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

作者： Raj, Arjun Wang, Lei Gedeon, Tom School of Computing Australian National University Canberra Australia School of Elec Eng Comp & Math Sci Curtin University Perth Australia

ISBN: (纸本)9798350368741

Accurately detecting and tracking high-speed, small objects, such as balls in sports videos, is challenging due to factors like motion blur and occlusion. Although recent deep learning frameworks like TrackNetV1, V2, and V3 have advanced tennis ball and shuttlecock tracking, they often struggle in scenarios with partial occlusion or low visibility. This is primarily because these models rely heavily on visual features without explicitly incorporating motion information, which is crucial for precise tracking and trajectory prediction. In this paper, we introduce an enhancement to the TrackNet family by fusing high-level visual features with learnable motion attention maps through a motion-aware fusion mechanism, effectively emphasizing the moving ball's location and improving tracking performance. Our approach uses frame differencing maps, modulated by a motion prompt layer, to highlight key motion regions over time. Experimental results on the tennis ball and shuttlecock datasets show that our method enhances the tracking performance of both TrackNetV2 and V3. We refer to our lightweight, plug-and-play solution, built on top of the existing TrackNets, as TrackNetV4. © 2025 IEEE.

关键词： fusion motion attention tracking

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Ant and Bear Dance for Dokweebah: Using a Skokomish Story to Engage Middle school Students in Event-Driven Programming 2023

Ant and Bear Dance for Dokweebah: Using a Skokomish Story to...

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2nd ACM Conference on Global computing Education, CompEd 2023

作者： Chipps, Joseph Fasy, Brittany Terese Hancock, Stacey A. McCoy, Bradley Montana State University School of Computing & Math. Sci. BozemanMT United States

ISBN: (纸本)9798400700484

Learning computer science (CS) is important for careers of tomorrow. Informal CS opportunities, however, are often limited by a student's socioeconomic disposition, location, ethnicity, gender, and ability. In Montana, these limitations are exemplified in rural communities where a dedicated CS teacher is not available. In order to make informal CS opportunities more equitable, we developed culturally responsive outreach modules for students across Montana by using storytelling as a basis of inquiry. In this paper, we present an outreach module based on the Skokomish story of How Daylight Came to Be. In this story, the two main characters-Ant and Bear-each dance for Dokweebah (the Changer). Students animate these dances using event-driven programming in the drag-and-drop programming environment Alice. While creating their dances, students construct knowledge of targeted CS concepts and make design decisions based on the context of the story. This outreach module reframes the context and activity of computing in an effort to transform the way in which students see themselves as potential future computer scientists, and democratize computing as a means of telling stories. By using Brayboy's Tribal Critical Race Theory as a theoretical framework for the development of the outreach program, we introduce computing from a lens of American Indian ways of knowing, culture, and power. To demonstrate the effectiveness of this unit in this exploratory study, we describe students' responses to the outreach programs in terms of perceptions of CS and perceptions of Alice as a culturally relevant programming tool. © 2023 Owner/Author.

关键词： Students

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Digital Forensics Investigations and Network Security Issues in Tracking the Trails of Cybercriminals

Digital Forensics Investigations and Network Security Issues...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Oyelakin, Oyetunji Ofori-Yeboah, Abel Ganiyu, Aishat Oguntoyinbo, Oluwole School of Computing and Eng University of West London London United Kingdom School of Eng. Phys. and Math. Royal Holloway University London United Kingdom

ISBN: (纸本)9798331539733

The application of digital forensics investigations (DFI) and the legal implications in network security have become imperative due to increased connectivity and cyberattacks. The DFI process requires investigating computers and their associated media to determine if they have been used to commit a crime or gain unauthorized access. However, in a network environment, attackers can intercept, interrupt, modify content, and modify contents and fabricate the victims. Thus, it is imperative that these cyberattacks are considered from a legal perspective considering the challenges. The paper explores the digital forensics investigations process and its legal implications from network security to assist in determining indicators of compromise, attribution, and prosecution. The contribution of the paper is threefold. First, we explore DFI challenges from a network security perspective, including the legal and the standards. Secondly, we implement attacks on a network and use DFI tools such as Splunk to detect interruptions, modifications, and disruptions during the investigation process. Finally, we discuss some DFI challenges that impinge on national and international law during investigations and prosecutions and recommend improvement. The results show that the DFI process in the network security environment is relevant in detecting, preventing, and prosecuting these threats. © 2024 IEEE.

关键词： Computer forensics

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Fortifying Cloud DevSecOps Security Using Terraform Infrastructure as Code Analysis Tools

Fortifying Cloud DevSecOps Security Using Terraform Infrastr...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Singh, Rashika Yeboah-Ofori, Abel Kumar, Saurabh Ganiyu, Aishat School of Computing and Eng University of West London United Kingdom University of Allahabad BTech in Computer Sc.and Eng India School of Eng Phys and Math Royal Holloway University United Kingdom

ISBN: (纸本)9798331539733

Fortifying Cloud Security has become inevitable due to challenges such as misconfigurations, coding errors, and compromised secrets or passwords that impact infrastructure as a service during infrastructure such as code automation (IaC). These challenges require code analysis tools to enhance security during infrastructure automation. Setting up a simple cloud architecture is quick, but human errors are still common, especially when cloud infrastructure can be deployed with just a few clicks. Terraform provides a ready-made infrastructure as code modules to build and scale cloud-hosted applications. However, cyber attackers exploit these vulnerabilities and gain access to sensitive data or resources without authorization due to configuration errors, inadequate storage, and infrastructure manipulation, resulting in unauthorized deployments or alterations. That affects the availability of resources during infrastructure deployment using attacks such as DoS attacks, injection attacks, Man in the Middle (MITM), malware spread, remote code execution (RCE), and phishing attacks to penetrate the cloud infrastructures. The paper aims to analyze Terraforms infrastructure as code in cloud security to fortify codes and assist DevSecOps engineers in identifying misconfiguration in Terraform scripts. The paper's contributions are threefold. First, we explore cloud security by securing IaC solutions on Terraform. We consider security issues, including misconfigurations and coding errors, present in Terraform IaC. Secondly, we implement a static analysis tool for terraform by comparatively analyzing existing tools. Finally, we provide a comparative analysis of terraform IaC on tools including Checkov, Tfsec, Tflint, and Terrascan for suitability based on their key features and performance metrics to enhance security. © 2024 IEEE.

关键词： Cloud security

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Effective and Lightweight Surface Defect Detection via Enhanced Deep Learning Models

Effective and Lightweight Surface Defect Detection via Enhan...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kayanzari, Kenny Pierrot Li, Hongzhou Zhang, Ji Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nanjing China Guilin University of Electronic Technology School of Life and Environmental Sciences Guilin China University of Southern Queensland School of Math Physics and Computing Toowoomba Australia

ISBN: (纸本)9798350362480

This study addresses limitations in industrial defect detection by enhancing YOLOv8 with EfficientNet-B0. Using MBConv, multiscale pathways, and C2f modules, the model improved precision from 0.935 to 0.983, the recall from 0.917 to 0.981, mAP@0.5 from 0.964 to 0.993, and mAP@0.5:0.95 from 0.787 to 0.9 on the NEU-DET dataset, enabling efficient, real-time defect detection for industrial applications. © 2024 IEEE.

关键词： Deep learning Efficient-Net Steel defect detection YOLOv8

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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BERT Goes to SQL school: Improving Automatic Grading of SQL Statements

BERT Goes to SQL School: Improving Automatic Grading of SQL ...

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2023 Congress in Computer Science, Computer Engineering, and Applied computing, CSCE 2023

作者： Rivas, Pablo Schwartz, Donald R. Quevedo, Ernesto School of Eng. & Computer Science Baylor University Dept. of Computer Science United States School of Computer Science & Math. Marist College Dept. of Computing Technology United States

ISBN: (纸本)9798350327595

Automated grading of SQL queries is a challenging task due to the complexity of the language and the variety of acceptable solutions for a given problem. In this paper, we propose a novel approach that leverages deep learning with a BERT model to understand the syntax and semantics of SQL statements. By training BERT on a dataset of SQL queries and their corresponding grades, we create a model that can automatically grade new questions accurately. Our experiments demonstrate that the proposed methodology achieves high accuracy and consistency in grading SQL queries, outperforming existing state-of-the-art models. Furthermore, we provide an analysis of the model's explainability, revealing a new capability that can be extremely beneficial for understanding the decision-making process. Overall, our work demonstrates the potential of deep learning with BERT for improving the efficiency and accuracy of SQL query grading. © 2023 IEEE.

关键词： bert deep learning machine learning natural language processing sql

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Managing Objective Archives for Solution Set Reduction in Many-Objective Optimization

Managing Objective Archives for Solution Set Reduction in Ma...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Peerlinck, Amy Sheppard, John Western Colorado State University Math and Computer Science Department GunnisonCO81230 United States Gianforte School of Computing Montana State University BozemanMT59717 United States

ISBN: (纸本)9781665430654

As objectives increase in many-objective optimization (MaOO), often so do the number of non-dominated solutions, potentially resulting in solution sets with thousands of non-dominated solutions. Such a larger final solution set increases difficulty in visualization and decision-making. This raises the question: how can we reduce this large solution set to a more manageable size? In this paper, we present a new objective archive management (OAM) strategy that performs post-optimization solution set reduction to help the end-user make an informed decision without requiring expert knowledge of the field of MaOO. We create separate archives for each objective, selecting solutions based on their fitness as well as diversity criteria in both the objective and variable space. We can then look for solutions that belong to more than one archive to create a reduced final solution set. We apply OAM to NSGA-II and compare our approach to environmental selection finding that the obtained solution set has better hypervolume and spread. Furthermore, we compare results found by OAM-NSGA-II to NSGA-III and get competitive results. Additionally, we apply OAM to reduce the solutions found by NSGA-III and find that the selected solutions perform well in terms of overall fitness, successfully reducing the number of solutions. © 2023 IEEE.

关键词： evolutionary algorithms many-objective optimization solution set reduction

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TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps

TrackNetV4: Enhancing Fast Sports Object Tracking with Motio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Arjun Raj Lei Wang Tom Gedeon School of Computing Australian National University Canberra Australia School of Elec Eng. Comp & Math Sci Curtin University Perth Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately detecting and tracking high-speed, small objects, such as balls in sports videos, is challenging due to factors like motion blur and occlusion. Although recent deep learning frameworks like TrackNetV1, V2, and V3 have advanced tennis ball and shuttlecock tracking, they often struggle in scenarios with partial occlusion or low visibility. This is primarily because these models rely heavily on visual features without explicitly incorporating motion information, which is crucial for precise tracking and trajectory prediction. In this paper, we introduce an enhancement to the TrackNet family by fusing high-level visual features with learnable motion attention maps through a motion-aware fusion mechanism, effectively emphasizing the moving ball’s location and improving tracking performance. Our approach uses frame differencing maps, modulated by a motion prompt layer, to highlight key motion regions over time. Experimental results on the tennis ball and shuttlecock datasets show that our method enhances the tracking performance of both TrackNetV2 and V3. We refer to our lightweight, plug-and-play solution, built on top of the existing TrackNets, as TrackNetV4.

关键词： Visualization Tracking Sports equipment Signal processing Predictive models Trajectory Object tracking Speech processing Sports Videos

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TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps

arXiv

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arXiv 2024年

作者： Raj, Arjun Wang, Lei Gedeon, Tom School of Computing Australian National University Canberra Australia School of Elec Eng Comp & Math Sci Curtin University Perth Australia

Accurately detecting and tracking high-speed, small objects, such as balls in sports videos, is challenging due to factors like motion blur and occlusion. Although recent deep learning frameworks like TrackNetV1, V2, and V3 have advanced tennis ball and shuttlecock tracking, they often struggle in scenarios with partial occlusion or low visibility. This is primarily because these models rely heavily on visual features without explicitly incorporating motion information, which is crucial for precise tracking and trajectory prediction. In this paper, we introduce an enhancement to the TrackNet family by fusing high-level visual features with learnable motion attention maps through a motion-aware fusion mechanism, effectively emphasizing the moving ball's location and improving tracking performance. Our approach leverages frame differencing maps, modulated by a motion prompt layer, to highlight key motion regions over time. Experimental results on the tennis ball and shuttlecock datasets show that our method enhances the tracking performance of both TrackNetV2 and V3. We refer to our lightweight, plug-and-play solution, built on top of the existing TrackNet, as TrackNetV4. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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