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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Lee, Seon-Woo Thi, Luong Nguyen Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Dalat University Faculty of Mathematics and Informatics Dalat Viet Nam Hallym University Division of Software Chuncheon Korea Republic of Dalat University Faculty of Information Technology Dalat Viet Nam Dalat University Department of Electronics and Telecommunications Dalat Viet Nam

ISBN: (纸本)9798350353983

Using WiFi Fingerprinting for indoor positioning is becoming increasingly popular due to its cost-effectiveness. However, to ensure the system can operate with high accuracy and stability, this technique requires a burden of data collection tasks from a dense distribution of reference points (RPs). To decrease the cost of data collection, as well as to improve the WiFi fingerprint database with irregular distribution of RPs, this study applies and compares different interpolation methods. Evaluating these methods in a public dataset including multiple sub-datasets with irregular distributions of RPs, the results highlight the Linear Barycentric interpolation (LB) as the most favorable method due to its low complexity and stable performance. The LB-based fingerprint database achieves an average interpolation error of less than 3 dBm compared to the original one. When applying the interpolated database to positioning tasks, it can help reduce acquisition costs by up to 50% while improving positioning accuracy by up to 11%. The study reveals the feasibility of interpolation in optimizing WiFi fingerprint data collection, especially in large and complex environments where regular distribution of RPs cannot be deployed. © 2024 IEEE.

关键词： Interpolation

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OnlineProver: Experience with a Visualisation Tool for Teaching Formal Proofs 13

OnlineProver: Experience with a Visualisation Tool for Teach...

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13th International Workshop on Theorem Proving Components for Educational Software, ThEdu 2024

作者： Perháč, Ján Novotný, Samuel Chodarev, Sergej Kristensen, Joachim Tilsted Tveito, Lars Shturmov, Oleks Thomsen, Michael Kirkedal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia Department of Informatics University of Oslo Oslo Norway Department of Computer Science University of Copenhagen Copenhagen Denmark

OnlineProver is an interactive proof assistant tailored for the educational setting. Its main features include a user-friendly interface for editing and checking proofs. The user interface provides feedback directly within the derivation, based on error messages from a proof-checking web service. A basic philosophy of the tool is that it should aid the student while still ensuring that the students construct the proofs as if they were working on paper. We gathered feedback on the tool through a questionnaire, and we conducted an intervention to assess its effectiveness for students in a classroom setting, alongside an evaluation of technical aspects. The initial intervention showed that students were satisfied with using OnlineProver as part of their coursework, providing initial confirmation of the learning approach behind it. This gives clear directions for future developments, with the potential to find and evaluate how OnlineProver can improve the teaching of natural deduction. © J. Perháč et al.

关键词： Teaching

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Adaptive Learning and Virtual Reality based Conceptual Framework for Dental Training

Adaptive Learning and Virtual Reality based Conceptual Frame...

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International Conference on computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt Department of Artifical Intellegence Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt Department of Software Enginerring Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt

Recently, there has been increasing interest in utilizing virtual reality (VR) technology in dental education and student training. VR based training can provide students with sufficient training and hands-on experience before entering the clinical setting, which in turn helps avoid risks ranging from small errors to severe consequences. However, previous research in this area is limited and none of the previous work proposed a complete, scalable framework for dental students’ education and training. This paper proposes an improved virtual environment for dental training, through an adaptive learning approach, integrated with the VR environment, which is - to the best of our knowledge - the first framework proposed for this purpose. The framework consists of eight modules that aim to provide students with a holistic dental training experience, equipping them with the essential skills and confidence required for their future clinical practice. Furthermore, it aims to improve patient care and safety. In order to guarantee the effectiveness and usability of the proposed framework, a thorough evaluation will be conducted, encompassing both a system perspective, which involves assessing software metrics, as well as evaluating its usability.

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Design and Implementation of Charging Circuit for Energy Storage System in Multi-Input Non-Isolated Converter with High Gain 27

Design and Implementation of Charging Circuit for Energy Sto...

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27th IEEE International Conference on Intelligent engineering Systems, INES 2023

作者： Taheri, Donya Shahgholian, Ghazanfar Mirtalaei, Mohammad Mehdi Mosavi, Amirhosein Fathollahi, Arman Varkonyi-Koczy, Annamaria Islamic Azad University Department of Electrical Engineering Najafabad Branch Najafabad Iran Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran John von Neumann Faculty of Informatics Budapest Hungary University of Public Service Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark Obuda University John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350328516

In this study, the topology of a multi-input high-gain non-isolated power converter is suggested. Within this converter, techniques involving voltage multiplier cells and a coupling inductor are utilized. It is possible to use switches that have low voltage stress and, as a result, low conductivity by combining these two methodologies. For applications dealing with clean energy, like green electrical power for public hospitals and etc., that consider a specific type of energy storage system, this particular configuration is an excellent choice. First, the charging performance of the energy storage system is explained, and then, for the design of the multi-input non-isolated power converter with a high voltage gain, the configuration and functionality of the proposed converter are fully investigated. This is accomplished so that the multi-input non-isolated converter can have high voltage gain. The correct operation of the converter is demonstrated through the presentation of a detailed design method, and numerical outcomes are shown for a variety of modes in which the converter can operate effectively. Eventually, to guarantee that the simulation outcomes are accurate, an experimental prototype of the proposed converter has been produced in a laboratory, and the results of the simulation have been validated with those obtained in practice. © 2023 IEEE.

关键词： Energy storage

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

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

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

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

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.

关键词： Measurement Fault detection Software algorithms Closed box Software quality Minimization Real-time systems

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Crafting an Inclusive Activity Planner for Persons with Down Syndrome

Crafting an Inclusive Activity Planner for Persons with Down...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Damir Pozderac Nejla Bečirspahić Dženis Muhić Šeila Bećirović Ramić Irfan Prazina Vensada Okanović Lejla Kafedžić Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina Faculty of Philosophy University of Sarajevo Sarajevo Bosnia and Herzegovina

The applications presented in this conference paper focus on the development of a mobile and web application serving as a planner with a focus on tracking persons with Down syndrome. These innovative technological solutions contribute to the development of independence and functionality for persons with Down syndrome while emphasizing the importance of inclusivity in society. In addition to focusing on organizing activities, the mobile and web applications provide support and facilitate daily tasks. The web application allows parents/guardians/teachers to add new activities to the planner and track the progress of these activities. On the other hand, the mobile application enables persons with Down syndrome to record their activities within the application, considering their specific challenges, and customizing the user interface to their needs.

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I-MCM: IoT Malware Counter Measures for Cross-Architecture IoT Malware Detection

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IEEE Access 2025年 13卷 95524-95534页

作者： Ibrahim Gulatas H. Hakan Kilinc A. Halim Zaim M. Ali Aydin Department of Computer Engineering National Defense University Istanbul Türkiye Department of Research and Development Orion Innovation Türkiye Istanbul Türkiye Faculty of Computer and Informatics Istanbul Technical University Istanbul Türkiye Department of Computer Engineering Istanbul University-Cerrahpaşa Istanbul Türkiye

The recent attacks initiated by malware-infected IoT devices illustrate that these attacks have tremendous impacts not only on the targeted systems but also on the entire internet infrastructure. Due to the vast number of infected IoT devices, the Quality of Service of the target system can be severely hampered by DDoS attacks. Despite the severity of the threat, the security measures against IoT malware are highly limited, and mostly, users are unaware that their devices are infected by malware. Due to the constrained nature of IoT devices, high-resource-demanding anti-malware tools cannot be run on edge devices, and lightweight malware detection systems are highly needed to secure those devices against IoT malware. In this regard, we propose the I-MCM framework for IoT malware detection on IoT devices by utilizing Tiny ML techniques based on static malware analysis data. Our proposed I-MCM framework is capable of detecting IoT malware in a Raspberry Pi Node deployed with Random Forest Classifier in 7.87 s. (including file analysis and data preprocessing) with 99.8% accuracy and in an Arduino Nano 33 BLE Sense microcontroller deployed with a Tiny ANN model in 8.16 s. (including file analysis and data preprocessing) with 97.1% accuracy. Besides, the I-MCM framework can detect IoT malware regardless of CPU architecture with only one ML model by utilizing our Opcode Purification Technique. Last but not least, this research presents a cross-architecture IoT Malware dataset that includes both malware and benignware samples.

关键词： Malware Internet of Things Accuracy computer architecture Security Purification Microprogramming Image edge detection Electronic mail Data preprocessing

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Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Khruahong, Sanya Surinta, Olarik Lam, Sinh Cong Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok Thailand Faculty of Informatics Mahasarakham University Mahasarakham Thailand Faculty of Electronics and Telecommunication VNU - University of Engineering and Technology Hanoi Viet Nam

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Currently, social networks, where people can express their opinion through content and comments, are fast developing and affect various areas of daily life;Particularly, some research on YouTube travel channels found that almost tourists and audiences leave comments about their attitudes to that place. Thus, mining the emotional recognition of comments through artificial intelligence can bring knowledge about the tourists’ general view. This article analyzes the relationship(s) between social media use and its effect on community-based tourism in Thailand using the Social Media Sensing framework (S-Sense) as sentiment analysis and the Bidirectional Long Short-Term Memory (BiLSTM) methods to analyze the text comments. This research collected 51,280 comments on 114 Youtube Videos, which are tourist attractions in various provinces in Thailand. The approach categorizes attractions based on sentiment analysis of 60% or more, including restaurants, markets, historical sites, temples, or natural attractions. The results show that 67.51% of the 19,391 clean-processed comments were satisfied with those attraction places. Therefore S-Sense and BiLSTM models can be sufficient to analyze the attitude of comments about attraction places with from 43 to remain 33 keywords of 1,603 comments. Furthermore, the offered sentiment analysis method has higher precision, recall, and F1 scores. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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CEGSet: Collection of standardized cause-effect graph specifications

CEGSet: Collection of standardized cause-effect graph specif...

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Mediterranean Conference on Embedded Computing (MECO)

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.

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Indoherb: Indonesia Medicinal Plants Recognition Using Transfer Learning and Deep Learning

SSRN

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SSRN 2023年

作者： Musyaffa, Muhammad Salman Ikrar Yudistira, Novanto Rahman, Muhammad Arif Informatics Engineering Faculty of Computer Science Brawijaya University East Java Malang65145 Indonesia

The rich diversity of herbal plants in Indonesia holds immense potential as alternative resources for traditional healing and ethnobotanical practices. However, the dwindling recognition of herbal plants due to modernization poses a significant challenge in preserving this valuable heritage. The accurate identification of these plants is crucial for the continuity of traditional practices and the utilization of their nutritional benefits. Nevertheless, the manual identification of herbal plants remains a time-consuming task, demanding expert knowledge and meticulous examination of plant characteristics. In response, the application of computer vision emerges as a promising solution to facilitate the efficient identification of herbal plants. This research addresses the task of classifying Indonesian herbal plants through the implementation of transfer learning of Convolutional Neural Networks (CNN). To support our study, we curated an extensive dataset of herbal plant images from Indonesia with careful manual selection. Subsequently, we conducted rigorous data preprocessing, classification utilizing transfer learning methodologies with three distinct CNN models: ResNet, DenseNet, and VGG. Our comprehensive analysis revealed that DenseNet achieved the highest accuracy, standing at an impressive 87.4%. Additionally, we conducted testing using a scratch model, resulting in an accuracy of 43.53%. The experimental setup featured essential hyperparameters, including the ExponentialLR scheduler with a gamma value of 0.9, a learning rate of 0.001, the Cross Entropy Loss function, the Adam optimizer, and a training epoch count of 50. This study's outcomes offer valuable insights and practical implications for the automated identification of Indonesian medicinal plants, contributing not only to the preservation of ethnobotanical knowledge but also to the enhancement of agricultural practices through the cultivation of these valuable resources. The Indonesia Medicinal Plant Da

关键词： Convolutional neural networks

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