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International Conference on Automatic Face and Gesture Recognition

作者： Nursena Boluk Hatice Kose Department of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

This study focuses on detecting two-dimensional (2D) gaze points in interactions between children both with and without autism and adults. Within the scope of this objective, a novel subset called ChildPlay-R has been created based on the open-source ChildPlay dataset. This new dataset includes explicitly the interactions of children with and without autism with adults in similar uncontrolled environments and relevant 2D gaze points. Moreover, the analyses using the Modified Spatiotemporal Gaze Detection (M-STGD) model, compared to the traditional Spatiotemporal Gaze Architecture (STGA), reveal significant improvements in the Area Under the Curve values and out-of-sample precision. These advancements indicate the M-STGD model's potential to provide a deeper understanding of the social engagement patterns of children with autism. This study contributes to the automatic detection of 2D gaze points in social interactions and supports the social development of children with autism.

关键词： Autism Analytical models Adaptation models Accuracy Two-dimensional displays Face recognition Gesture recognition

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Micro Frontend Architecture

Micro Frontend Architecture

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Vladimír Kunštnár Patrik Podhorský Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9798350349153

ISBN: (纸本)9798350349160

Our paper focuses on software architectures in web application environments, specifically their evolution in client interface development. We will focus on implementing client interfaces with Micro Frontends, which are based on the principle of microservices. We will explain how to implement this architecture and integrate multiple Micro Frontends into a single web application. Additionally, we will evaluate the efficiency and performance of Micro Frontends compared to monolithic client interfaces. Finally, we will compare the performances of the two architectures and propose an improvement to implementing Micro Frontends. Our improvement proposal will explain how Micro Frontends can be implemented to create more modular and reusable user interfaces using well-known methodologies and design patterns.

关键词： Technological innovation Software architecture Design methodology Microservice architectures computer architecture User interfaces Proposals

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DDoS Attack Detection and Mitigation in Software-Defined Networking-Based 5G Mobile Networks with Multiple Controllers 9

DDoS Attack Detection and Mitigation in Software-Defined Net...

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9th International Conference on Future Internet of Things and Cloud, FiCloud 2022

作者： Sheibani, Morteza Konur, Savas Awan, Irfan University of Bradford Faculty of Engineering and Informatics Department of Computer Science Bradford United Kingdom

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

The increased risk of Distributed Denial-of Service (DDoS) attacks is one of the crucial security issues for 5G networks. Software defined networking (SDN) is one of the key solutions for the centralised control of network behavior for detection and mitigation of network attacks. Single centralised controllers for the SDN-based 5G networks lead to many problems concerning scalability, reliability and other system-related performance. In this respect, employing dispersed controllers will provide a vital solution. However, there are still some issues such as how to equalise the processing burden among these controllers and how to employ multi-controllers to improve the security aspects of the SDN-based 5G networks. In this paper, we propose a new method to equalise the processing burden among the dispersed controllers in SDN-based 5G networks in a dynamic manner. The proposed system employs two levels of controllers: dispersed controllers and a master controller. The master controller manages and performs the grouping of the dispersed controllers. The decision of the groups' reconstruction is the responsibility of the master controller and it is based on the processing burden of the dispersed controllers. The proposed dispersed algorithm is employed in 5G networks to detect and mitigate the DDoS attacks. The simulation results prove that the proposed system performs well in equalising the processing burden among controllers and detection and mitigation of DDoS attacks. © 2022 IEEE.

关键词： Controllers

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Mask Detection Using Convolutional Neural Network

Mask Detection Using Convolutional Neural Network

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Abdussalam Pramudya, Elkaf Rachmawan Setiono, Oki Muslih, Muslih Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

COVID-19 virus has hit Indonesia since early March 2020. One of the government's efforts to prevent the spread of COVID-19 is to do physical distancing to require people to wear masks when doing activities outside the home. One way to overcome this problem is by detecting mask users to be more obedient and obedient to the rules, then the identification process is carried out for mask users and those who do not use masks. The process is carried out using the Convolutional Neural Network method. CNN is known to be superior and does not require pre-processing so it saves more time. In terms of algorithmic competence, CNN is considered capable of carrying out the data detection process well. Of the 1376 datasets used, 30 epochs, accuracy = 0.988, recall = 0.990, precision = 0.987, and F1 = 0.988 with the required detection time for each image between 4 to 5 seconds. © 2022 IEEE.

关键词： Convolutional neural networks

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Retrieval Augmented Generation in Large Language Models: Development of AI Chatbot for Student Support 15

Retrieval Augmented Generation in Large Language Models: Dev...

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15th International Conference on eLearning, eLearning 2024

作者： Oreški, Dijana Vlahek, Dino University of Zagreb Faculty of Organization and Informatics Pavlinska 2 Varaždin42000 Croatia University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 MariborSI-2000 Slovenia

Large Language Models (LLMs) are being employed in various domains to support different tasks. There are many challenges when working with LLMs, such as hallucination and domain knowledge gaps. Retrieval Augmented Generation (RAG) has emerged as one of the best paradigms for enabling LLMs to access domain-specific data and as a mechanism for mitigating hallucinations. In this paper, we are employing RAG and investigating how large language models use repositories of pre-existing knowledge to enhance the quality and relevance of generated responses. The focus of the paper is the development of real-world RAG applications in the educational domain. The research aims to develop an AI-based chatbot for university students that could answer students` frequently asked questions. Such a chatbot solves several challenges faced by the faculty administration and guides the improvement of the student's study experience. In the implementation, a low-code approach of Flowise AI is used. As a result, the prototype is developed. From the domain point of view, the prototype represents a step toward an AI-powered educational system that has the potential to further enhance the level of individualized educational support. From the technological point of view, such implementation emphasizes the benefits of LLM and RAG integration and the advantages of generative artificial intelligence. © 2024 Copyright for this paper by its authors.

关键词： Students

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Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

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computer Modeling in engineering & Sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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A Mixed Reality Interface for Tactical Data Management in Geographic Information Systems 7

A Mixed Reality Interface for Tactical Data Management in Ge...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Keskin, Fatih Ince, Gokhan Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey Aselsan A.Ş.

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

The requirements of security systems evolve as the complexity of risks and dangers increases in modern times. Due to the nature of security operations, the operator's stress and cognitive load increase, as the operators must strictly report events that occur in certain situations. In this paper, we present an alternative interface design for a command and control software traditionally used in regional security systems. We propose a mixed reality application that uses holograms and superimposition methods to enhance the operator's experience. The proposed interface is tested on four different security operation scenarios which are operational planning, unit tracking, surveillance, and alarm detection. According to the results of applied questionnaires such as NASA TLX, PSSUQ, and SUS, it has been observed that the created interface is more informative and more usable compared to the traditional command and control software in terms of security operations. © 2022 IEEE.

关键词： Augmented reality

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Interactive Multimodal Learning: Towards Using Pedagogical Agents for Inclusive Education

Interactive Multimodal Learning: Towards Using Pedagogical A...

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2023 IEEE International Humanitarian Technology Conference, IHTC 2023

作者： Al Omoush, Muhammad H. Salih, Sumaia E. Kishore, Sameer Mehigan, Tracey Dublin City University Faculty of Engineering and Computing Dublin Ireland Dublin Ireland Middlesex University Department of Computer Engineering and Informatics Dubai United Arab Emirates

ISBN: (纸本)9798350314311

A pedagogical agent is an animated interface in an interactive online learning environment. Its role involves guiding users through instructions and participating in direct discussions. Numerous research studies underline the advantages of pedagogical agents in delivering instruction, tailoring learning experiences, and promoting inclusivity. The authors utilize machine learning techniques to develop an interactive multimodal application (combining visuals and audio) designed as a pedagogical agent. This application is intended to assist in teaching primary school students (aged 6 to 8) the recognition of colors and letters. Additionally, it incorporates voice interaction to assess their learning progress. The overarching objective of this pedagogical agent is to seamlessly integrate artificial intelligence (AI) into educational settings, thereby amplifying student engagement and motivation during the learning process. This paper will explore the journey of creating a machine learning-based application and its potential utility in supporting educators. Furthermore, the authors will investigate potential augmentations that could render the application more adept at aiding students with dyslexia. The paper will emphasize the significance of human-computer interaction (HCI), as it significantly shapes pedagogical agents' design and operational aspects. © 2023 IEEE.

关键词： Graphical user interfaces

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Clustering Approach for Modeling Course Difficulty Level in Adaptive Learning

Clustering Approach for Modeling Course Difficulty Level in ...

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2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2024

作者： Imamah Yuhana, Umi Laili Djunaidy, Arif Pardede, Eric Purnomo, Mauridhi Hery Departement of Electrical Engineering Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Informatics Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Information Systems Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Computer Science Information Technology La Trobe University BundooraVIC3086 Australia

ISBN: (纸本)9798350322996

The variations in students' abilities present challenges in achieving desired learning outcomes. Students exhibit different levels of comprehension regarding the complexity of learning materials. In this study, we aim to model the difficulty levels of learning materials using a clustering approach to offer personalized learning material recommendations within an adaptive learning framework. Data were collected from student scores of Data Structure course obtained between 2020 and 2022 at the University of Trunojoyo Madura. The dataset modeling process adheres to the CRISP-DM Methodology, employing the K-Means clustering method and Euclidean Distance. We employed the Within-Cluster Sum of Squares (WCSS) metric and silhouette score to identify the optimal K-value, which resulted in a value of 5, accompanied by a silhouette score of 0.629. Our analysis pinpointed Stack and Deque as the easiest, while Linked List emerged as the most challenging course material. Accordingly, we will assign a label of 1 to the easiest materials, 2 to those of medium difficulty, and 3 to the most challenging ones. These labels will serve as criteria for recommending suitable learning materials to students based on their knowledge level. We will assess the ratio between the overall knowledge level score and the difficulty level of the material. The ratio value will serve as a reference for determining material recommendations for students. Materials categorized with the highest difficulty levels are exclusively suggested to students demonstrating faster learning abilities with a high value of ratio, and vice versa. This approach is feasible for implementation in adaptive learning. © 2024 IEEE.

关键词： Contrastive Learning

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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