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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Guo, Yaoqi Ma, Yun Shao, Zijian Bi, Weichen Wang, Yanfeng School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Department of Comprehensive Oncology National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China

ISBN: (纸本)9798400701726

Cancer patients face a heightened risk of venous thromboembolism (VTE), emerging as the second most prevalent cause of death within this population. Central venous catheterization (CVC), a routine procedure in cancer care, amplifies the VTE risk, leading to catheter-related thrombosis (CRT). Although traditional risk-assessment models and certain AI methods exist for VTE prediction, their capability and application in CRT risk prediciton for cancer patients remains limited. This paper addresses the shortcomings of current models (RAMs) by crafting a dedicated AI model to predict CRT risks for cancer patients. Leveraging a dataset encompassing 10,512 cancer patients undergoing catheterization over a decade, we meticulously select nine specific features for model construction, resulting in an impressive 0.794 AUROC in prediction, 54.9% higher than baseline. Furthermore, we estimate CRT-free probability using the Kaplan–Meier method. We also develop a WeChat Mini Program designed for efficient data collection and risk prediction, enhancing the efficiency of CRT risk detection for both doctors and patients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Machine learning

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PDC-ViT: Source Camera Identification using Pixel Difference Convolution and Vision Transformer

arXiv

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arXiv 2025年

作者： Elharrouss, Omar Akbari, Younes Almaadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad Bouridane, Ahmed Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Computer Science and Engineering Qatar University Doha Qatar Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom Center for Data Analytics and Cybernetics University of Sharjah Sharjah United Arab Emirates

Source camera identification has emerged as a vital solution to unlock incidents involving critical cases like terrorism, violence, and other criminal activities. The ability to trace the origin of an image/video can aid law enforcement agencies in gathering evidence and constructing the timeline of events. Moreover, identifying the owner of a certain device narrows down the area of search in a criminal investigation where smartphone devices are involved. This paper proposes a new pixel-based method for source camera identification, integrating Pixel Difference Convolution (PDC) with a Vision Transformer network (ViT), and named PDC-ViT. While the PDC acts as the backbone for feature extraction by exploiting Angular PDC (APDC) and Radial PDC (RPDC). These techniques enhance the capability to capture subtle variations in pixel information, which are crucial for distinguishing between different source cameras. The second part of the methodology focuses on classification, which is based on a Vision Transformer network. Unlike traditional methods that utilize image patches directly for training the classification network, the proposed approach uniquely inputs PDC features into the Vision Transformer network. To demonstrate the effectiveness of the PDC-ViT approach, it has been assessed on five different datasets, which include various image contents and video scenes. The method has also been compared with state-of-the-art source camera identification methods. Experimental results demonstrate the effectiveness and superiority of the proposed system in terms of accuracy and robustness when compared to its competitors. For example, our proposed PDC-ViT has achieved an accuracy of 94.30%, 84%, 94.22% and 92.29% using the Vision dataset, Daxing dataset, Socrates dataset and QUFVD dataset, respectively. © 2025, CC BY.

关键词： Convolution

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Multi-Modal Medical Vision-and-Language Learning for Retinal Vein Occlusion Classification

健康数据科学（英文）

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健康数据科学（英文） 2023年第4期 42-43页

作者： Weibin Liao Yanfeng Liao Zhimin Fan Jiexuan Zhang Shuochen Li Jiarui Yang Liantao Ma National Engineering Research Center for Software Engineering Peking UniversityBeijingChina Computer Science and Technology Beijing Institute of TechnologyBeijingChina Department of Ophthalmology Peking University Third HospitalBeijing Key Laboratory of Restoration of Damaged Ocular NervePeking University Third HospitalBeijingChina National Engineering Research Center for Software Engineering Peking UniversityBeijingChina

Background:Retinal vein occlusion(RVO)is one of the most common retinal vascular diseases leading to vision loss if not diagnosed and treated in *** diagnosis of central and branch RVO(CRVO and BRVO)can alleviate the workload of ophthalmologists while facilitating early detection and treat-ment of RVO and laying the foundation for subsequent symptom grading,treatment planning,and ***,the development of a fast,high-performance,and robust diagnostic model is a crucial step toward achieving quantitative and accu-rate assessment of RVO *** there is an extensive research using fundus visual images for clinical assisted diagno-sis with deep learning models,there is a lack of focus on incor-porating doctors'text reports to further enhance the models. Objectives:In this study,we propose a multi-modal medical visual-and-language learning model that utilizes fundus fluo-rescein angiography(FFA)images and physician analysis reports to classify BRVO and CRVO cases. Methods:As shown in Fig.1,the proposed model utilizes an advanced convolutional neural network to extract visual features from FFA images for patient visual representation *** language representation learning,the model first extracts basic patient features such as gender,age,vision,and blood pressure,and then uses regular expression matching to obtain typical patient symptoms from expert text ***,we cre-ated a sign vocabulary for RVO patients,including exudate,macular edema,and intraretinal hemorrhage,among *** on this vocabulary,the proposed model analyzes patient symptom manifestations and learns symptom presentation(such as exudate stage and location).Finally,based on the patient's visual and text representation,the proposed model uses a fully connected(FC)layer for classification tasks. Results:We evaluated the proposed model on a private dataset consisting of 101 patients and 1,265 FFA images,composed of 58 BRVO patients and 43 CRVO patie

关键词： language vision learning classification medical multi-modal occlusion retinal vein

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Person Re-Identification Based on Instance Segmentation and Pose Estimation

Person Re-Identification Based on Instance Segmentation and ...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Yao Zhou Yusong Qin Song Wang Zengxi Huang Dazhi Zhang School of Computer Science and Software Engineering Xihua University Chengdu China School of Computer and Software Engineering and the Sichuan Xihua Jiaotong Forensics Center Xihua University Chengdu China Criminal Science and Technology Department Sichuan Police College Luzhou China

Person Re-Identification (ReID) is a crucial task in computer vision that aims to match persons captured from non-overlapping camera views. In this paper, to alleviate the impacts of background and occlusion, we propose to use instance segmentation and pose estimation methods to create masks for global feature extraction. Furthermore, we divide the pedestrian images into three regions according to pedestrian keypoints, trying to eliminate the alignment errors. This part-based matching strategy also helps to address the occlusion issue. Overall, we construct a deep learning network with three branches, including two global branches and a part-based branch. The two global branches extract global features using segmentation-based mask and the mask derived from pedestrian keypoint heatmaps, respectively. In the end, a weighted fusion strategy is used to combine the global scores and part-based scores for final classification. This network enables us to acquire robust global feature of pedestrians by excluding background and occlusion, and simultaneously address the alignment errors to some extent. Experimental results on these three widely used datasets demonstrate the effectiveness of our method: Specifically, it achieves 64.5% rank-1 accuracy and 54.3% mAP on Occluded-Duke, and 94.4% and 87.1% rank-1 accuracies on Market-1501 and DukeMTMC-reID, respectively.

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Enhancing Low-Latency and High-Reliability Data Forwarding for In-Vehicle Time-Sensitive Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Sun, Haitong Zhang, Haijun Guan, Wanqing Liu, Xiangnan Ma, Hui Leung, Victor C.M. Hebei Key Laboratory of Space-Air-Ground Intelligent Communication China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Department of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China

To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task Group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time-critical services in networks. This paper resolves the following issue: how to minimize the worst-case response time (WCRT) of frame forwarding in TSN under no-wait scenarios to guarantee low-latency transmission, stability, and high success rate for in-vehicle TSN networks? Currently, some work resolves this problem by leveraging integer linear programming, and others apply heuristic methods to avoid the time complexity problem, but the success rate is affected since they are prone to get trapped in local optima. To reach a balance between time-complexity and success rate, a scheme named Frame Dynamic Priority Scheduling Mechanism (FDPSM) is proposed, where the frame dynamic priority levels are set up and frames are scheduled accordingly. The target is to maximize the success rate and minimize the WCRT for in-vehicle networks. We evaluate the proposed mechanism from aspects such as traffic forwarding time, size of windows, number of hops, and the network load, together with success rate and the simulation results demonstrate the advantages of the FDPSM for in-vehicle TSN networks. © 2025 IEEE. All rights reserved,

关键词： Integer programming

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Fault-tolerant Mobile Service Offloading in Mobile Edge Computing

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IEEE Transactions on Automation Science and engineering 2025年

作者： Long, Tingyan Xia, Yunni Zhou, Mengchu Li, Jianqi Ma, Yong Ai-Turki, Yusuf Guizhou University School of Computer Science and Technology Guiyang550025 China Chongqing University College of Computer Science Chongqing400030 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Global energy interconnection research institute Co. Ltd Beijing102209 China Jiangxi Normal University School of Computer and Information Engineering Nanchang China King Abdulaziz University K. A. Care Energy Research and Innovation Center The Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods. © 2004-2012 IEEE.

关键词： Mobile applications

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Predictive Modeling of House Prices;A Regression Approach

Predictive Modeling of House Prices;A Regression Approach

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International Conference on Emerging Technologies, ICET

作者： Natasha Saeed Ahsan Saeed Mariam Bashir Lismer Andres Caceres-Najarro Department of Computer & Software Engineering National University of Sciences and Technology Islamabad Pakistan Department of Computer Science National Excellence Institute Islamabad Pakistan Korea Institute of Energy Technology Naju-si South Korea

Housing prices have always been an important issue among the business community. The sudden increase in property prices has an impact on economic cycles as well as our means of living. However, understanding several features that affect residential real estate prices is still challenging. Traditional techniques have been unable to choose essential features for precise predictions, which frequently leads to reduced prediction accuracy. In this research work, we utilized machine learning techniques to increase prediction accuracy and get data-driven insights into property evaluation. This study compares Random Forest Regressor and the Decision Tree Regressor using an extensive dataset of Bangalore that includes nine property features. The findings show that our models achieved an optimal accuracy level and provide insights into the variances in model performance, assisting policymakers in choosing the most suitable approaches to get accurate house prices across various market circumstances.

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Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data 7

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation. © 2023 IEEE.

关键词： Internet of things

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Brain organoids in motor cortex involved in motor output 24

Brain organoids in motor cortex involved in motor output

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9th International Conference on Biomedical Signal and Image Processing, ICBIP 2024

作者： Fan, Xiaoman Xu, Yuchen Xu, Shibo Yang, Yuqi Shi, Lufeng Liu, Yan Zhang, Shaomin Qiushi Academy for Advanced Studies Department of Biomedical Engineering Zhejiang University China Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies School of Engineering Westlake University China State Key Laboratory of Reproductive Medicine Institute for Stem Cell and Neural Regeneration Nanjing Medical University China Qiushi Academy for Advanced Studies Department of Computer Science and Technology Zhejiang University China Department of Neurosurgery Sir Run Run Shaw Hospital Medical College Zhejiang University China

ISBN: (纸本)9798400717970

Motor function is essential in daily life while brain damage in motor system such as stroke would lead to long term motor function deficits. Brain organoids, a novel in vitro stem cell culture technology, offer a new idea for motor function recovery. In this study, we pumped out the motor cortex of forelimbs to mimic brain injury in mice and implanted human brain organoids into the cavity combined with microwire electrodes to simultaneously record neural signals from both the organoids and the host animals. Our findings indicate that the brain organoids can be involved in motor output and the involvement can be improved through behavioral training. © 2024 Copyright held by the owner/author(s).

关键词： Stem cells

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GRASSMARLIN-based Metadata Extraction of Cyber-Physical Systems Intrusion Detection in CyberSCADA Networks

GRASSMARLIN-based Metadata Extraction of Cyber-Physical Syst...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： John E. Efiong Bodunde O. Akinyemi Emmanuel A. Olajubu Ganiu A. Aderounmu Department of Computer Science & Engineering Obafemi Awolowo University lIe-Ife Nigeria Department of Computer Science & Engineering African Centre of Excellence in Software Engineering Obafemi Awolowo University Ile-Ife Nigeria

Cyber-Physical Systems such as those in the SCADA architecture and the entire industrial control system networks have now become essential components of the cyberspace due to their integration with modern IT networks. Cyber attackers have taken advantage of these vulnerable critical assets that have suddenly found themselves in an unprepared situation enabled by the Internet of Things Paradigm. Intrusions into the new CyberSCADA networks seem to go unabated as the proprietary communication protocols in these systems lack security mechanisms to help intrusion detection and analysis. In addition, existing network analyzers are weak in identifying granular details from traffic in these legacy networks that could help track, detect and investigate intrusions. This study performed a passive fingerprinting exercise on captured network data using Grassmarlin and identified useful metadata of critical network devices. The results demonstrate that fingerprints, metadata and critical assets' inventory on Grassmarlin would help industry-based cybersecurity personnel improve intrusion detections on CyberSCADA networks.

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