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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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Keynote Talk: Leveraging the Edge-Cloud Continuum to Manage the Impact of Wildfires on Air Quality

Keynote Talk: Leveraging the Edge-Cloud Continuum to Manage ...

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2023 IEEE international conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Balouek-Thomert, Daniel Perez, Ismael Faulstich, Sam D. Holmes, Heather A Altintas, Ilkay Parashar, Manish Imt Atlantique Nantes Université École Centrale Nantes Cnrs Inria LS2N Umr 6004 NantesF-44000 France University of Utah Salt Lake CityUT United States University of California La Jolla San DiegoCA United States

ISBN: (纸本)9781665453813

The emergence of large-scale cyberinfrastructure composed of heterogeneous computing capabilities and diverse sensors and other data sources are enabling new classes of dynamic data-driven 'urgent' applications. However, as the variety of data sources, and the volume and velocity of data grow, processing this data while considering the uncertainty of infrastructure and timeliness constraints of urgent application workflows can be nontrivial and presents a new set of challenges. In this paper, we use an application workflow that monitors and manage the air quality impacts of remote wildfires to illustrate how the R-Pulsar programming system, leveraging the SAGE and WIFIRE platforms, can enable urgent analytics across the computing continuum. R-Pulsar supports urgent data-processing pipelines that tradeoff the content of data, cost of computation, and urgency of the results to support such workflows. We also discuss research challenges associated with programming urgent application workflows and managing resources in an autonomic manner. © 2023 IEEE.

关键词： data handling

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data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task 23rd

Data Augmentation Method Based on Partial Noise Diffusion St...

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23rd international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Chen, Weiwen Zhang, Yong Ke, Wenlong Huzhou Univ Sch Informat Engn Huzhou Peoples R China Liaoning Normal Univ Sch Comp & Informat Technol Dalian Peoples R China

ISBN: (纸本)9789819708109;9789819708116

One-class defect detection has proven to be an effective technique. However, the performance of complex models is often limited by existing data augmentation methods. To address this issue, this paper proposes a novel data augmentation method based on a denoising diffusion probability model. This approach generates high-quality image samples using partial noise diffusion, eliminating the need for extensive training on large-scale datasets. Experimental results demonstrate that the proposed method outperforms current methods in one-class defect detection tasks. The proposed method offers a new perspective on data augmentation and demonstrates its potential to tackle challenging computer vision problems.

关键词： Defect detection Denoising diffusion probability model data augmentation Deep learning Image generation

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Accelerating Stream processing Queries with Congestion-aware Scheduling and Real-time Linux Threads 23

Accelerating Stream Processing Queries with Congestion-aware...

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20th ACM international conference on computing Frontiers (CF)

作者： Frasca, Fausto Gulisano, Vincenzo Mencagli, Gabriele Palyvos-Giannas, Dimitris Torquati, Massimo Univ Pisa Pisa Italy Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9798400701405

Stream processing Engines (SPEs) have been used by companies and industries to develop queries able to extract insights from data streams. The Edge/IoT context poses additional challenges, since streaming queries need to run closer to data producers to save latency, i.e., on resource-constrained devices. Lachesis is a middleware helping Linux to schedule more efficiently threads of the SPE, which revealed useful especially for devices with limited CPU resources. Lachesis does not require any architectural change to the SPE implementation. It collects metrics from the SPE, and computes high-level priorities that are converted into hints to the Operating System to affect its actual scheduling of threads. This paper extends the initial contribution of Lachesis in two main directions: i) we optimize the policy assigning to threads a priority proportional to their actual load by accurately studying the implementation of STORM and FLINK, two popular SPEs;ii) instead of restricting the OS scheduling to traditional SCHED_OTHER threads as done previously by Lachesis, we leverage the real-time capability of the modern Linux kernel. Our experimental evaluation shows that both enhancements provide important benefits compared with the previous version of LACHESIS: we get +9.75% (average) throughput (+19% peak) with -27% latency on average (-40% peak).

关键词： data Stream processing Apache Storm Apache Flink Real-time Threads Linux Scheduler

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Tabular data Extraction From Documents 11th

Tabular Data Extraction From Documents

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11th international conference on Recent Trends in computing, ICRTC 2022

作者： Madake, Jyoti Pandey, Sameeran Vishwakarma Institute of Technology Maharashtra Pune India

ISBN: (纸本)9789811988240

data is everywhere, and data is the new oil, meaning it is omnipresent and valuable. Unfortunately, much of this data is not digitized and that calls for a technology capable of translating a hardcopy into usable softcopy. Text extraction or "textraction" is the key. Using image processing and advanced machine learning, a technique to perform text extraction comes into the picture which is optical character recognition. But, the OCR’s scope is limited to just extraction of data. Making sense of that data by reorganizing it is a different story altogether. This project proposes a couple of methods to extract data from tables and then re-tabulate it in the database using the two approaches. The first method straightaway utilizes the processing power of Google’s vision API and later processes the data obtained from its OCR while the second method focuses on image processing algorithms to extract the data from the columns of the tables and use it to repopulate the digital database. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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Utilizing Machine Learning to Develop an Improved Real-Time data Analysis System 3

Utilizing Machine Learning to Develop an Improved Real-Time ...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Khandari, Harsimrat Sahoo, Girija Shankar Raja Praveen, K.N. Shree, Ritu Umamaheswari, B. Yashwant, V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records © 2023 IEEE.

关键词： Statistics

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Authentication Protocol for Secure data Access in Fog computing-Based Internet of Things 11th

Authentication Protocol for Secure Data Access in Fog Comput...

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11th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2023

作者： Surendran, Priyanka Thomas, Bindhya John, Densy Prasanth, Anupama Winston, Joy Jilani, AbdulKhadar College of Computer Studies University of Technology Bahrain Salmabad Bahrain

ISBN: (纸本)9789819967056

The Internet of things (IoT) is mainly an infrastructure in millions of computers and sensors that produce massive amounts of information. computing paradigms are required near the edge devices due to latency, bandwidth, and storage constraints. Fog computing (FC) is a more sophisticated form of cloud computing, and it gives the end-users access to some of its features. FC offers end-users with processing, storage, and network resources as part of the IoT. One of the primary issues with FC is authentication and safe data access between fog servers and fog nodes, as well as fog nodes and IoT users. This research presents a technique for safe information access and authentication between fog and IoT end-users. Initial encryption is performed using SHA1 and AES algorithms. The homomorphic approach was utilized to provide additional data security in the first phase. Homomorphic encryption is a cryptographic approach that permits computations on encrypted data, avoiding the required to reveal the original message to intermediaries (servers) that are merely providing a service and not a data consumer. In this paper, an effective mutual authentication technique between cloud and fog is proposed. The performance of the proposed system is evaluated in terms of execution time. The technique is suitable for FC in an IoT setting, according to the performance and security studies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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Forecasting the Rate of Crimes Committed Against Women Using Machine Learning Approach and data Mining Techniques 7

Forecasting the Rate of Crimes Committed Against Women Using...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kumar, Sarthak Parhad, Om Pathak, Dhruv Irlewad, Shivani Parira, Payel Pawar, Rajendra Mit World Peace University Department of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350304268

Prediction of the probability of crime in any region is a serious problem, especially in developing countries. Several factors can influence the crime rate at any location. There have been no effective solutions or dedicated research studies on the increasing crime cases, especially among women. Thus, we have trained several machine learning algorithms and a neural network to come up with the best probability prediction model for different crime types. The data used was gathered from 50 districts in Madhya Pradesh, India. The trained models were evaluated based on their accuracy and f1-score in predicting the probability of different types of crimes. © 2023 IEEE.

关键词： Crime Prediction Demographic Features Neural Network Random Forest

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An Object-Based Extremely Weak Supervised Learning Paradigm: Focusing on the Introduction of Algorithm

An Object-Based Extremely Weak Supervised Learning Paradigm:...

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2023 international conference on Machine Vision, Image processing and Imaging Technology, MVIPIT 2023

作者： Zhou, Min Luo, Xiaoyan Zhao, Bo Tang, Panpan Jiaxing Statistical Investigation Center Jiaxing Bureau of Statistics Jiaxing China Nanhu Laboratory Research Center of Big Data Technology Jiaxing China

ISBN: (纸本)9798350306545

Although deep learning-based algorithms have achieved satisfactory classification performance, they require a large amount of annotated training data. To overcome this issue, we proposed an extremely-weak supervised learning algorithm for remote sensing (RS) classification tasks, which only requires several carefully chosen ground-feature sample labels as input. To be specific, our method consists of three subnetworks working in tandem and under the object-based framework. The first one produces pseudo labels by using two unsupervised autoencoders with shared weights, the second one attempts to optimize the pseudo labels through noisy learning and post-processing, and the third one involves running inference with a powerful residual network for labeling the unseen data. To the extent of our information, we are the first to introduce in the RS classification task the autoencoder and noisy learning modules, which minimized the data acquisition and human labeling efforts. Due to the space limitations, this article focuses only on the algorithms and theory of computation. Readers are referred to the companion piece for detailed case studies. © 2023 IEEE.

关键词： Remote sensing

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A design to realize power loss data protection based on Non-volatile DIMM in domestic platform

A design to realize power loss data protection based on Non-...

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international conference on Internet of Things and Machine Learning, IoTML 2023

作者： Yang, Jiadong Guo, Qingqing Ma, Xiaofeng Wang, Qian The 52nd Research Institute of China Electronics Technology Corporation Zhejiang Hangzhou China

ISBN: (纸本)9781510671805

With the development of information technology, storage-computing devices are widely used in the information field. Most of storage-computing devices usually have to maintain their storage and computing performance by introducing data cache technology, such compromise put the storage-computing devices in the dilemma of being challenged by the risk of losing their cache data when the power loses accidentally. In order to prevent the cache data loss caused by the accidental power loss of the system, this paper designs a method to realize power loss data protection based on Non-volatile DIMM in domestic platform, Specifically, install the Non-volatile DIMM memory in the storage-computing devices, the BMC is used to trigger the curing preservation operation of the memory in the case of power loss .When the system is suddenly power off, under the endurance of the super-capacitor, the processing unit in non-volatile memory migrates the data in DRAM to NAND Flash for curing storage, this ensures that data will not lost even in the event of a sudden power loss,achieving the purpose of memory data non-volatile, ensuring the data security of the device. © 2023 SPIE.

关键词： Dynamic random access storage

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Analysis Techniques for Pharmaceutical Drugs Biomedicine Big data Analytics and Machine Learning 7

Analysis Techniques for Pharmaceutical Drugs Biomedicine Big...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Padmaavathy, P.A. Celina, A. Devi, R. Prameela Arathi, B.N. Sureshkumar, G. Ramachandran, G. Faculty of Arts Science Commerce and Management Department of Management Karpagam Academy of Higher Education Coimbatore India College of Management SRMIST Kattankulathur India CVR College of Engineering Department of ECE Telangana Hyderabad India PES University Dept of Computer Science and Engineering Karnataka Bengaluru India ECE Vinayaka Mission's Kirupananda Variyar Engineering College Salem India

ISBN: (纸本)9798350397253

The phases of drug discovery and development, starting with drug design and moving through crucial clinical trials to clinical practice, are being revolutionized by machine learning techniques. A more sophisticated kind of learning is machine learning (ML), which may be used to identify patterns in the solutions to solve current problems and learn to solve unsolved problems in future. Following the pre-processing of the necessary data, the objective is to learn from the data and identify patterns. The results can then be obtained by applying the patterns found to a different dataset. In general, when using larger, more agile data samples on high-dimensional datasets, ML algorithms can make predictions that are more accurate. Along with the rapid development of bioinformatics, there has been a significant global evolution in drug discovery and design technologies over the past ten years. Various artificial intelligence and machine learning (ML) methodologies have been used in drug discovery and design, leading to changes in all of their stages, including the time-consuming drug-target interaction (DTI) prediction and medicines and compounds discrimination. © 2023 IEEE.

关键词： Big data

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