lessons learned and experiences gained through the creation of EduGym. © 2025 IEEE.

关键词： Game design

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

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Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. Data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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Explainable ensemble deep learning-based model for brain tumor detection and classification

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Neural Computing and Applications 2025年第3期37卷 1289-1306页

作者： Hosny, Khalid M. Mohammed, Mahmoud A. Salama, Rania A. Elshewey, Ahmed M. Department of Information Technology Faculty of Computers and Information Zagazig University P.O.BOX 44519 Zagazig Egypt Department of Computer Science Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt Department of Information Technology Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt

Brain tumors are very dangerous as they cause death. A lot of people die every year because of brain tumors. Therefore, accurate classification and detection in the early stages can help in recovery. Various deep learning techniques have achieved good results in brain tumor classification. The traditional deep learning methods and training the neural network from scratch are time-consuming and can last for weeks of training. Therefore, in this work, we proposed an ensemble approach depending on transfer learning that utilizes pre-trained models of DenseNet121 and InceptionV3 to detect three forms of brain tumors: meningioma, glioma, and pituitary. While developing the ensemble model, some changes were made to the architecture of pre-trained models by replacing their classifiers (fully connected and SoftMax layers) with a new classifier to adopt the recent task. In addition, gradient-weighted class activation maps (Grad-CAM) are an explainable model to verify results and achieve high confidence. The suggested model was validated using a publicly available dataset and achieved 99.02% accuracy, 98.75% precision, 98.98% recall, and a 98.86% F1 score. The suggested approach outperformed others in detecting and classifying brain tumor MRI data, and verifying results using the explainable model achieved a high degree of trust. © The Author(s) 2024.

关键词： Transfer learning

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