IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.icte.2025.12.003

Abdul Wahid Soomro , Miss Laiha Mat Kiah , Rafidah Md Noor , Salim Newaz Kazi , Kaleemullah Shaikh , Wajahat Ahmed Khan , Ihsan Ali

引用次数: 0

Partial encryption-based Shamir secret sharing for low-latency and secure networks 面向低延迟和安全网络的基于部分加密的Shamir秘密共享

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-12-17 DOI: 10.1016/j.icte.2025.12.006

Sun-Jin Lee, So-Eun Jeon, Il-Gu Lee

引用次数: 0

Selective channel inversion protocol for over-the-air computation 无线计算的选择信道反转协议

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.icte.2025.11.016

Hyunwoo Jung , Jung-Bin Kim

{"title":"Selective channel inversion protocol for over-the-air computation","authors":"Hyunwoo Jung , Jung-Bin Kim","doi":"10.1016/j.icte.2025.11.016","DOIUrl":"10.1016/j.icte.2025.11.016","url":null,"abstract":"<div><div>This paper introduces a novel selective channel inversion (SCI) protocol for over-the-air computation (AirComp) networks. The proposed SCI protocol reduces the overhead associated with channel state information (CSI) by broadcasting a predefined channel threshold. Furthermore, it improves mean squared error (MSE) performance by permitting transmission only from sensor nodes with favorable channel conditions. For an arbitrary channel distribution, an exact closed-form expression for the MSE is derived, from which an asymptotic expression for a large number of sensor nodes is obtained. Numerical results demonstrate that the asymptotic expression closely matches the exact result, even for a relatively small number of sensor nodes. An optimization problem is formulated to determine the optimal channel threshold under Nakagami-<span><math><mi>m</mi></math></span> fading channels, and it is analytically proven that the problem is convex. An algorithm for dynamically determining the threshold under arbitrary channel distributions is also presented. Numerical results demonstrate that SCI AirComp outperforms CI AirComp in scenarios with low signal-to-noise ratio (SNR), high fading severity, and a large number of sensor nodes.</div><div>2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (<span><span>http://creativecommons.org/licenses/by-nc-nd/4.0/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 1","pages":"Pages 6-12"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid-YOLO: Lightweight Mamba-Transformer Hybrid with multi-scale fusion for real-world traffic detection Hybrid- yolo：轻量级Mamba-Transformer混合动力车，具有多尺度融合，用于现实世界的流量检测

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-09-05 DOI: 10.1016/j.icte.2025.09.002

Hongqing Wang , Jun Kit Chaw , Marizuana Mat Daud , Liantao Shi , Nannan Huang , Tin Tin Ting , Liuzhen Pu

{"title":"Hybrid-YOLO: Lightweight Mamba-Transformer Hybrid with multi-scale fusion for real-world traffic detection","authors":"Hongqing Wang , Jun Kit Chaw , Marizuana Mat Daud , Liantao Shi , Nannan Huang , Tin Tin Ting , Liuzhen Pu","doi":"10.1016/j.icte.2025.09.002","DOIUrl":"10.1016/j.icte.2025.09.002","url":null,"abstract":"<div><div>Vehicle detection in complex traffic scenes remains challenging due to frequent occlusions, lighting variations, and extreme weather. We present Hybrid-YOLO, a real-time detection framework that unifies Mamba-based state space modeling, Transformer-driven global attention, and multi-scale feature fusion to achieve high accuracy at low computational cost. At its core, Hybrid-YOLO introduces a Dynamic Residual Stem (DR Stem) for adaptive feature calibration, a Hexa-Scan Selective Block (HSSBlock) for six-directional structural perception, and a Selective State Space Model (SSM) for efficient long-range dependency modeling. A Cross-Stage Scales Feature Extraction (CSSFE) module enriches spatial semantics for small-object detection, while a Sparse-Queries Cascade Self-Attention (SCS) module focuses computation on informative regions, enhancing robustness to clutter and background noise. Extensive experiments on KITTI, BDD100K, and IITM-HeTra show that Hybrid-YOLO achieves 90.11 [email protected] at 66.3 FPS, surpassing state-of-the-art methods in both accuracy and efficiency, and offering a promising solution for real-world intelligent transportation systems.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 1","pages":"Pages 214-222"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward storage-aware learning with compressed data an empirical exploratory study on JPEG 基于压缩数据的存储感知学习在JPEG上的实证探索性研究

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.icte.2025.12.005

Kichang Lee , JeongGil Ko , Songkuk Kim , JaeYeon Park

{"title":"Toward storage-aware learning with compressed data an empirical exploratory study on JPEG","authors":"Kichang Lee , JeongGil Ko , Songkuk Kim , JaeYeon Park","doi":"10.1016/j.icte.2025.12.005","DOIUrl":"10.1016/j.icte.2025.12.005","url":null,"abstract":"<div><div>On-device machine learning is fundamentally constrained by limited storage, especially in continuous data collection scenarios where sensor or vision streams accumulate rapidly. This paper empirically investigates storage-aware learning, characterizing the trade-off between data quantity and data quality under lossy compression. Using the CIFAR-10 dataset as a controlled benchmark, we systematically vary both the amount and the fidelity of training data to understand their joint impact on model performance. Our results reveal that (1) neither maximizing quantity nor quality alone yields optimal accuracy, emphasizing that the optimal trade-off between them depends nonlinearly on the available storage budget, and (2) data samples exhibit differential sensitivity to compression, motivating a sample-wise adaptive compression policy. These findings challenge uniform data-retention strategies such as naive data dropping or fixed-rate compression, and establish a foundation for adaptive, storage-efficient learning systems on resource-limited devices. This work opens new directions toward generalizable, storage-aware on-device intelligence.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 1","pages":"Pages 50-54"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Macroeconomic context-aware graph topology learning for stock price forecasting using graph neural network 基于图神经网络的宏观经济上下文感知图拓扑学习股票价格预测

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-12-03 DOI: 10.1016/j.icte.2025.12.002

Amna Sarwar , Fizza Bukhari , Asma Sattar , Maryam Bukhari , Zahoor ur Rehman , Sungwoo Park , Seungmin Rho

引用次数: 0

A lightweight-to-diffusion framework for semantic image communications 语义图像通信的轻量化扩散框架

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-12-29 DOI: 10.1016/j.icte.2025.12.013

Thien Huynh-The , Toan Van Nguyen , Phuong Luu Vo , Huu-Tai Nguyen

引用次数: 0

Action recognition: A comprehensive survey of tasks, methods, and challenges 行动识别：对任务、方法和挑战的全面调查

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-11-29 DOI: 10.1016/j.icte.2025.11.015

Sehwan Heo , Junbeom Moon , Soon Ki Jung

{"title":"Action recognition: A comprehensive survey of tasks, methods, and challenges","authors":"Sehwan Heo , Junbeom Moon , Soon Ki Jung","doi":"10.1016/j.icte.2025.11.015","DOIUrl":"10.1016/j.icte.2025.11.015","url":null,"abstract":"<div><div>Action recognition has emerged as a central research problem in computer vision, aiming to identify and understand human actions from video data. Over the past decade, the field has advanced from early convolutional approaches to sophisticated architectures capable of capturing complex spatio-temporal dependencies. This survey provides a comprehensive overview of action recognition across six major tasks: Action Classification, Temporal Action Localization, Spatio-temporal Action Localization, Temporal Action Segmentation, Online Action Detection, and Action Anticipation. For each task, we trace the methodological evolution from foundational models to recent state-of-the-art approaches, highlighting how key challenges such as long-range temporal modeling, viewpoint variation, boundary precision, over-segmentation, real-time inference, and future uncertainty have been addressed. We also reorganize benchmark results and evaluation metrics, presenting a unified perspective that facilitates fair comparisons and reproducible research. In addition, we analyze representative datasets, ranging from early benchmarks like UCF101 and HMDB51 to large-scale collections such as Kinetics, ActivityNet, and Epic-Kitchens, which have enabled rapid progress in both supervised and self-supervised learning. We discuss open issues and unresolved challenges, including the use of State Space Models for efficient sequence modeling, multimodal fusion techniques that dynamically assess modality reliability, synthetic data and weak supervision for reducing annotation costs, and fairness-aware frameworks that ensure ethical applicability. By consolidating a decade of progress, this survey offers a structured understanding of the action recognition landscape and aims to inspire further research toward robust, scalable, and responsible video understanding systems.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 1","pages":"Pages 32-49"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal beamforming in over-the-air federated learning for efficient model aggregation 基于高效模型聚合的空中联合学习中最优波束形成

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-07-10 DOI: 10.1016/j.icte.2025.06.016

Sangwoo Choi , Minsik Kim , Daeyoung Park

引用次数: 0

Transformer-based localization in UAV-RIS enabled non-terrestrial networks 基于变压器的无人机- ris非地面网络定位

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.icte.2025.11.017

Seungseok Sin , Sangmi Moon , Cheol Hong Kim , Intae Hwang

{"title":"Transformer-based localization in UAV-RIS enabled non-terrestrial networks","authors":"Seungseok Sin , Sangmi Moon , Cheol Hong Kim , Intae Hwang","doi":"10.1016/j.icte.2025.11.017","DOIUrl":"10.1016/j.icte.2025.11.017","url":null,"abstract":"<div><div>Accurate localization is essential for next-generation wireless systems. Traditional millimeter-wave (mmWave) techniques rely heavily on line-of-sight (LOS) paths, which limits their performance in non-line-of-sight (NLOS) environments. To overcome this challenge, we propose a non-terrestrial network (NTN) framework that employs an unmanned aerial vehicle–mounted reconfigurable intelligent surface (UAV-RIS) in conjunction with a Transformer-based refinement model. Unlike conventional regression or filtering approaches, the Transformer leverages self-attention mechanisms to refine coarse geometric estimates. Simulations using the DeepMIMO dataset show that more than 90% of users achieve sub-meter localization accuracy, representing a 35% improvement over existing baselines. These results demonstrate the novelty and effectiveness of integrating RIS adaptability with Transformer-based learning to enable robust, high-precision localization.</div><div>© 2025 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open-access article under the CC BY license (<span><span>http://creativecommons.org/licenses/by/4.0/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 1","pages":"Pages 20-25"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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