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33rd ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA 2025

作者： Cheng, Jianyi Wang, Lianghui Jiang, Zijian Bao, Yungang Shi, Kan School of Informatics The University of Edinburgh Edinburgh United Kingdom Institute of Computing Technology Chinese Academy of Sciences University of Science and Technology of China Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400713965

Dataflow high-level synthesis (HLS) tools automatically map a high-level software program to a dataflow hardware design. When testing the design, the HLS tool takes a testing function written in the same software language and translates it into the corresponding hardware testbench for cycle-accurate simulation. However, the generated testbench only follows a fixed schedule to feed inputs and collect outputs, without considering external dynamic behaviors of the test data (also known as latency insensitivity testing). Overlooking these test cases could miss bugs introduced by the designer that occur in certain events, such as late input arrival and output backpressure, leading to higher debugging costs. In this paper, we present an HLS testing toolflow named PEDIA that automatically generates testbenches to detect latency insensitivity bugs. We expand the test space from varying input values to varying input delays. PEDIA automatically determines an efficient test space of input delays based on the HLS schedule for better scalability;and tests the HLS design on a parallel hardware testing platform for high performance. Over two representative sets of existing benchmarks, we show that PEDIA can help identify potential bugs missed by existing approaches and achieves an average speedup of 4.53× on running 1500 test cases. © 2025 Copyright held by the owner/author(s).

关键词： Software testing

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Cultural Heritage in The Digital Age: Evaluating User Experience in Virtual Reality Cultural Heritage Learning Application 27

Cultural Heritage in The Digital Age: Evaluating User Experi...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Afendi bin Abdul Ghani, Mohamad Shahfik Kiffli, Sudirman bin Kuan, Tenh Hock Shamsuddin, Syadiah Nor binti Wan Rahim, Normala binti Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan Kelantan Bachok16300 Malaysia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Kuala Nerus21300 Malaysia

ISBN: (纸本)9791188428137

The Fourth Industrial Revolution (IR4.0) has transformed various sectors, including cultural heritage preservation, education, and tourism, through advanced technologies like virtual reality (VR). This study explores the intersection of technology and cultural heritage learning, highlighting the crucial role of user experience (UX) in VR applications. While VR holds significant potential for cultural heritage learning, existing research often emphasizes utility and usability over UX. To address this gap, the study evaluates the UX of VR applications designed for cultural heritage, specifically focusing on the Istana Jahar historical site. The objectives include assessing dimensions such as effectiveness, efficiency, attractiveness, satisfaction, emotion, engagement, attention, and perception. The research employs quantitative methods using the Design Science Research Methodology (DSRM) to guide the development and evaluation of the VR application. A cluster random sampling technique was used to recruit 113 students from Universiti Malaysia Kelantan (UMK), ensuring a representative sample with relevant educational backgrounds. Participants interacted with the VR application and completed a survey based on the developed conceptual framework. Findings indicate high mean scores across UX dimensions: Effectiveness (4.487), Efficiency (4.363), Attractiveness (4.528), Satisfaction (4.363), Emotion (4.401), Engagement (4.385), Attention (4.366), and Perception (4.416). These results demonstrate that the elements within the conceptual framework effectively enhance the UX in cultural heritage learning applications. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Virtual environments

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Exploring Quantum Processors in Decentralized Edge computing for Improved Data Processing

Exploring Quantum Processors in Decentralized Edge Computing...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Harshad Pitkar Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems.

关键词： Quantum computing Accuracy Program processors Computational modeling Autoencoders Machine learning Real-time systems Computational efficiency Internet of Things Edge computing

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Understanding the Student Experience Better: Analyzing Remote Learning Tools Through Engagement, Participation, and Liveness Concepts

Understanding the Student Experience Better: Analyzing Remot...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Gomes, Genildo Silva, Paulo Magalhães, Sâmila Galvão, Leandro Lauschner, Tanara Steinmacher, Igor Conte, Tayana Gadelha, Bruno Institute of Computing Federal University of Amazonas AM Manaus Brazil Department of Design and Graphic Expression Federal University of Amazonas AM Manaus Brazil School of Informatics Computing and Cyber Systems Nothern Arizona University FlagstaffAZ United States

ISBN: (纸本)9789897587467

Technologies have changed how students engage and participate during remote classes, whether by encouraging interaction via chat on platforms such as Google Meet or by using voting features such as Poll. Our research aims to better understand the role of these tools in the educational context and how their interactive features influence student experience through the lens of engagement, participation, and liveness. In this sense, we address the following question: How can educational technologies be classified by the Engagement, Participation, and Liveness lens? To explore the relationship between the lens and the interactive features of Mentimeter, Google Meet, and Google Presentations, we conducted a study with three independent evaluations, measuring agreement using Cohen's Kappa coefficient. Our results suggest that these interactive features can be used as a lens to understand different types of experiences and how they can be applied to promote various dynamics, such as competitive and collaborative activities. Our results expand the professor's perspective on the student experience, providing ways for professors to understand the emotional and student behavioral reactions from using interactive tools in remote learning environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Efficient Medical File Protection via Adaptive CRP-based Ephemeral Key Encapsulation and Robust Error Correction

Efficient Medical File Protection via Adaptive CRP-based Eph...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Adil Alshammari Dina Ghanai Miandoab Sareh Assiri Bertrand Cambou Brit Riggs School of Informatics and Computing Northern Arizona University Flagstaff Arizona USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Ahstract- The healthcare sector faces significant challenges in balancing adaptability, security, and resource efficiency in medi-cal file management. This paper introduces a lightweight protocol that optimizes cryptographic components for using ephemeral key encapsulation, combining adaptive Challenge Response Pair with robust error correction. Designed for limited resource environments, the proposed approach minimizes computational overhead, ensures reliable key recovery, and provides privacy protection. The execution results validate its performance, making it a practical solution for a decentralized healthcare system.

关键词： Encapsulation Technological innovation Privacy Protocols Medical services Reliability engineering Error correction Cryptography Protection Low latency communication

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Parkinson’s Disease Prediction Using Spectrograms and Deep Belief Networks

Parkinson’s Disease Prediction Using Spectrograms and Deep ...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Sandeep C S Martin Margala School of Computing and Informatics University of Louisiana at Lafayette United States of America

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

This research presents a way to forecast Parkinson's disease in the early stages by means of spectrogram investigation and deep belief networks (DBNs). The aforesaid way concentrates on the use of spectrograms in order to get the best results and the extraction of innovative features and patterns from the signal of the speech or motor, which is indicative of Parkinson's disease. By using DBNs, which is a resilient deep learning mechanism, the system has the good feature of learning deep representations of the data, which makes the detection and classification accurate. The coupling of spectrograms with DBNs gives the model perspective to notice even the less severe differences in the input signals which are often missed out by traditional methods. The outlined aggregate demonstrates a prologue to a system that would be an accurate and automated solution to the problem, which in the end could be a help to healthcare professionals in a timely procurement of intervention and treatment. The proposed model makes clear the the ability of advanced machine learning techniques to solve hard neurological disorders and enhance patient outcomes.

关键词： Neurological diseases Procurement Accuracy Parkinson's disease Feature extraction Motors Regulation Slabs Spectrogram Medical diagnostic imaging

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Optimizing VPN Security in Financial Services: Implementing 256-Bit Encryption and Tunneling Protocols for Secure Data Transmission 19

Optimizing VPN Security in Financial Services: Implementing ...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ilias, Irma Syarlina Che Sameon, Sera Syarmila Zulkifli, Muhammad Zaqwan Ezzad Salleh, Mohammad Zahir Mat Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia College of Computing and Informatics Universiti Tenaga Nasional Selangor Malaysia CyberSecurity RawSec Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

This study examines the essential importance of security in contemporary network settings, emphasizing the increasing dependence on Virtual Private Networks (VPNs) for safe data transfer. The emphasis is on assessing the security attributes of VPNs to enhance existing network systems. This study specifically contrasts the performance and efficacy of two tunneling protocols—Layer 2 Tunneling Protocol version 3 with IPsec (L2TPv3/IPsec) and Generic Routing Encapsulation (GRE) utilizing IPsec—in a site-to-site connection. The study offers an economical solution by leveraging current infrastructure, minimizing the requirement for extra gear like antennas and rented space. Moreover, the study follows the National Institute of Standards and Technology (NIST) security protocols, specifically designed for the financial services sector, guaranteeing a secure and cost-effective approach for data transfer. Initial findings indicate that the best arrangement of VPN tunnels improves security in financial services networks, providing an effective method for protecting direct connections. © 2025 IEEE.

关键词： Cost effectiveness

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DR3DH: A DoS resistant extended triple Diffie–Hellman for mobile edge networks

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Computer Networks 2025年 265卷

作者： Altaweel, Ala Bouridane, Ahmed Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Mobile Edge Networks (MENs) are wireless networks that establish "close-to-end-users-clouds" to provide storage, computation, and communication services. MENs employ the Extended Triple Diffie–Hellman (X3DH) protocol to establish secret keys that provide end-to-end encryption among their nodes. X3DH relies on a cloud server that stores the public keys of the nodes to address the asynchronous scenarios when they operate offline. The cloud server in X3DH protocol represents a single point of failure (SPoF), as the exchange of secure keys is disrupted if the server fails or is compromised. Moreover, a malicious attacker, Eve, can launch various DoS attacks against the MEN nodes (e.g., packet dropping or packet modification) and wireless links (e.g., jamming) to disrupt the execution of X3DH during secure key exchange. This paper proposes the DoS-Resistant-X3DH (DR3DH) protocol to resolve the SPoF of the cloud server in X3DH and to address packet dropping, packet modification, and jamming DoS attacks. DR3DH employs Reed–Solomon Erasure Coding to encode the X3DH public keys into fragments that are distributed into a set of storage nodes. These storage nodes, which are determined by an Integer Linear Programming (ILP) solver, are optimum. That is, they have lowest probabilities to launch packet dropping/modification attacks and lowest probabilities that the wireless links toward them are jammed. The authors evaluated DR3DH through trace-driven simulations in MATLAB using two mobility traces and a proof-of-concept implementation in Java. The authors also compared DR3DH with two approaches that employ Random and Greedy policies when selecting the storage nodes for keys fragments. The results demonstrate the resistance of DR3DH to DoS attacks, achieving a high success rate that outperforms the Random and Greedy approaches by between 13.6% and 83.0%. Additionally, the results confirm the feasibility of DR3DH under different mobility models and node speeds, as demonstrated by

关键词： Packet loss

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MLDA-Net: Multi-Level Deep Aggregation Network for 3D Nuclei Instance Segmentation

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IEEE Journal of Biomedical and Health informatics 2025年第5期29卷 3516-3525页

作者： Hu, Bin Ye, Zhiwei Wei, Zimei Snezhko, Eduard Kovalev, Vassili Ye, Mang Hubei University of Technology School of Computer Wuhan China Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan China United Institute of Informatics Problems of the National Academy of Sciences of Belarus Belarus Wuhan University School of Computer Science Wuhan China

Segmentation of cell nuclei from three-dimensional (3D) volumetric fluorescence microscopy images is crucial for biological and clinical analyses. In recent years, convolutional neural networks have become the reliable 3D medical image segmentation standard. However, convolutional layers are limited by their finite receptive fields and weight-sharing mechanisms. Consequently, they struggle to effectively model long-range dependencies and spatial correlations, which may lead to inadequate nuclei segmentation. Moreover, the diversity in nuclear appearance and density poses additional challenges. This work proposes a lightweight multi-layer deep aggregation network, MLDA-Net, incorporating Wide Receptive Field Attention (WRFA). This module effectively simulates the large receptive field generated by self-attention in the Swin Transformer while requiring fewer model parameters. This design implements an extended global sensory field that enhances the ability to capture a wide range of spatial information. In addition, the multiple cross-attention (MCA) module in MLDA-Net enhances the output features of different resolutions from the encoder while maintaining global effectiveness. The Multi-Path Aggregation Feature Pyramid Network (MAFPN) receives multi-scale outputs from the MCA module, generating a robust hierarchical feature pyramid for the final prediction. MLDA-Net outperforms state-of-the-art networks, including 3DU-Net, nnFormer, UNETR, SwinUNETR, and 3DUXNET, on the 3D volumetric datasets NucMM and MitoEM. It achieves average performance improvements of 4% to 7% in F1 score, MIoU, and PQ metrics, thereby establishing new benchmark results. © 2013 IEEE.

关键词： Fluorescence microscopy

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LSTM-QGAN: Scalable NISQ Generative Adversarial Network

LSTM-QGAN: Scalable NISQ Generative Adversarial Network

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

作者： Cheng Chu Aishwarya Hastak Fan Chen Luddy School of Informatics Computing and Engineering Indiana University Bloomington USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN’s effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN, a QGAN architecture that eliminates PCA preprocessing and integrates quantum long short-term memory (QLSTM) to ensure scalable performance. Our experiments show that LSTM-QGAN significantly enhances both performance and scalability over state-of-the-art QGAN models, with visual data improvements, reduced Fréchet Inception Distance scores, and reductions of 5× in qubit counts, 5× in single-qubit gates, and 12× in two-qubit gates.

关键词： Visualization Scalability Qubit Logic gates Signal processing Generative adversarial networks Generators Speech processing Long short term memory Principal component analysis

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