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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Liu, Yuankun Xing, Lumin Wang, Di Nie, Xin Zhang, Xueyan Wu, Di Liu, Wenjian The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Chongqing Jianzhu College Chongqing China Shandong Youth University of Political Science School of Information Work Office Jinan China University of Southern Queensland School of Mathematics Physics and Computing QLD Australia

ISBN: (纸本)9798350391367

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively. © 2024 IEEE.

关键词： Urology

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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Moving towards FAIR mappings and crosswalks

Moving towards FAIR mappings and crosswalks

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Martínková, Jana Juty, Nick Beltran, Alejandra Gonzalez Goble, Carole Le Franc, Yann e-Science Data Factory S.A.S.U. Montpellier France Czech Technical University in Prague Faculty of Information Technology Prague160 00 Czech Republic The University of Manchester Department of Computer Science Oxford Road ManchesterM13 9PL United Kingdom Scientific Computing Science and Technology Facilities Council Harwell Campus OX11 0QX United Kingdom

Mappings and crosswalks are key elements to ensure semantic interoperability as well as metadata and data integration between different information systems. Designing FAIR compliant systems requires making sure all the elements that constitute the systems are themselves FAIR to support machine-actionability and automation. This paper describes the ongoing European and international effort to build a framework for FAIR mappings and crosswalks. This framework aims to be generic enough to capture the diverse set of use cases and methodologies across domains and communities. It should be composed of a set of technical recommendations to aid compliance with FAIR principles, a set of models for machine-actionable mappings and crosswalks as well as a practical framework with aligned good practices to support the creation of mappings by scientific communities. Developed in the context of FAIR IMPACT, a Horizon Europe project, this work will be pursued within a more international context as a Research data Alliance Working Group. © 2024 Copyright for this paper by its authors.

关键词： Mapping

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Towards Resource-Efficient Reactive and Proactive Auto-Scaling for Microservice Architectures

SSRN

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SSRN 2024年

作者： Ahmad, Hussain Treude, Christoph Wagner, Markus Szabo, Claudia School of Computer and Mathematical Sciences University of Adelaide Australia School of Computing and Information Systems Singapore Management University Singapore Department of Data Science and AI Faculty of IT The Monash University Australia

Microservice architectures have become increasingly popular in both academia and industry, providing enhanced agility, elasticity, and maintainability in software development and deployment. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust microservice resources to accommodate fluctuating workloads. However, existing HPAs are not suitable for resource-constrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability, resource mismanagement, and financial losses. Furthermore, the inherent delay in initializing and terminating microservice pods hinders HPAs from timely responding to workload fluctuations, further exacerbating these issues. In addition, the architectures of existing HPAs exhibit several challenges, including single point of failure and the absence of coordinated scaling operations. To address these concerns, we propose Smart HPA and ProSmart HPA, reactive and proactive horizontal pod auto-scalers. These auto-scalers integrate centralized and decentralized styles. Smart HPA optimizes resource exchange in constrained environments, while ProSmart HPA uses machine learning to manage resource demands proactively, reducing delays and enabling preemptive resource sharing. Our experiments show Smart HPA outperforms Kubernetes HPA, and ProSmart HPA surpasses both by reducing resource overutilization, overprovisioning, underprovisioning, and increasing resource allocation to microservice applications. © 2024, The Authors. All rights reserved.

关键词： Losses

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Enhancing Blockchain Storage Efficiency in Mobile IoT through Multidimensional Structures and Collective Signing

Enhancing Blockchain Storage Efficiency in Mobile IoT throug...

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IEEE International Conference on Blockchain (Blockchain)

作者： Hussein Zangoti Alex Pissinou Makki Wazir Zada Khan Niki Pissinou School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia Frost Institute of Data Science and Computing University of Miami Miami FL USA University of Wah Wah Cantt Pakistan

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

The rapidly evolving mobile IoT landscape, driven by mobile financial applications, self-driving cars, wearables, and health monitoring devices, confronts substantial blockchain storage challenges due to decentralized networks, limited band-width, and high storage overhead. To tackle these issues, we propose the Collective Signing-Based Blockchain Storage Optimization (CSBSO) algorithm. CSBSO mitigates storage over-head by leveraging the Collective Signing (CoSi) protocol and employing a multidimensional blockchain structure for efficient block management and retrieval. It focuses on identifying and pruning irrelevant blocks and implementing streamlined data management. Evaluations with Ethereum Classic Blockchain and Facebook Users datasets show CSBSO achieves up to 92% storage optimization, surpassing current models. These results underscore the effectiveness of CoSi-based strategies in reducing blockchain storage overhead in resource-constrained environments.

关键词： Social networking (online) Scalability Smart contracts Blockchains Cryptocurrency Autonomous automobiles Wearable devices

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Development of a Sensor-Based Wearable Fetal and Maternal Health Monitoring System 1

Development of a Sensor-Based Wearable Fetal and Maternal He...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Jayalath, Yugashi Maduranga, M.W.P. Tilwari, Valmik Wijayarathna, Wmsrb Wanniarachchi, Waam Sharma, Debashree School Of Pharmacy & Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Sri Lanka Technology Campus Faculty of Computing and IT Department of Data Science Padukka Sri Lanka Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India General Sir Jhon Kotelawala Defence University Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9798350364590

According to the World Health Organization, approximately 360,000 pregnant women are registered annually in Sri Lanka. Among these, about 328,000 babies are born healthy, while 5,800 are born with birth defects, some of which are severe and impact daily life. To address the Sri Lankan stillbirth rate of 6.01%, continuous monitoring of fetal and maternal health is essential. Although commercial devices exist to fulfill this requirement, their high costs and safety issues often preclude regular use. To overcome these challenges, there is a need for a passive, wearable, and portable monitoring system for fetal and maternal health. This system should track key parameters such as fetal heart rate, fetal movements, and maternal heart rate to mitigate health risks for both the fetus and the mother. In this study, we developed a device using the Arduino, appropriate sensors, and a microcontroller to monitor these vital parameters. The device is user-friendly and suitable for regular use. MATLAB was utilized for the signal processing requirements of the proposed device. Test results demonstrated that fetal movement detection was accurate, although potential errors may arise during real-time testing. For pulse sensor readings, an average percentage difference of 3.025% was observed compared to pulse oximeter readings, likely due to placement errors and sensor instability in the proposed device. © 2024 IEEE.

关键词： Electronic health record

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Descriptor: Egocentric Action Recognition for Bengali Sign Language Detection dataset (Ego-SLD)

IEEE Data Descriptions

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IEEE data Descriptions 2025年 2卷 102-112页

作者： Asfak Ali Bibek Das Ahmad Sami Al-Shamayleh Sujatro Sarkar Chinmoyee Ray Sayoni Mandal Saifuddin Sk Adnan Akhunzada Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Electrical Engineering Jadavpur University Kolkata India College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

Sign language recognition integrates computer vision and natural language processing to automatically interpret hand gestures and translate them into spoken or written Bengali. The primary goal is to bridge the communication gap between sign language users and non-users by recognizing gestures, movements, postures, and facial expressions that correspond to spoken language elements. Since hand gestures are the cornerstone of sign language communication, they play a pivotal role in improving the accuracy of sign language recognition systems. This article introduces Ego-SLD, a video dataset specifically designed for egocentric action recognition of Bangla sign language used in daily life situations. The dataset contains videos of 16 commonly used words, collected from 12 individuals (ten males and two females) aged between 16 and 42 years. Each participant provided two samples of each word in an indoor environment with standard lighting conditions. Ego-SLD is a crucial resource for the automatic recognition of Bangla sign language and holds significant potential to benefit the deaf community. Moreover, it serves as a valuable dataset for researchers focusing on vision-based sign language detection, hand gesture recognition, and egocentric action recognition. IEEE SOCIETY/COUNCIL Computer Society data TYPE/LOCATION Video; Jadavpur university, Kolkata, West Bengal, India data DOI/PID https://***/10.21227/kbeg-fa06

关键词： Sign language Hands Recording Annotations Machine learning Accuracy Lighting Face recognition Cleaning Training

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Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms

arXiv

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arXiv 2025年

作者： Wang, Xiaoyu Kasprzak, Mikolaj J. Negrea, Jeffrey Bourguin, Solesne Huggins, Jonathan H. Department of Mathematics & Statistics Boston University United States Department of Information Systems Data Analytics & Operations ESSEC Business School France Department of Statistics & Actuarial Science University of Waterloo Canada Faculty of Computing & Data Sciences Boston University United States

Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional problems in machine learning, statistics, and engineering. Numerous works have bounded the parameter error in, and characterized the uncertainty of, these approximations. One common approach has been to use scaling limit analyses to relate the distribution of algorithm sample paths to a continuous-time stochastic process approximation, particularly in asymptotic setups. Focusing on the univariate setting, in this paper, we build on previous work to derive non-asymptotic functional approximation error bounds between the algorithm sample paths and the Ornstein-Uhlenbeck approximation using an infinite-dimensional version of Stein's method of exchangeable pairs. We show that this bound implies weak convergence under modest additional assumptions and leads to a bound on the error of the variance of the iterate averages of the algorithm. Furthermore, we use our main result to construct error bounds in terms of two common metrics: the Lévy-Prokhorov and bounded Wasserstein distances. Our results provide a foundation for developing similar error bounds for the multivariate setting and for more sophisticated stochastic approximation *** Codes 60F17 (Primary) 60J60, 62-08, 68T05, 62E17 (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Capturing the Complexity of Human Strategic Decision-Making with Machine Learning

arXiv

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arXiv 2024年

作者： Zhu, Jian-Qiao Peterson, Joshua C. Enke, Benjamin Griffiths, Thomas L. Department of Computer Science Princeton University United States Faculty of Computing and Data Science Boston University United States Department of Economics Harvard University NBER United States Department of Psychology Princeton University United States

Understanding how people behave in strategic settings–where they make decisions based on their expectations about the behavior of others–is a longstanding problem in the behavioral sciences. We conduct the largest study to date of strategic decision-making in the context of initial play in two-player matrix games, analyzing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on these data predicts people’s choices better than leading theories of strategic behavior, indicating that there is systematic variation that is not explained by those theories. We then modify the network to produce a new, interpretable behavioral model, revealing what the original network learned about people: their ability to optimally respond and their capacity to reason about others are dependent on the complexity of individual games. This context-dependence is critical in explaining deviations from the rational Nash equilibrium, response times, and uncertainty in strategic decisions. More broadly, our results demonstrate how machine learning can be applied beyond prediction to further help generate novel explanations of complex human behavior. © 2024, CC BY.

关键词： Adversarial machine learning

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LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition

arXiv

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arXiv 2024年

作者： Hu, Youbing Cheng, Yun Lu, Anqi Cao, Zhiqiang Wei, Dawei Liu, Jie Li, Zhijun AIoT Lab. Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland School of Cyber Engineering Xidian University China

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization and Focus Vision Transformer (LF-ViT). This model operates by strategically curtailing computational demands without impinging on performance. In the Localization phase, a reduced-resolution image is processed;if a definitive prediction remains elusive, our pioneering Neighborhood Global Class Attention (NGCA) mechanism is triggered, effectively identifying and spotlighting class-discriminative regions based on initial findings. Subsequently, in the Focus phase, this designated region is used from the original image to enhance recognition. Uniquely, LF-ViT employs consistent parameters across both phases, ensuring seamless end-to-end optimization. Our empirical tests affirm LF-ViT’s prowess: it remarkably decreases Deit-S’s FLOPs by 63% and concurrently amplifies throughput twofold. Code of this project is at https://***/edgeai1/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Image recognition

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