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Cloud-Driven AI: Enhancing Machine Learning Performance Through distributed Computing 4

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

引用

4th IEEE International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dixit, Kunal Himani Bhardwaj, Parul Sachdeva, Ritu Manav Rachna International Institute of Research & Studies School of Computer Applications Faridabad India

ISBN: (纸本)9798331518981

The exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. This research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application's efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. This framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5-12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. These results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today's AI applications while opening the door to new advancements in ML throughout different domains. © 2024 IEEE.

关键词： distributed cloud

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Correction of Secret images Reconstructed from Noised Shared images 11

Correction of Secret Images Reconstructed from Noised Shared...

引用

11th International conference on image processing Theory, Tools and Applications (IPTA)

作者： Bertojo, Laura Puech, William Univ Montpellier LIRMM Montpellier France

ISBN: (纸本)9781665469647

Protecting sensitive images is nowadays a key issue in information security domain. As such, numerous techniques have emerged to securely transmit or store such multimedia data, as encryption, steganography or secret sharing. Most of today's secret image sharing methods relie on the polynomialbased scheme proposed by Shamir. However, some of the shared images distributed to the participants may be noised between their creation and their use to retrieve the secret image. Noise can be added to a shared image during transmission, storage or JPEG compression for example. However, to our knowledge, to date no analysis has been made on the impact of using a noised shared image in the reconstruction process of a secret image. In this paper, we propose a method to correct the errors during the reconstruction of a secret image using noised shared images.

关键词： Poynomial secret image sharing Noised shared image Error correction

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Tensor-Train-based Lightweight Encryption in Federated Cloud Environments 29

Tensor-Train-based Lightweight Encryption in Federated Cloud...

引用

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

作者： Ma, Jiangtao Zhang, Yunfan Liu, Huazhong Tan, Peng Ding, Jihong Pan, Qiao Hainan University School of Computer Science and Technology Haikou China Hainan Credit Investigation Company Limited Haikou China Donghua University School of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350330717

In the era of big data, data security poses a paramount challenge in dealing with large-scale data in cloud computing environments. Traditional encryption methods such as RSA and Paillier face the problems that, as the data volume increases, the encryption consumes too much computation resource and the storage size of ciphertext is too large. To tackle these problems, this paper explores secure and lightweight tensor train (TT) based encryption methods for large-scale data in federated cloud environments. First, we develop a novel encryption method called Orthogonalized TT Encryption (OTT-EPT), which can encrypt large-scale plaintext data into a list of small-size ciphertext (TT cores) and store them in different clouds. Second, to tackle the unique characteristics of TT decomposition that adjacent TT cores have the same rank, which could be exploited by attackers to threaten encrypted data, we further propose a Noise-Added Orthogonalized TT Encryption (NOTT-EPT) method. NOTT-EPT can change arbitrary TT-rank by adding noise TT-cores on specific order. Furthermore, we present a TT-based encryption framework and a suite of secure homomorphic operation protocols for large-scale data computation in federated cloud environments. The experimental results indicate that, compared to state-of-the-art methods, our proposed methods not only ensure the integrity and security of encrypted data but also reduce both time and space consumption by about 1000 times. © 2023 IEEE.

关键词： big data encrypted data processing federated cloud homomorphic encryption privacy preserving secure computing protocols tensor-train decomposition

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parallel Computing Optimization of Tourism Big Data based on Crawler Programs 1

Parallel Computing Optimization of Tourism Big Data based on...

引用

1st International conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Zhou, Xiaoyu Wuhan Donghu University School of Management Hubei Wuhan China

ISBN: (纸本)9798331529246

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy. © 2025 IEEE.

关键词： Cloud platforms

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Traffic processing and Fingerprint Generation for Smart Home Device Event 28

Traffic Processing and Fingerprint Generation for Smart Home...

引用

IEEE 28th International conference on parallel and distributed Systems (IEEE ICPADS)

作者： Yao, Yunhao Hou, Jiahui Zhang, Sijia Xu, Zhengyuan Li, Xiang-Yang USTC Sch Comp Sci & Technol Hefei Peoples R China USTC Sch Informat Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781665473156

Recent studies show that smart home devices are vulnerable to passive network observers, referred to as adversaries. An adversary can use mobile devices as a sniffer to infer device events (e.g., a door is opened/closed) even through encrypted WiFi traffic. However, to obtain high event identification accuracy, existing works heavily rely on either machine learning (ML) or large traffic feature sizes, which results in a costly retraining or pairing process. In this paper, we generate an event traffic fingerprint and propose an event identification method with good extensibility. To address the interference of network fluctuations and unrelated traffic, we filter all redundant and event-unrelated packets and extract event packet sequences based on time interval. After processing, we generate a representative packet-level fingerprint for each event and identify the event based on fingerprint matching. The precision and recall of event identification reach 96.77% and 92.31% on average. Our traffic processing method can work as an add-on to existing ML-based event inference methods, which leads to at least 12% increase on the accuracy.

关键词： smart home traffic processing event fingerprint

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A parallel Attention Mechanism for image Manipulation Detection and Localization 48

A Parallel Attention Mechanism for Image Manipulation Detect...

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zeng, Qiang Wang, Hongxia Zhou, Yang Zhang, Rui Meng, Sijiang Sichuan University School of Cyber Science and Engineering Chengdu China

ISBN: (纸本)9781728163277

Existing image manipulation detection and localization methods tend to detect the trail of manipulation and achieve decent performance. So far, however, there has been little discussion about the category imbalance. In this paper, we propose a parallel attention mechanism based network to localize tampered regions, which is inclined to have better generalization, while it possesses higher model capacity. In addition, we designed a trainable parameter constrained shifted-window dual attention module to strengthen the manipulated features. Finally, to settle the difficulty of category imbalance, we conduct a category-based normalization loss function, which allows the model to pay more attention to the manipulated regions and further improves the generalization capability. Extensive experimental results demonstrate that the proposed approach can effectively reconcile the weights of different categories during training and produce state-of-the-art performance in various benchmark datasets. © 2023 IEEE.

关键词： Convolution

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Melanlysis: A mobile deep learning approach for early detection of skin cancer 28

Melanlysis: A mobile deep learning approach for early detect...

引用

IEEE 28th International conference on parallel and distributed Systems (IEEE ICPADS)

作者： Arani, Samen Anjum Zhang, Yu Rahman, Md Tanvir Yang, Hui Northwestern Polytech Univ Ningbo Inst Key Lab Flexible Elect Zhejiang Provience Ningbo Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Sch Life Sci Xian Peoples R China

ISBN: (纸本)9781665473156

Early detection of melanocytes can save lives from melanoma. Most individuals can't be professionally diagnosed since it's time-consuming, costly, and inconvenient. Smartphone-based early skin cancer diagnosis has emerged as a new approach. The existing computer-aided skin cancer diagnosis methods and mobile deep learning technology have been studied, and it is found that the existing smartphone-based skin cancer detection and identification methods rely on the support of background cloud services. Accuracy, reaction time, and patient data confidentiality are issues. A novel early detection and recognition model of melanoma skin cancer based on mobile deep learning, Melanlysis, is proposed. The model uses the EfficientNetLite0 deep learning model to have low latency and considers the imbalance of the existing open-source skin image dataset. The proposed classification model is implemented and evaluated. Experimental results show that compared with the existing EfficientNetLite-0, MobileNet V2, and ResNet-50 models, the accuracy of correctly identifying malignant or non-melanoma is over 94%. At the same time, an Android application based on this mobile deep learning model was developed to diagnose potential malignant melanoma. Users can quickly obtain the classification results of melanoma through the application.

关键词： Melanlysis Melanoma Dermoscopic image Mobile deep learning TensorFlow Lite Smart Health

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AN EXPERIMENTAL STUDY ON TRANSFERRING DATA-DRIVEN image COMPRESSIVE SENSING TO BIOELECTRIC SIGNALS 47

AN EXPERIMENTAL STUDY ON TRANSFERRING DATA-DRIVEN IMAGE COMP...

引用

47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhang, Zhikang Zhao, Jonathan Ren, Fengbo Arizona State Univ Parallel Syst & Comp Lab Sch Comp & Augmented Intelligence Tempe AZ 85287 USA

ISBN: (纸本)9781665405409

The emerging area of bioelectric signal compressive sensing(CS) has shown great potential in health care applications. However, improving the reconstruction accuracy of compressively sensed bioelectric signals remains a challenging problem. In recent years, data-driven image CS methods have achieved significant improvements in reconstruction accuracy over conventional model-based image CS methods. In this paper, we conduct an experimental study on transferring existing data-driven image CS methods to bioelectric signals. Through our investigation of five critical factors affecting the reconstruction performance of bioelectric signals, we conclude that existing data-driven image CS methods can be transferred to ECG signals with high reconstruction accuracy. Our experimental results show that transferred data-driven image CS methods can achieve up to 8.08-2.73 SNR improvement over the reference method on ECG signal reconstruction across compression ratios of 2-8x.

关键词： compressive sensing bioelectric signal deep learning

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Reachability Labeling for distributed Graphs 38

Reachability Labeling for Distributed Graphs

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38th IEEE International conference on Data Engineering (ICDE)

作者： Zhang, Junhua Li, Wentao Qin, Lu Zhang, Ying Wen, Dong Cui, Lizhen Lin, Xuemin Univ Technol Sydney FEIT AAII Ultimo Australia Univ New South Wales Sydney NSW Australia Shandong Univ Jinan Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Real-world graphs are typically distributed across multiple data centers. When performing reachability queries on these distributed graphs, reachability labeling methods ensure fast query processing by using lightweight indexes. One of the best-known labeling methods is TOL;however, TOL is a serial algorithm and cannot handle distributed graphs. The main goal of this paper is to design new labeling methods that can work in parallel while producing the same index as TOL. To this end, we investigate the limitation of TOL and thus propose a filtering-and-refinement framework for index creation. This framework first obtains a super-set of each vertex's label sets and then eliminates the invalid elements. Based on this framework, we design distributed labeling algorithms and then use batch processing to improve efficiency. Experimental results on real-world graphs show that the proposed algorithms can index distributed graphs efficiently.

关键词： Data centers Heuristic algorithms Query processing Design methodology conferences Filtering algorithms Data engineering

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distributed Simulation System Design for Power Communication Networks 24

Distributed Simulation System Design for Power Communication...

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

作者： Zhao, Yanheng Zhang, Liting Zhang, Han Huang, Kuilin Liu, Guangpeng Liu, Lu State Grid of China Technology College China Beijing Kedong Electric Power Control System Co. Ltd China

ISBN: (纸本)9798400718137

In modern society, power communication networks play a crucial role, and their stable operation is essential for the safety and efficiency of power grids. In this paper, a distributed simulation system for power communication networks is proposed, aiming to improve the accuracy and efficiency of simulation. The system is based on Service Oriented Architecture (SOA) and adopts a modular design, which realizes parallel computation of the simulation engine, real-time processing of data management and protocol optimization of the communication interface. The experimental results show that compared with the traditional methods, this system has significant advantages in terms of communication delay, packet loss rate and system stability. The contribution of this paper is to provide a new theory and method for the simulation study of power communication network, which is of great significance to improve the stability and reliability of the network. © 2024 Copyright held by the owner/author(s).

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