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Image Representations of Numerical Simulations for Training Neural Networks

computer Modeling in engineering & sciences 2023年第2期134卷 821-833页

作者： Yiming Zhang Zhiran Gao Xueya Wang Qi Liu School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality *** the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation ***,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and *** this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by *** temporal and spatial information is kept and the data are greatly *** addition,the results are readable for not only computers but also human *** examples are given,indicating the effectiveness of the proposed strategy.

关键词： Numerical simulations neural network pre-/post-processing data compression

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Security and Privacy in Metaverse: A Comprehensive Survey

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Big Data Mining and Analytics 2023年第2期6卷 234-247页

作者： Yan Huang Yi(Joy)Li Zhipeng Cai Department of Software Engineering and Game Development Kennesaw State UniversityAtlantaCA 30060USA Department of Computer Science Georgia State UniversityAtlantaCA 30303USA

Metaverse describes a new shape of cyberspace and has become a hot-trending word since *** are many explanations about what Meterverse is and attempts to provide a formal standard or definition of ***,these definitions could hardly reach universal *** than providing a formal definition of the Metaverse,we list four must-have characteristics of the Metaverse:socialization,immersive interaction,real world-building,and *** characteristics not only carve the Metaverse into a novel and fantastic digital world,but also make it suffer from all security/privacy risks,such as personal information leakage,eavesdropping,unauthorized access,phishing,data injection,broken authentication,insecure design,and *** paper first introduces the four characteristics,then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic *** on the four characteristics and the findings of the current progress,the security and privacy issues in the Metaverse are *** then identify and discuss more potential critical security and privacy issues that can be caused by combining the four ***,the paper also raises some other concerns regarding society and humanity.

关键词： Metaverse cybersecurity privacy protection cyber infrastructure extended reality

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Keyword extraction method based on complex network

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International Journal of Information and Communication Technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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Optimization of Heliostat Field Arrangement Model Based on Geometric Relationship and Particle Swarm Algorithm 4

Optimization of Heliostat Field Arrangement Model Based on G...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Liu, Zhishuai Wei, Zhengyang Li, Jiangnan School of Mathematics Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798350359589

With the continuous progress of photovoltaic (PV) technology and the steady reduction of related costs, solar energy, as an important renewable energy source, shows an increasingly strong competitiveness in energy market competition. In the tower solar thermal power station, the arrangement of the heliostat field directly affects the power generation efficiency of the tower power generation system as well as the working cost. Therefore, this paper presents a model based on geometric relationship and particle swarm algorithm for the optimization of the heliostat field arrangement, mathematical modeling and calculating cosine efficiency, truncation efficiency, etc., and effectively improves the output thermal power as well as the optical efficiency of the heliostat field. Geometric planning is utilized to determine the location of the absorption tower, the coordinates of the heliostat arrangement and other layout parameters to optimize the layout of the tower solar system. Drawing on the idea of clustering, the model analyzes the characteristics of the heliostat mirrors in the same region, uses same or similar parameters to minimize the cost of computation, improving the overall optical efficiency of the mirror field. The particle swarm algorithm is utilized to solve the parameters such as mirror length and mirror width of the heliostat to get the suitable size of the heliostat for the heliostat mirror field. After completing all the calculation and optimization steps, the final solution of the model is carried out in this paper. The layout scheme of the heliostat field optimized by the implementation of the model gets a significant performance improvement. Specifically, the average annual thermal power output of the heliostat field is improved by 33.2309 MW, while the average annual optical efficiency is also improved by 43.2%. These improvements effectively enhance the power generation efficiency of the whole system, confirming the effectiveness of the optimization

关键词： Campo arrangement method Geometric relationship Mirror field optimization arrangement Particle swarm algorithm Spatial transformation

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Future Intelligent Autonomous Robots, Ethical by Design. Lessons Learned from Autonomous Cars Ethics 14

Future Intelligent Autonomous Robots, Ethical by Design. Les...

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14th International Conference on Society and Information Technologies, ICSIT 2023

作者： Dodig-Crnkovic, Gordana Holstein, Tobias Pelliccione, Patrizio Thavarasa, Jathoosh Division of Computer Science and Software Engineering Mälardalen University Västerås Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Information Engineering Computer Science and Mathematics University of l'Aquila L'Aquila Italy Software Engineering Efrei Paris Villejuif France

ISBN: (纸本)9781950492701

The "ethical by design" approach involves examining all stages of a lifecycle of technology to ensure that they are ethically justifiable and socially sustainable. Building on our work on the ethics of autonomous intelligent robocars, and studies of the literature on the ethics of robotics, we propose for robot applications a set of values and ethical principles including safety, security, privacy, transparency, and explainability, accountability, fairness, human control, well-being, autonomy and freedom, and sustainability. This may help stakeholders in the field of intelligent autonomous robotics to connect ethical principles with their applications. Most ethical considerations we identified in our work on autonomous cars are relevant to all AI-powered robots, but robots require additional examination depending on their application domain, such as social robots (care robots, personal companions, robots used in education, health care, elderly care, education, entertainment, chat-bots), industrial robots, etcetera. Thus, existing ethical frameworks need to be applied in a context-sensitive way, by assessments in interdisciplinary, multi-competent teams through multi-criteria analysis. Furthermore, we argue for the need for continuous development of ethical principles, guidelines, and regulations, informed by the progress of technologies and involving relevant stakeholders. This implies designing the socio-technical system as an intelligent learning ecology. © 2023 Proceedings ICSIT, International Conference on Society and Information Technologies. All rights reserved.

关键词： Intelligent robots

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Uni4DAL: A Unified Baseline for Multi-dataset 4D Auto-Labeling 27th

Uni4DAL: A Unified Baseline for Multi-dataset 4D Auto-Label...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Zhiyuan Wang, Xuekuan Zhang, Wei Tan, Xiao Lu, Jinchen Wang, Jingdong Ding, Errui Lai, Zhihui Zhao, Cairong Department of Computer Science and Technology Tongji University Shanghai201804 China Baidu Inc. Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031781124

The 4D auto-labeling system, with its potential to enhance data annotation efficiency for 3D object detection, has garnered significant attention. However, its adoption has been hampered by the high costs associated with temporally annotated long-sequential training data and limited generalization capabilities across diverse scenarios. In this paper, we hypothesize that a multi-dataset approach can address these challenges and, accordingly, first introduce a Unified training pipeline for multi-dataset 4D Auto-Labeling, namely Uni4DAL. We recognize that this is a challenging task, primarily due to data-level variations and feature-level inconsistencies among various datasets. Motivated by this understanding, we initially propose a series of Data-Level Alignment (DLA) operations to mitigate potential discrepancies between diverse datasets and ensure synchronized training progress across samples from multiple datasets. Furthermore, to address feature-level inconsistencies, we introduce the Mixed Expert Models Voxel Feature Encoding (MoE-VFE) module, which aims to extract both domain-specific and domain-generalizable features. Additionally, we employ a Domain-Adaptive Hard Example Mining (DA-HEM) technique to leverage both data-level and feature-level consistencies, ensuring that the model pays enhanced attention to the challenging samples during multi-dataset training. Finally, comprehensive experiments demonstrate that Uni4DAL significantly improves performance on the nuScenes, Argoverse2, and Waymo datasets, and exhibits greater robustness with insufficient training data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 4D auto-labeling Multi-dataset training Offline 3D object detection

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An Integrated Machine Learning Framework Based Liver Disease Diagnosis System

An Integrated Machine Learning Framework Based Liver Disease...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Imtiaz, Irsa Qaiser, Ayesha Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Sciences and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Viral hepatitis increases the likelihood of developing liver cancer, which is a critical public health concern. It is a severe disorder that can be fatal if not recognized and treated promptly. The most common causes of liver cancer are hepatitis viruses. Early-stage diagnosis of HCC is crucial for improving patient outcomes, particularly when hepatitis virus infection is the underlying cause. This study looks into the use of ML algorithms for early detection of liver cancer in hepatitis virus-infected patients. The study will present a unified approach for the early detection of liver cancer that is highly accurate. Early detection of liver cancer is critical in reducing mortality and assisting practitioners in treating patients on time based on results. Machine learning algorithms have been intensively researched and applied to the diagnosis of liver cancer, especially in recent years. Researchers employed a variety of machine learning techniques to create models for detecting liver cancer at different stages, including early diagnosis. To enhance the model's flexibility and efficiency, We used three datasets that contained all of the information required for diagnosing liver cancer. Several machine-learning approaches were used to improve the model's accuracy. Our proposed model acquired a 98.10% accuracy rate with Bagging. Using several datasets in machine learning for liver disease detection enhances model performance by diversifying the training examples, resulting in a more robust and generalizable model with lower overfitting risk. This study aims to improve the quality of life for people who are at risk of or have been diagnosed with liver cancer. © 2024 IEEE.

关键词： Viruses

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Optimal Resource Allocation in Fog Computing for Healthcare Applications

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computers, Materials & Continua 2022年第6期71卷 6147-6163页

作者： Salman Khan Ibrar Ali Shah Nasser Tairan Habib Shah Muhammad Faisal Nadeem Department of Computer Software Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaK.S.A Informatics Complex H-8Islamabad44000Pakistan

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and ***,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT ***,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time ***,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of *** paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the *** proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load *** consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average *** proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been *** experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

关键词： Fog computing internet of health things resource management

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Experience as Source for Anticipation and Planning: Experiential Policy Learning for Target-driven Recommendation Dialogues

Experience as Source for Anticipation and Planning: Experien...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Dao, Huy Deng, Yang Bui, Khanh-Huyen Le, Dung D. Liao, Lizi Singapore Management University Singapore FPT Software AI Center College of Engineering and Computer Science VinUniversity Viet Nam

ISBN: (纸本)9798891761681

Target-driven recommendation dialogues present unique challenges in dialogue management due to the necessity of anticipating user interactions for successful conversations. Current methods face significant limitations: (I) inadequate capabilities for conversation anticipation, (II) computational inefficiencies due to costly simulations, and (III) neglect of valuable past dialogue experiences. To address these limitations, we propose a new framework, Experiential Policy Learning (EPL), to enhance such dialogues. Specifically, EPL embodies the principle of Learning From Experience, facilitating anticipation with an experiential scoring function that estimates dialogue state potential using similar past interactions stored in long-term memory. Moreover, to demonstrate its flexibility, we introduce Tree-structured EPL (T-EPL) as one possible training-free realization with Large Language Models (LLMs) and Monte-Carlo Tree Search (MCTS). In particular, T-EPL assesses past dialogue states with LLMs while utilizing MCTS to achieve hierarchical and multi-level reasoning. Extensive experiments on two published datasets demonstrate the superiority and efficacy of T-EPL. Code and data are available here. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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An Approach for Early Detection of Schizophrenia 15

An Approach for Early Detection of Schizophrenia

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Paranagama, Sachin Uditha, Isiwara Daundasekara, Pamodya Hettiarachchi, Shehani Weerasinghe, Lokesha Kasthurirathna, Dharshana Sri Lanka Institue Of Information Technology Faculty Of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350370249

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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