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共 2976 条记录，以下是221-230 订阅

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Containerized AI Framework on Secure Shared Multi-GPU Systems 7

Containerized AI Framework on Secure Shared Multi-GPU System...

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7th international conference on parallel, Distributed and Grid Computing, PDGC 2022

作者： Kumar, Mandeep Kaur, Gagandeep Chandigarh University University Institute of Computing Mohali India Locuz Enterprise Solutions Ltd Delhi India

ISBN: (纸本)9781665454018

Nowadays, technology has reached the point where Graphics processing Unit (GPU) drive Artificial Intelligence (AI) workloads with large datasets to speed up the process of solving more complex real-world problems. the DevOps movement has brought container-based deployment models into the world of software development and application deployment. Data scientists can quickly prototype, build, and deploy a containerized application or service, allowing them to manage AI workloads more effectively with portability. Docker is a widely used containerization platform, requiring root privileges for execution, which has associated security concerns and may not be readily adopted in secure shared multi-GPU systems. Singularity addresses the needs of container-based virtualization in a secure environment, which allows the same Docker containers to be used without any changes or security concerns. this work presents the challenges and approaches for quickly deploying complex AI workloads on multi-GPU systems without compromising security by ensuring that AI frameworks run in an isolated container environment with user credentials. In addition, a comprehensive performance evaluation of containers converted from Docker to Singularity in the context of native is performed on a multi-GPU system to solve more complex real-world problems with AI workflow containerization. © 2022 IEEE.

关键词： Graphics processing unit

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Shisha: Online Scheduling of CNN Pipelines on Heterogeneous architectures 14th

Shisha: Online Scheduling of CNN Pipelines on Heterogeneou...

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14th international conference on parallel processing and Applied Mathematics, PPAM 2022

作者： Soomro, Pirah Noor Abduljabbar, Mustafa Castrillon, Jeronimo Pericàs, Miquel Department Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Ohio State University Columbus United States Chair for Compiler Construction Technische Universität Dresden Dresden Germany

ISBN: (纸本)9783031304415

Many modern multicore processors integrate asymmetric core clusters. With the trend towards Multi-Chip-Modules (MCMs) and interposer-based packaging technologies, platforms will feature heterogeneity at the level of cores, memory subsystem and the interconnect. Due to their potential high memory throughput and energy efficient core modules, these platforms are prominent targets for emerging machine learning applications, such as Convolutional Neural Networks (CNNs). To exploit and adapt to the diversity of modern heterogeneous chips, CNNs need to be quickly optimized in terms of scheduling and workload distribution among computing resources. To address this we propose Shisha, an online approach to generate and schedule parallel CNN pipelines on heterogeneous MCM-based architectures. Shisha targets heterogeneity in compute performance and memory bandwidth and tunes the pipeline schedule through a fast online exploration technique. We compare Shisha with Simulated Annealing, Hill Climbing and Pipe-Search. On average, the convergence time is improved by ∼ 35 × in Shisha compared to other exploration algorithms. Despite the quick exploration, Shisha’s solution is often better than that of other heuristic exploration algorithms. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network architecture

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BachLedger: Orchestrating parallel Execution with Dynamic Dependency Detection and Seamless Scheduling

BachLedger: Orchestrating Parallel Execution with Dynamic De...

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international conference on parallel and Distributed Systems (ICPADS)

作者： Yi Yang Guangyong Shang Guangpeng Qi Zhen Ma Yaxiong Liu Jiazhou Tian Aocheng Duan Meng Zhang Jingying Li Xuan Ding School of Software Tsinghua University Beijing China Inspur Yunzhou Industrial Internet Co. Ltd Jinan Shandong China School of Cyber Engineering Xidian University Xi’an Shaanxi China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Blockchain technology inherently necessitates redundant computation to achieve consensus among untrusted parties because of its fundamental threat model. this requirement, however, compromises system performance and impedes the widespread adoption of blockchain. To leverage existing physical resources, current research on high-performance consortium blockchain algorithms and architectures frequently employs cluster-node architectures to expand the parallel processing capability of traditional single physical nodes. Our investigation reveals a significant trend as the parallel capability of individual nodes improves. the idle time caused by synchronization of all transactions within each block, previously considered negligible, has become increasingly significant. To address this, we present BachLedger, which implements Seamless Scheduling to fully utilize inter-block thread idle time, thereby augmenting system resource utilization and achieving overall performance improvements. Our experimental results demonstrate that our algorithm surpasses current state-of-the-art (SOTA) performance levels in high-performance consortium blockchains and effectively resolves the aforementioned synchronization issue. Furthermore, this scheduling algorithm offers enhanced scalability for BachLedger, positioning it as a promising solution for future blockchain implementations.

关键词： threat modeling Scheduling algorithms System performance Scalability Computer architecture parallel processing Dynamic scheduling Blockchains Synchronization Resource management

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Learning-based Task Offloading in Dynamic Orbital Edge Computing Network 23

Learning-based Task Offloading in Dynamic Orbital Edge Compu...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Fang, Jiang Liu, Haitao Sun, Jiyan Li, Ting Liu, Yinlong Guo, Zhaorui Zhuang, Shangyuan Geng, Liru Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Computer Network Information Center Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665494571

In existing satellite communication model (SCM), satellites receive remote commands from ground station, then downlink raw sensed data to ground station directly. To tackle the challenges of high delay, intermittent downlink and congested ground station, edge computing for SCM is proposed, in which the flying satellites can locally process raw data. In SCM, the computing resources are time-variant and difficult to be obtained accurately due to rapid mobility of satellites. Multi-armed bandit (MAB) is a well-known scheme that has great potential to achieve nearly optimal task offloading for edge computing in mobility cases. However, existing MAB based works are difficult to apply in SCM. First, the distance difference between each satellite may be very large and the existing works have ignored the distance factor in exploration. this may cause huge errors for exploration cost evaluation and inevitable offloading failures. Second, the exploration and exploitation may have several order of magnitude difference while the existing work only use a constant to adjust the trade-off between the exploration and exploitation. As a result, it may take a long learning process to balance the trade-off, causing low offloading efficiency. To address the above challenges, in this work, we propose DMUCB as a Distance-aware and Mobility-aware task offloading algorithm based on MAB theory. the offloading decisions are made without any prior knowledge of computing resources. First, DMUCB provide a novel exploration method by jointly considering the distance, task size and the accumulative selected times to achieve an accurate exploration cost. Second, the difference of exploration and the new exploitation is largely reduced by our well-designed normalization mechanism for fast converge to the optimal offloading. third, we present a time-variant parameter to effectively adapt the dynamic satellite communication environment. Simulation results show that DMUCB achieves close-to-optimal tas

关键词： Satellites Costs Smart cities Simulation parallel processing Downlink Orbits

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Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector 7th

Beqi: Revitalize the Senegalese Wolof Language with a Rob...

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7th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Mbaye, Derguene Diallo, Moussa Baamtu Dakar Senegal Université Cheikh Anta Diop Dakar Senegal

ISBN: (纸本)9783031864926

the progress of Natural Language processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role in many NLP applications. this is particularly the case for automatic spell checkers. Several approaches have been studied to address this task and the one modeling spelling correction as a translation task from misspelled (noisy) text to well-spelled (correct) text shows promising results. However, this approach requires a parallel corpus of noisy data on the one hand and correct data on the other hand, whereas Wolof is a low-resource language and does not have such a corpus. In this paper, we present a way to address the constraint related to the lack of data by generating synthetic data and we present sequence-to-sequence models using Deep Learning for spelling correction in Wolof. We evaluated these models in three different scenarios depending on the subwording method applied to the data and showed that the latter had a significant impact on the performance of the models, which opens the way for future research in Wolof spelling correction. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Natural language processing systems

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U-ASK: A Unified Architecture for kNN Spatial-Keyword Queries Supporting Negative Keyword Predicates 22

U-ASK: A Unified Architecture for kNN Spatial-Keyword Querie...

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30th ACM SIGSPATIAL international conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Liu, Yongyi Magdy, Amr Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781450395298

Spatial keyword queries have been popular in the research community for over a decade due to the explosive growth in user-generated data and its prime applications in different domains. kNN queries make a major category of spatial keyword queries that is heavily studied. However, the expressiveness of existing kNN queries is limited in supporting negative keyword predicates, e.g., find tweets with keywords "Chipotle" but NOT "Chipotle sauce", which have prime applications. In addition, existing architectures suffer from a lack of generality for different types of kNN queries. this paper proposes U-ASK;a Unified Architecture for Spatial-Keyword query supporting negative keyword predicates. U-ASK includes an indexing framework named TEQ (Textual-Enhanced Quadtree) and a query processor POWER (parallel bOttom-up search With incrEmental pRuning) that handle various forms of kNN spatial keyword queries with negative keyword predicates. the experimental evaluation on real tweet datasets demonstrates up to 30x faster runtime compared to the state-of-the-art algorithms.

关键词： Spatial-keyword Query processing Spatial-Textual Indexing

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Augmented-High Resolution Network for Image Segmentation

Augmented-High Resolution Network for Image Segmentation

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Aditya Mandsorwale Joseph Zacharias Dept. of Electronics and Communication Engg. College of Engineering Trivandrum Kerala Affiliated to APJ Abdul Kalam Technological University Trivandrum Kerala India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Convolutional Neural Network (CNN) based architectures have shown superior performance in computer vision. But in semantic segmentation, CNN based models suffer from loss of contextual information due to reduction in resolution. High Resolution Network (HRNet) addresses this issue by preserving parallel processing layers at high resolution, but it also suffers from loss of information due to low and high resolution fusion. In this work, a unique approach to do semantic segmentation of images using Augmented HRNet (Au-HRNet) is presented. To prevent information loss during fusion of low resolution with high we have added channel and spatial attention blocks. these blocks are added at each stage and within the fusion module. the augmented HRNet output is then passed to contextual representation segmentation header. By enriching pixel through weighted aggregation using object contextual representation, pixel’s label matching to the object category is ensured. through this approach, we have achieved improved results on widely used datasets like PASCAL VOC+AUG and PASCAL CONTEXT.

关键词： Semantic segmentation Computational modeling Pose estimation Computer architecture Object detection parallel processing Convolutional neural networks

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A Deep Multimodal Voice Pathology Classifier with Electroglottographic Signal processing Capabilities 7

A Deep Multimodal Voice Pathology Classifier with Electroglo...

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7th international conference on Frontiers of Signal processing, ICFSP 2022

作者： Miliaresi, Ioanna Pikrakis, Aggelos Poutos, Kyriakos University of Piraeus Department of Informatics Greece

ISBN: (数字)9781665481588

ISBN: (纸本)9781665481588

In this paper, we present a study on the integration of multimodal data into a modular deep learning architecture, for the task of automatic classification of voice pathology, with an emphasis on the role of electroglottographic signals as a new modality. the proposed architecture fuses audio descriptors, categorical medical data and features derived from electroglottographic signals into a modular deep learning architecture. More specifically, a cascade of convolutional layers processes a sequence of short-term audio feature vectors consisting of Mel-Frequency Cepstral Coefficients, their derivatives and Mel filterbank outputs. Furthermore, medical records and mid-term audio features are processed by a standard feed-forward branch and, finally, in a parallel, convolutional branch, 'wavegrams' of the glottal chords are processed as a new modality to increase system accuracy. Our study focuses on the sustained vowel /a/ in a subset of the Saarbrucken voice database. the resulting problem is class-imbalanced and the gathered dataset includes healthy subjects and three types of pathology types, namely hyperfunctional dysphonia, laryngitis and recurrent laryngeal nerve paralysis. the experimental results indicate that the integration of various modalities and in particular the inclusion of electroglottographic signals, increases the system's classification capabilities, yielding a state-of-the-art classification accuracy of 89.3%. © 2022 IEEE.

关键词： Pathology

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Deep Learning Based Novel Cascaded Approach for Skin Lesion Analysis 7th

Deep Learning Based Novel Cascaded Approach for Skin Lesion ...

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7th international conference on Computer Vision and Image processing, CVIP 2022

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Baid, Ujjwal Talbar, Sanjay Center of Excellence Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin can72;cer diagnosis and ensures effective treatment. the computer-aided diag72;... 详细信息

ISBN: (纸本)9783031314063

Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin can¬cer diagnosis and ensures effective treatment. the computer-aided diag¬nosis of such skin cancer in dermoscopic images can significantly reduce the clinicians’ workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detec¬tion and accurate identification of skin lesions remain challenging. this research focuses on a two-step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effective¬ness of deep convolutional neural network (CNN) based architectures by designing an encoder-decoder architecture for skin lesion segmenta¬tion and CNN based classification network. the proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union(mIoU) and Dice Similarity Coefficient. Our cascaded end-to-end deep learning-based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation. the code is available at https://***/shubhaminnani/skin/lesion. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image enhancement

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Transfer Learning Models for MRI-Based Brain Tumor Detection

Transfer Learning Models for MRI-Based Brain Tumor Detection

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international conference on Advanced Technologies for Signal and Image processing (ATSIP)

作者： Moriba Bérété Amira Echtioui Lamia Sellami Ahmed Ben Hamida Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’ National Engineering School of Sfax (ENIS) Sfax University Sfax Tunisia Department IS College of Computer Science King Khaled University ‘KKU’ Abha Kingdom of Saudi Arabia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Human health, especially brain health, is paramount for overall well-being. Magnetic Resonance Imaging (MRI) serves as a vital diagnostic tool for assessing critical organs such as the brain. the abundance of data generated by MRI scans presents a significant opportunity for artificial intelligence (AI) to aid in health decision-making processes. In this paper, we focus on using AI techniques, particularly transfer learning, to classify brain tumors from MRI images. By leveraging transfer learning, we aim to enhance the performance of image processing algorithms in the classification of brain tumors. Our approach involves employing established architectures such as Xception, ResNet50, and DenseNet201, adapted through transfer learning for this specific task. Among the architectures tested, our results highlight the superiority of the ResNet50 model in binary classification of brain tumors using MRI scans. the proposed model achieves remarkable performance metrics, including an accuracy of $99 \%$, precision of $99.5 \%$, recall of $99.5 \%$, and F1 Score of $99 \%$. this research underscores the potential of transfer learning in advancing diagnostic capabilities for brain health assessment.

关键词： Measurement Accuracy Magnetic resonance imaging Image processing Transfer learning Brain modeling Artificial intelligence

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