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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Shaffi, Noushath Viswan, Vimbi Mahmud, Mufti Hajamohideen, Faizal Subramanian, Karthikeyan College of Computing and Inf. Sc Utas Oman Nottingham Trent University Department of Computer Science Nottingham United Kingdom

ISBN: (纸本)9781665430654

In this study, we focus on Parkinson's Disease (PD) classification and present a comparative analysis of prominent machine learning models using two distinct and independent modalities: Magnetic Resonance Imaging (MRI) and Acoustic data. Unlike many existing works that typically focus on a single modality, our research study provides performance evaluation on the performance of various algorithms on both MRI and Acoustic data. Through a detailed investigation, we provide an understanding of how different models perform when applied to each modality individually. Furthermore, our study extends beyond this comparative framework by introducing an ensemble approach aimed at enhancing the performance of machine learning models for PD classification using the acoustic data. Notably, our ensemble approach yields around a 12 % increase in overall performance. © 2023 IEEE.

关键词： Accoustic Ensemble KNN Machine Learning MRI Parkinson's Disease SVM

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Using Object Detection to Select Grasp Type & Control Functional Electrical Stimulation for Hand Rehabilitation 10

Using Object Detection to Select Grasp Type & Control Functi...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Bhagat, Nikunj Arunkumar Ruppa, Manikanta Indian Institute of Science Dept. of Electrical Engineering Bengaluru India Indian Inst. of Inf. Tech. & Mgmt Dept. of Computer Science Thiruvananthapuram India

ISBN: (纸本)9781665490993

Unilateral muscle weakness and hand paralysis is the most common outcome after a stroke. Functional electrical stimulation (FES) is effective in assisting hand prehension, but conventional stimulators require users to manually select the grasp type (e.g. by pressing a button), which is challenging for patients with severe paralysis. Also, patients need to frequently divert attention from their task to operate the stimulator, which is cumbersome and reduces their engagement in the therapy. In this study, we develop a novel deep learning-based object detection approach to select multiple grasp types and control an electrical stimulator, in order to assist grasping. Object detection was performed using a state-of-the-art YOLOv5 algorithm, which achieved above 93% mean average precision. The algorithm tracked the positions of the hand and objects and selected a grasp type based on the object nearest to the hand. Once the grasp type was selected, a custom-built FES stimulator was activated to execute pre-defined stimulation sequences and allow a person to grasp the nearest object. This contactless, vision-based solution is beneficial for patients opting for homebased rehabilitation since it doesn't require additional setup time or help from caregivers. The future scope of this work includes testing the object detection-based FES on stroke patients and determining its efficacy in restoring hand movements. © 2023 IEEE.

关键词： Object detection

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Demo - Unmasking the Unseen: CryptojackingTrap, the Most Resilient Evasion-Proof Cryptojacking Malware Detection Algorithm

Demo - Unmasking the Unseen: CryptojackingTrap, the Most Res...

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IEEE International Workshop on inf.rmation Forensics and Security (WIFS)

作者： Atefeh Zareh Chahoki Hamid Reza Shahriari Marco Roveri Inf. Eng. and Computer Science Dept. University of Trento Trento Italy Computer Engineering Dept. Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350364422

ISBN: (纸本)9798350364439

This demonstration presents CryptojackingTrap, an advanced cryptojacking malware detection solution that stands out among similar works due to its superior evasion resilience. The demonstration covers the architecture, design, and implementation of CryptojackingTrap, highlighting its effectiveness in detecting cryptojacking attempts, even in scenarios where attackers employ evasion techniques to reduce the hash rate by up to tenfold. The evaluation includes rigorous experimental testing against both miner and non-miner executables. This demonstration provides cybersecurity specialists with a deep understanding of the algorithm, showcasing its robustness and potential for extension, and offering insights into benchmarking detection solutions using publicly available datasets and opensource codebases.

关键词： Instruments Forensics Conferences Benchmark testing Malware Robustness Detection algorithms computer security Resilience

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Enhancing C-V2X Vehicle Platooning: A Dual-Strategy of Time Synchronisation and Partition-Based Scheduling

Enhancing C-V2X Vehicle Platooning: A Dual-Strategy of Time ...

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IEEE International Workshop on Measurements and Networking Proceedings (M&N)

作者： Hossein Khalilnasl Salvatore Dello Iacono Paolo Ferrari Alessandra Flammini Brian McCarthy Emiliano Sisinni Dept. of Information Engineering University of Brescia Brescia Italy Sch. of Computer Science and Inf. Technology University College Cork Cork Ireland

ISBN: (数字)9798350370539

ISBN: (纸本)9798350370546

There is clear evidence indicating that the standardized Sensing-Based Semi-Persistent Scheduling scheme in Out-of-coverage possesses specific vulnerabilities. This unsatisfactory performance lies in the absence of coordination in channel scheduling and transmission time. Specifically, the determination to re-assign a resource after the reservation period expires is currently based on a random selection process, without consideration of the actual interference encountered by the utilized resources. In this study, a novel scheme is proposed to markedly improve the random selection method for vehicle positioning.

关键词： Interference Sensors Synchronization Standards

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Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data

Towards Automated Classification of Parkinson's Disease: Com...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Noushath Shaffi Vimbi Viswan Mufti Mahmud Faizal Hajamohideen Karthikeyan Subramanian College of Computing and Inf. Sc UTAS Oman Department of Computer Science Nottingham Trent University Nottingham United Kingdom

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Wireless Embedded Index Coded Transmissions with Multiple Antennas 14

Wireless Embedded Index Coded Transmissions with Multiple An...

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14th International Conference on inf.rmation and Communication Technology Convergence, ICTC 2023

作者： Song, Heekang Son, Kyungrak Choi, Wan Korea Advanced Institute of Science and Technology School of Electrical Engineering Daejeon Korea Republic of Dept. of Inf. Commun. Engineering Yongin Korea Republic of Seoul National University Institute of New Media and Communications Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

Embedded index coding, known as device-to-device index coding, is a promising transmission scheme that effectively reduces the number of transmissions required in device-to-device communications. Leveraging spatial multiplexing gain attained with multiple antennas in wireless environment, this paper explores the benefits of spatially multiplexed embedded index codes through the joint design of embedded index coding and multicast beamformers. Through our simulations, we are able to demonstrate significant improvements obtained by the proposed scheme in terms of spatially multiplexed embedded index coding. © 2023 IEEE.

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Work in Progress: VR-based Robotics Training for AEC Industry

Work in Progress: VR-based Robotics Training for AEC Industr...

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IEEE World engineering Education Conference (EDUNINE)

作者： Biayna Bogosian Shahin Vassigh Bhavleen Kaur Narula Seth Corrigan Giancarlo Perez Mohammadreza Akbari Lor Bhanu Vodinepally Tisa Islam Erana Mark Alan Finlayson Shu-Ching Chen The Design School Arizona State University Tempe AZ USA Department of Architecture Florida International University Miami FL USA School for Inf. and Computer Sci. University of California Irvine CA USA School of Science and Engineering University of Missouri Kansas City MO USA School of Computer and Inf. Sci. Florida International University Miami FL USA

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

This paper introduces the Intelligent Learning Platform for Robotics Operations (IL-PRO), a Virtual Reality (VR) system designed to enhance robotics training in the Architecture, engineering, and Construction (AEC) industry. IL-PRO addresses the growing need for effective training methods as the AEC sector adopts robotic automation. The system integrates VR technology with game-assisted learning, combining online multimedia lessons for theory with immersive VR tasks for practical skills. Developed iteratively using Design-Based Research principles, IL-PRO incorporates realistic robot simulations and progressive task complexity. The VR environment, built in Unity, aims to enhance engagement, motor coordination, and spatial awareness in robotics training. While future goals include AI-driven personalized instruction, this work-in-progress focuses on VR curriculum development and implementation. The paper concludes by discussing future directions, including curriculum expansion and cross-institutional adoption, to establish new benchmarks in innovative robotics education for the AEC industry.

关键词： Training Industries Solid modeling Service robots Robot kinematics Multimedia systems Curriculum development Motor coordination engineering education Immersive learning

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Automated Boundary Identification for Machine Learning Classifiers

Automated Boundary Identification for Machine Learning Class...

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Search-Based and Fuzz Testing (SBFT), IEEE/ACM International Workshop on

作者： Felix Dobslaw Robert Feldt Dept. of Quality Mngmt Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (数字)9798400705625

ISBN: (纸本)9798350376012

AI and Machine Learning (ML) models are increasingly used as (critical) components in software systems, even safety-critical ones. This puts new demands on the degree to which we need to test them and requires new and expanded testing methods. Recent boundary-value identification methods have been developed and shown to automatically find boundary candidates for traditional, non-ML software: pairs of nearby inputs that result in (highly) differing outputs. These can be shown to developers and testers, who can judge if the boundary is where it is supposed to ***, we explore how this method can identify decision boundaries of ML classification models. The resulting ML Boundary Spanning Algorithm (ML-BSA) is a search-based method extending previous work in two main ways. We empirically evaluate ML-BSA on seven ML datasets and show that it better spans and thus better identifies the entire classification boundary(ies). The diversity objective helps spread out the boundary pairs more broadly and evenly. This, we argue, can help testers and developers better judge where a classification boundary actually is, compare to expectations, and then focus further testing, validation, and even further training and model refinement on parts of the boundary where behaviour is not ideal.

关键词： Training Conferences Machine learning Fuzzing Software systems Classification algorithms Testing

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Building Collaborative Learning: Exploring Social Annotation in Introductory Programming

arXiv

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arXiv 2024年

作者： de Oliveira Neto, Francisco Gomes Dobslaw, Felix Chalmers The University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden Mid Sweden University Dept. of Quality Mngmt Communication and Inf. Systems Östersund Sweden

The increasing demand for software engineering education presents learning challenges in courses due to the diverse range of topics that require practical applications, such as programming or software design, all of which are supported by group work and interaction. Social Annotation (SA) is an approach to teaching that can enhance collaborative learning among students. In SA, both students and teachers utilize platforms like Feedback Fruits, Perusall, and Diigo to collaboratively annotate and discuss course materials. This approach encourages students to share their thoughts and answers with their peers, fostering a more interactive learning environment. We share our experience of implementing social annotation via Perusall as a preparatory tool for lectures in an introductory programming course aimed at undergraduate students in Software engineering. We report the impact of Perusall on the examination results of 112 students. Our results show that 81% of students engaged in meaningful social annotation successfully passed the course. Notably, the proportion of students passing the exam tends to rise as they complete more Perusall assignments. In contrast, only 56% of students who did not participate in Perusall discussions managed to pass the exam. We did not enforce mandatory Perusall participation in the course. Yet, the feedback from our course evaluation questionnaire reveals that most students ranked Perusall among their favorite components of the course and that their interest in the subject has increased. © 2024, CC BY.

关键词： Students

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Building Collaborative Learning: Exploring Social Annotation in Introductory Programming

Building Collaborative Learning: Exploring Social Annotation...

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Software engineering: Software engineering Education and Training (ICSE-SEET), IEEE/ACM International Conference on

作者： Francisco Gomes De Oliveira Neto Felix Dobslaw Dept. of Computer Science and Engineering Chalmers and the University of Gothenburg Gothenburg Sweden Dept. of Quality Mngmt Communication and Inf. Systems Mid Sweden University Östersund Sweden

ISBN: (数字)9798400704987

ISBN: (纸本)9798350352115

关键词： Training Software design Annotations Federated learning Buildings Programming profession Software engineering

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