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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Tünde Kaufman Jozsef Katona National Media and Infocommunications Authority Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary

ISBN: (数字)9798331515478

ISBN: (纸本)9798331515485

The increasing demand for IoT devices in industries has generated a high level of cybersecurity threats, with botnets including Mozi exploiting poor passwords and unpatched vulnerabilities to target networked infrastructure. Peer-to-peer (P2P) topology of Mozi, employing Distributed Hash Tables (DHTs) for Command-and-Control (C2) communications, makes it resistant to takedown operations and capable of high-scale Distributed Denial-of-Service (DDoS) attack, data exfiltration, and remote command execution. Traditional one-class, machine learning techniques such as OneClass Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) have been widely used for discovering malwarerelated anomalies. However, such techniques require high labelled training datasets, whose availability in dynamically changing environments of malwares proves challenging. In this work, an AutoRegressive Integrated Moving Average (ARIMA)-based mechanism for discovering time-series anomalies in IoT-infected devices with Mozi infection is proposed. In an experimental setup using a Raspberry Pi running Pi-hole, baseline profiles of CPU consumption, RAM usage, and network activity are first established under normal conditions. Next, a system is subjected to a simulation of infection with Mozi malware and monitored for deviation with ARIMA forecasting and Local Outlier Factor (LOF) for discovering anomalies. By comparing predicted values with actual values, effective intrusion detection for malware-related anomalies is attained, providing a real-time, adaptive mechanism for early intrusion detection. In conclusion, with its effectiveness in discovering stealthy behaviour of malwares, employing ARIMA for discovering time-series anomalies in IoTinfected devices proves effective, and its employment in IoT cybersecurity aids in providing real-time mechanism for discovering threats for system admins and security professionals. As future work, a combination of ARIMA with deep neural networks such as Long

关键词： Training Botnet Intrusion detection Malware Real-time systems Peer-to-peer computing Topology Internet of Things Long short term memory Anomaly detection

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Virtual Reality-Based Teleoperation System for Robot Forklifts

Virtual Reality-Based Teleoperation System for Robot Forklif...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Manuel B. Couto Marcelo R. Petry Abel Mendes Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal FEUP - Faculty of Engineering University of Porto Porto Portugal Flowbotic Coimbra Portugal ISEP - Instituto Superior de Engenharia do Porto Instituto Politécnico do Porto Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

The growing reliance on e-commerce and the demand for efficient intralogistics operations have increased the need for automation, while labour shortages continue to pose significant challenges. When combined with the inherent risks of forklift operation, this circumstance prompted businesses to look for robotic solutions for intralogistics tasks. However, robots are still limited when they come across situations that are outside of their programming scope and often need assistance from humans. To achieve the long-term goal of enhancing intralogistics operation, we propose the development of a virtual reality-based teleoperation system that allows remote operation of robot forklifts with minimal latency. Considering the specificities of the teleoperation process and network dynamics, we conduct detailed modelling to analyse latency factors, optimise system performance, and ensure a seamless user experience. Experimental results on a mobile robot have shown that the proposed teleoperation system achieves an average glass-to-glass latency of 368 ms, with capturing latency contributing to approximately 60% of the total delay. The results also indicate that network os-cillations significantly impact image quality and user experience, emphasising the importance of a stable network infrastructure.

关键词： Radio frequency Visualization 5G mobile communication System performance Robot vision systems Virtual reality Cameras User experience Wireless fidelity Testing

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N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 22

N-Gram-Based Machine Learning Approach for Bot or Human Dete...

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6th International Conference on Intelligent systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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COVID-19 Infection Detection in X-Ray Images Using Enhanced Image Segmentation and Machine Learning Models

COVID-19 Infection Detection in X-Ray Images Using Enhanced ...

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Information Technology (ACIT)

作者： Khalid M.O. Nahar Mohammad Ebrahim Megdad Rabia Emhamed Al Mamlook Mohammad Aljaidi Besan K.M. Nahar Mohammad K. M. Nahar Samar Hussni Anbarkhan Hayel Khafajeh Waleed Amer Faculty of Computer Studies Arab Open University Riyadh Saudi Arabia Computer Science Department Yarmouk University Irbid Jordan Dept. of Mechanical and Industrial Engineering University of Zawia Zawia Libya Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Faculty of Medicine Yarmouk University Irbid Jordan Faculty of Applied Medical Science Jordan University of Science and Technology Irbid Jordan Information Systems Department Northern Border University Rafha Saudi Arabia Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cybersecurity Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The COVID-19 pandemic has shown a significant challenge to healthcare associations worldwide demanding the progress of reliable diagnostic techniques. Chest X-ray imaging is an easily available and cost-effective tool for evaluating lung defects combined with COVID-19. This study aims to introduce a novel COVID-19 infection detection and recognition method in X-ray images through image segmentation. By employing machine learning (ML) methods, particularly CNN-based U-Net architectures, the lung sections are accurately segmented, and infection sections are classified based on specific radiological features. The model was trained on a large dataset of annotated X-ray images assisting it to distinguish between healthy lungs, COVID-19-affected lungs, and other lung pathologies. The proposed method apprehended a segmentation accuracy of 96.6% with a Dice coefficient of 94.36% offering a potential solution for mechanical detection and 98.4% with KNN analysis of COVID-19 infection in chest X-rays in that way assisting radiologists in making faster and more accurate diagnoses.

关键词： COVID-19 Support vector machines Image segmentation Accuracy Image recognition Pandemics Lungs Nearest neighbor methods X-ray imaging Biomedical imaging

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Hybrid IoT Programming with Data Flow Diagrams and mruby

Hybrid IoT Programming with Data Flow Diagrams and mruby

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Kazuaki Tanaka Sota Ogura R. Krishnamoorthy Faculty of Information Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India

ISBN: (数字)9798331522728

ISBN: (纸本)9798331522735

IoT is increasingly being used in different areas of society. IoT is used in society by collecting values acquired by sensor devices through networks and analysing the data. IoT software is needed to use the IoT, but efficient software development methods are desired because software for an increasingly complex society tends to become more complex. This research aims to improve the efficiency of IoT software and develop an intuitive method for developing IoT *** paper proposes a development methodology that combines a data-flow-based programming approach with a procedural programming language. When the focus is on data, data flow programming is intuitive and efficient. On the other hand, when the focus is on data processing, it is better to use a procedural programming language because data is processed sequentially[1].In the hybrid IoT programming environment proposed in this paper, the code of a procedural programming language is generated from the description by data flow programming, and the program is executed by a micro *** products of this research are available as open source software[2], [3].

关键词： Hands computer languages Codes Programming Data processing Hybrid power systems Open source software Software development management Programming environments

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Adaptive Genetic Algorithm for Slm Ofdm Papr Time Reduction for 5g-Internet of Things System

SSRN

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SSRN 2023年

作者： Hagras, Esam A.A. Desouky, Sameh F. Faculty of Engineering Delta University for Science and Technology International Coastal Road Dakahlia Gamasa City35712 Egypt Systems and Computer Eng. Carleton University Ottawa Canada

Abstract—In recent years, wireless communication systems have increasingly adopted orthogonal frequency division multiplexing (OFDM) due to its high transmission data rate and resistance to frequency selective fading. The transmitted OFDM signal's high peak-to-average power ratio (PAPR), however, represents a significant flaw in the OFDM system at the transmitter. To get a satisfactory PAPR reduction without signal distortion, the selected mapping (SLM) technique is applied. This paper proposes a unique Adaptive Genetic Algorithm (AGA)-based PAPR time reduction method. The suggested method is used to search for the best phase rotation factors in an SLM-OFDM system in order to reduce PAPR as much as possible. According to the simulation results, the proposed approach reduces PAPR by roughly 3.87 dB when compared to SLM-OFDM. Comparing the suggested AGA SLM-OFDM to the traditional GA SLMOFDM with the same parameters, an excellent learning time reduction of roughly 95.6% is also achieved. © 2023, The Authors. All rights reserved.

关键词： Peak to average power ratio

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Modulation Recognition of the OFDM Signals in the UAV-RIS Based AI-enabled 6G Network for Smart Agriculture

Modulation Recognition of the OFDM Signals in the UAV-RIS Ba...

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International Bhurban Conference on Applied sciences & Technology, IBCAST

作者： Adil Sheraz Muhammad Farrukh Shahid Zuhaib Ashfaq Khan Tariq Jamil. S. Khanzada Department of Computer Science FAST-NUCES Karachi Pakistan Department of Electronic and Communication Engineering (ECE) School of Architecture Technology and Engineering (ATE) University of Brighton UK Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Department Mehran UET Jamshoro Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learning (ML) technology and the high-speed, low-latency communication capabilities of 6G networks, we can create intelligent farming systems that can enhance productivity, reduce resource wastage, and ensure sustainable agricultural practices. To tackle resource wastage and power efficiency of 6G-enabled devices, we present an AI-based solution that utilizes pre-trained Deep Learning (DL) models to classify Orthogonal Frequency Division Multiplexing (OFDM) modulated Quadrature Amplitude Modulation (QAM) signals. The proposed approach involves recognizing signals based on the classification results at the receiving nodes, prompting them to switch their radio modulation schemes to options such as 4-QAM, 16-QAM, and 64-QAM. Taking advantage of DL models for QAM classification and dynamically adjusting modulation schemes, the proposed solution can optimize resource utilization, improve data transmission quality, and enhance overall network performance in agricultural applications. We used pre-trained ResNet50, MobileNetV2, and a scratch CNN model. Our results show that pretrained models performed better than scratch CNN models and demonstrate the feasibility of using pretrained DL models for modulation scheme classification, offering promising opportunities for the development of adaptive and intelligent agricultural systems.

关键词： 6G mobile communication Smart agriculture Adaptation models Adaptive systems Quadrature amplitude modulation OFDM Modulation Switches Resource management Residual neural networks

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Have Java Production Methods Co-Evolved With Test Methods Properly?: A Fine-Grained Repository-Based Co-Evolution Analysis

Have Java Production Methods Co-Evolved With Test Methods Pr...

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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Tenma Kitai Hirohisa Aman Sousuke Amasaki Tomoyuki Yokogawa Minoru Kawahara Department of Computer Science Faculty of Engineering Ehime University Matsuyama Ehime Japan Center for Information Technology Ehime University Matsuyama Ehime Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja Okayama Japan

Any source code of a software product (production code) is expected to be tested to ensure its correct behavior. Whenever a developer updates production code, the developer should also update or create the corresponding test code to check if the updated parts still work correctly. Such a desirable co-evolution relationship between production and test code forms a logical coupling. Although the logical coupling is detectable through an association analysis on the code repository such as Git, the detection granularity is coarse because the conventional repository is at the file level. For observing those logical couplings as precisely as possible, this paper utilizes the finer-grained, Java method-level repository (FinerGit). Then the paper proposes a metric measuring the extent to which a production method has co-evolved with test methods and conducts a case study using ten open-source projects. The results show that most Java methods (98% on average) have co-evolved with test methods, but some have not; The proposed metric helps detect those methods having the potential risk that the developers might not test adequately.

关键词： Couplings Measurement Java Codes Source coding Production Software

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An Improved Approximation Algorithm for the Max-3-Section Problem

arXiv

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arXiv 2023年

作者： Katzelnick, Dor Pillai, Aditya Schwartz, Roy Singh, Mohit The Henry and Marilyn Taub Faculty of Computer Science Technion Israel H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States

We consider the Max-3-Section problem, where we are given an undirected graph G = (V,E) equipped with non-negative edge weights w : E → + and the goal is to find a partition of V into three equisized parts while maximizing the total weight of edges crossing between different parts. Max-3-Section is closely related to other well-studied graph partitioning problems, e.g., Max-Cut, Max-3-Cut, and Max-Bisection. We present a polynomial time algorithm achieving an approximation of 0.795, that improves upon the previous best known approximation of 0.673. The requirement of multiple parts that have equal sizes renders Max-3-Section much harder to cope with compared to, e.g., Max-Bisection. We show a new algorithm that combines the existing approach of Lassere hierarchy along with a random cut strategy that suffices to give our result. © 2023, CC BY-NC-ND.

关键词： Approximation algorithms

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Exploiting Mode-Switching between Aerial-RIS and Active Radio in UAV-Assisted Wireless Networks

Exploiting Mode-Switching between Aerial-RIS and Active Radi...

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IEEE Conference on Wireless Communications and Networking

作者： Songhan Zhao Yusi Long Bo Gu Nguyen Cong Luong Bin Lyu Shimin Gong School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Faculty of Computer Science Phenikaa University Vietnam School of Communications and Information Engineering Nanjing University of Posts and Telecommunications China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode.

关键词： Radio frequency Trajectory planning Wireless networks Collaboration Switches Time-varying channels Throughput

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