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SSRN 2023年

作者： Nguyen, Phu Cuong Aragon, Nicole K. Park, Taejoon Pourboghrat, Farhang Ryu, Ill Department of Mechanical Engineering The University of Texas at Dallas RichardsonTX75080 United States Department of Computational Materials and Data Science Sandia National Laboratories AlbuquerqueNM87185 United States Department of Integrated Systems Engineering The Ohio State University ColumbusOH43210 United States

The collective motion of defects and their interaction are the basic building blocks for plastic deformation and corresponding mechanical behaviors of crystalline metals. Especially, dislocations among various defects are the "carrier" of plastic deformation in many crystalline materials, particularly ductile materials. To get a fundamental understanding of plastic deformation mechanisms, it calls for an integrated computational platform to simultaneously capture detailed defects characteristics across several length scales together with corresponding macroscopic mechanical response. In this paper, we present a three-dimensional mesoscale defect dynamics model to directly couple the three dimensional discrete dislocation dynamics model with continuum finite element method, aiming at capturing both size dependent plasticity at micron-, and submicron scale and constitutive behaviors at larger scales where such size-dependence disappear. Using non-singular dislocation theories, our model could accurately consider both short- and long-range elastic interactions between multiple dislocation segments with even higher computational efficiency than traditional dislocation dynamics simulations, together with the crystal rotation and finite deformation in the coupled framework. In addition, our model could directly model dislocation nucleation from stress concentrators such as a void, crack and indentor tip, which could allow us to investigate various defects’ motion and their mutual interactions, predicting macroscopic mechanical response of complex structures. The developed concurrently coupled model could also consider multiphysical phenomena by solving coupled governing equations in finite element framework, which could shed light on complex defect behaviors under various physical environments. © 2023, The Authors. All rights reserved.

关键词： Finite element method

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Proactive Energy Management for Renewable-Integrated Smart Grids with Real-Time Optimization using a Novel Hybrid PSO-GA Algorithm

Proactive Energy Management for Renewable-Integrated Smart G...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Divya Nimma V.Kanpur Rani Shamim Ahmad Khan Amit Kumar Mishra Mithun D'Souza I Infant Raj Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Electrical and Electronics Engineering Hindustan Institute of Technology and Science India Glocal School of Science & Technology Glocal University U.P India Department of CSE Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science St. Joseph's University Bangalore India Department of CSE K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The increasing integration of renewable energy sources (RES) into smart grids presents significant challenges related to variability, stability, and energy management. This research proposes a novel hybrid PSO-GA algorithm for real-time decision-making and optimization in smart grid systems. The proposed PSO-GA combines the exploration capabilities of Particle Swarm Optimization (PSO) with the capabilities of Genetic Algorithm (GA), enabling it to effectively address the dynamic and challenging nature of smart grid environments. The hybrid PSO-GA algorithm demonstrates significant improvements in transmission and distribution losses, reducing them by up to 6%. Additionally, it enhances energy efficiency, achieving an increase of 88% compared to traditional and non-hybrid optimized systems. The proposed system offers lower operational costs and improved power system stability, facilitating the integration of high levels of renewable power. Furthermore, the system's ability to respond to real-time demands and proactively address dynamic grid situations ensures reliable and efficient power delivery. This research contributes to the development of advanced energy management strategies for smart grids, promoting the increased adoption of renewable energy and paving the way for a more sustainable and efficient energy future.

关键词： Renewable energy sources Heuristic algorithms Power system dynamics Power system stability Real-time systems Stability analysis Smart grids Reliability Optimization Genetic algorithms

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Robust Cross-Domain Speaker Verification with Multi-Level Domain Adapters

Robust Cross-Domain Speaker Verification with Multi-Level Do...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wen Huang Bing Han Shuai Wang Zhengyang Chen Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Speaker verification encounters significant challenges when confronted with diverse domain data, often resulting in performance degradation due to domain mismatch. To enhance performance in cross-domain scenarios, we introduce the Domain Adapter, an adaptable module designed for specific domains. This module learns and integrates domain-specific information with speaker-related data, mitigating domain-related variations and promoting convergence of utterance embeddings from the same speaker across diverse domains. It offers configurability across multiple levels and is adaptable to various backbone architectures. Our proposed module substantially enhances cross-domain performance with minimal parameter increments while effectively generalizing to previously unseen domains. In our experiments, we present results on the 3D-Speaker dataset, which provides acoustically-relevant attributes crucial for domain categorization and the subsequent learning of domain information. The top-performing system integrated with domain adapters achieved 10.8%, 14.8%, and 21.1% EER improvements over the baseline across three 3D-Speaker dataset trials.

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When data disappear: public health pays as US policy strays

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The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

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AI Tools for Enhanced English Learning: Leveraging Machine Learning for Improved Outcomes 1

AI Tools for Enhanced English Learning: Leveraging Machine L...

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1st IEEE International Conference on Green Industrial Electronics and Sustainable Technologies, GIEST 2024

作者： Sharma, Priya Nimma, Divya Sweetline, S. Punithaasree, K.S. Sreela, B. Mohamed Faizal, M.K. Chandigarh University UITTR Department Mohali India University of Southern Mississippi Data Analyst in UMMC Computational Science United States Vel Tech Rangarajan Dr.Sagunthala R&D Institue of Science and Technology Department of English Chennai India Department of English Erode India Prathyusha Engineering College Department of English India K.Ramakrishnan College of Engineering Department of AI & DS Tamil Nadu Samayapuram India

ISBN: (纸本)9798331540142

First, AI has implemented English language learning by providing a tailor-made approach to education. Yet, the current methods based on AI have certain drawbacks, including exclusive reliance on such static characteristics of a learner as study routine, rigid flexibility, and poor interpretation of the context in which the speech is delivered', which may lead to speech suppression;unaddressed individual learning requirements - low learning attainment and engagement;This paper outlines a new approach to English as a foreign language based on the application of AI and advanced ML methods such as NLP and adaptive The proposed system can also support flexible learning and Delivery mode that engages learners with their own skills, writing and speaking feedback, and participation with on gamified learning experience. Further, the rationale for the middle section focuses on the utilization of real-time speech recognition to enhance word pronunciation of students besides employing text-recognition technologies enhanced by artificial intelligence to augment learning activities. The general approach of the overall methodology comprises of NLP based context comprehension, along with the process of gamification, and the coming together of flexibility of curriculum and simulation of actual practice. Algorithms collected data to employ it for the customization of the learning interactions and improving the structure of the platform throughout learners' engagement. In addition to overcoming the existing shortcomings, the present approach suggests elements that can promote the most suitable and effective method of teaching English to diverse groups of learners. © 2024 IEEE.

关键词： Natural language processing systems

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Detection Of Counterfeit Images Through Deep Learning based Yolov3 Model

Detection Of Counterfeit Images Through Deep Learning based ...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Jobez J. C Golden Nancy R Syed Rabiya Mohamed Ali Muthukumaran N Raghavan Sheeja Jasmine Gnana Malar A Department of AIML Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. Engineering College Gummidipoondi Tamil Nadu India Department of ECE PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Image forgery is the procedure of analysing and detecting any fraudulent or unauthorised alterations made to digital images. The detection in images is difficult for achieving a high accuracy rate and the process is time-consuming. Researchers and forensic experts are developing tools for detecting fake images. The identification of computer-generated picture forgeries is a notable area of study, aiming to provide a comprehensive review of detection tools and techniques. In this work, a novel deep-learning model is proposed for detecting forgery in images. The images are pre-processed using an adaptive Gaussian bilateral (AGB) filter to enhance the quality of the images. The enhanced photos are fed into the Mobile Net for extracting the relevant features in the pictures. The modified Yolov3 is utilized for object detection in images and detecting the real and forgery images. According to the result, the proposed model attains 99.55% for forgery detection in images. The modified Yolov3 yields 99.55% accuracy as opposed to Mask RCNN, RCNN, and SSD's 0.68%, 0.65%, and 1.16% accuracy. CNN, ResNetV2 and DCNN are all outperformed the proposed model in terms of overall accuracy by 0.11%, 1.10%, and 5.55%, respectively.

关键词： YOLO Deep learning Adaptation models Accuracy Reviews computational modeling Forensics Digital images Feature extraction Forgery

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A Compounded Burr Probability Distribution for Fitting Heavy-Tailed data with Applications to Biological Networks

arXiv

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arXiv 2024年

作者： Chakraborty, Tanujit Chattopadhyay, Swarup Das, Suchismita Naik, Shraddha M. Hens, Chittaranjan SAFIR Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Sorbonne University Paris France Department of Computer Science & Engineering XIM University Bhubaneswar India Department of Data Science SP Jain School of Global Management Mumbai India Department of Computer Science Khalifa University United Arab Emirates Center for Computational Natural Science and Bioinformatics International Institute of Information Technology Hyderabad India

Complex biological networks, encompassing metabolic pathways, gene regulatory systems, and protein–protein interaction networks, often exhibit scale-free structures characterized by heavy-tailed degree distributions. However, empirical studies reveal significant deviations from ideal power-law behavior, underscoring the need for more flexible and accurate probabilistic models. In this work, we propose the Compounded Burr (CBurr) distribution, a novel four-parameter family derived by compounding the Burr distribution with a discrete mixing process. This model is specifically designed to capture both the body and tail behavior of real-world network degree distributions with applications to biological networks. We rigorously derive its statistical properties, including moments, hazard and risk functions, and tail behavior, and develop an efficient maximum likelihood estimation framework. The CBurr model demonstrates broad applicability to networks with complex connectivity patterns, particularly in biological, social, and technological domains. Extensive experiments on large-scale biological network datasets show that CBurr consistently outperforms classical power-law, log-normal, and other heavy-tailed models across the full degree spectrum. By providing a statistically grounded and interpretable framework, the CBurr model enhances our ability to characterize the structural heterogeneity of biological networks. © 2024, CC BY.

关键词： Maximum likelihood estimation

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LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment

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Computers in Biology and Medicine 2024年 173卷 108382-108382页

作者： Sardari, Sara Sharifzadeh, Sara Daneshkhah, Alireza Loke, Seng W. Palade, Vasile Duncan, Michael J. Nakisa, Bahareh Research Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Geelong Vic Australia Department of Computer Science Swansea University Swansea United Kingdom School of Mathematics and Data Science Emirates Aviation University Dubai United Arab Emirates Centre for Sport Exercise and Life Sciences Coventry University Coventry United Kingdom

Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial–Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity. © 2024 The Authors

关键词： Convolution

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Accuracy of a one-dimensional reduction of dynamical systems on networks

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Physical Review E 2022年第2期105卷 024305-024305页

作者： Prosenjit Kundu Hiroshi Kori Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Department of Complexity Science and Engineering The University of Tokyo Chiba 277-8561 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.

关键词： Complex systems Dynamics of networks Network resilience

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Prototype and Instance Contrastive Learning for Unsupervised Domain Adaptation in Speaker Verification

arXiv

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arXiv 2024年

作者： Huang, Wen Han, Bing Chen, Zhengyang Wang, Shuai Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Speaker verification system trained on one domain usually suffers performance degradation when applied to another domain. To address this challenge, researchers commonly use feature distribution matching-based methods in unsupervised domain adaptation scenarios where some unlabeled target domain data is available. However, these methods often have limited performance improvement and lack generalization in various mismatch situations. In this paper, we propose Prototype and Instance Contrastive Learning (PICL), a novel method for unsupervised domain adaptation in speaker verification through dual-level contrastive learning. For prototype contrastive learning, we generate pseudo labels via clustering to create dynamically updated prototype representations, aligning instances with their corresponding class or cluster prototypes. For instance contrastive learning, we minimize the distance between different views or augmentations of the same instance, ensuring robust and invariant representations resilient to variations like noise. This dual-level approach provides both high-level and low-level supervision, leading to improved generalization and robustness of the speaker verification model. Unlike previous studies that only evaluated mismatches in one situation, we have conducted relevant explorations on various datasets and achieved state-of-the-art performance currently, which also proves the generalization of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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