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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11317 条记录，以下是961-970 订阅

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Understanding the Intrinsic Characteristics of Spatial Partitioning in distributed Spatial Join

Understanding the Intrinsic Characteristics of Spatial Parti...

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IEEE international conference on Big Data

作者： Zhuohan Xu Dejun Teng Zhaohui Peng Fusheng Wang Shandong University China Stony Brook University USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Spatial join has become a frequently used yet resource-intensive operation in geospatial applications, driven by the increasing volume and complexity of geospatial data. With Hadoop and Spark becoming the de facto standard platforms for distributed computing, scalable spatial data processing is primarily achieved by partitioning the input space to form parallel units on these platforms. Effective spatial data partitioning is critical for task parallelization and load balancing, but it faces significant challenges due to data skew and the geometric and topological complexity of spatial objects, particularly in supporting spatial joins. This paper examines the interplay among query performance, spatial data partitioning, query types, data, and system characteristics. We qualitatively and quantitatively analyze the features of representative partitioning algorithms that impact overall query performance. Along with these analyses, we propose a data sampling-based approach for selecting optimized partitioning strategies. Extensive experiments on large and complex datasets using MapReduce frameworks are conducted to validate the correctness of our analysis and the effectiveness of our optimization approach.

关键词： Load management Spatial databases Partitioning algorithms Geospatial analysis Complexity theory Sparks distributed computing Standards Optimization Faces

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Curse of Feature Selection: a Comparison Experiment of DDoS Detection Using Classification techniques

Curse of Feature Selection: a Comparison Experiment of DDoS ...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Wenjia Wang Seyed Masoud Sadjadi Naphtali Rishe Knight Foundation School of Computing and Information Sciences Florida International University Miami USA

distributed denial-of-service (DDoS) attack is a malicious cybersecurity attack that has become a global threat. Machine learning (ML) as an advanced technology has been proven to be an effective way against DDoS attacks. Feature selection is a crucial step in ML, and researchers have put endless efforts to mitigate the “Curse of Dimensionality”. Feature selection is also causing problems to ML models, such as a decrease in prediction accuracy. Four supervised classification techniques, namely, Decision Tree (DT), k-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF), are tested using mutual information score ranking to study the necessity of feature selection in DDoS detection.

关键词： Training Radio frequency Measurement Predictive models Feature extraction Denial-of-service attack Decision trees

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Enhancing DETR Efficiency with Inter-Object Relationship and Semantic Spectral Decomposition-Based Distillation

Enhancing DETR Efficiency with Inter-Object Relationship and...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ziyu Huang College of Design and Engineering National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

DEtection TRansformer (DETR) is an innovative object detection framework based on transformer architecture that achieves end-to-end detection using a self-attention mechanism, moving away from traditional CNN-based approaches. However, the high computational cost of DETR models limits their deployment in practical applications. To address this issue, we propose a novel distillation framework specifically designed for DETR-based models, incorporating two key techniques: Inter-Object Relationship (IOR) distillation and Semantic Spectral Decomposition (SSD) distillation. The IOR distillation method captures and transfers complex dependencies between objects, enhancing the student model’s understanding of contextual relationships. In parallel, the SSD distillation method focuses on extracting and refining semantic information across spectral levels, improving the model’s focus on relevant features for more accurate detection. Extensive experiments on the MS COCO dataset demonstrate that our framework significantly enhances student model performance, achieving state-of-the-art results while effectively reducing the computational demands of DETR models without compromising accuracy.

关键词： Accuracy Computational modeling Semantics Refining Object detection Signal processing Transformers Feature extraction Computational efficiency Speech processing

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Reconfigurable HW-SW Co-design Platform for Lung Cancer Detection and Classification 34

Reconfigurable HW-SW Co-design Platform for Lung Cancer Dete...

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34th international conference on VLSI Design / 20th international conference on Embedded Systems (VLSID)

作者： Sharma, Ayushparth Lata, Kusum LNM Inst Informat Technol Jaipur Rajasthan India

ISBN: (纸本)9781665440875

High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Energy-efficient systems are required to enable the development of complex medical imaging applications. This article presents an energy-efficient hardware-software (HW-SW) co-design of a scalable and reconfigurable image segmentation/classification streaming-based processing platform explored at various design abstraction levels. Optimized algorithms and architectural techniques achieve significant savings in energy consumption and operational time. The proposed platform has been implemented on Xilinx Spartan-6 FPGA board and co-simulated with Xilinx system generator, enabled real-time processing of CT scans for pulmonary nodule detection. Optimized pipelining and scheduling have minimized the memory requirements to few kB. parallel architecture has been employed achieving 10x higher energy-efficiency compared to serial counterpart and reduced execution period by 70x. Clinical validation shows that parallel architecture introduces 5-7% error in nodule characteristic determination in comparison to serial one.

关键词： lung cancer detection delay structures medical image processing energy-efficient platform reconfigurability

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Classification of Multi-view mammogram images using a parallel pre-trained models system

Classification of Multi-view mammogram images using a parall...

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international conference on Image and Signal processing and their applications (ISPA)

作者： Abdelhafidh Kacher Medjeded Merati Saïd Mahmoudi Computer Science Department LIM Research Laboratory University of Tiaret Algeria ILIA Department Faculty of Engineering University of Mons Belgium

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Breast cancer is the main lead of women’s cancer-related mortalities. Therefore, early detection is imperative for preventing breast cancer from developing to advanced stages. Moreover, the emergence of Computer-aided diagnosis systems combined with Deep Learning techniques improve breast cancer diagnosis at its early stages. This study proposes a system for classifying mammogram images scanned from different views based on Deep learning techniques. In this paper, we propose combining four pre-trained models in one system employing the Transfer Learning technique with different input images of the same lesion. This proposed system provides a binary classification of the lesion into malignant or benign classes. The Mini-DDSM dataset images were used in this study; the images were pre-processed and augmented to achieve the best results. Several well-known pre-trained models for image classification (ResNet, Xception, DenseNet, MobileNet, and Inception) were experimented with for the proposed model construction. The study results showed that our proposed model achieved an accuracy of 94.18%, a precision of 94.26%, a recall of 94.16%, and an F1-Score of 94.21%, outperforming the pre-trained model on classifying mammogram images of the same dataset.

关键词： Deep learning Computational modeling Transfer learning Signal processing Lead Mammography Breast cancer

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Resource-Limited Localized Adaptive Adversarial Training for Machine Learning Model

Resource-Limited Localized Adaptive Adversarial Training for...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Mohammed Rajhi Niki Pissinou School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. This study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. This adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats.

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Adaptive Progressive Image Enhancement for Edge-Assisted Mobile Vision 18

Adaptive Progressive Image Enhancement for Edge-Assisted Mob...

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18th IEEE international conference on Mobility, Sensing and Networking (MSN)

作者： Feng, Daipeng Zeng, Liekang Pu, Lingjun Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China

ISBN: (纸本)9781665464574

Recent advances in deep learning models have pushed Super-Resolution (SR) techniques to an unprecedented altitude, enabling high-quality image rendering with variable scaling size and natural fidelity. To deploy them on resource-constrained mobile devices, however, confronts significant challenges of excessively long latency and poor user experience. To this end, we propose Apie, an edge-assisted adaptive image rendering system that allows low-latency, progressive image enhancement for a smooth user experience. Apie adopts a data parallel strategy across the end device and the edge server, along with a residual learning mechanism to judiciously retrieve information for SR models. Besides, a novel progressive image reconstruction is developed by exploiting content-aware image blocking and incremental image rendering, towards improved quality of user experience. Furthermore, Apie can dynamically adjust the choice of employed SR models with respect to the networking conditions, striking a good balance upon the latency-quality trade-off. Extensive evaluations show that Apie performs 7.33x faster than on-device GPU execution and 1.42x faster compared to the partial offloading method, while achieves 2.84dB higher PSNR compared to the interpolation method using conventional JPEG image compression and 0.74dB higher PSNR compared to the partial offloading method.

关键词： Edge intelligence image enhancement parallel processing user experience

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Optimizing Random Forest Algorithms for LargeScale Data Analysis

Optimizing Random Forest Algorithms for LargeScale Data Anal...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Shuchi Bhadula Myasar Mundher Adnan Rakesh Kumar Ajay Rana Gopal Kaliyaperumal Bolleddu Devananda Rao Nandini Shirish Boob Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Computer Engineering & Applications GLA University Mathura Amity University Greater Noida Uttar Pradesh India Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The exponential growth of data in recent years has necessitated the development of more efficient and scalable machine learning algorithms to handle large-scale data analysis. Random Forest (RF) algorithms, known for their robustness and accuracy, find extensive use in the realms of classification and regression. However, their performance can be significantly hindered when dealing with massive datasets due to increased computational complexity and resource demands. This paper presents a comprehensive approach to optimizing Random Forest algorithms for large-scale data analysis. We explore various strategies, including data partitioning, algorithmic changes and parallel processing to make RF algorithms more efficient and scalable. Additionally, we introduce novel techniques for feature selection and hyperparameter tuning to improve model performance. Extensive experiments conducted on multiple large-scale datasets demonstrate the effectiveness of the proposed optimizations in reducing computation time and memory usage while maintaining or even enhancing predictive accuracy. From genomics to finance, the optimised Random Forest method shows promise as a robust tool for large-scale data processing in many applications, according to the findings.

关键词： Radio frequency Data analysis Accuracy parallel processing Prediction algorithms Partitioning algorithms Classification algorithms Random forests Tuning Optimization

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CBP: Coordinated management of cache partitioning, bandwidth partitioning and prefetch throttling 30

CBP: Coordinated management of cache partitioning, bandwidth...

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30th international conference on parallel Architectures and Compilation techniques (PACT)

作者： Holtryd, Nadja Ramhoj Manivannan, Madhavan Stenstrom, Per Pericas, Miquel Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden

ISBN: (纸本)9781665442787

Reducing the average memory access time is crucial for improving the performance of applications running on multi-core architectures. With workload consolidation this becomes increasingly challenging due to shared resource contention. techniques for partitioning of shared resources - cache and bandwidth - and prefetch throttling have been proposed to mitigate contention and reduce the average memory access time. However, existing proposals only employ a single or a subset of these techniques and are therefore not able to exploit the full potential of coordinated management of cache, bandwidth and prefetching. Our characterization results show that application performance, in several cases, is sensitive to prefetching, cache and bandwidth allocation altogether. Furthermore, the results show that managing these together provides higher performance potential during workload consolidation as it enables more resource trade-offs. In this paper, we propose CBP a coordination mechanism for dynamically managing prefetching throttling, cache and bandwidth partitioning, in order to reduce average memory access time and improve performance. CBP works by employing individual resource managers to determine the appropriate setting for each resource and a coordinating mechanism in order to enable inter-resource trade-offs. Our evaluation on a 16-core CMP shows that CBP, on average, improves performance by 11% compared to the state-of-the-art technique that manages cache partitioning and prefetching and by 50% compared to the baseline without cache partitioning, bandwidth partitioning and prefetch throttling.

关键词： Multicore processing Prefetching Bandwidth Channel allocation Resource management Proposals

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Applying Fuzzy Logic to Efficiently Manage Shared Edge Infrastructure

Applying Fuzzy Logic to Efficiently Manage Shared Edge Infra...

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2023 IEEE international conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2023

作者： Sylla, Tidiane Chalouf, Mohamed Aymen Mendiboure, Leo Krief, Francine Aniss, Hasnaa Alouache, Lylia Univ. of Sciences Techniques and Technologies of Bamako Dept. ISA-GEII Bamako Mali University of Rennes 1 IRISA Lab LannionF-22300 France Univ. Gustave Eiffel COSYS-ERENA Marne-la-ValléeF-77454 France Bordeaux INP LaBRI Lab BordeauxF-33000 France CY Cergy Paris University ETIS UMR 8051 CNRS lab CergyF-95000 France

ISBN: (纸本)9798350350432

Edge Computing promise a bright future for Internet of Things (IoT). Edge Computing servers can be geographically distributed to be closer to users and ensure low latency communications and real-time data processing for the third-party applications using this infrastructure. However, user mobility and limited Edge servers capabilities (CPU, memory, bandwidth) may cause many services placement failures. That's why in this article we propose a new strategy aimed at enabling a fair sharing of available Edge resources through a dynamic and real-time IoT Service placement. Our strategy uses Fuzzy Logic to enable 1) quick and low-cost deployment of the solution and 2) real-time modification of the policies defined by the Edge operator. It takes advantage, among others, of a microservice decomposition to optimize the use of the Edge architecture. Experiments demonstrate the benefits of our approach in terms of placement reliability, execution time and resources utilization. © 2023 IEEE.

关键词： Fuzzy logic

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