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5th International conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Veerla, Harsha Chindukuri, Vinay Mohammed, Yaseen Junaid Palakonda, Vinay Chintala, Radhika Rani Koneru Lakshmaiah Education Foundation Computer Science and Engineering A.P Vaddeswaram India

ISBN: (纸本)9798350321425

High Performance Computing (HPC) is generally a powerful computing system and software which is used to solve complex computational problems that the today's computing methods fail to solve. HPC systems use parallel processing methods, distributed computing, and specialized hardware like FPGA (Field programmable gate arrays) and GPU (Graphics processing unit) to get high level of performance. Developing of HPC systems has become rapidly evolving field, to bring out the new technologies of hardware and software for optimization techniques and managing to get better performance. HPC systems are used in wide range of machine learning, scientific and engineering applications including weather forecasting etc. Users can also use HPC to run complex modelling problems such as nuclear physics, AI (Artificial Intelligence), climate modelling etc. It is applied to solve transaction processing, data ware houses like business uses. It will perform high number of operations in no time and completes the operations under a tight deadline. In this computing method user can solve many computational things parallelly. It is also known as Supercomputing which performs operations at highest rate on computers which can perform more than a quadrillion floating-point calculations per second. In this article the present and emerging application domains of HPC and the role of it in shaping the future of networking are addressed. © 2023 IEEE.

关键词： Graphics processing unit

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A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation 29

A Drug Box Recognition System Based on Deep Learning and Clo...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zhu, Honglei Dong, Anming Yu, Jiguo School of Computer Science and Technology Jinan China Big Data Institute Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor's prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it's hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement. © 2023 IEEE.

关键词： Cloud-Edge Collaboration image Recognition Pharmacy Automation Text Recognition

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image denoising based on Swin Transformer Residual Conv U-Net

Image denoising based on Swin Transformer Residual Conv U-Ne...

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ACIS International conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Yong Gan Shaohui Zhou Haonan Chen Yuefeng Wang Zhengzhou University of Technology Zhengzhou China College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural Networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at $\sigma=15,33.87$ and 0.915 at $\sigma=25$, and 28.96 and 0.810 at $\sigma=50$.

关键词： Training Visualization AWGN Computational modeling Noise reduction Transformers Data augmentation

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Quantifiability: Correctness of Concurrent Programs in Vector Space 29

Quantifiability: Correctness of Concurrent Programs in Vecto...

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

作者： Cook, Victor Peterson, Christina Painter, Zachary Dechev, Damian Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781665414555

Architectural imperatives due to the slowing it Moore's Law, the broad acceptance of relaxed semantics and the O(nl) worst case verification complexity of generating sequential histories motivate a new approach to concurrent correctness. Desiderata for a new correctness condition are that it he independent of sequential histories, compositional, flexible as to timing, modular as to semantics and free of inherent locking or waiting. We propose Quantifiability, a novel correctness condition that models a system in vector space to launch a new mathematical analysis of concurrency. The vector space model is suitable for a wide range of concurrent systems and their associated data structures. This paper formally defines quantifiability and demonstrates that quantifiability is compositional and non-blocking. Analysis is facilitated with linear algebra, better supported and of much more efficient time complexity than traditional combinatorial methods.

关键词： concurrent correctness multicore performance formal methods

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Detecting Adversarial Samples for Deep Learning Models: A Comparative Study

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第1期9卷 231-244页

作者： Zhang, Shigeng Chen, Shuxin Liu, Xuan Hua, Chengyao Wang, Weiping Chen, Kai Zhang, Jian Wang, Jianxin Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Chinese Acad Sci State Key Lab Informat Secur Inst Informat Engn Beijing 100093 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Sci & Technol Parallel & Distributed Proc Laborat Changsha 410073 Peoples R China Chinese Acad Sci Inst Informat Engn SKLOIS Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secu Rity Beijing Peoples R China

Deep learning techniques such as convolutional neural networks (CNNs) have been used in a wide range of fields due to their superior performance, e.g., image classification, autonomous driving and natural language processing. However, recent progress shows that deep learning models are vulnerable to adversarial samples, which are crafted by adding small perturbations on normal samples that are imperceptible to human beings but can mislead the deep learning models to output incorrect results. Many adversarial attack models are proposed and many adversarial detection methods are developed to detect adversarial samples generated by these attack models. However, the evaluations of these detection methods are fragmented and scatter in separate literature, and the community still lacks a comprehensive understanding of the ability and performance of existing adversarial detection methods when facing different attack models on different datasets. In this paper, by using image classification as the example application scenario, we conduct a comprehensive study on the performance of five mainstream adversarial detection methods against five major attack models on four widely used benchmark datasets. We find that the detection accuracy of different methods interleaves for different attack models and dataset. Moreover, besides detection accuracy, we also evaluate the time efficiency of different detection methods. The findings reported in this paper can provide useful insights when designing systems to detect adversarial samples and act as a guideline to design new methods to detect adversarial samples.

关键词： Training Neural networks Deep learning Detectors Robustness Predictive models Safety Adversarial detection efficiency adversarial samples detection deep learning attacks image classification

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A Comprehensive Scheme for Transaction and Fund Tracing in distributed Anonymous Transactions 24th

A Comprehensive Scheme for Transaction and Fund Tracing in...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Liu, Hai Li, Fangqiong Peng, Hongye Key Laboratory of Blockchain and Fintech of Department of Education of Guizhou Province Guizhou University of Finance and Economics Guiyang China

ISBN: (纸本)9789819615247

Anonymous transaction technologies are crucial for safeguarding the privacy of users’ transactions, but they also pose challenges to traditional regulatory methods. However, existing supervision schemes for anonymous transactions often suffer from poor performance due to the use of complex cryptographic techniques. This paper proposes a transaction and fund tracing scheme for distributed anonymous transactions. To achieve traceability, the proposed scheme provides two models of supervision: transaction tracing to de-anonymize specific transactions and fund tracing to trace the flow of funds from illicit users. We have ingeniously designed a mechanism to distribute the critical information necessary for regulation among three parties, reducing the risk of misconduct by the supervisor and the platforms. Theoretical analysis shows that even in the face of external attacks and internal malicious activities, the proposed scheme can maintain the anonymity and traceability of transactions. Additionally, the performance of the proposal is evaluated through extensive experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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distributed and parallel Sparse Computing for Very Large Graph Neural Networks

Distributed and Parallel Sparse Computing for Very Large Gra...

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2022 IEEE International conference on Big Data, Big Data 2022

作者： Petit, Quentin R. Li, Chong Emad, Nahid Huawei Paris Research Center Université Paris-Saclay Boulogne-Billancourt France Huawei Paris Research Center Distributed and Parallel Software Lab Boulogne-Billancourt France Université Versailles Saint-Quentin Maison de la Simulation LI-PaRAD Saclay France

ISBN: (纸本)9781665480451

Deep learning (DL) requires high-performance processing on big data. Graph Neural Networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph's sparse data structures is one of the key *** first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore. © 2022 IEEE.

关键词： Matrix algebra

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Developing A parallel Program For image Similarity Search Using Hashing methods

Developing A Parallel Program For Image Similarity Search Us...

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2022 International conference on Data Analytics for Business and Industry, ICDABI 2022

作者： Aldossary, Yasmeen Ebrahim, Majeed Alqaddoumi, Abdallah University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology College of Science Bahrain

ISBN: (纸本)9781665490580

Nowadays, images play an essential role in people's lives as they document their memories and affairs. Therefore, a large number of images have been generated in different storage. As a result, there was a need to search for similar images in this colossal quantity. While enormous data processing requires speed in execution, it needs the parallel processing method. This study aims to develop a program to search for similar images using the image hashing methods, the Average Hashing, and the Difference Hashing, then to compare their performance. In addition, it aims to explore the effect of using parallelization on the speedup and efficiency of the program. The first part's results stated that the performance of the Average Hashing and the Difference Hashing methods was affected by the type of data used, despite their effectiveness in detecting identical images. As for the second part, the parallel program reduced the execution time by almost half using two workers, while the best speedup value was when using eight workers. In terms of efficiency, it achieved the highest results with a value greater than 0.9 when using two workers. © 2022 IEEE.

关键词： Efficiency

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A parallelizable Counterfactual Generation Method Based on Gradient Optimization 29

A Parallelizable Counterfactual Generation Method Based on G...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Ding, Haorun Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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A distributed and parallel Tensor Hierarchical Tucker Decomposition on Clouds 9

A Distributed and Parallel Tensor Hierarchical Tucker Decomp...

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9th International conference on Advanced Cloud and Big Data, CBD 2021

作者： Ning, Weikai Wu, Zebin Sun, Jin Yang, Jiandong Zhang, Yi Zhu, Yaoqin Wei, Zhihui Xia, Lei Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China China Satellite Maritime Tracking and Control Department Jiangyin China China Railway Shanghai Group Co. Ltd Shanghai China

ISBN: (纸本)9781665407458

Hierarchical Tucker decomposition is widely used in image processing, data mining and many other fields. With the increase of data volumes, the hierarchical Tucker decomposition requires much more amounts of memories and computing capacities, beyond the computing resources of a single computer. Accordingly, we propose a distributed and parallel tensor hierarchical Tucker decomposition on a cloud computing platform. Both coarse-grained parallelism and fine-grained parallelism are exploited in the hierarchical Tucker decomposition by splitting the computation tasks on the dimension tree nodes. Each individual node in the dimension tree is independent on the tree topology and can thus be executed in parallel. Experimental results indicate that the proposed method is effective in processing high-dimensional tensor data with significant speedups and high robustness. © 2022 IEEE.

关键词： Tensors

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