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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Krupalija, Ehlimana Cogo, Emir Becirovic, Seila Prazina, Irfan Pozderac, Damir Besic, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350322910

Cause-effect graphs are a commonly used black-box testing method, and many different algorithms for converting system requirements to cause-effect graph specifications and deriving test case suites have been proposed. However, in order to test the efficiency of black-box testing algorithms on a variety of cause-effect graphs containing different numbers of nodes, logical relations and dependency constraints, a dataset containing a collection of cause-effect graph specifications created by authors of existing papers is necessary. This paper presents CEGSet, the first collection of existing cause-effect graph specifications. The dataset contains a total of 65 graphs collected from the available relevant literature. The specifications were created by using the ETF-RI-CEG graphical software tool and can be used by future authors of papers focusing on the cause-effect graphing technique. The collected graphs can be re-imported in the tool and used for the desired purposes. The collection also includes the specification of system requirements in the form of natural language from which the cause-effect graphs were derived where possible. This will encourage future work on automatizing the process of converting system requirements to cause-effect graph specifications. © 2023 IEEE.

关键词： Black-box testing

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Optimization of the test case minimization algorithm based on forward-propagation in cause-effect graphs 22

Optimization of the test case minimization algorithm based o...

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22nd International Symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Krupalija, Ehlimana Cogo, Emir Bećirović, Šeila Prazina, Irfan Pozderac, Damir Bešić, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9781665475464

Many different methods are used for generating blackbox test case suites. Test case minimization is used for reducing the feasible test case suite size in order to minimize the cost of testing while ensuring maximum fault detection. This paper presents an optimization of the existing test case minimization algorithm based on forward-propagation of the cause-effect graphing method. The algorithm performs test case prioritization based on test case strength, a newly introduced test case selection metric. The optimized version of the minimization algorithm was evaluated by using thirteen different examples from the available literature. In cases where the existing algorithm did not generate the minimum test case subsets, significant improvements of test effect coverage metric values were achieved. Test effect coverage metric values were not improved only in cases where maximum optimization was already achieved by using the existing algorithm. © 2023 IEEE.

关键词： Black-box testing

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Generating animatable 3D cartoon faces from single portraits

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虚拟现实与智能硬件（中英文） 2024年第4期6卷 292-307页

作者： Chuanyu PAN Guowei YANG Taijiang MU Yu-Kun LAI Department of Electrical Engineering and Computer Sciences University of California BerkeleyBerkeley CA 94704USA Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK

Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D ***,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being *** study presents a novel framework for generating animatable 3D cartoon faces from a single portrait *** First,we transferred an input real-world portrait to a stylized cartoon image using *** then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed *** two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark ***,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation *** Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity ***,we demonstrated the capability of the proposed 3D model for real-time facial animation.

关键词： 3D reconstruction Cartoon face reconstruction Face rigging Stylized reconstruction Virtual reality

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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Cyber Attacks Detection using Deep Learning Methods for Resilient Operation in DC Shipboard Microgrids

Cyber Attacks Detection using Deep Learning Methods for Resi...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ali, Zulfiqar Hussain, Tahir Su, Chun-Lien Jurcut, Anca Delia Baloch, Shazia Sadiq, Muhammad National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung city Taiwan Graduate School of Informatics and Engineering The University of Electro-Communication Department of Informatics Tokyo Japan School of Computer Science University College Dublin BelfieldDublin 4 Ireland Balochistan University of Engineering and Technology Department of Electrical Engineering Khuzdar Pakistan

ISBN: (纸本)9798350352290

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity. © 2024 IEEE.

关键词： Network intrusion

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Relevance Vector Machine for Code Smell Detection 22

Relevance Vector Machine for Code Smell Detection

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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Putro, Hanson Prihantoro Yuhana, Umi Laili Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Engineering Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798331527471

Various approaches have been proposed to detect code smells, including machine learning models. However, there are still challenges to improving detection accuracy and selecting appropriate quality metrics. This research introduces the use of Relevance Vector Machine (RVM) for code smell detection, especially Magic Value smells, which often affect source code readability and maintainability in a software development industry. This approach utilizes source code analysis using two source code representations (raw source code and token stream) and feature extraction using two text mining techniques (Bag of Words/BoW and Term Frequency-Inverse Document Frequency/TF-IDF). The experiment involves analyzing 983 records of labeled source code datasets. The results show that RVM can detect code smells with a very high level of accuracy, reaching 99.9% on some kernel configurations, also nearly perfect precision, and F1 scores. This paper contributes to introducing the use of RVM for detecting code smells in source code, explores source code representation and feature extraction techniques, and shows that RVM can achieve high detection accuracy. © 2024 IEEE.

关键词： Software design

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A spatio-temporal matrix representation for trajectory classification 24

A spatio-temporal matrix representation for trajectory class...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Kontopoulos, Ioannis Varlamis, Iraklis Makris, Antonios Tserpes, Konstantinos Department of Informatics and Telematics Harokopio University of Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798400711077

Fish piracy remains widespread globally despite national and international efforts. Experts estimate it accounts for about 20% of the total seafood catch worldwide. Technology is playing a key role in detecting illegal fishing, with satellite imagery and sensors being used to track vessels and monitor fishing practices. Since fishing boats broadcast their positions using a vessel tracking system, this data can be processed to detect illegal activity. This study focuses on classifying fishing vessel trajectories using only positional data. A novel trajectory representation and a Convolutional Neural Network is employed, showing promising results compared to traditional methods. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Exploring Energy Culture and Efficiency Awareness: Insights from Private and Public Sectors in Romania 3

Exploring Energy Culture and Efficiency Awareness: Insights ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Muresan, Claudia Stet, Denisa Cirstea, Stefan Czumbil, Levente Polycarpou, Alexis Micu, Dan D. Technical University of Cluj-Napoca Energy Transition Research Center Cluj-Napoca Romania Frederick University Computer Engineering and Informatics Department of Electrical Engineering Nicosia Cyprus

ISBN: (纸本)9798350375923

This paper focuses on the energy transition as a social transition and highlights the importance of understanding individual and collective behaviors, aspirations and perceptions regarding energy consumption. The conceptual framework of energy cultures serves as a basic tool for investigating energy behavior, facilitating a deeper understanding of the factors influencing energy consumption. The study, carried out within the ENERGEIA project, focuses on assessing energy culture and energy efficiency awareness in both private entities and public institutions in Romania. Using a comprehensive questionnaire tool, the research aims to uncover the main challenges and barriers faced by organizations in implementing energy efficiency measures. The paper presents a quantitative analysis of the survey data aiming to gather information about the energy culture of companies and public bodies to identify common opportunities for action to improve energy culture and awareness levels. © 2024 IEEE.

关键词： awareness energy culture energy culture framework energy efficiency sustainability

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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Optimizing Item-based Collaborative Filtering for Course Recommendation: A Case Study with Coursera Data

Optimizing Item-based Collaborative Filtering for Course Rec...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Suryani, Mira Pangestu, Risyad Pradana, Aditya Universitas Padjadjaran Departement of Computer Science Sumedang Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798331511470

In e-learning environments, the sheer volume of available courses often overwhelms users, leading to decision fatigue and disengagement Learners vary widely in their preferences, learning styles, and goals, yet generic recommendations fail to account for these differences, resulting in suboptimal learning experiences. Therefore, personalized recommendations which are generally found in recommendation systems are needed especially in e-learning systems. Collaborative filtering is a popular method in recommendation systems, generating predictions by analyzing user ratings to forecast items of interest. This study implements an item-based collaborative filtering approach, calculating item similarity to recommend courses effectively. The study involves developing a web-based recommendation system utilizing a dataset of 623 Coursera courses and 1,454,711 course review entries. Prediction accuracy is enhanced through weighted sum and adjusted weighted sum calculations, employing cosine similarity and adjusted cosine similarity to refine similarity scores. Prediction errors are evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. Testing various K-nearest neighbor values reveals that an optimal K value of 150 yields an MAE of 0.4791 and an RMSE of 0.7882 for weighted sum predictions. For adjusted weighted sum predictions, an optimal K value of 160 produces an MAE of 0.4792 and an RMSE of 0.7638. These results highlight the effectiveness of item-based collaborative filtering in delivering accurate course recommendations and showcase the impact of similarity measures on predictive accuracy. © 2024 IEEE.

关键词： Collaborative filtering

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