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International Conference on Communications (COMM)

作者： Alexandru Chiș Titus-Constantin Bălan Petru-Adrian Cotfas Daniel-Tudor Cotfas Faculty Electrical Engineering and Computer Science Transylvania University Brașov Romania

ISBN: (数字)9798350355956

ISBN: (纸本)9798350355963

Cloud computing offers benefits in the digital world, such as scalability and agility, but securing these environments requires a high level of knowledge and have the potential for misconfiguration. While cloud platforms offer user-friendly interfaces, ensuring robust cloud security requires a balance between user experience and technical expertise to avoid security vulnerabilities that arise from misconfiguration. This paper proposes a fundamental framework for improving the security posture while using the cloud. The framework uses a configurable building block approach to simplify migration and development efforts for cloud-based solutions. The framework focuses on Amazon Web Services (AWS) and automates deployment with infrastructure-as-code (IaC) principles, minimizing the need for deep infrastructure knowledge. This allows users to develop and deploy secure cloud solutions with built-in security tools and monitoring, minimizing the need for extensive infrastructure knowledge on the user's part. This framework aims to provide a hardened environment suited for cloud appropriation. The framework is leveraging security best practices and monitoring tools, harnessing a more secure approach to the use of public cloud environments.

关键词： Knowledge engineering Web services Cloud computing security Scalability Pipelines User experience Threat assessment Regulation Security Monitoring

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An Empirical Survey of Students’ and Professors’ Perspectives on computer science University Teaching

An Empirical Survey of Students’ and Professors’ Perspecti...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Marco Klopp Faculty of Engineering and Computer Science University of Applied Sciences Aschaffenburg Aschaffenburg Germany

ISBN: (数字)9798350376234

ISBN: (纸本)9798350376241

This paper presents a study which examines the pedagogical-interactional dimensions in computer science education at universities of applied sciences, focusing on the expectations and perceptions of students and professors. Data were collected using a modified version of the Learning Culture Inventory (LCI) questionnaire and analyzed both descriptively and *** descriptive statistics reveal differences in the mean values between the ideal and real states of educational conditions as perceived by students and professors. Inferential statistical analyses using t-tests identified significant differences between the perceptions of the two groups. The effect size Cohen’s d was also calculated to determine the strength of the existing difference. The analyses show that there are significant differences in many dimensions of the LCI between the perspectives of students and professors. These differences are often associated with high effect sizes. The results therefore indicate that the students' expectations and perceptions deviate (considerably) from the professors’ perspectives.

关键词： computer science Surveys Statistical analysis Education Focusing computer science education

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Determining category metadata in open data portals – an approach based on Formal Concept Analysis

Determining category metadata in open data portals – an app...

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Telecommunications Forum (TELFOR)

作者： Milena Frtunić Gligorijević Miloš Bogdanović Leonid Stoimenov Department for Computer Science Faculty of Electronic Engineering University of Niš Niš Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

The efforts for data transparency and open government initiatives have resulted in a large amount of data being published on open data portals. These portals are organized to enhance published data accessibility by providing search mechanisms that utilize metadata such as category, tags, format, and publisher. However, due to the metadata incompleteness, such as missing category information, datasets are harder to find. Therefore, within this paper, we address the issue of missing category information by proposing an approach for categorizing datasets using keywords and their usage within categories. The presented approach is based on the Formal Concept Analysis method, which is used to create a hierarchical order of keywords grounded on their usage within categories on one open data portal. Further, we present the evaluation of the presented method using data from the Canadian open data portal.

关键词： Formal concept analysis Knowledge based systems Government Lattices Metadata Market research Iterative algorithms Telecommunications Portals Open data

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MakedonASRDataset - A Dataset for Speech Recognition in the Macedonian Language 15th

MakedonASRDataset - A Dataset for Speech Recognition in th...

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15th International Conference on ICT Innovations, ICT Innovations 2023

作者： Mishev, Martin Penkova, Blagica Mitreska, Maja Kostoska, Magdalena Todorovska, Ana Simjanoska, Monika Mishev, Kostadin Faculty of Computer Science and Engineering Ss Cyril and Methodiuos University Skopje Macedonia

ISBN: (纸本)9783031543203

Using dataset analysis as a research method is becoming more popular among many researchers with diverse data collection and analysis backgrounds. This paper provides the first publicly available dataset consisting of audio segments and appropriate textual transcription in the Macedonian language. It is appropriately preprocessed and prepared for direct utilization in the automatic speech recognition pipelines. The dataset was created by students at the faculty of computer science and engineering as part of the elective course, ‘Digital Libraries’, with the audio segments sourced from a YouTube channel. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Speech recognition

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An Automated Platform for Gathering and Managing Open-Source Cyber Threat Intelligence 2

An Automated Platform for Gathering and Managing Open-Source...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Al-Dmour, Nidal A. Kamrul Hasan, Mohammad Ajmal, Masood Ali, Muhammad Naseer, Iftikhar Ali, Atif Hamadi, Hussam Al Ali, Nasir Mutah University College of Engineering Department of Computer Engineering Jordan Center for Cyber Security Malaysia Riphah International University Islamabad Pakistan University of Central Punjab Pakistan The Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Cyberjaya Malaysia College of Engineering and IT University of Dubai United Arab Emirates School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The community has begun paying more attention to source OSCTI Cyber Threat Intelligence to stay informed about the rapidly changing cyber threat landscape. Numerous reports from the OSCTI frequently provide Information about dangers. However, current OSCTI gathering and management tools have mainly concentrated on individual minor compromise indicators, despite the urgent need for high-quality OSCTI. The relationship between higher-level notions (including the strategies, methods, and processes) and the connections between them, which hold crucial Information about dangerous behaviors and are crucial to revealing the full dangerous situation, have been disregarded. Therefore, we present SecurityKG, an automated OSCTI collection and administration system. SecurityKG collects OSCTI to extract high-fidelity knowledge about threat behaviours to address the void. Using a mixture of AI and NLP approaches, a security know-how graph is then constructed from a wide variety of sources. To facilitate knowledge graph exploration, SecurityKG provides a user interface (UI) that supports multiple forms of interactivity. © 2023 IEEE.

关键词： User interfaces

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Facial Palsy Image Classification Using Landmarks Extraction and Extreme Learning Machine

Facial Palsy Image Classification Using Landmarks Extraction...

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IEEE EMBS Conference on Biomedical engineering and sciences (IECBES)

作者： Frisca P. A. Febriyanti Fitra Abdurrachman Bachtiar Agus W. Widodo Informatics Engineering Department Faculty of Computer Science University of Brawijaya Malang Indonesia

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

Facial palsy is a condition characterized by facial paralysis, affecting the patient's motor function. Detection is typically done through a clinical expert's direct observation of facial muscles. However, manual examinations are less effective as patients tend to go for health checks when symptoms indicate severe dysfunction. Due to these challenges, a classification system is built as early screening for facial palsy. This research aims to propose the detection of facial palsy using Extreme Learning Machine algorithm. In this research, we use three datasets: CK+, JAFFE, and Youtube Facial Palsy (YFP). The dataset is divided into training and testing data in an 80:20 ratio. Applied preprocessing techniques of landmark facial feature extraction, selection, and Euclidean distance calculation at specific face points. We employ k-fold cross-validation to find the optimal parameter during the training process. We also implemented oversampling and undersampling techniques to handle imbalanced data. The system classifies facial palsy into normal, mild, moderate, and severe dysfunction. The ELM model achieved Accuracy values of 0.9949, Precision of 0.9888, Recall of 0.9887, and F1-score of 0.9888 on balanced data using an oversampling technique. The current research indicates that the implemented techniques can handle imbalanced data, and contribute to better model performance.

关键词： Training Accuracy Video on demand Extreme learning machines Biological system modeling Data models Robustness Data mining Web sites Testing

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Current Trends, Challenges and Techniques in XAI Field;A Tertiary Study of XAI Research

Current Trends, Challenges and Techniques in XAI Field;A Ter...

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Proceedings of the International Convention MIPRO

作者： Saša Brdnik Boštjan Šumak Institute of Informatics Faculty of Electrical Engineering and Computer Science University of Maribor (Slovenia)

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores current explanation techniques, providing insights for researchers, practitioners, and policymakers interested in enhancing AI transparency. Notably, the increasing number of systematic literature reviews (SLRs) in XAI publications indicates a growing interest in the field. The review offers an annotated catalogue for human-computer interaction-focused XAI stakeholders, emphasising practitioner guidelines. Automated searches across ACM, IEEE, and science Direct databases identified SLRs published between 2019 and May 2023, covering diverse application domains and topics. While adhering to methodological guidelines, the SLRs often lack primary study quality assessments. The review highlights ongoing challenges and future opportunities related to XAI evaluation standardisation, its impact on users, and interdisciplinary research on ethics and GDPR aspects. The 29 SLRs, analysing over two thousand papers, include five directly relevant to practitioners. Additionally, references from the SLRs were analysed to compile a list of frequently cited papers, serving as recommended foundational readings for newcomers to the field.

关键词： Surveys Ethics Systematics Explainable AI Reviews Bibliographies Market research

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Revolutionizing Dota 2 Viewing: Automated Battle Highlights in Esports Videos 12

Revolutionizing Dota 2 Viewing: Automated Battle Highlights ...

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12th International Electrical engineering Congress, iEECON 2024

作者： Arthayukti, Chananate Malathong, Kritthanit Konpang, Jessada Sirikham, Adisorn Musika, Chanwit Thitimahatthanakusol, Phatsakul Rajamangala University Of Technology Krungthep Faculty Of Science And Technology Department Of Computer Science 2 Nanglingee Rd. Sathorn Thungmahamek Bangkok10120 Thailand Rajamangala University Of Technology Krungthep Faculty Of Engineering Department Of Electrical And Telecommunication Engineering 2 Nanglingee Rd. Sathorn Thungmahamek Bangkok10120 Thailand Rajamangala University Of Technology Isan Faculty Of Engineering And Technology Department Of Telecommunication Engineering 744 Sura Narai Rd Mueang Nakhon Ratchasima Nakorn Ratchasima Nai Mueang30000 Thailand

ISBN: (纸本)9798350383591

This research outlines a structured process for automatically generating video highlights from Dota 2, a multiplayer online battle arena (MOBA) game. Dota 2's dynamic battles make determining their duration challenging, often requiring manual intervention. However, the game's API provides developers access to critical in-game information, particularly player deaths, which serve as key indicators of battles. The process unfolds in six steps: capturing competition videos, extracting match details, refining the data to account for in-game time variations and game start times, automatic video trimming with attention to overlap resolution, consolidation of video segments, and, finally, the publication of the highlight video. This approach ensures engaging and concise highlight videos, enhancing the viewer experience. © 2024 IEEE.

关键词： Trimming

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Study of the impact of Glass Fiber Reinforced Concrete (GFRC) on contemporary facade design evolution

Journal of Umm Al-Qura University for Engineering and Archit...

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Journal of Umm Al-Qura University for engineering and Architecture 2025年 1-18页

作者： Zid, Khaled Ali Mohamed Ali Civil and Architectural Engineering Department College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Architectural Engineering Department Faculty of Engineering Mataria Helwan University Cairo Egypt

This study examined the role of Glass Fiber Reinforced Concrete (GFRC) in addressing contemporary facade design challenges, including the erosion of architectural identity caused by modern materials and high skilled labor costs. It explored how GFRC could revive classical heritage elements while balancing aesthetic innovation, economic efficiency, and environmental sustainability. Using a mixed methodology of comparative quantitative analysis and qualitative case studies, the research found that GFRC not only enabled cost-effective revival of classical styles but also facilitated innovative facade designs unachievable through traditional methods. Examples included (Palazzo Italia (complex modern facades and (Jabal Omar Towers (integration of heritage and modernity. The study recommended standardizing digital manufacturing criteria and analyzing architectural trends. It proposed future research directions, such as integrating 3D printing into GFRC production and developing sustainable glass fibers for harsh climates, positioning GFRC as a bridge between heritage preservation and contemporary innovation.

关键词： Facade design Glass Fiber Reinforced Concrete (GFRC) High-performance finishes Lightweight construction Modular facade systems Prefabricated GFRC components Sustainable facades

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Towards Industry 4.0: Machine Malfunction Prediction Based on IIoT Streaming Data 18

Towards Industry 4.0: Machine Malfunction Prediction Based o...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Nikolova, Dragana Lameski, Petre Pires, Ivan Miguel Zdravevski, Eftim Faculty of Computer Science and Engineering Ss Cyril and Methodius University Skopje Macedonia Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal

ISBN: (纸本)9788396744784

The manufacturing industry relies on continuous optimization to meet quality and safety standards, which is part of the Industry 4.0 concept. Predicting when a specific part of a product will fail to meet these standards is of utmost importance and requires vast amounts of data, which often are collected from variety of sensors, often reffered to as Industrial Internet of Things (IIoT). Using a published dataset from Bosch, that describes the process at every step of production, we aim to train a machine learning model that can accurately predict faults in the manufacturing process. The dataset provides two years of production data across four production lines and 52 stations. Considering that the data generated from each production part includes more than four thousand features, we investigate various feature selection and data preprocessing methods. The obtained results exhibit Area Under the Receiver Operating Characteristic Curve (AUC ROC) of up to 0.997, which is remarkable and promising even for real-life production use. © 2023 Polish Information Processing Society.

关键词： Forecasting

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