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2024 IEEE International Conference on Big Data, BigData 2024

作者： Papatsaroucha, Dimitra Astyrakakis, Nikolaos Pallis, Evangelos Grammatikis, Panagiotis I. Radoglou Sarigiannidis, Panagiotis G. Markakis, Evangelos K. Hellenic Mediterranean University Department of Computer & Electrical Engineering Heraklion Greece University of West Attica Department of Industrial Design and Production Engineering Athens Greece K3Y Sofia Bulgaria University of Western Macedonia Department of Informatics and Telecommunication Engineering Kozani Greece

ISBN: (纸本)9798350362480

Nowadays, numerous devices are utilizing the IoT world, connecting and providing access to data and sensor measurements in vast networks of interconnected objects and devices. Considering the great communication distances that need to be covered occasionally, the LoRaWAN network was proposed as it employs Low Power (LP) and Long Range (LoRa) protocols that reduce device energy consumption while maximizing communication range. A gateway to the cloud authenticates LoRaWAN IoT devices before data transmission. This procedure begins with an unencrypted Join Request. A Join Request includes, among others, a Message Integrity Code (MIC), which is the result of encrypting the unencrypted contents of the message using an AppKey that is securely stored both in the cloud and the IoT device. However, malicious actors acting as Man-In-the-Middle (MITM) can interfere in the communication channel, reverse engineer the MIC value, and derive the AppKey. They can then initiate a Join Request that is misinterpreted as coming from a legitimate device and gain access to the communication channel. This paper introduces a novel approach that focuses on the continuous regeneration of the AppKey, necessitating frequent re-joining and re-authentication of IoT devices within the network. The suggested method, which can be added as an extra layer of security in LoRaWAN networks, uses a key rolling technique similar to the one used in automobile central locking systems, and is developed as an optimised and scalable microservice for various LoRaWAN installations and versions. Through the evaluation process, significant findings emerged, demonstrating the effectiveness of the proposed security solution in mitigating replay attacks. The system successfully prevented the server from getting flooded by malicious packets, distinguishing it from a system lacking the proposed mechanism. Remarkably, this accomplishment was made without causing any noticeable delay to the communication process. In addit

关键词： Network security

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Detecting Abusive Activity with Enhanced Convolutional Neural Networks

Detecting Abusive Activity with Enhanced Convolutional Neura...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Khalaf, Suadad Zaidan Shujaa, Mohamed Ibrahim Alwahhab, Ahmed Bahaaulddin A. Middle Technical University Department of Computer Techniques Engineering Baghdad Iraq Middle Technical University Technical College of Management Department of Informatics Baghdad Iraq

ISBN: (纸本)9798350341881

There is a growing reliance on surveillance systems for law enforcement and public safety purposes. Video surveillance scenarios, such as those seen in train stations, schools, and hospitals, must automatically detect hostile and suspicious behavior to avoid social, economic, and environmental harm. Human contact can now be seen using intelligent video surveillance systems. Crowds and the camera's field of view reduce the system's effectiveness as a security procedure. As a result, there is a lot of study on the many strategies for detecting violent behavior. The method of detection discussed in this study can be divided into three categories. These groups were established as a result of the classification procedures that were used. Among the categorizations is a refined CNN for using AI to identify instances of violence. Principal Component Analysis (PCA) is used to develop techniques for feature extraction and violence detection. The AIRTLab dataset was utilized to analyze models based on dataset learning for violence detection by testing the resilience of algorithms against false positives. To evaluate the proposed model's robustness against false positives, we calculated accuracy measures and utilized them to establish a baseline on the AIRTLab dataset. The study literature claims a 99.98% success rate for the suggested models, with transfer learning-based networks boasting greater generalization abilities. © 2023 IEEE.

关键词： computer vision

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The Evolution of Generative AI: Trends and Applications

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IEEE Access 2025年 13卷 98504-98529页

作者： Trigka, Maria Dritsas, Elias University of West Attica Egaleo Park Campus Department of Informatics and Computer Engineering Athens12243 Greece

Generative artificial intelligence (AI) has revolutionized AI by enabling high-fidelity content creation across text, images, audio, and structured data. This survey explores the core methodologies, advancements, applications, and challenges of generative AI, covering key models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformer-based architectures. These innovations have driven breakthroughs in healthcare, scientific computing, Natural Language Processing (NLP), computer vision, and autonomous systems. Despite its progress, generative AI faces challenges in bias mitigation, interpretability, computational efficiency, and ethical governance, necessitating research into scalable architectures, explainability, and AI safety mechanisms. Integrating Reinforcement Learning (RL), multi-modal learning, and self-supervised techniques enhances controllability and adaptability in generative models. Additionally, as AI reshapes industrial automation, digital media, and scientific discovery, its societal and economic implications demand robust policy frameworks. This survey provides a comprehensive analysis of generative AI's current state and future directions, highlighting innovations in efficient generative modelling, AI-driven scientific reasoning, adversarial robustness, and ethical deployment. By consolidating theoretical insights and real-world applications, it offers a structured foundation for researchers, industry professionals, and policymakers to navigate the evolving landscape of generative AI. © 2013 IEEE.

关键词： Reinforcement learning

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Smart vegetable cutter for Smart Home 1

Smart vegetable cutter for Smart Home

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Singhal, Saurabh Mangal, Deepak Kumar, Rakesh Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications Mathura India Jain University Department of Computer Science and IT Bangalore India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665472753

Modern society strives for simpler, more efficient, dependable, speedier, and more automated lifestyle. Existing vegetable cutters in the market have drawbacks such as high investment costs, additional manpower, and time consumption. Moreover, none of them provide the feature of cleaning, peeling, and cutting in a single machine. The veggies offered in the market have pesticides and chemicals, and many people, in their haste, do not wash them properly, endangering their health. To reduce human labor while also providing good health care, a solution to this problem is required. This paper offers a novel method for developing a vegetable cutter and chopper using an Android application and IoT devices. In addition, smart vegetable cutters clean, peel, and cut veggies all in one piece of equipment. This strategy will assist human society in reducing the amount of additional manpower needed and would save them time. © 2023 IEEE.

关键词： Real time systems

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Leveraging Digital Twin Technologies for Public Space Protection and Vulnerability Assessment

Leveraging Digital Twin Technologies for Public Space Protec...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Stefanidou, Artemis Cani, Jorgen Papadopoulos, Thomas Radoglou-Grammatikis, Panagiotis Sarigiannidis, Panagiotis Varlamis, Iraklis Papadopoulos, Georgios Th. Harokopio University Department of Informatics and Telematics Athens Greece Regional Unit of Kastoria Region of Western Macedonia Kastoria Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350362480

In recent years, the protection of so-called "soft targets", has become an increasingly important and challenging issue. The complexity and seriousness of this security threat have been growing exponentially, particularly with the advent of advanced technologies such as Artificial Intelligence (AI), Autonomous Vehicles (AVs), and 3D printing, especially in the context of large-scale, popular, and diverse public spaces. In this paper, a novel Digital Twin-as-a-Security-Service (DTaaSS) architecture is introduced for holistically and significantly enhancing the protection of public spaces (e.g. metro stations, leisure sites, urban squares, etc.). The proposed framework combines a Digital Twin (DT) conceptualization with additional cutting-edge technologies, including Internet of Things (IoT), cloud computing, Big Data analytics and AI. In particular, DTaaSS comprises a holistic, real-time, large-scale, comprehensive and data-driven security solution for the efficient/robust protection of public spaces, supporting: a) data collection and analytics, b) area monitoring/control and proactive threat detection, c) incident/attack prediction, and d) quantitative and data-driven vulnerability assessment. Overall, the designed architecture exhibits increased potential in handling complex, hybrid and combined threats over large, critical and popular soft-targets. The applicability and robustness of DTaaSS is discussed in detail against representative and diverse real-world application scenarios, including complex attacks to: a) a metro station, b) a leisure site, and c) a cathedral square. © 2024 IEEE.

关键词： Subway stations

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A Channel Estimation Method for Modified Walsh-Hadamard Code Division Multiplexing in Helicopter Satellite Communications and Its Application to Initial Acquisition 16

A Channel Estimation Method for Modified Walsh-Hadamard Code...

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16th International Conference on Advanced Technologies for Communications, ATC 2023

作者： Yoshinaka, Yuto Kojima, Toshiharu The University of Electro-Communications Graduate School of Informatics and Engineering Department of Computer and Network Engineering 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan

ISBN: (纸本)9798350301328

Modified Walsh-Hadamard code division multiplexing (MWHCDM) is a promising solution for the degradation of bit error rate (BER) performance due to the periodic blockage (PB) of the channel caused by rotor blades in helicopter satellite communications. While zero-forcing (ZF) enhances the anti-PB capability of MWHCDM, it requires channel information. This paper proposes a channel estimation method for MWHCDM, discussing its application to initial acquisition. The proposed channel estimation method reproduces the transmitted signal from the received signal by utilizing the structure of MWHCDM signals. It estimates the channel information based on the cross-correlation coefficient between the reproduced transmitted signal and the received signal. computer simulation shows that introducing ZF combined with the proposed channel estimation method to initial acquisition significantly improves the acquisition performance in the PB channel, similar to the BER performance. © 2023 IEEE.

关键词： Bit error rate

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Detection System for Lung Tumours Based on CT Scan Images Using Cellular Automata

Detection System for Lung Tumours Based on CT Scan Images Us...

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2024 International Conference on Electrical and Information Technology, IEIT 2024

作者： Latif Hayati, Fierly Harsono, Tri Yuniarti, Heny Ferriastuti, Widiana Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Airlangga Faculty of Medicine Dr. Soetomo General Academic Hospital Department of Radiology Surabaya Indonesia

ISBN: (纸本)9798331516888

Lung tumours are masses of tissue that arise abnormally in the lung organ. In the field of medicine, One means of detecting and analyzing lung tumours is to use Computed Tomography (CT) scan images, where doctors see with the naked eye on the image and make a diagnosis. The existence of a lung tumour area detection system using a computer program is needed as a companion system in diagnosing. This research develops a lung tumour detection system using Cellular Automata (CA) with the system input is CT scan image. The quality of CT Scan image is improved through pre-processing, then segmentation is performed to separate the lung object from the background. Binaryization is applied to the segmented image to clarify the presence of lung tumour area. The detection process by CA is conducted on the binaryized image. The experiment was implemented on 20 CT scan images for normal lung and 20 other images for abnormal lung obtained from The Lung Image Database Concortium (LIDC) and Image Database Resource Initiative (IDRI). Analyzing the experimental data using performance score, it was found that the accuracy of detection was 95%, 100% precision, 90% sensitivity, and 100% specificity. These conditions indicate that the detection system has good reliability. © 2024 IEEE.

关键词： Lung cancer

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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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Designing an Advanced Situational Awareness Platform Using Intelligent Multi-Drones for Smart Farming Towards Agriculture 5.0 11

Designing an Advanced Situational Awareness Platform Using I...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350389654

With regard to Agriculture 5.0, the goal of this research is to create an intelligent platform for multi-drone collaboration that will improve situational awareness. This study examines the crucial phases in the Sensing, Understanding, and Action processes by fusing cutting-edge drone technology with creative coordination methods. Creating the architecture and foundation for drone collaboration, utilizing artificial intelligence for real-time data processing, and creating user interfaces that work are all part of the study technique. Area coverage, data quality, processing speed, and decision-making effectiveness are some of the performance evaluation measures. The goal of the research is to identify strategies for enhancing large-scale farming methods' resource management and operational effectiveness. The results will greatly enhance Agriculture 5.0 technologies and create avenues for the deployment of multi-drone collaboration platforms in a variety of high-situational awareness businesses. © 2024 IEEE.

关键词： Data quality

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Open-Source Online Mission-Planning in Emergent Environments with PDDL for Multi-robot Applications 13th

Open-Source Online Mission-Planning in Emergent Environments...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Michailidis, Iakovos T. Michailidis, Panagiotis Kosmatopoulos, Elias Bassiliades, Nick Information Technologies Institute Thessaloniki Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783031632266

The current study focuses on the development of an open-source framework which is outsourcing the lack of expressivity of the standardized Planning Domain Definition Language – PDDL, leveraging the capacity and flexibility of the hosting environment in Python 3.8. The implementation was based on the definition and logic of a PDDL domain modeling an object stacking/collecting problem, in specified order and locations, by a fleet of autonomous synergetic vehicles by a centralized management agent. A PDDL-parser was utilized to instantiate the solver. Random changes were imposed in the fully-observable environment during mission execution. The proposed framework considered dynamic replanning of the currently valid mission, every time the state space – and consequently the corresponding state graph representation – is stochastically changed. Appropriate test scenarios were defined, to validate the capacity of the implemented framework and establish that it could serve as an interoperable and extensible foundation for other add-ons and graph-searching tools. The evidence from the simulation results indicates the speed and flexibility of the implemented environment for emulating the dynamic replanning problems in highly emergent scenario cases where the state space changes stochastically. © IFIP International Federation for Information Processing 2024.

关键词： Autonomous agents

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