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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Banik, Swarnali Adithya, Akhilesh Mitra, Bivas Sen, Sougata Saha, Snehanshu Ghosh, Surjya Amazon India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India BITS Pilani K K Birla Goa Campus Department of Computer Science and Information Systems India BITS Pilani K K Birla Goa Campus APPCAIR Goa India

ISBN: (纸本)9798331531195

We present an annotation tool designed to collect fine-grain continuous emotion ground truth labels and physiological responses as users watch different videos. In the existing literature, continuous emotion annotations are typically collected as self-reports based on the 2D Circumplex model through auxiliary devices (a joystick or similar tools) during video consumption. However, the users face challenges as they need to use an auxiliary device and rely on the Circumplex model's valence and arousal axis without clear indication of distinct emotion labels. Therefore, to overcome these challenges, we introduce PResCon, a tool that allows to collect emotion annotations using keyboard arrow keys only (without using any auxiliary device) and guiding users towards the discrete emotions (e.g., amused, stressed, bored, relaxed) by embedding them on the Circumplex plane. The tool encompasses two major components - (a) a web interface that displays various stimuli videos and provision of collecting emotion labels (valence and arousal) by moving the keyboard arrow keys and (b) a sensor setup (based on Arduino) that allows to collect the physiological responses (heart rate and galvanic skin response) passively during video consumption. In summary, the PResCon tool facilitates research in the domain of affective computing by allowing to collect fine-grain continuous emotion annotations and the physiological responses in real-time during video consumption. © 2025 IEEE.

关键词： Auxiliary equipment

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Multilevel Discrete Cosine Transform and CNN-Based Image Steganography 3

Multilevel Discrete Cosine Transform and CNN-Based Image Ste...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Subburaj, Brindha Sai, Aditya Shantanu, Shantanu Won, Daehan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Binghamton University Department of Systems Science and Industrial Engineering United States

ISBN: (纸本)9798331523893

This paper investigates the growing role of steganography in cybersecurity and presents a hybrid implementation called Multi-level Discrete Cosine Convolution (MDCC) that applies a Multi-level Discrete Cosine Transform (MDCT) and a Convolutional Neural Network (CNN). Steganography provides some advantages compared to cryptography, mainly its invisibility, but there are weaknesses in information capacity and resistance to attacks in traditional techniques. The paper suggests solving these difficulties with a new steganography method using deep learning. The proposed MDCC method provides an opportunity to encrypt large amounts of data in realistic images that are beyond human visual perception. The system is trained on the ImageNet dataset by using a loss function to balance the quality of the cover image and the information it conceals within it. The MDCC model proposed yields accurate results for all metrics since PSNR, SSIM, and bits per pixel come out to be 80.13dB, 0.9918, and 12.5 bpp, respectively. © 2025 IEEE.

关键词： Convolutional Neural Networks Deep Learning Discrete Cosine Transform Embedding Steganography

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Understanding and Identifying Technical Debt in the Co-Evolution of Production and Test Code

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IEEE Transactions on Software engineering 2025年第5期51卷 1415-1436页

作者： Guo, Yimeng Chen, Zhifei Xiao, Lu Chen, Lin Li, Yanhui Zhou, Yuming Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Stevens Institute of Technology Department of Systems and Enterprises Charles V. Schaefer Jr. School of Engineering and Science HobokenNJ07030 United States

The co-evolution of production and test code (PT co-evolution) has received increasing attention in recent years. However, we found that existing work did not comprehensively study various PT co-evolution scenarios, such as the qualification and persistence of their effects on software. Inspired by technical debt (TD), we refer to TD generated during the co-evolution between production and test code as PT co-evolution technical debt (PTCoTD). To better understand PT co-evolution, we first conducted an exploratory study on its characteristics on 15 open-source projects, finding unbalanced PT co-evolution prevalent and summarizing five potential PT flaws. Then we proposed an approach to identify and quantify PTCoTDs of these flaw patterns, considering evolutionary and structural relationships. We also built prediction models to describe cost trajectories and rank all PTCoTDs to prioritize expensive ones. The evaluation on the 15 projects shows that our approach can identify PTCoTDs that deserve attention. The identified PTCoTDs account for about half of the project's total maintenance costs, and the cost proportion of the expensive Top-5 is 1.8x more than the file proportion they contain. Almost all covered maintenance costs persist as PTCoTD in the future, with an average increase of 6.8% between the last two releases. Our approach also accurately predicts the costs of PTCoTD with an average prediction deviation of only 8.3%. Our study provides valuable insights into PT co-evolution scenarios and their effects, which can guide practices and inspire future work on software testing and maintenance. © 1976-2012 IEEE.

关键词： Software testing

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Cardiac MRI Segmentation of Ventricular Structures and Myocardium Using U-Net Variants 5

Cardiac MRI Segmentation of Ventricular Structures and Myoca...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Wijesinghe, Chinthalanka B. Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka Ulster University School of Computing Engineering and Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798331530983

Accurate segmentation of the ventricular structures and myocardium from Cardiac Magnetic Resonance (CMR) images is essential to diagnose and manage cardiovascular diseases. This study systematically evaluates the performance of five U-Net variants in cardiac MRI segmentation using the Automated Cardiac Diagnosis Challenge (ACDC) dataset and a hybrid loss function combining Cross-Entropy and dice losses. Among the variants, the Feature Pyramid U-Net achieved the best performance, with Dice coefficients of 0.9388 (Left Ventricle), 0.8759 (Right Ventricle), and 0.8426 (Myocardium), showcasing its superior ability to capture multi-scale features and segment complex anatomical structures. The comprehensive and standardized evaluation conducted in this study provides valuable insights into the strengths and limitations of these architectures for cardiac segmentation. © 2025 IEEE.

关键词： Deep learning

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Scanning the Issue - May 2025

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IEEE Transactions on Intelligent Transportation systems 2025年第5期26卷 5627-5648页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

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Evaluating the Impact on Usability and Acceptance of ECAs in m-Health Applications for Older Adults 11

Evaluating the Impact on Usability and Acceptance of ECAs in...

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11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Gilaberte, Raquel Lacuesta Bagdasari, Eva Cerezo Navarro-Alamán, Javier EUPT Universidad de Zaragoza Spain EINA Universidad de Zaragoza Spain Department of Computer Science and Engineering of Systems EUPT Universidad de Zaragoza Spain

ISBN: (纸本)9789897587436

In the context of m-health applications, developing user-friendly interfaces to improve usability and acceptance by older adults has become a prominent research topic. The use of embodied conversational agents (ECAs) seems promising as they allow interacting through natural verbal and nonverbal communication. However, analyses about the design and acceptance of embodied conversational agents embedded in m-health applications for older adults are needed. In this paper, we present a study carried out to analyse the usability and acceptance of ECAs interfaces in m-health applications, compared to traditional tactile text interfaces. The study carried out has involved 23 users over 65 years old with promising results. The ECA interface was positively assessed and its acceptance increased compared to the traditional one, but although the feelings arisen are positive, users still claim for less complexity and a more careful design. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Avatars ECAs m-Health Older Adults

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Scanning the Issue

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IEEE Transactions on Intelligent Transportation systems 2025年第1期26卷 3-21页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

“Scanning the Issue.“

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Modeling Local Demand for Mobile Spectrum: An Interpretable Machine Learning Approach

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4063-4082页

作者： Parekh, Janaki Yackoboski, Elizabeth Ghasemi, Amir Yanikomeroglu, Halim Communications Research Centre Canada Department of Data Science OttawaONK2H 8S2 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

With the expansion of 5G networks and the ongoing development of future 6G networks, the demand for mobile spectrum is expected to continue to grow, particularly at a local level. In response, spectrum regulators globally are exhibiting growing interest in enhancing their understanding of current mobile spectrum demand. The goal is twofold: to maximize the socioeconomic benefits of this finite resource and to ensure that spectrum policy and licensing decisions continue to drive innovation within the wireless industry. Despite its importance, research in modeling mobile spectrum demand has been notably scarce, particularly at the granularity required in the spectrum regulatory domain. To address this gap, this paper presents a data-driven approach to estimate localized mobile spectrum demand within the context of spectrum regulation. A novel demand proxy is first introduced, derived from a large and diverse dataset of crowdsourced commercial mobile measurements. Subsequently, spectrum demand modeling is formulated as a regression task and a variety of classical machine learning models are explored, leveraging publicly available geospatial data as input features. The top-performing model successfully achieves an R2 of 0.76 and a Root Mean Square Error of 51.02 on the hold-out test set. Finally, a machine learning interpretability technique is applied to demonstrate how these models can be used for regulatory decision-making, particularly in scenarios requiring transparency and accountability. © 2020 IEEE.

关键词： 5G mobile communication systems

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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