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arXiv 2025年

作者： Bagchi, Mayukh Doctoral Programme in Information Engineering and Computer Science Department of Information Engineering and Computer Science University of Trento Italy

Semantic heterogeneity is conventionally understood as the existence of variance in the representation of the same target reality when computationally modelled by independent parties. It can have serious implications in critical application scenarios like that of Knowledge Graph-based multilingual data integration. In view of the above, the thesis argues that the current understanding of the problem of semantic heterogeneity as the ‘existence of variance’, while being crucially necessary, is not sufficient and under-characterized. There can be no variance without a prior notion of a unifying reference taken as the basis for computing the variance itself. To that end, the thesis proposes the problem of representation heterogeneity to emphasize the fact that heterogeneity is an intrinsic property of any representation, wherein, different observers encode different representations of the same target reality in a stratified manner using different concepts, language and knowledge (as well as data). The thesis then advances a top-down solution approach to the above stratified problem of representation heterogeneity in terms of several solution components, namely: (i) a representation formalism stratified into concept level, language level, knowledge level and data level to accommodate representation heterogeneity, (ii) a top-down language representation using Universal Knowledge Core (UKC), UKC namespaces and domain languages to tackle the conceptual and language level heterogeneity, (iii) a top-down knowledge representation using the notions of language teleontology and knowledge teleontology to tackle the knowledge level heterogeneity, (iv) the usage and further development of the existing LiveKnowledge catalog for enforcing iterative reuse and sharing of language and knowledge representations, and, (v) the kTelos methodology integrating the solution components above to iteratively generate the language and knowledge representations absolving representation heterogeneit

关键词： Knowledge graph

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Deciphering Stem Cell Fate with an Integrative Multi-Omics Examination of Microenvironmental Dynamics

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SN computer science 2025年第1期6卷 1-14页

作者： Shanthanam, Radha Rammohan Selvam, Janani Vajravelu, Ashok Pradeep, T. Post Doctoral Fellowship (PDF) programme in Computer Science Engineering Lincoln University College Petaling Jaya Malaysia Faculty of Engineering Lincoln University College Petaling Jaya Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Kongu Engineering College Tamilnadu Erode India

The evidence-based literature on healthcare is akin to a vast universe, continually expanding with new discoveries and insights. In this dynamic landscape, the integration oftools and multi-omics approaches a beacon of hope, illuminating the intricate pathways that govern stem cell fate within the microenvironment. Still, figuring out how stem cell fate is determined is complicated and calls for a multifaceted understanding that cuts beyond traditional discipline lines. This review navigates through the evolutionary trajectory of AI integration in medicine, tracing its transformative impact on hematology and beyond. It explores the burgeoning field of AI applications in hematopoietic cell transplantation (HCT), from diagnosis to prognosis, heralding a new era of precision medicine. Through the lens of integrative multi-omics methodologies, the review delves into the molecular signatures intricately woven within the stem cell niche, deciphering the regulatory networks orchestrating stem cell fate decisions. Emerging technologies like single-cell multi-omics profiling and spatial transcriptomics offer newfound vistas to explore cellular heterogeneity and spatial organization within the stem cell niche, paving the way for transformative advances in regenerative medicine. Harnessing the power of AI and multi-omics methodologies, this review embarks on a quest to dissect stem cell fate with unparalleled precision, ushering in a new era of personalized therapeutics and regenerative interventions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cellular heterogeneity Hematology Molecular signatures Precision medicine Stem cell niche

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A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach

arXiv

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arXiv 2025年

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Gong, Jiaqi Wells, Kristen J. Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA United States Department of Computer Science The University of Alabama TuscaloosaAL United States Department of Psychology San Diego State University San DiegoCA United States

Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision – 92.04%, Recall – 93.15%, Specificity – 77.50%), and weekly models, an accuracy of 76.04% (Precision – 75.83%, Recall – 85.80%, Specificity – 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more compre

关键词： Endocrinology

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CONVEXITY AND CHARACTERIZATION OF OPTIMAL POLICIES IN A DYNAMIC ROUTING PROBLEM

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1984年第1期44卷 105-136页

作者： TSITSIKLIS, JN Doctoral Student Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts

An infinite horizon, expected average cost, dynamic routing problem is formulated for a simple failure-prone queueing system, modelled as a continuous time, continuous state controlled stochastic process. We prove that the optimal average cost is independent of the initial state and that the cost-to-go functions of dynamic programming are convex. These results, together with a set of optimality conditions, lead to the conclusion that optimal policies are switching policies, characterized by a set of switching curves (or regions), each curve corresponding to a particular state of the nodes (servers).

关键词： Stochastic control unreliable queueing systems average cost jump disturbances

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Traffic Congestion Prevention Using Ant Colony Optimization 8

Traffic Congestion Prevention Using Ant Colony Optimization

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8th International engineering Conference on Renewable Energy and Sustainability, ieCRES 2023

作者： Abusamra, Aiman Ashour, Abeer Ghazal, Mai Aldahdooh, Jehad Abuarja, Raghad Islamic University of Gaza Computer Engineering Dept. Gaza Palestine University of Helsinki Doctoral Programme in Computer Science Helsinki Finland Islamic University of Gaza Computer Engineering Gaza Palestine

ISBN: (纸本)9798350300758

Managing traffic congestion is crucial for improving mobility, reducing fuel consumption, and mitigating environmental impacts in urban areas. To address this challenge, we present a novel framework named TCP-ACO for detecting traffic congestion that classifies congestion into three distinct types: expected, unexpected, and real-time. The framework utilizes data from various sources, including databases, Ant colony optimization (ACO) systems, and computer vision techniques, to precisely detect and handle traffic congestion. Expected congestion is identified by analyzing historical traffic data and scheduled events, while unexpected congestion is detected by leveraging real-time data from ACO systems. Real-time congestion is detected by employing computer vision techniques, such as analyzing video footage from cameras or drones. The proposed framework has the potential, by recognizing and managing various types of congestion, to improve traffic flow, shorten travel times, and decrease environmental impacts. Additionally, it also offers a precise and effective solution for traffic congestion detection, which is a crucial aspect of smart city traffic management systems. Our analysis shows that the ACO algorithm adapted in TCP-ACO is more effective in finding the shortest path between two cities (result obtained: 4.014) compared to the result obtained from the shortest path technique compounded with computer vision (which yields a score of 6.224 when the path is free). This indicates the effectiveness of the proposed framework in addressing the challenges of traffic congestion, offering a promising solution for smart city traffic management systems to improve mobility and reduce environmental impacts in urban areas. © 2023 IEEE.

关键词： Traffic congestion

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Cross-camera vehicle tracking via affine invariant object matching for video forensics applications

Cross-camera vehicle tracking via affine invariant object ma...

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2013 IEEE International Conference on Multimedia and Expo, ICME 2013

作者： Hsu, Chao-Yung Kang, Li-Wei Mark Liao, Hong-Yuan Institute of Information Science Academia Sinica Taipei Taiwan Graduate School of Engineering Science and Technology-Doctoral Program Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

ISBN: (纸本)9781479900152

The recent deployment of very large-scale camera networks consisting of fixed/moving surveillance cameras and vehicle video recorders, has led to a novel field in object tracking problem. The major goal is to detect and track each vehicle within a large area, which can be applied to video forensics. For example, a suspected vehicle can be automatically identified for mining digital criminal evidences from a large amount of video data. In this paper, we propose an efficient cross-camera vehicle tracking technique via affine invariant object matching. More specifically, we formulate the problem as invariant image feature matching among different viewpoints of cameras. To achieve vehicle matching, we first extract invariant image feature based on ASIFT (affine and scale-invariant feature transform) for each detected vehicle in a camera network. Then, to improve the accuracy of ASIFT feature matching between images from different viewpoints, we propose to efficiently match feature points based on our observed spatially invariant property of ASIFT, as well as the min-hash technique. As a result, cross-camera vehicle tracking can be efficiently and accurately achieved. Experimental results demonstrate the efficacy of the proposed algorithm and the feasibility to video forensics applications. © 2013 IEEE.

关键词： Tracking (position)

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Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities

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Heliyon 2025年第1期11卷 e40855页

作者： Fan, Yi-Ling Hsu, Ching-Han Wu, Ju-Yu Tsai, Ying-Ying Chen, Wei J. Lee, Min-Shi Hsu, Fang-Rong Liao, Lun-De Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan Department of Biomedical Engineering & Environmental Sciences National Tsing-Hua University Hsinchu Taiwan Doctoral Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung Taiwan Center for Neuropsychiatric Research National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung 407 Taiwan

Introduction: The COVID-19 pandemic has created an urgent demand for research, which has spurred the development of enhanced biosafety protocols in biosafety level (BSL)-3 laboratories to safeguard against the risks associated with handling highly contagious pathogens. Laboratory management failures can pose significant hazards. Methods: An external system captured images of personnel entering a laboratory, which were then analyzed by an AI-based system to verify their compliance with personal protective equipment (PPE) regulations, thereby introducing an additional layer of protection. A deep learning model was trained to detect the presence of essential PPE items, such as clothing, masks, hoods, double-layer gloves, shoe covers, and respirators, ensuring adherence to World Health Organization (WHO) standards. The internal laboratory management system used a deep learning model to delineate alert zones and monitor compliance with the imposed safety protocols. Results: The external detection system was trained on a dataset consisting of 4112 images divided into 15 PPE compliance classes. The model achieved an accuracy of 97.52 % and a recall of 97.03 %. The identification results were presented in real time via a visual interface and simultaneously stored on the administrator's dashboard for future reference. We trained the internal management system on 3347 images, achieving 90 % accuracy and 85 % recall. The results were transmitted in JSON format to the internal monitoring system, which triggered alerts in response to violations of safe practices or alert zones. Real-time notifications were sent to the administrators when the safety thresholds were met. Conclusion: The BSL-3 laboratory monitoring system significantly reduces the risk of exposure to pathogens for personnel during laboratory operations. By ensuring the correct use of PPE and enhancing adherence to the imposed safety protocols, this system contributes to maintaining the integrity of BSL-3 facilities

关键词： Bioengineering Biomedical instrumentation Biomedical laboratory safety Biosafety level-3 laboratory

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Adaptive Spectrum Handoff Scheme in Cognitive Radio Ad-Hoc Networks

Adaptive Spectrum Handoff Scheme in Cognitive Radio Ad-Hoc N...

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2018 International Conference on Advances in Computing and Communication engineering, ICACCE 2018

作者： Shakeel, Atif Hussain, Riaz Iqbal, Adeel Latif, Irfan Malik, Shahzad A. Omoniwa, Babatunji Department of Electrical Engineering COMSATS Institute of Information Technology Islamabad Pakistan Computer Science Programme National Mathematical Centre Abuja Nigeria

ISBN: (纸本)9781538644850

Cognitive radio improves the spectrum utilization by permitting the secondary users in accessing the underutilized spectrum band of the primary network in an opportunistic manner. Spectrum handoff is considered as one of the important phases in cognitive cycle, where the secondary users have to vacate the occupied channel on the appearance of primary user for maintaining the QoS requirements of both primary and secondary communication. It also helps secondary users to find an appropriate target channel to resume its undone transmission either through switching handoff scheme or non-switching handoff process. The process of spectrum handoff becomes more critical in cognitive radio ad-hoc networks, as the coordination among the secondary users is a challenging task. This research presents an analytical model for the performance evaluation of secondary users, in terms of extended data delivery time for both switching and non-switching spectrum handoff in ad-hoc network environment. Channel rendezvous among secondary users is achieved usings of common control channel. The results achieved are based on preemptive resume priority M/M/1 queuing network, considering varying number of secondary users, their average arrival and service time. An adaptive spectrum handoff is proposed based on the primary user traffic intensity, number of SU and total number of primary channels. © 2018 IEEE.

关键词： Switching

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An Improvement Data Hiding Scheme Based on Formula Fully Exploiting Modification Directions and Pixel Value Differencing Method 11

An Improvement Data Hiding Scheme Based on Formula Fully Exp...

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11th Asia Joint Conference on information Security, AsiaJCIS 2016

作者： Kuo, Wen-Chung Li, Jyun-Jia Wang, Chun-Cheng Wuu, Lih-Chyau Huang, Yu-Chih Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Taiwan Graduate School of Engineering Science and Technology Doctoral Program National Yunlin University of Science and Technology Taiwan Department of Information Management Tainan University of Technology Taiwan

ISBN: (纸本)9781509022854

Recently, a modified data hiding scheme based on pixel value differencing and improving exploiting modification directions is proposed by Shen and Huang. There are two major contributions in this scheme. One is to enhance the embedding rate and good embedding capacity and the other is an optimization problem used to solve the overflow/ underflow problem. In fact, they propose the similar brute force method to solve overflow/underflow problem in their proposed scheme. To overcome the overflow/underflow problem exactly, we will propose the close form to solve this problem in this paper. Then, an improvement data hiding scheme based on FFEMD (Formula Fully Exploiting Modification Directions) and pixel value differencing method is proposed. According to our analysis, it is not only to keep the advantages of Shen-Huang scheme but also to use the close form to solve the overflow/ underflow problem. © 2016 IEEE.

关键词： Pixels

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