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Endoscopy-assisted lightweight diagnosis system based on transformers for colon polyp detection

Optoelectronics Letters 2025年第1期21卷 57-64页

作者： FAN Weiming YU Jiahui JU Zhaojie School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang 110159China Department of Biomedical Engineering Zhejiang UniversityHangzhou 310058China School of Computing University of PortsmouthPO13HEUK

The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases,with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer(CRC).Although deep learning holds promise in mitigating missed polyp rates,modern endoscopy examinations pose additional challenges,such as image blurring and *** study explores lightweight yet powerful attention mechanisms,introducing the spatial-channel transformer(SCT),an innovative approach that leverages spatial channel relationships for attention weight *** method utilizes rotation operations for inter-dimensional dependencies,followed by residual transformation,encoding inter-channel and spatial information with minimal computational *** experiments on the CVC-Clinic DB polyp detection dataset,addressing endoscopy pitfalls,underscore the superiority of our SCT over other state-of-the-art *** proposed model maintains high performance,even in challenging scenarios.

关键词： diagnosis utilize holds

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Robust facial expression recognition via lightweight reinforcement learning for rehabilitation robotics

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Optoelectronics Letters 2025年第2期21卷 97-104页

作者： CHEN Yifan FAN Weiming GAO Hongwei YU Jiahui JU Zhaojie School of Computing University of PortsmouthPO13HEUK School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang 110159China Department of Biomedical Engineering Zhejiang UniversityHangzhou 310027China

This paper proposes a lightweight reinforcement network (LRN) and auxiliary label distribution learning (ALDL)based robust facial expression recognition (FER) *** designed representation reinforcement (RR) network mainly comprises two modules,i.e.,the RR module and the auxiliary label space construction (ALSC) *** RR module highlights key feature messaging nodes in feature maps,and ALSC allows multiple labels with different intensities to be linked to one ***,LRN has a more robust feature extraction capability when model parameters are greatly reduced,and ALDL is proposed to contribute to the training effect of LRN in the condition of ambiguous training *** tested our method on FER-Plus and RAF-DB datasets,and the experiment demonstrates the feasibility of our method in practice during rehabilitation robots.

关键词： rehabilitation lightweight auxiliary

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VITMST++: Efficient Hyperspectral Reconstruction Through Vision Transformer-Based Spatial Compression

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 398-404页

作者： Silveira, Ana C. Caznok do Carmo, Diedre S. Ueda, Lucas H. Fantinato, Denis G. Costa, Paula D. P. Rittner, Leticia Universidade Estadual de Campinas Department of Computer Engineering and Automation School of Electrical and Computer Engineering Campinas13083-852 Brazil Universidade Estadual de Campinas CPQD Recod.AI Institute of Computing Campinas13083-852 Brazil

Hyperspectralchannel reconstruction transforms a subsampled multispectral image into hyperspectral imaging, providing higher spectral resolution without a dedicated acquisition hardware and camera. Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (MST++) is a state-of-the-art channel reconstruction technique, but it faces memory limitations for high spatial-resolution images. In this context, we introduced VITMST++, a novel architecture incorporating Vision Transformer embeddings for spatial compression, multi-resolution image context, and a custom channel-weighted loss. Developed for the ICASSP 2024 HyperSkin Challenge, VITMST++ outperforms the state-of-the-art MST++ in both performance and computational efficiency in channel reconstruction. In this work, we perform a deeper analysis on the main aspects of VITMST++ efficiency, quantitative performance, and generalization to other datasets. Results show that VITMST++ achieves similar values of SAM and SSIM hyperspectral reconstruction metrics when compared to state-of-the-art methods, while consuming up to three fold less memory and needing up to 10 times fewer multiply-add operations. © 2020 IEEE.

关键词： Image compression

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Research on Garbled Circuits-Based Identity Authentication and Ciphertext Hidden Access Control in Vehicular Networks 24

Research on Garbled Circuits-Based Identity Authentication a...

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9th International Conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Jiao, Zhilong Bai, Fenhua Zhang, Chi Zhang, Xiaohui Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology China Kunming China Department of Computing Beijing University of Technology China Beijing China

ISBN: (纸本)9798400717161

Since most communications in vehicular networks rely on wireless transmissions, information broadcast by vehicles is highly susceptible to interception and eavesdropping by third parties, making it vulnerable to various attacks. This can result in communication messages being tampered with and the leakage of vehicle locations and identity privacy. From a security perspective, this paper introduces Yao's garbled circuit mechanism, combining random bytes with the solutions of linear equations as identity information assigned to vehicle nodes. By employing dual authentication, the proposed approach addresses issues such as privacy protection, anonymity, replay attacks, and collusion attacks in vehicular networks, thereby enhancing security. Moreover, considering the post-authentication access control scenario, this paper proposes a novel LSSS matrix transformation algorithm for ciphertext hidden access control strategy. It realizes a partially hidden policy, which makes matrix generation more efficient by supporting both AND and OR gates, thus improving the access control efficiency while ensuring security. Performance analysis indicates that in terms of identity authentication, the scheme reduces computational costs by an average of 96.25%, and in access control, the scheme reduces key generation time by an average of 80%. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ciphertext

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The Challenges of Cyber Resilience in the Maritime Sector: Addressing the Weak Awareness of the Dangers Caused by Cyber Threats

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Journal of Marine Science and engineering 2025年第4期13卷 762-762页

作者： Ćelić, Jasmin Vukšić, Marko Baždarić, Robert Cuculić, Aleksandar Department of Electrical Engineering Automation and Computing Faculty of Maritime Studies University of Rijeka Rijeka51000 Croatia

The maritime industry plays a key role in the global supply chain. Advanced digital technologies bring significant economic benefits to ports and shipowners, but at the same time increase the risks of cyber threats and attacks. This article aims to provide guidelines and examples of good practice that will help in the effective implementation of cyber risk assessment, cyber resilience and cyber sustainability, which are the products of increasingly pronounced challenges. The interconnection of ports requires operators to achieve and maintain a baseline level of cybersecurity to ensure security across the entire port ecosystem. The development of new technologies in areas such as the Internet of Things, cloud computing, artificial intelligence, etc., contributes to the fact that monitoring and control systems in the maritime industry are becoming increasingly exposed to cyber threats and various forms of cyberattacks. The connection of vessels with systems on land in real time presents a necessary element in meeting the intended goals in the digital transformation of the maritime sector. This results in increasingly frequent work on specific software solutions within the maritime sector. With the adoption of new operational technologies (OT) and information technologies (IT), the desire for more efficient supply chains and operations of shipping in general has been realized, but at the same time the level of cybersecurity has decreased. The research results aim to encourage port operators and shippers to develop a series of good practices in order to develop an appropriate level of cybersecurity, resilience, and sustainability. © 2025 by the authors.

关键词： Marine industry

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On Measures Resulting from the Choquet Integration 20th

On Measures Resulting from the Choquet Integration

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20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

作者： Ontkovičová, Zuzana Torra, Vicenç Department of Computing Science Umeå University Umeå Sweden Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (纸本)9783031739996

In this paper, we study measures arising as a result of the Choquet integration with respect to a particular class of measures. The initial insight is provided with several classes of additive and fuzzy measures. It can be seen that some classes are closed regarding the integration, e.g. probabilities, while some are not, such as distorted Lebesgue measures. Knowing both an integration measure and a resulting measure after the Choquet integration directly leads to a fuzzy analogue of the Radon-Nikodym derivatives. For them, a completely different possible approach to their existence is presented for a specific pair of measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Integration

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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud computing

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IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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A review of recent artificial intelligence for traditional medicine

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Journal of Traditional and Complementary Medicine 2025年第3期15卷 215-228页

作者： Hou, Chengbin Gao, Yanzhuo Lin, Xinyu Wu, Jinchao Li, Ning Lv, Hairong Chu, William Cheng-Chung School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fujian 350109 China Maynooth International Engineering College Fuzhou University Fuzhou 350108 China MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China

Traditional Medicine (TM) has played a crucial role in global healthcare due to its long history and holistic approach. Artificial Intelligence (AI) has emerged as a revolutionary technology, offering exceptional capabilities in areas such as data mining, pattern recognition, and decision-making. The integration of Artificial Intelligence for Traditional Medicine (AITM) presents a promising frontier in advancing medicine and healthcare. In this review, we explore AITM from two perspectives: recent AI techniques and TM applications. Specifically, we investigate how Machine Learning, Deep Learning, and Large Language Models are applied to TM, covering applications such as diagnosis (before, during, after) and research (drug research, structured knowledge, data analysis). By leveraging advanced algorithms and models, AI can improve decision-making efficiency, optimize diagnosis accuracy, enhance patient experience, and reduce costs. We anticipate this review can bridge the gap between AI and TM communities. And the goal is to foster collaboration and innovation between both communities, enabling them to exploit the state-of-the-art AI techniques to advance TM diagnosis and research, ultimately contributing to the enhancement of human health. © 2025 Center for Food and Biomolecules, National Taiwan University

关键词： AI for health AI for medicine Artificial intelligence Deep learning Large language models Machine learning Traditional medicine

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Vision-Language Meets the Skeleton: Progressively Distillation With Cross-Modal Knowledge for 3D Action Representation Learning

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IEEE Transactions on Multimedia 2025年 27卷 2293-2303页

作者： Chen, Yang He, Tian Fu, Junfeng Wang, Ling Guo, Jingcai Hu, Ting Cheng, Hong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China The Hong Kong Polytechnic University Department of Computing Hong Kong999077 Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518000 China No.1 Orthopedic Hospital of Chengdu Chengdu611731 China

Skeleton-based action representation learning aims to interpret and understand human behaviors by encoding the skeleton sequences, which can be categorized into two primary training paradigms: supervised learning and self-supervised learning. However, the former one-hot classification requires labor-intensive predefined action categories annotations, while the latter involves skeleton transformations (e.g., cropping) in the pretext tasks that may impair the skeleton structure. To address these challenges, we introduce a novel skeleton-based training framework (C2VL) based on Cross-modal Contrastive learning that uses the progressive distillation to learn task-agnostic human skeleton action representation from the Vision-Language knowledge prompts. Specifically, we establish the vision-language action concept space through vision-language knowledge prompts generated by pre-trained large multimodal models (LMMs), which enrich the fine-grained details that the skeleton action space lacks. Moreover, we propose the intra-modal self-similarity and inter-modal cross-consistency softened targets in the cross-modal representation learning process to progressively control and guide the degree of pulling vision-language knowledge prompts and corresponding skeletons closer. These soft instance discrimination and self-knowledge distillation strategies contribute to the learning of better skeleton-based action representations from the noisy skeleton-vision-language pairs. During the inference phase, our method requires only the skeleton data as the input for action recognition and no longer for vision-language prompts. Extensive experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that our method outperforms the previous methods and achieves state-of-the-art results. © 1999-2012 IEEE.

关键词： Musculoskeletal system

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