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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Venkata Gadiraju Hao-Yu Tsai Hsiao-Chun Wu Manali Singha C.-H. Scott Huang Guannan Liu Shih Yu Chang Yiyan Wu School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge USA Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Innovation Center for AI Applications Yuan Ze University Chungli Taiwan Institute for Genomic Medicine (IGM) Nationwide Children’s Hospital Columbus USA Department of Applied Data Science San Jose State University San Jose USA Communications Research Centre Ottawa Canada

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

In the realm of fine-tuning pre-trained language models in modern prompt engineering, we introduce a novel graph-theoretical approach to address the resource-intensive challenges to the conventional data fine-tuning methods for prompt engineering. Leveraging semantic and contextual prompt relationships, we propose to form a novel prompt graph, which facilitates a new comprehensive representation of prompt similarities. Building upon this new graph structure, our proposed approach can minimize the training time during the fine-tuning process for small language models by identifying and utilizing cliques corresponding to condensed subsets of highly similar prompts. This new strategic reduction in training data can greatly reduce the training time, particularly for resource-constrained applications in practice. Our proposed new approach leads to a significant reduction in the original prompt-graph order and a more focused and streamlined fine-tuning process. This data-reduction strategy demonstrates the potential to enable finetuning language models for prompt engineering with smaller datasets subject to less computational resource. The real run-time analysis for the training process of a small language model GPT2 have been undertaken to show the advantage of our proposed new approach.

关键词： Training Computational modeling Simulation Semantics Training data data models Prompt engineering

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Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML

Predicting the Impacts of the Environment on the Probability...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Araddhana Arvind Deshmukh K Parish Venkata Kumar B.Anjanee Kumar Pramoda Patro Pavitar Parkash Singh N. Ashokkumar Department of Artificial Intelligence and Data science Marathwada Mitra Mandal College of Engineering Pune India Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous) Vijayawada India Electronics and Communication Engineering Dadi institute of engineering and technology Visakhapatnam India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Management Lovely Professional University Phagwara India Department of ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The acceleration of global warming and environmental degradation has been attributed to industrial growth and fossil fuel utilization. Consequently, this phenomenon has resulted in a rise in the frequency and intensity of natural disasters. Due to their predominantly outdoor installation, generators are subject to significant impacts. The occurrence of natural disasters has led to an increase in accidents associated with electrical equipment. This paper analyses statistical data on electrical fires, electric shock accidents, and electricity-related accidents caused by climate change in domestic settings to determine the associated risk *** this study, we propose a risk assessment model for electrical fires, explicitly focusing on every month. Our approach involves analysing data on electrical hazards in different regional areas, particularly metropolitan cities, considering climatic conditions such as temperature and humidity. Additionally, we aim to evaluate the accident risk rating associated with natural disasters, specifically those related to low and high-voltage equipment. In this study, a risk analysis model has been developed to assess electrical hazards in different regions and equipment types. The model presented herein serves as a foundational predictive tool. Hence, offering electrical security amenities in the forthcoming period is feasible by giving a risk estimation map delineating potential dangers specific to each geographical area and wiring category via web platforms or mobile applications utilizing the investigation data provided. Additionally, it is imperative to enhance the resilience and dependability of electrical components as a proactive measure against electrocute ions resulting from climate change-induced emergencies.

关键词： Wiring Temperature distribution Electric potential Analytical models Disasters Urban areas Humidity

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F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

arXiv

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arXiv 2020年

作者： Li, Wenhao Jin, Bo Wang, Xiangfeng Yan, Junchi Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai200062 China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a flexible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multiagent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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The Middle of the Specified Node Set of Shortest Path Algorithm

The Middle of the Specified Node Set of Shortest Path Algori...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Jie Jia Jeng-Shyang Pan Hua-Rong Xu Innovative Information Industry Research Center Shenzhen Graduate SchoolHarbin Institute of Technology IIIRCHITSZ Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Innovative Information Industry Research Center Shenzhen Graduate SchoolHarbin Institute of Technology Department of Computer Science and Technology Xiamen University of Technology CSTXMUT

the vast majority of researches about the shortest path algorithm, nowadays, focus just on the case starting from the beginning point and ending at the ending point. If additional condition that the shortest path must go through some given nodes of which number is uncertain must be met, then most of the existing classic algorithms are not applicable. A general method based on the Virus Co-Evolutionary Genetic algorithm and greedy algorithm is presented to solve this kind of problem. The time complexity of the algorithm is given by theoretical analysis and the effectiveness of the algorithm is verified by programming calculation. Furthermore, this algorithm in HUAWEI Code Craft 2016 makes 17 th score in preliminary to the semi-finals.

关键词： shortest path algorithm VEGA greedy algorithm path planning

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A Lightweight Feature Selection Method Based on Rankability

SSRN

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SSRN 2024年

作者： Kong, Lingping Velasquez, Juan D. Perfilieva, Irina Pant, Millie Pan, Jeng-Shyang Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile Institute for Research and Applications of Fuzzy Modeling Centre of Excellence IT4Innovations division of The University of Ostrava Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India School of Artificial Intelligence Nanjing University of Information Science and Technology China Instituto Sistemas Complejos de Ingeniería Santiago Chile Santiago Chile Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków Poland Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India College of Computer Science and Engineering Shandong University of Science and Technology China

Within the realm of data mining and machine learning, feature selection stands out as an essential technique for dimensionality reduction, presenting both popularity and complexity. Feature selection involves a nuanced trade-off that results in information loss. This distinction underscores the necessity for the development of lightweight, efficient, and practical methods capable of minimizing information loss while executing dimensional reduction. In this paper, we introduce a rankability-based feature selection method. The concept of rankability, proposed in 2019 and similar to the entropy concept, remains relatively unexplored. Our proposed method distinguishes itself by its lightweight complexity, eliminating the need for iterative *** evaluate the effectiveness of our approach, we conducted experiments on forty-one datasets and compared the results with four other algorithms using refined data with 10\%, 30\%, and 50\% feature dimension reduction. The findings reveal that our rankability-based feature selection method consistently outperforms the other four methods across a majority of datasets in terms of accuracy, precision, recall, and F1-macro performance. Specifically, the classification score exceeds 0.9 on accuracy for 50\% feature dimension reduction in twelve datasets, with nine datasets achieving scores over 0.9 in recall, F1-macro, and precision. Overall, our proposed feature selection method demonstrates promising performance. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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Prevention of external force damage to overhead power lines based on network and intelligent positioning

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第3期6卷 551-583页

作者： Xie, Bo-Lin Pan, Jeng-Shyang Lin, Yuh-Chung Xie, Yu-Hong Pan, Tien-Szu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Information Science and Technology Sanda University Shanghai201209 China Guangdong Power Grid Heyuan Power Supply Bureau Transmission Management Office China Southern Power Grid Heyuan517000 China Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung807 Taiwan

In the research on the prevention of external damage to power transmission lines, it is a difficult problem to prevent large machinery vehicles from damaging the overhead transmission lines. After the emergence of video surveillance technology, the accident rate has dropped significantly. But still unable to completely eliminate such accidents. The anti-external damage solution based on network and intelligent positioning is very effective for early warning of accidents. This gives us the opportunity to completely eliminate such accidents. In this paper we utilize the satellite positioning data of the power towers to establish a warning zone along the power line. When the system detects that some large machinery vehicles enter the warning zone, early warning messages will be sent to these vehicles. Afterward, the connection between vehicle drivers and management center can be established such that further instructions can be ordered to avoid accidents. According to our simulations, it presents our proposed mechanism can effectively prevent the damage occurrences which may be caused by large machinery vehicles. © 2021 Journal of Network Intelligence.

关键词： Accidents

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Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers

arXiv

引用

arXiv 2021年

作者： Khan, Asad Huerta, E.A. Zheng, Huihuo Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States

MSC Codes 68T10, 85-08, 83C35, 83C57We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers. We used the NRHybSur3dq8 surrogate model to produce train, validation and test sets of = |m| = 2 waveforms that cover the parameter space of binary black hole mergers with mass-ratios q ≤ 8 and individual spins |sz{1, 2}| ≤ 0.8. These waveforms cover the time range t ∈ [−5000M, 130M], where t = 0M marks the merger event, defined as the maximum value of the waveform amplitude. We harnessed the ThetaGPU supercomputer at the Argonne Leadership Computing Facility to train our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, to fully train our model within 3.5 hours. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms in the time range t ∈ [−100M, 130M]. Sampling a test set of 190,000 waveforms, we find that the average overlap between target and predicted waveforms is & 99% over the entire parameter space under consideration. We also combined scientific visualization and accelerated computing to identify what components of our model take in knowledge from the early and late-time waveform evolution to accurately forecast the latter part of numerical relativity waveforms. This work aims to accelerate the creation of scalable, computationally efficient and interpretable artificial intelligence models for gravitational wave astrophysics. Copyright © 2021, The Authors. All rights reserved.

关键词： Merging

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AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

arXiv

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arXiv 2021年

作者： Khan, Asad Huerta, E.A. Kumar, Prayush Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Chicago ChicagoIL60637 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India

We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to ` ≤ 4 and (5, 5), except for (4, 0) and (4, 1), that describe binaries with mass-ratios q ≤ 8, individual spins sz{1,2} ∈ [−0.8, 0.8], and inclination angle θ ∈ [0, π]. Our probabilistic AI surrogates can accurately constrain the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. We compared the predictions of our AI models with Gaussian process regression, random forest, k-nearest neighbors, and linear regression, and with traditional Bayesian inference methods through the PyCBC Inference toolkit, finding that AI outperforms all these approaches in terms of accuracy, and are between three to four orders of magnitude faster than traditional Bayesian inference methods. Our AI surrogates were trained within 3.4 hours using distributed training on 1,536 NVIDIA V100 GPUs in the Summit *** Codes 68T10, 85-08, 83C35, 83C57 Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Optimizing multiple ontology alignments through hybrid evolutionary algorithm based on a new elitist strategy

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ICIC Express Letters, Part B: applications 2016年第6期7卷 1241-1248页

作者： Jiang, Li Tsai, Pei-Wei Xue, Xingsi Computer Department Fuzhou Polytechnic No. 8 Lianrong Road University Town Minhou Fuzhou350108 China College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China

In this paper, we propose a hybrid evolutionary algorithm based on a new elitist strategy to address the problem of optimizing the multiple ontology alignments simultaneously. Comparing with the conventional approach, our method is able to deal with multiple pair of ontologies at a time, and through a new elitist strategy, avoid the bias improvement caused by f-measure. Experimental results show that our approach is effective. © 2016 ICIC International.

关键词： Ontology

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