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Learning and Technology Conference (L&T)

作者： Jawahir Che Mustapha Muhammad Azmin Mohamed Ghazali Munaisyah Abdullah Nor Intan Saniah Sulaiman Hezlin Aryani Abd Rahman Informatics & Analytics Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Computer Engineering Tech. Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia College of Arts and Science School of Quantitative Sciences Universiti Utara Malaysia Kedah Malaysia Centre of Statistical and Decision Science Studies Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Selangor Malaysia

This research investigates the complex relationships between social media, body image, and bullying among adolescent boarding school students, with an emphasis on the Malaysian context. Utilizing science fiction as a conceptual lens, the study presents hypothetical future scenarios to investigate how technological advancements could impact these interconnected issues. The paper seeks to provide nuanced insights into the ethical and policy implications of social media use in boarding schools by drawing on key theories such as Social Comparison Theory, Social-Ecological Model and Uses and Gratifications Theory. Through comparative analysis of hypothetical scenarios, this study aims to provide educators, policymakers, and technologists with a comprehensive understanding of the challenges and opportunities at this intersection. The ultimate objective is to contribute to the ongoing discourse concerning adolescent mental health and to propose actionable strategies for mitigating negative outcomes in the unique context of Malaysian boarding schools.

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Evaluation of highway-pilot function based on FMEA safety analysis

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Periodica Polytechnica Transportation engineering 2020年第3期48卷 253-259页

作者： Bényei, Ádám Vida, Gabor Pintér, Krisztián Szalay, Zsolt Ágoston, György Department of Automotive Technologies Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics P. O. B. 91 BudapestH-1521 Hungary Department of Software Development and Application Institute of Informatics University of Dunaújváros Dunaújváros P. O. B. 152 H-2401 Hungary

Cybersecurity is becoming more and more relevant. Autonomous vehicles handle large amounts of data and can connect to more and more existing devices, smartphones, tablets, or even other cars and systems. This poses the risk of unauthorized access to data. Theoretically cars have separate computer units, operate in isolation, and are not connected, so there is less possibility to be attacked. However if the vehicles are interconnected, hackers can have easier access to personal data. They can get information about the location of the car owner, their typical trips, and, for example, allow an intruder to know when the tracked person is not at home. Furthermore it can also be happened that the vehicle operation is maliciously disturbed, which can result in a security risk for the passengers. In extreme cases, computer terrorist attacks can also be prepared - large-scale interventions on roads can lead to chaos across a region or country. In accordance with the introduced threats, it is a crucial objective of this research to indicate specific methods, which can help the industry to evaluate and prepare for these kinds of attacks in a proper way. © 2020 Budapest University of Technology and Economics. All rights reserved.

关键词： Vehicle safety

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Towards a Building Information Modeling System for Identification and Retrofit Planning of Stone Damages 8th

Towards a Building Information Modeling System for Identific...

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8th European-Mediterranean Conference, EuroMed 2020

作者： Luig, Klaus Mustedanagic, Dino Jansen, Dieter Fuchs, Sebastian Schülbe, Robert Katranuschkov, Peter Hamdan, Al-Hakam Franzen, Christoph Hiemann, Kristin Scherer, Raimar 3L Architekten Horlecke 46 Menden58706 Germany TragWerk Software Prellerstraße 9 Dresden01309 Germany TU Dresden Faculty of Civil Engineering Institute of Construction Informatics Dresden01062 Germany Institut für Diagnostik und Konservierung an Denkmalen in Sachsen und Sachsen-Anhalt e.V. Schlossplatz 1 Dresden01076 Germany

ISBN: (纸本)9783030730420

Nowadays building works, especially of historic buildings with facades out of natural stone, require continuous maintenance, repair and retrofit works. In order to fulfill the needs for a completely digitized natural stone retrofit process, works are to be projected, planned, conducted and cleared with instrumentation of Building Information Modeling (BIM). Due to this need, a novel knowledge-based stone damage identification system focused on natural stone damage on the basis of BIM is developed, which will present implicitly existing knowledge and information from the building survey explicitly and objectively by using semantic data structures. BIM-SIS is an adaptive damage identification system for natural stone, which allows to virtually merge different natural stone damages, recorded by different information systems and with different procedures, into a holistic damage model. This model is used to assess the damages integrative and in detail, supported by knowledge-based methods, and to develop a uniform and cost-stable remediation strategy. Therefore, the BIM-SIS methodology consists of BIMification for information processes, Ontologies for knowledge representation and Multimodels for data interoperability. The so formed continuous interoperable digital construction representation consists of separate but interlinked domain models. This model structure is then extended for remediation execution management and allows to simply incorporate subsequently detected defects during execution. The complete damage profile is the basis for all further retrofit-creation and calculation processes in BIM-SIS which will automatically lead to user customized retrofit variants presented in VR and AR. © 2021, Springer Nature Switzerland AG.

关键词： Knowledge representation

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software engineering knowledge transfer channels between university and medical device industry: a gap analysis

Software engineering knowledge transfer channels between uni...

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Fachtagung des GI-Fachbereichs softwaretechnik, software engineering 2020 - Conference of the GI Special Interest Group on software engineering, software engineering 2020

作者： Stapić, Zlatko Lechner, Nadica Hrgarek Mijač, Marko University of Zagreb Faculty of Organization and Informatics Laboratory of Applied Software Engineering Pavlinska 2 Varaždin42000 Croatia MED-EL Elektromedizinische Geräte GmbH Fürstenweg 77a Innsbruck6020 Austria

ISBN: (纸本)9783885796947

This paper describes a number of different channels for bidirectional software engineering (SE) knowledge transfer between academia and medical device industry. The paper also brings the results of the gap analysis showing the discrepancy between the medical device industry needs and the curricula of relevant courses. We perform a case study of the faculty of Organization and informatics (FOI) at the University of Zagreb (Croatia). © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： software engineering

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A lightweight hybrid approach for intrusion detection systems using a chi-square feature selection approach in IoT

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Internet of Things 2025年 32卷

作者： Hafsa Benaddi Mohammed Jouhari Omar Elharrouss LSIA Laboratory Moroccan School of Engineering Sciences (EMSI) Tanger Morocco Laboratory of Research in Informatics (LaRI) Faculty of Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates

Protecting IoT networks from cyber threats is challenging, especially with resource-constrained devices. This paper proposes an efficient, lightweight hybrid intrusion detection system (IDS) specifically optimized for IoT devices. Our innovative approach integrates convolutional neural networks (CNN) for effective spatial feature extraction and bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies. Crucially, we employ a chi-square ( χ 2 ) feature selection method, significantly reducing input complexity by selecting the 20 most relevant features from the UNSW-NB15 dataset. Benchmarking against recent IDS methods, our model achieved outstanding accuracy: 97.90% for binary classification and 97.09% for multiclass scenarios, clearly outperforming existing approaches. Additionally, computational performance evaluation reveals rapid prediction times (1.1 s binary; 2.10 s multiclass), demonstrating suitability for real-time IoT deployment. This study illustrates a balanced trade-off between high accuracy and low computational demand, highlighting the practical benefits of advanced, resource-efficient IDS solutions for IoT security.

关键词： Convolutional neural networks Deep learning Feature selection Intrusion detection systems Long-short-term memory UNSW-NB15

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Open IE-Triples Inference- Corpora Development and DNN Architectures 15

Open IE-Triples Inference- Corpora Development and DNN Archi...

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15th Federated Conference on Computer Science and Information Systems, FedCSIS 2020

作者： Vita, Martin Skoda, Petr Masaryk University Botanická 68a Nlp Centre Faculty of Informatics Brno602 00 Czech Republic Charles University Department of Software Engineering Malostranské nám. 2/25 Prague118 25 Czech Republic

ISBN: (纸本)9788395541674

Natural language inference (NLI) is a well established part of natural language understanding (NLU). This task is usually stated as a 3-way classification of sentence pairs with respect to entailment relation (entailment, neutral, contradiction). In this work, we focus on a derived task of relation inference: we propose a method of transforming a general NLI corpus to an annotated corpus for relation inference that utilizes existing NLI annotations. We subsequently introduce a novel relation inference corpus obtained from a well known SNLI corpus and provide its brief characterization. We investigate several DNN siamese architectures for this task and this particular corresponding corpus. We set several baselines including hypothesis only baseline. Our best architecture achieved 96.92% accuracy. © 2020 Polish Information Processing Society - as it is since 2011.

关键词： Architecture

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A Trust Assisted Matrix Factorization based Improved Product Recommender System

A Trust Assisted Matrix Factorization based Improved Product...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Rahim, Asma Maqsood, Muazzam Mehmood, Irfan Muhammad, Khan Kim, Mucheol Comsats University Islamabad Department of Computer Science Pakistan University of Bradford Faculty of Engineering Informatics Department of Media Design and Technology United Kingdom Sejong University Software Department Seoul Korea Republic of Chung-Ang University School of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9781728176246

Smart services is an efficient concept to provide services to the citizen in an efficient manner. The online shopping and recommender system play an important role in this scenario that provides efficient item recommendations to the citizens. Though, the majority of the latest recommender systems can't get effective and efficient prediction accuracy because of the sparsity of the item matrix against each user. Additionally, the recommendations are not reliable when tested upon larger datasets. To handle these problems, a trust-based technique is proposed, called trustasvd++, which fuses a user's trust data in the MF con. The offered strategy combines trust data and rating values to deal with the sparsity and cold start user's issues. Matrix Factorization (MF) has been recognized as a persuasive method for the formation of an effective Recommender System. Pearson correlation coefficient (PCC) is used as a similarity metric in the proposed technique. To assess the efficiency of the offered strategy, numerous datasets have been done on datasets including Epinions, Filmtrust, and Ciao. © 2020 IEEE.

关键词： Recommender systems

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Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model 2

Latent fingerprint enhancement and segmentation technique ba...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Miaikil, Othman A.M. Mahmoud Altrad, Abdullah Baker El-Ebiary, Yousef A. Hilles, Mohanad M Contreras, Jennifer Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia University College of Applied Science Information Technology Department Gaza Palestine FEU Institute of Technology Department of Information Technology Manila Philippines

ISBN: (纸本)9781665432221

Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it's still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation © 2021 IEEE.

关键词： Image enhancement

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Enriched DNA strands classification using CGR images and convolutional neural network 8

Enriched DNA strands classification using CGR images and con...

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8th International Conference on Bioinformatics and Biomedical Science, ICBBS 2019

作者： Safoury, Sarah Hussein, Walid Software Engineering Department Faculty of Informatics and Computer Science British University in Egypt Egypt Computer Science Department Faculty of Informatics and Computer Science British University in Egypt Egypt

ISBN: (纸本)9781450372510

Bioinformatics is the biological study which applies programming techniques for more understanding and analysis of living objects such as the study of genome structure. The genome structure could be represented in the form of an image. Chaos Game Representation (CGR) is the practice of converting the DNA sequence (i.e., genomes) into images, where each image is a graphical appearance for an individual DNA strand's signature. CGR is a method of converting a long one-dimensional DNA sequence into a graphical form. This method provides a visual image of a DNA sequence different from the traditional manual linear arrangement of nucleotides polymerase chain reaction. In the recent years, CGR was introduced to automatically classify genomes not only by archival references but also through its' unique signature. In this paper, a novel CGR classification approach is developed combining the advances of image processing and pattern recognition approaches. The approach starts by declaring the genome and using the CGR technique to map it to the graphical interface (i.e., 16x16 signature images). Then, an image processing procedure is prepared to handle complex geometric shapes, analyze structured and visualized genome sequences and fractal point the included nucleotides of these images. Finally, the convolutional neural network was designed and well-Trained by those signatures to classify each genome tested. © 2019 Association for Computing Machinery.

关键词： Genome

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National Institute of Standard and Technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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