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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Theodora Kremantala Stefanos Balaskas Maria Rigou Computer Engineering and Informatics Department University of Patras Patras Greece Department of Management Science and Technology University of Patras Patras Greece

ISBN: (纸本)9798400713170

This study investigates the effectiveness of various types of native ads in terms of capturing users' attention and engagement using eye-tracking data to analyze user interactions. To this end, 47 participants compared four types of native ads with two traditional banner ads embedded within a website designed specifically for the needs of the study. Results confirm that native ads, and particularly content integrated ones like recommendation widgets and native ads in-feed, attract higher levels of attention compared to traditional banner ads. These ads maintain high deception rates, with users mostly failing to recognize them, a fact that raises questions about users' trust and annoyance. The variation that creates the highest click-through rate is the promoted listing, while typical banner advertisements attracted limited engagement suffering from banner blindness. This study highlights the delicate balance between effective ad placement and ethical transparency by advertisers, emphasizing the need for clear labelling to maintain user trust. These findings present useful insights for marketers and interface designers, with guidance on creating effective yet usable ad designs in digital spaces.

关键词： dark design patterns

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Closed-Loop Clustering-Based Global Bandwidth Prediction in Real-Time Video Streaming

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 448-462页

作者： Afshar, Sepideh Razavi, Reza Moshirpour, Mohammad Department of Electrical and Software Engineering University of Calgary CalgaryABT2N 1N4 Canada Userful CalgaryABT2G 4Y5 Canada Department of Informatics Donald Bren School of Information and Computer Sciences University of California at Irvine IrvineCA92697 United States

Accurate throughput forecasting is essential for ensuring the seamless operation of Real-Time Communication (RTC) applications. These demands for accurate throughput forecasting become particularly challenging when dealing with wireless access links, as they inherently exhibit fluctuating bandwidth. Ensuring an exceptional user Quality of Experience (QoE) in this scenario depends on accurately predicting available bandwidth in the short term since it plays a pivotal role in guiding video rate adaptation. Yet, current methodologies for short-term bandwidth prediction (SBP) struggle to perform adequately in dynamically changing real-world network environments and lack generalizability to adapt across varied network conditions. Also, acquiring long and representative traces that capture real-world network complexity is challenging. To overcome these challenges, we propose closed-loop clustering-based Global Forecasting Models (GFMs) for SBP. Unlike local models, GFMs apply the same function to all traces enabling cross-learning, and leveraging relationships among traces to address the performance issues seen in current SBP algorithms. To address potential heterogeneity within the data and improve prediction quality, a clustered-wise GFM is utilized to group similar traces based on prediction accuracy. Finally, the proposed method is validated using real-world datasets of HSDPA 3G, NYC LTE, and Irish 5G data demonstrating significant improvements in accuracy and generalizability. © 2023 CCBY.

关键词： Video streaming

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3D Reconstruction of Colonic Polyps: A Methodology for Improved Visualization and Analysis

3D Reconstruction of Colonic Polyps: A Methodology for Impro...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Ishtiaque Ahmed Pranto Saha Md. Saimim Islam Khan Hamim Irfan Ahmad Alvi Md. Zahid Hasan Md. Hasan Imam Bijoy Department of Computer Science and Engineering Health Informatics Research Laboratory (HIRL) Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Polyps are critical abnormalities that may indicate the initial phases of colon cancer, making accurate detection and analysis essential in medical diagnostics. Traditional 2D medical imaging techniques provide limited insight into the shape and structure of polyps, which is where 3D reconstruction can play a transformative role. By converting 2D polyp images into 3D models, healthcare professionals can gain enhanced visualization, enabling better diagnosis, treatment planning, and surgical precision. This study demonstrates a robust process for reconstructing polyp images in 3D using the Kvasir-Seg dataset, which includes 1,000 polyp images with expertly annotated ground truth masks. Our methodology includes the generation of depth maps using the Intel DPT-Large model, the extraction of point clouds with a customized approach, and the construction of precise mesh objects with Open3D. We extract geometric properties, including linearity, planarity, and curvature change, from the point clouds and meshes for a more in-depth analysis of the 3D objects. The unsupervised analysis of the resulting density plots indicates the presence of two distinct clusters within the feature distributions, highlighting the significance of 3D reconstruction for improved polyp characterization. Furthermore, the evaluation included 2D features such as edge density, contrast, dissimilarity, homogeneity, energy, correlation, entropy, number of lines, circularity, and Gabor energy. The analysis of p-values reveals that the 3D features related to these two clusters show notably lower p-values, thereby demonstrating their significance in the comprehensive analysis.

关键词： Point cloud compression Visualization Solid modeling Three-dimensional displays Accuracy Shape Surgery Transforms Planning Image reconstruction

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A Contend Node-Restricted Receiver-Initiated MAC Protocol With Consecutive Packet Transmission

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Yuxin Ota, Kaoru Dong, Mianxiong Wang, Ruoyu Changsha University College of Computer Science and Engineering Changsha410022 China Muroran Institute of Technology Department of Sciences and Informatics Japan Central South University School of Computer Science and Engineering Changsha410083 China

The medium access control (MAC) protocol plays a crucial role in wireless communications, influencing key performance metrics, such as energy consumption, delay, collision, and throughput. This paper introduces the Contend Node Restricted Joint Consecutive Packet Transmissions Receiver-Initiated (CNR-CPT-RI) protocol, specifically designed to minimize collisions and reduce energy consumption in wake-up radio-enabled wireless sensor networks (WSNs). The CNR-CPT-RI protocol is an effective communication protocol that is mainly applied with nodes in the one-hop range of the sink. Since the sink has the ability to control each node, it becomes feasible to manage the nodes in a way that effectively reduces collisions and enhances network lifetime as well as throughput. Moreover, the CNR-CPT-RI protocol can adaptively adjust the number of nodes participating in channel contention, maintaining it within an optimal range to balance reduced collisions, decreased energy consumption, and sustained high throughput. Furthermore, the winning node in channel contention employs Consecutive Packet Transmissions (CPTs) method to further diminish competition. Comprehensive theoretical and experimental results demonstrate that the number of collisions of the CNR-CPT-RI protocol is only 42.4%, 35.9%, and 32.5% of the baseline protocol when the number of nodes is 10, 20, and 30, respectively. The energy consumption of the CNR-CPT-RI protocol is reduced by 5.7%, 15%, and 22.6%, and the delay is decreased by 6.7%, 16%, and 32%, respectively, compared with that of the existing protocol. © 2017 IEEE.

关键词： Internet protocols

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ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing

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computers in Biology and Medicine 2025年 187卷 109729-109729页

作者： Kim, Junku Jang, Hojoong Park, Youngjun Jung, Inuk Jo, Kyuri Department of Computer Engineering Chungbuk National University Cheongju Korea Republic of Department of Medical Informatics University Medical Center Göttingen Göttingen Germany School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of

Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, effectively linking these data to actionable drug discovery for clinical application remains challenging. In this paper, we introduce ExPDrug, a neural network (NN) model that integrates biological pathways from transcriptomic data with a biomedical knowledge graph to facilitate pathway-based drug repurposing. ExPDrug enhances disease phenotype prediction by capturing the complex relationships between genes and pathways. Using layer-wise relevance propagation (LRP), the model interprets the contribution of each pathway using relevance scores applied in a random walk-with-restart (RWR) algorithm to prioritize potential drug candidates in the biomedical network. ExPDrug outperforms existing methods in predicting phenotypes for the three diseases and identifying drug candidates, as supported by the literature. This model offers a transformative approach for advancing precision medicine by linking transcriptomic insights directly to clinical drug repurposing, thereby potentially improving treatment strategies for complex diseases. © 2025 Elsevier Ltd

关键词： Knowledge graph

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Exploring UAV Interfaces: An Eye-Tracking Study on Novice and Experienced Users 24

Exploring UAV Interfaces: An Eye-Tracking Study on Novice an...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Andreas Kousoulos Angelos Fotopoulos Dimosthenis Minas Michalis Xenos Department of Computer Engineering & Informatics / Software Quality and Human-Computer Interaction Laboratory University of Patras Patras Greece University of Patras Patras Greece

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Design of mmWave Broadband Rotary Joint and 360° Beam-Steering Rotenna Based on Gap Waveguide Technology

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IEEE Transactions on Antennas and Propagation 2025年

作者： Farahbakhsh, Ali Zarifi, Davood Mrozowski, Michal Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Microwave and Antenna Engineering Gdansk80-233 Poland Graduate University of Advanced Technology Department of Electrical and Computer Engineering Kerman Iran University of Kashan School of Electrical and Computer Engineering Kashan Iran

This work presents the design and fabrication of a groove gap waveguide-based rotary joint and a rotenna (rotary antenna) with 360° mechanical beam-steering for wideband mmWave applications. The proposed rotary joint incorporates two WR-22 to groove gap waveguide transitions, connected back-to-back with an innovative transformer. A key advantage of gap waveguide technology is its ability to operate without physical contact between the rotary joint's components, allowing the rotor to rotate freely near the stator with an air gap, while wave leakage is suppressed. By replacing common coaxial connectors with standard waveguide flanges and introducing a novel transformer between the rotor and stator, we have successfully developed a wideband rotary joint suitable for mmWaves. To demonstrate the practical applicability of this concept, we designed a rotenna with 360° mechanical beam-steering. This design addresses the common limitations of electronically steerable antennas, such as limited steering range, gain degradation, and high cost and complexity. To validate the concept, prototypes of the proposed rotary joint and rotenna were fabricated and measured. The measured and simulated results show good agreement. The rotary joint exhibits a 44.4% fractional bandwidth over the frequency range of 35 to 55 GHz with an insertion loss below 0.68 dB. Additionally, the engineered rotenna demonstrates a peak realized gain of 13 dBi with no scan loss over the full 360° steering range. © 1963-2012 IEEE.

关键词： Steerable antennas

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Practical big data techniques for end-to-end machine learning deployment: a comprehensive review

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Discover Data 2025年第1期3卷 1-18页

作者： El-Sayed, Ahmed Abougabal, Mohamed Lazem, Shaimaa Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Informatics Research Institute City of Scientific Research and Technological Applications New Borg El-Arab Egypt

In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data". Although various methods and platforms have been proposed to tackle these issues, there is still a need for a conceptual guide that synthesizes state-of-the-art tools and techniques across the full machine learning (ML) workflow. In this survey, we consolidate research on big data infrastructures, distributed processing frameworks, and ML methods, mapping out an end-to-end conceptual framework that can serve as a reference architecture for end-to-end deployment. Specifically, we discuss key aspects of big data analytics (storage solutions, platforms like Hadoop and Spark, NoSQL databases), preprocessing approaches (dimensionality reduction, instance selection, noise handling), ML algorithms (supervised, unsupervised, and emerging deep learning paradigms), and deployment considerations (monitoring, continuous integration, and versioning). By gathering and integrating these elements, our survey provides a comprehensive overview of existing solutions, clarifies the design choices developers must consider, and identifies research gaps that remain to be addressed.

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Joint Content Caching and Request Routing for User-Centric Many-Objective Metaverse Services

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1911-1925页

作者： Hu, Zhaoming Fang, Chao Wang, Zhuwei Chen, Jining Tseng, Shu-Ming Dong, Mianxiong Beijing University of Technology College of Computer Science Beijing100021 China Beijing University of Technology School of Information Science and Technology Beijing100021 China Guangxi Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530000 China National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan Muroran Institute of Technology Department of Sciences and Informatics Muroran050-0071 Japan

Metaverse, as a revolutionary technology that changes the way of human interaction, brings new challenges to content delivery services due to the extensive data transmission and personalized service requirements. To ensure a personalized user experiences while improving the utilization of heterogeneous network resources, a user-centric many-objective metaverse content delivery framework is proposed to optimize content delivery through user attention awareness. This framework addresses two key subproblems in metaverse content delivery by investigating user-centric many-objective cooperative content caching and deep reinforcement learning (DRL)-based request routing. The user-centric many-objective cooperative content caching is proposed to dynamically combine three basic preference prediction results to predict user preferences and control network resource allocation, which can simultaneously optimize prediction precision, delay, offloaded traffic, and load balancing. In DRL-based request routing, the reward function is designed to enable the optimization of multiple objectives. The multi-objective DRL routing algorithm is employed to continuously observe network states and make adaptive routing decisions in response to user requests. In the simulation, a movie dataset is employed to simulate user requests and support user attention awareness. The results show that the proposed content delivery framework outperforms existing basic prediction algorithms and other content delivery algorithms on four evaluation indicators. © 2013 IEEE.

关键词： Deep reinforcement learning

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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