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arXiv 2024年

作者： Varotto, Matteo Valentin, Stefan Ardizzon, Francesco Marzotto, Samuele Tomasin, Stefano Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering University of Padova Italy

Fifth-generation (5G) mobile networks are vulnerable to jamming attacks that may jeopardize valuable applications such as industry automation. In this paper, we propose to analyze radio signals with a dedicated device to detect jamming attacks. We pursue a learning approach, with the detector being a convolutional neural network (CNN) implementing a generalized likelihood ratio test (GLRT). To this end, the CNN is trained as a two-class classifier using two datasets: one of real legitimate signals and another generated artificially so that the resulting classifier implements the GLRT. The artificial dataset is generated mimicking different types of jamming signals. We evaluate the performance of this detector using experimental data obtained from a private 5G network and several jamming signals, showing the technique's effectiveness in detecting the attacks. © 2024, CC BY-NC-ND.

关键词： 5G mobile communication systems

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One-Class Classification as GLRT for Jamming Detection in Private 5G Networks

One-Class Classification as GLRT for Jamming Detection in Pr...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Matteo Varotto Stefan Valentin Francesco Ardizzon Samuele Marzotto Stefano Tomasin Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering University of Padova Italy

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

关键词： Wireless communication Accuracy 5G mobile communication Supervised learning Detectors Signal processing Threat assessment Convolutional neural networks Jamming Unsupervised learning

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The Impact of GPU on Containerized computer Vision Applications on Edge Nodes 8

The Impact of GPU on Containerized Computer Vision Applicati...

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8th IEEE International Conference on Edge Computing and Communications, EDGE 2024

作者： Alqaisi, Osamah I. Tosun, Ali Saman Korkmaz, Turgay University of Texas at San Antonio Department of Computer Science San AntonioTX78249 United States The University of North Carolina at Pembroke Dep. of Math. and Computer Science NC28372 United States

ISBN: (纸本)9798350368499

Edge computing revolutionizes computer vision, but faces challenges due to computational demands and complexities in dep.oyment and platform integration. In addressing these hurdles, container technology emerges as a crucial solution, particularly in mitigating dep.oyment and security issues. However, the impact of GPU acceleration on containerized computer vision applications remains understudied. This paper explores containerized computer vision applications across ARM edge devices, analyzing performance on different CPUs and GPUs. Insights cover processing time, CPU usage, memory utilization, and application performance, highlighting the importance of ARM processor selection. Integrating CPU and GPU enhances execution time but leads to significant memory usage increases. Workload escalation strains the GPU, resulting in slowed processing, and its performance becomes comparable to that of the CPU. Furthermore, the performance of computer vision applications varies with different containers. These findings underscore the delicate balance between CPU and GPU utilization and the pivotal role of algorithm selection in optimizing containerized computer vision applications on edge devices. © 2024 IEEE.

关键词： ARM processors

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Parallel and hierarchically-distributed Shoreline Alert Model (SAM) 31

Parallel and hierarchically-distributed Shoreline Alert Mode...

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31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： De Vita, Ciro Giuseppe Mellone, Gennaro Florio, Aniello Charles, Catherine Alessandra Torres Di Luccio, Diana Lapegna, Marco Benassai, Guido Budillon, Giorgio Montella, Raffaele University of Naples 'Parthenope' Dep. of Science and Technologies Naples Italy University of Madrid Carlos Iii Dep. of Computer Science and Engineering Madrid Spain University of Naples 'Federico II' Dept. of Mathematics and Applications Naples Italy

ISBN: (纸本)9798350337631

In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment. © 2023 IEEE.

关键词： Parallel architectures

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Predictions of COVID-19 Spread by Using Supervised Data Mining Techniques

Predictions of COVID-19 Spread by Using Supervised Data Mini...

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2020 Ibn Al-Haitham International Conference for Pure and Applied sciences, IHICPS 2020

作者： Awadh, Wid Akeel Alasady, Ali Salah Mustafa, Hadeel Ismail Dep. of Computer Information System College of Computer Science and Information Technology University of Basrah Iraq Dep. of Computer Science College of Computer Science and Information Technology University of Basrah Iraq

In the wake of the outbreak of the new coronavirus, the countries in the world have fought to combat the spread of infection and imposed preventive measures to compel the population to social distancing, which led to a global crisis. Important strategies must be studied and identified to prevent and control the spread of coronavirus COVID-19 disease 2019. In this paper, the effect of preventive strategies on COVID-19 spread was studied, a model based on supervised data mining algorithms was presented and the best algorithm was suggested on the basis of accuracy. In this model, three classifiers (Naive Bayes, Multilayer Perceptron and J48) dep.nded on the questionnaires filled out by Basra City respondents. The questionnaires consisted of 25 questions that covered fields most related to and that affect the prevention of COVID-19 spread, including demographic, psychological, health management, cognitive, awareness and preventive factors. A total of 1017 respondents were collected. This model was developed using Weka 3.8 tool. Results showed that quarantine played an important role in controlling the spread of the disease. By comparing the accuracy of the algorithms used, the best algorithm was found to be J48. © Published under licence by IOP Publishing Ltd.

关键词： COVID-19

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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

Planing It by Ear: Convolutional Neural Networks for Acousti...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Deschênes, Anthony Georges, Rémi Subakan, Cem Ugulino, Bruna Henry, Antoine Morin, Michael Dep. of Wood and Forest Sciences Université Laval QC Canada Dep. of Computer Science and Software Engineering Université Laval QC Canada Dep. of Operations and Decision Systems Université Laval QC Canada FORAC Research Consortium Université Laval QC Canada Mila-Quebec AI Institute QC Canada FPInnovations QC Canada Concordia University QC Canada

ISBN: (纸本)9798350368741

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities. © 2025 IEEE.

关键词： Acoustic Anomaly detection Convolutional neural networks Transformer Wood planer

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Enhancing SAT Solving with GNN for Clause Weight Prediction

Enhancing SAT Solving with GNN for Clause Weight Prediction

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Abdelraouf Ishtaiwi Zaid Momani Amani Abu Zaid Dep. of Data Science and AI University of Petra Amman Jordan Dep. of Computer Science Zarqa University Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Boolean satisfiability (SAT) solving is foundational in various computational domains but faces scalability challenges with large instances. This paper introduces a novel method employing Graph Neural Networks (GNNs) to predict clause weights dynamically within SAT solvers. By representing SAT problems as bipartite graphs and integrating GNNs for clause weight prediction, our approach achieves a 40% reduction in solving time and a 35% improvement in solution quality compared to state-of-the-art solvers such as MapleSAT and GAN-SAT. Extensive experiments on benchmarks and real-world applications validate the effectiveness of our method, indicating significant potential for enhancing SAT solver performance in practical settings.

关键词： Machine learning algorithms Scalability Heuristic algorithms Prediction algorithms Search problems Graph neural networks Complexity theory Bipartite graph Faces Computational intelligence

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A Proposed Approach for Risk Planning and Management 25

A Proposed Approach for Risk Planning and Management

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25th International Arab Conference on information technology, ACIT 2024

作者： Al Refai, Mohammed N. Al-Smadi, Yazan Al-Momani, Adai Jamhawi, Zeyad M. Otoom, Ahmed Ali Atoum, Issa Yaseen, Ahid Ababneh, Jafar Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Applied Science Private University Department of Computer Science Amman Jordan Applied Science Private University Department of Cyber Security Amman Jordan Philadelphia University Faculty of Information Technology Software Engineering Department Jordan Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan. © 2024 IEEE.

关键词： Project management

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Gradual divergence for seamless adaptation: a novel domain incremental learning method 24

Gradual divergence for seamless adaptation: a novel domain i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Kishaan Jeeveswaran Elahe Arani Bahram Zonooz Dep. of Mathematics and Computer Science Eindhoven University of Technology NL Dep. of Mathematics and Computer Science Eindhoven University of Technology NL and Wayve Technologies Ltd London UK

Domain incremental learning (DIL) poses a significant challenge in real-world scenarios, as models need to be sequentially trained on diverse domains over time, all the while avoiding catastrophic forgetting. Mitigating representation drift, which refers to the phenomenon of learned representations undergoing changes as the model adapts to new tasks, can help alleviate catastrophic forgetting. In this study, we propose a novel DIL method named DARE, featuring a three-stage training process: Divergence, Adaptation, and REfinement. This process gradually adapts the representations associated with new tasks into the feature space spanned by samples from previous tasks, simultaneously integrating task-specific decision boundaries. Additionally, we introduce a novel strategy for buffer sampling and demonstrate the effectiveness of our proposed method, combined with this sampling strategy, in reducing representation drift within the feature encoder. This contribution effectively alleviates catastrophic forgetting across multiple DIL benchmarks. Furthermore, our approach prevents sudden representation drift at task boundaries, resulting in a well-calibrated DIL model that maintains the performance on previous tasks.

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Standardization Review on 5G LAN-Type Service 15

Standardization Review on 5G LAN-Type Service

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15th International Conference on information and Communication technology Convergence, ICTC 2024

作者： Kyung, Yeunwoong Lee, Donghun Lee, Jaewook Kim, Youngjun Ko, Haneul Oh, Seungmin Lee, Jaehwan Kongju National University Div. of Info. & Comm. Eng. Cheonan Korea Republic of Pukyong National University Dep. of Info. and Comm. Engineering Busan Korea Republic of School of Computer Science and Eng. Kyungnam University Changwon Korea Republic of Kyung Hee University Dep. of Electronic Eng. Yongin-si Korea Republic of Kongju National University Dep. of Computer Science and Eng. Cheonan Korea Republic of

ISBN: (纸本)9798350364637

The 5G LAN-type service refers to a service that supports communication between users within a group through the 5G system. It provides functions similar to those of traditional Local Area Networks (LAN) or Virtual Private Networks (VPN), but it gains attention across various services due to the enhanced performance of 5G such as high throughput, low latency, mobility management, and security. The 3rd Generation Partnership Project (3GPP) introduced the 5G LAN-type service in Release 16 and defined the architecture, protocols, and requirements to support the 5G LAN-type service. In this paper, we briefly examine the architecture and components of the 5G network based on the 3GPP standard and introduce the structure and protocols for supporting 5G LAN-type services in a 5G network. © 2024 IEEE.

关键词： 5G mobile communication systems

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