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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Rajasa, Mahesa Cadi Rahma, Fayruz Rachmadi, Reza Fuad Pratomo, Baskoro Adi Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Islam Indonesia Department of Informatics Yogyakarta Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350344691

The rapid development of network connections and the widespread use of Internet of Things (IoT) devices has increased network traffic. The surge in network traffic has created new vulnerabilities in cyberspace, making it vulnerable to cyber-attacks. To address this challenge, researchers have turned to intelligent techniques, especially machine learning and deep learning, to improve the detection of network traffic attacks. However, a common problem arises: the data imbalance problem, where normal samples occur more often than attack samples, which hurts the performance and classification of machine learning or deep learning models. This study conducted a systematic literature review to identify the imbalanced datasets and the use of resampling techniques for addressing data imbalances in network intrusion detection research. We found four widely-used imbalanced datasets: NSL-KDD, CIC-IDS2017, UNSW-NB15, and KDD-Cup 1999. Researchers used three resampling approaches to tackle the imbalance problem: oversampling, undersampling, and hybrid sampling (combining oversampling and undersampling approaches). Researchers and practitioners can improve the security and efficiency of attack detection across network traffic by applying resampling techniques. © 2023 IEEE.

关键词： Internet of things

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IoT for Sustainable Hospitality: A Systematic Review of Opportunities and Challenges for the Hospitality Industry Revolution 19

IoT for Sustainable Hospitality: A Systematic Review of Oppo...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Poullas, Marios S. Kakoulli, Elena Neapolis University Pafos Intelligent Systems Laboratory Department of Computer Science Pafos Cyprus Cell and Developmental Biology University College London London United Kingdom Cyprus University of Technology Electrical Engineering and Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350346497

Technology has influenced the hospitality industry, and the Internet of Things (IoT) has become a focus for sustainability, revenue growth, and problem-solving. This systematic review examined the opportunities and challenges of implementing IoT technology for a sustainable hospitality. Through a thorough analysis of 28 studies, we found that IoT technology has great potential to address sustainability issues in the hospitality industry. However, there are still significant challenges that need to be addressed for the successful implementation of IoT technology in the hospitality industry. Overall, this study highlights the potential of IoT technology to revolutionize the hospitality industry and make it more sustainable. Further research is needed to address the challenges of IoT implementation in the hospitality industry and to evaluate the effectiveness of IoT technology in achieving sustainability goals. © 2023 IEEE.

关键词： Energy efficiency

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35th International Conference on Concurrency Theory, CONCUR 2024

作者： Reghem, Gaspard van Glabbeek, Rob J. ENS Paris-Saclay Université Paris-Saclay France School of Informatics University of Edinburgh United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9783959773393

This paper provides an adaptation of branching bisimilarity to reactive systems with time-outs. Multiple equivalent definitions are procured, along with a modal characterisation and a proof of its congruence property for a standard process algebra with recursion. The last section presents a complete axiomatisation for guarded processes without infinite sequences of unobservable actions. © Gaspard Reghem and Rob J. van Glabbeek.

关键词： Modal analysis

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Progressive Motion Boosting for Video Frame Interpolation

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IEEE Transactions on Multimedia 2023年 25卷 8076-8090页

作者： Xiao, Jing Xu, Kangmin Hu, Mengshun Liao, Liang Wang, Zheng Lin, Chia-Wen Wang, Mi Satoh, Shin'Ichi The School of Computer Science Wuhan University Wuhan430072 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430072 China The Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Video frame interpolation has made great progress in estimating advanced optical flow and synthesizing in-between frames sequentially. However, frame interpolation involving various resolutions and motions remains challenging due to limited or fixed pre-trained networks. Inspired by the success of the coarse-to-fine scheme for video frame interpolation, i.e., gradually interpolating frames of different resolutions, we propose a progressive boosting network (ProBoost-Net) based on a multi-scale framework to achieve flexible recurrent scales and then gradually optimize optical flow estimation and frame interpolation. Specifically, we designed a dense motion boosting (DMB) module to transfer features close to real motion to the decoded features from the later scales, which provides complementary information to refine the motion further. Furthermore, to ensure the accuracy of the estimated motion features at each scale, we propose a motion adaptive fusion (MAF) module that adaptively deals with motions with different receptive fields according to the motion conditions. Thanks to the framework’s flexible recurrent scales, we can customize the number of scales and make trade-offs between computation and quality depending on the application scenario. Extensive experiments with various datasets demonstrated the superiority of our proposed method over state-of-the-art approaches in various scenarios. © 2023 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Motion estimation

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A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

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computers, Materials & Continua 2023年第11期77卷 1445-1465页

作者： Arif Hussain Magsi Ali Ghulam Saifullah Memon Khalid Javeed Musaed Alhussein Imad Rida State Key Laboratory of Networking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Information Technology Center Sindh Agriculture UniversityTandojam70050Pakistan Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirate Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting ***,existing NDN faces three significant challenges,including security,privacy,and *** particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious *** instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road *** such a situation,trust in the content-providing vehicles brings a new *** the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another ***,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in *** this connection,our contribution is *** existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate *** on ML evaluation results,vehicles accept or discard served ***,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ***,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)*** implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive *** qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

关键词： Named data networking vehicular networks reputation caching machine-learning

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Utilizing Fuzzy Weights for Enhanced User Experience in Virtual Museums

Utilizing Fuzzy Weights for Enhanced User Experience in Virt...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Christos Papakostas Christos Troussas Akrivi Krouska Phivos Mylonas Cleo Sgouropoulou Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

In the digital age, the existence and experience of art, history, and culture change worldwide, and the rise of virtual museums ensures that the boundaries are eliminated and interactions with the population diversities are improved for education and culture preservation. This paper analyzes fuzzy weights for personalizing user experiences in virtual museums. Fuzzy weights propose recommendations and subtle content adaptation from user preferences and behaviors to enhance further engagement in the content exposed. Based on a case study of VirtuMuse, we have developed a model to calculate fuzzy weights from user interactions, which are integrated into content management and recommendation systems. User engagement was high, personalization effective and the users were satisfied based on forty university students’ evaluation. In conclusion, these findings underscore its effectiveness in matching contents according to individual users’ preferences, thereby enhancing their overall experience. Such research highlights the potential for virtual museums to act as preservers of digital cultural heritage and providers of personalized immersive educational experiences.

关键词： Art Navigation Immersive experience Virtual museums Information age User experience Cultural differences History Recommender systems Engines

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Crude Palm Oil Price Forecasting Based on ECoS-MARS: in Data Science Models 11

Crude Palm Oil Price Forecasting Based on ECoS-MARS: in Data...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Al-Khowarizmi Ramlan, Fitria Wulandari Efendi, Syahril Lubis, Arif Ridho Riza, Ferdy Amrullah Sary, Yoshida Universitas Muhammadiyah Sumatera Utara Department of Information Technology Medan Indonesia National University Ireland College of Engineering and Informatics Galway Ireland Universitas Sumatera Utara Department of Computer Science Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering & Informatics Medan Indonesia Universitas Muhammadiyah Sumatera Utara Department of Information System Medan Indonesia

ISBN: (纸本)9798350305968

The crude palm oil (CPO) industry is highly competitive due to fluctuating and unpredictable prices. To accurately predict future CPO prices, a forecasting technique is necessary. This can be achieved through various regression techniques and data mining approaches. This paper proposes a Multivariate Adaptive Regression Splines (MARS) model, which utilizes the ECoS machine learning algorithm to obtain optimal forecasting values. This research mainly focuses on forecasting CPO prices using the ECoS-MARS approach. The functional basis (BF) validation has yielded an accuracy of 0.0532%. Furthermore, validation has been conducted to achieve accurate forecasting values, resulting in an accuracy of 0.11714%. The best parameter combination for this forecasting model consists of a learning rate of 0.6 for both learning rates 1 and 2, a sensitivity threshold of 0.3, and an error threshold of 0.05. © 2023 IEEE.

关键词： Forecasting

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Assessing the Effectiveness of Oversampling and Undersampling Techniques for Intrusion Detection on an Imbalanced Dataset 4

Assessing the Effectiveness of Oversampling and Undersamplin...

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4th IEEE Industrial Electronics and Applications Conference, IEACon 2023

作者： Rahma, Fayruz Rachmadi, Reza Fuad Pratomo, Baskoro Adi Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Islam Indonesia Department of Informatics Yogyakarta Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350347517

The imbalanced class distribution in intrusion detection systems has been a significant issue. Imbalanced class distribution can negatively impact the performance of intrusion detection systems as they may be biased towards the majority class. We explore the effectiveness of oversampling and under-sampling techniques to address this issue. Oversampling and undersampling techniques aim to balance the class distribution and improve the performance of the intrusion detection system. Oversampling increases the number of records in the minority class to make it closer in size to the majority class. Conversely, undersampling reduces the number of records in the majority class so that it is closer in size to the minority class. We assess the effectiveness of different oversampling and undersampling techniques, including Random OverSampling, SMOTE, ADASYN, Random UnderSampling, AllKNN, TomekLinks, SMOTEENN, and SMOTETomek. The experiment's findings indicate that the raw data achieved the highest accuracy score, 0.965. On the other hand, the Random Oversampling method yielded the highest F1 score, reaching a score of 0.589. When we see the evaluation scores of each class, the recall & F1 scores generally show high contrast between classes with a large amount of data and classes with (previously) a small amount of data, even though the data for training has been more balanced. We found that oversampling and undersampling can improve the performance of intrusion detection systems in specific ways, but this still needs improvement. These results can serve as a reference for researchers developing intrusion detection systems. © 2023 IEEE.

关键词： Intrusion detection

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TCLaneNet: Task-Conditioned Lane Detection Network Driven by Vibration Information

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-14页

作者： Gong, Yan Jiang, Xinmin Wang, Lu Xu, Lisheng Lu, Jianli Liu, Hao Lin, Lei Zhang, Xinyu School of Computer Science and Engineering Northeastern University Shenyang China College of Medicine and Biomedical Information Engineering Northeastern University Shenyang China School of Vehicle and Mobility Tsinghua University Beijing China Autonomous Driving Department of X Divsion JD Logistics Beijing China Department of Artificial Intelligence School of Informatics Xiamen University Xiamen China

Lane detection is an important task in autonomous driving. However, it poses great challenges in occlusion and low-light conditions. To deal with these problems, we propose to utilize the vibration signals generated when vehicles pass over the vibration marking lines as supervision for lane occlusion prediction, whose features are then used to adaptively adjust the weights of lane detection network to improve its performance. Specifically, we develop a Task-Conditioned Lane Detection Network (TCLaneNet) that consists of the main lane detection branch and an lane occlusion classification branch. To effectively refine the lane detection features, Conditioned Adaptation Modules (CAMs) are proposed to obtain the conditioned parameters (i.e., channel weights) from the feature embedding of the lane occlusion classification task to adjust the lane detection features. In addition, as lane lines are elongated, while the receptive field of the convolution operation is spatially limited, we propose a Contextual Spatial Information Attention Module (CSIAM) to capture the global shape features of the lanes. Substantial experiments have been carried out on a vibration-based lane detection dataset VBLane that we constructed. The dataset consists of synchronously collected visual images and vibration signals, as well as the labels for the lane lines. Experimental results show that the proposed TCLaneNet achieves an Intersection over Union of 94.29%, which is better than the state-of-the-art lane detection methods. In addition, the TCLaneNet is lightweight (0.84 M) and runs in real-time (43.44 FPS), and the vibration information is not needed in the inference phase. Code and data at available on https://***/gongyan1/TCLaneNet. IEEE

关键词： Feature extraction

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