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Construction of Innovation and Entrepreneurship MOOC Education Resource Library based on distributed Information Storage and Retrieval Intelligence

Construction of Innovation and Entrepreneurship MOOC Educati...

引用

2022 international conference on Edge Computing and applications, ICECAA 2022

作者： Feilong, Yu YiWu Industrial & Commercial College ZheJiang YiWu322000 China

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

An improved algorithm of the topic relevance model is designed, and the cosine value of the angle between the space vectors is used to measure the similarity between the block documents. By analyzing the development status of innovation and entrepreneurship education, this paper proposes innovative and entrepreneurial talents that integrate MOOCs Cultivating new ideas for curriculum reform, expounding from the aspects of curriculum system, integration mechanism, resource platform construction, and innovation and entrepreneurship mentor team building. In addition, the efficiency of distributed file storage and retrieval system based on MapReduce is much better than standalone processing and MPI parallel system. However, there is still a threshold problem in order to comprehensively improve the quality of innovative and entrepreneurial talent training. © 2022 IEEE.

关键词： MapReduce

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Large Language Models and Adversarial Reinforcement Learning to Automate PLCs Programming: A Preliminary Investigation 9

Large Language Models and Adversarial Reinforcement Learning...

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9th international conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Boudribila, Abderrahmane Chadi, Mohamed-Amine Tajer, Abdelouahed Boulghasoul, Zakaria Cadi Ayyad University National School of Applied Sciences Systems Engineering Applications Laboratory Marrakech Morocco Cadi Ayyad University Faculty of Sciences Semlalia Informatics Systems Engineering Laboratory Marrakech Morocco

ISBN: (纸本)9798350311402

The manual programming of Programmable Logic Controllers (PLCs) is a time-consuming and error-prone task, particularly for complex systems. Researchers have proposed various techniques, such as the modular, decentralized, hierarchical, and distributed approaches, which all fall under the Supervisory Control Theory (SCT) whose main goal is to generate a supervisor that ensures that the controller's behavior satisfies the specifications. While these techniques have demonstrated efficiency in systems of low complexity, high-complexity systems and probabilistic ones remain a major challenge. In this paper, we propose a novel pipeline that relies solely on Artificial Intelligence (AI)-techniques, namely, Reinforcement Learning (RL) and Natural Language processing (NLP) techniques, including Named Entity Recognition (NER) and Large Language Models (LLMs). As a preliminary result, we demonstrate the potential of the proposed pipeline using the recent ChatGPT model by making a sample dataset in this context and generating Structured Text code from specifications presented in a natural language format. Finally, we outline future directions toward a fully automated AI-based PLC programming tool. © 2023 IEEE.

关键词： Reinforcement learning

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Empowering Health Insurance with Personalization: A Comprehensive Architecture Leveraging Federated Learning and BlockChain

Empowering Health Insurance with Personalization: A Comprehe...

引用

international conference on Advances and applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Jain, Anant Gupta, Gauranshi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819795062

Historically, the health insurance domain has adhered to uniform premium pricing structures, often sidelining the distinct health nuances of each policyholder. Such broad-brush approaches can result in misaligned premiums, not truly reflective of individual health dynamics. Venturing into uncharted territories, this study proposes an innovative fusion of Federated Machine Learning (FML) and BlockChain methodologies to recalibrate health insurance pricing. FML, a cutting-edge evolution in the machine learning sphere, emphasizes decentralized data processing. By harnessing FML, timely insights can be gathered that mirror an individual’s health trajectory. This continuous insight generation facilitates agile adjustments to insurance premiums, ensuring they resonate with real-time health conditions. BlockChain’s decentralized ledger system acts as the bedrock of this model. Each premium modification, steered by FML-derived insights, is indelibly etched onto the BlockChain. This architecture not only masters transparency but also instills a heightened sense of trust, given the unalterable nature of BlockChain entries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Challenges of Data Consistency in High-Concurrency Environments: Algorithms and Implementation for the Electric Power Industrial Internet Platform

Challenges of Data Consistency in High-Concurrency Environme...

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Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： Zhongran Zhou Lili Wang Chunhe Song Yaowei Shen Mafeng Li Sai Liu NARI Information & Communication Technology Co.Ltd Nanjing China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information Technology Liaoning University Shenyang China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

With the rapid development of the Electric Power Industrial Internet, challenges in high-concurrency data processing have become increasingly prominent, particularly issues related to data consistency, which directly affect the stability and reliability of the system. This study addresses the distributed data consistency problems in the Electric Power Industrial Internet platform, proposing an optimized algorithmic framework designed specifically for high concurrency and dynamic environments to enhance the response speed and data processing capabilities of the power system. This paper first analyzes common data consistency challenges in high-concurrency environments within the power system, including data synchronization delays, complexity in fault recovery, and resource allocation issues. Based on these challenges, we introduce and optimize distributed data consistency algorithms, combined with load balancing and resource optimization techniques, significantly reducing communication costs and enhancing processing efficiency. Additionally, the paper designs a robust fault tolerance mechanism to ensure data integrity and consistency in the event of node failures. Through simulation experiments, we have confirmed the effectiveness of the proposed algorithms in terms of processing speed, system response time, and fault recovery, significantly optimizing the performance of the Electric Power Industrial Internet platform. This research not only enhances the robustness of the power system in facing high-concurrency data challenges but also provides important technical support and a theoretical basis for future research and applications in the Electric Power Industrial Internet.

关键词： Protocols Heuristic algorithms distributed databases Process control Power system stability Data processing Power systems

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Research on Chinese License Plate Automatic Recognition Based on Transfer Learning 24

Research on Chinese License Plate Automatic Recognition Base...

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9th international conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Cai, Xiaocheng Liao, Guomin Zhao, Haiyu School of Digital Engineering Chongqing College of Architecture and Technology China Chongqing College of International Business and Economics China

ISBN: (纸本)9798400718137

This paper introduces an optimized approach that harnesses the power of transfer learning, specifically the ResNet34 architecture, to transcend the limitations of existing self-developed model in recognizing Chinese license plates. By integrating advanced digital image processing (DIP) techniques with the robustness of the ResNet34 model, that not only refines the accuracy and efficiency of the automatic license plate recognition (ALPR) system but also underscores the pivotal role of DIP in enhancing feature extraction and image quality enhancement. This method leverages a suite of sophisticated, open-source DIP tools, namely the OpenCV framework and Python Imaging Library (PIL), complemented by the versatility of artificial neural networks (ANN). This synergy enables the concurrent generation of training, validation, and test datasets, thereby streamlining the model development pipeline. Innovating further, our deep learning training paradigm operates in parallel, managing data generation, processing, and training simultaneously. This approach not only refines the recognition capabilities of the ALPR system but also optimizes the utilization of computational resources. Fine-tuning the established parameters of the ResNet34 model alongside specific training adjustments has led to a remarkable improvement in accuracy, ranging from 3.12% to 12.50% across the seven characters of the license plate, outperforming our self-developed CNN baseline model. Despite an increase in the model’s parameter count from 9,023,463 to 22,919,879, the implementation efficiency has impressively increased by over 80%. The significance of this research lies in its potential to evolve traffic management, security monitoring, and parking systems by providing a more accurate, efficient, and robust solution for Chinese license plate recognition. The integration of DIP with deep learning models, as demonstrated in this study, sets a new resolution for future research and applications in the ALPR

关键词： Information management

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An Intelligent parallel distributed Streaming Framework for near Real-time Science Sensors and High-Resolution Medical Images 21

An Intelligent Parallel Distributed Streaming Framework for ...

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50th international conference on parallel processing (ICPP)

作者： Shivadekar, Samit Mangalagiri, Jayalakshmi Phuong Nguyen Chapman, David Halem, Milton Gite, Rahul Univ Maryland Baltimore Cty Baltimore MD 21228 USA OpenKneck Inc Halethorpe MD USA

ISBN: (纸本)9781450384414

Our goals are to address challenges such as latency, scalability, throughput and heterogeneous data sources of streaming analytics and deep learning pipelines in science sensors and medical imaging applications. We present a prototype Intelligent parallel distributed Streaming Framework (IPDSF) that is capable of distributed streaming processing as well as performing distributed deep training in batch mode. IPDSF is designed to run streaming Artificial Intelligent (AI) analytic tasks using data parallelism including partitions of multiple streams of short time sensing data and high-resolution 3D medical images, and fine grain tasks distribution. We will show the implementation of IPDSF for two real world applications, (i) an Air Quality Index based on near real time streaming of aerosol Lidar backscatter and (ii) data generation of Covid-19 Computing Tomography (CT) scans using deep learning. We evaluate the latency, throughput, scalability, and quantitative evaluation of training and prediction compared against a baseline single instance. As the results, IPDSF scales to process thousands of streaming science sensors in parallel for Air Quality Index application. IPDSF uses novel 3D conditional Generative Adversarial Network (cGAN) training using parallel distributed Graphic processing Units (GPU) nodes to generate realistic 3D high resolution Computed Tomography scans of Covid-19 patient lungs. We will show that IPDSF can reduce cGAN training time linearly with the number of GPUs.

关键词： distributed streaming Data parallelism Deep Learning Lidar backscatter Computed Tomography cGAN

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High performance deep learning libraries for biomedical applications

High performance deep learning libraries for biomedical appl...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Luca Barillaro Giuseppe Agapito Mario Cannataro Dep Surgical and Medical Sciences Magna Grœecia University Catanzaro Italy Dep Law Economics and Social Sciences Magna Græcia University Catanzaro Italy

Deep learning approaches are a topic of growing interest since they can achieve high precision in machine learning tasks and may be useful in several scenarios, while high performance computing (HPC) is one of the driving factors for deep learning applications since they require massive computational power. One of these scenarios is related to biomedical context since the massive growth of data generated by several medical procedures. Deep learning techniques, applied on these data may be useful both for medical procedures and for further knowledge discovery in specific field (in example gene interaction related to some diseases). Therefore the importance to have a deep learning library tailored for these task is evident. This paper aims to discuss about some libraries specifically designed to provide convenient high performance computing oriented deep learning support to biomedical applications. We describe two libraries developed inside a European project, named the Deep Health Project, to support both deep learning basic operations and computer vision tasks, oriented to a distributed computing fashion and with some special features for managing biomedical data. In addition we highlight some differences and comparisons with popular environments like Keras and Tensorflow by describing a simple use case.

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Enhancing Code Generation for Dataflow Programming: Fine-Tuning Large Language Models with the DFCPP Dataset

Enhancing Code Generation for Dataflow Programming: Fine-Tun...

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international Symposium on parallel and distributed processing with applications, ISPA

作者： Luo Qiuming Ma Xi Wei Xuan College of Computer Science and Software Engineering Shenzhen University Shenzhen China Tianjin Yuyi Kaihong Intelligent Technology Tianjin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In recent years, large language models (LLMs) based on the Transformer architecture have demonstrated excellent performance in code generation, but there have been fewer studies on data flow languages. This study proposes a scheme for fine-tuning large language models based on the DFCPP dataset. We demonstrate the model's ability to generate dataflow graph (DAG) topologies and achieve significant performance improvements. Experimental results show that the BLEU score of the fine-tuned model in the DFCPP code generation task reaches 0.193, which is an increase of 112.1% compared to the non-fine-tuned model (0.091). This demonstrates the effectiveness of fine-tuning techniques in domain-specific code generation.

关键词： distributed processing Codes Large language models Programming Transformers Topology Error correction codes Complexity theory

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High-Performance parallel Fault Simulation for Multi-Core Systems 29

High-Performance Parallel Fault Simulation for Multi-Core Sy...

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29th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Karami, Masoomeh Haghbayan, Mohammad-hashem Ebrahimi, Masoumeh Nejatollahi, Hamid Tenhunen, Hannu Plosila, Juha Univ Turku Dept Future Technol Turku Finland KTH Royal Inst Technol Dept Elect & Embedded Syst Kista Sweden Univ Calif Irvine Irvine CA USA

ISBN: (纸本)9781665414555

Fault simulation is a time-consuming process that requires customized methods and techniques to accelerate it. Multi-threading and Multi-core approaches are two promising techniques that can be exploited to accelerate the fault simulation process by using different parts of the hardware at the same time. However, an efficient parallelization is obtained only by the refinement of software with respect to the hardware platform. In this paper, a parallel multi-thread fault simulation technique is proposed to accelerate the simulation process on multi-core platforms. In this approach, the gate input values are independently assigned to each thread. Each input value carries the information of several parallel simulation processes. This provides a multithread parallel fault simulation environment. The experimental results show that the proposed technique can efficiently use the hardware platform. In a single-core platform. the proposed technique can reduce the time by 25% while in a dual-core increasing the thread approximately halves the execution time.

关键词： Multi-core system multi-threading parallelization fault simulation

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ECG Signals Analysis based on Compressed Sensing and Learning techniques for Heart Disease Recognition 7

ECG Signals Analysis based on Compressed Sensing and Learnin...

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7th international Multi-Topic ICT conference: IMTIC 2023

作者： Lal, Bharat Li, Qimeng Gravina, Raffaele Corsonello, Pasquale Dimes University of Calabria Rende Italy

ISBN: (纸本)9798350338461

Advancements in the acquisition and processing of physiological signals have facilitated deeper analysis aimed at disease identification and diagnosis. However, with the significant rise in data size and power consumption, it is necessary to use effective data compression, signal acquisition, transmission, and processing techniques, especially in tele-health and mobile-Health applications. A new area of research involves integrating Compressed Sensing (CS) and Compressed Learning (CL) with an electrocardiogram (ECG) signals to overcome such issues. This research paper investigates the use of CS and CL techniques on ECG signals from three distinct groups of individuals: those with normal sinus rhythms (NSR), congestive heart failure (CHF), and cardiac arrhythmia (ARR). The study employs three measurement matrices, Random Gaussian Matrix (RGM), Random Bernoulli Matrix (RBM), and Structured Fourier Matrix (SFM) with the Block Sparse Bayesian Learning reconstruction algorithm. The features extracted from the compressed signal and reconstructed signals using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms in both compressed sensing and compressed learning. The results show that the LSTM and SVM algorithms achieve high accuracy in both training and testing phases, with LSTM performing better in CL with SFM, while both SVM and LSTM perform better in SC with RGM and RBM. This study demonstrates the potential of CS and CL techniques in extracting relevant features from ECG signals, which could have implications for diagnosing and treating cardiac conditions. © 2023 IEEE.

关键词： Electrocardiograms

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