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arXiv 2020年

作者： Li, Boyun Gou, Yuanbiao Gu, Shuhang Liu, Jerry Zitao Zhou, Joey Tianyi Peng, Xi College of Computer Science Sichuan University Chengdu610065 China School of Electrical and Information Engineering University of Sydney TAL AI Lab Beijing China Institute of High Performance Computing ASTAR Singapore

In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and a image collection (untrained). An unsupervised neural network will avoid the intensive labor collection of hazy-clean image pairs, and an untrained model is a "real" single image dehazing approach which could remove haze based on only the observed hazy image itself and no extra images is used. Motivated by the layer disentanglement idea, we propose a novel method, called you only look yourself (YOLY) which could be one of the first unsupervised and untrained neural networks for image dehazing. In brief, YOLY employs three jointly subnetworks to separate the observed hazy image into several latent layers, i.e., scene radiance layer, transmission map layer, and atmospheric light layer. After that, these three layers are further composed to the hazy image in a self-supervised manner. Thanks to the unsupervised and untrained characteristics of YOLY, our method bypasses the conventional training paradigm of deep models on hazy-clean pairs or a large scale dataset, thus avoids the labor-intensive data collection and the domain shift issue. Besides, our method also provides an effective learning-based haze transfer solution thanks to its layer disentanglement mechanism. Extensive experiments show the promising performance of our method in image dehazing compared with 14 methods on four databases. Copyright © 2020, The Authors. All rights reserved.

关键词： Demulsification

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Virtual Memory versus File Interface for Large, Memory-Intensive Scientific Applications

Virtual Memory versus File Interface for Large, Memory-Inten...

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Supercomputing Conference

作者： Yoonho Park R. Scott S. Sechrest High performance computing center University of Houston Houston TX USA Software Systems Research Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

Scientific applications often require some strategy for temporary data storage to do the largest possible simulations. The use of virtual memory for temporary data storage has received criticism because of performance problems. However, modern virtual memory found in recent operating systems such as Cenju-3/DE give application writers control over virtual memory policies. We demonstrate that custom virtual memory policies can dramatically reduce virtual memory overhead and allow applications to run out-of-core efficiently. We also demonstrate that the main advantage of virtual memory, namely programming simplicity, is not lost.

关键词： Memory Operating systems Control systems

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DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree

DisClose: Discovering colossal closed itemsets via a memory ...

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PAKDD 2012 International Workshops: 3rd Data Mining for Healthcare Management, DMHM 2012, Multi-View Data, high-Dimensionality, External Knowledge: Striving for a Unified Approach to Clustering, 3Clust 2012, GeoDoc 2012 and 2nd DSDM 2012

作者： Zulkurnain, Nurul F. Haglin, David J. Keane, John A. Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia P.O. Box 10 Kuala Lumpur 50728 Malaysia High Performance Computing Pacific Northwest National Laboratory MSIN J4-30 P.O. Box 999 Richland WA 99352 United States School of Computer Science University of Manchester Oxford Road Manchester M13 9PL United Kingdom

ISBN: (纸本)9783642367779

A recent focus in itemset mining has been the discovery of frequent itemsets from high-dimensional datasets. With exponentially increasing running time as average row length increases, mining such datasets renders most conventional algorithms impractical. Unfortunately, large cardinality itemsets are likely to be more informative than small cardinality itemsets in this type of dataset. This paper proposes an approach, termed DisClose, to extract large cardinality (colossal) closed itemsets from high-dimensional datasets. The approach relies on a Compact Row-Tree data structure to represent itemsets during the search process. Large cardinality itemsets are enumerated first followed by smaller ones. In addition, we utilize a minimum cardinality threshold to further reduce the search space. Experimental results show that DisClose can achieve extraction of colossal closed itemsets in the discovered datasets, even for low support thresholds. The algorithm immediately discovers closed itemsets without needing to check if each new closed itemset has previously been found. © 2013 Springer-Verlag.

关键词： Trees (mathematics)

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Parallel Erasure Coding: Exploring Task Parallelism in Erasure Coding for Enhanced Bandwidth and Energy Efficiency

Parallel Erasure Coding: Exploring Task Parallelism in Erasu...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Hsing-bung Chen Song Fu High Performance Computing-Design Group Los Alamos National Laboratory Los Alamos New Mexico USA Department of Computer Science and Engineering University of North Texas Denton Texas USA

ISBN: (纸本)9781509033164

Very large data sets within the range of megabytes to terabytes generated daily from checkpoint-and- restart processes are seen in today's scientific simulations. Reliability and durability are two important factors to build an archive storage system. Erasure code based object storage systems are becoming popular choices for archive storage systems due to cost-effective storage space saving schemes and higher fault-resilience capabilities. Both erasure code encoding and decoding procedures involve heavy array, matrix, and table-lookup compute intensive operations. Current solutions of the erasure coding process are based on single process approach which is not capable of processing very large data sets efficient and effectively. In this paper, we address the bottleneck problem of single process erasure encoding by leveraging task parallelism offered by multi-core computers. We add parallel processing capability to the erasure coding process. More specifically, we develop a parallel erasure coding software, called parEC. It explores the MPI run time parallel I/O environment and integrates data placement process for distributing encoded data blocks to destination storage devices. We evaluate the performance of parEC in terms of both encoding throughput and energy efficiency. We also compare the performance of two task scheduling algorithms for parEC. Our experimental results show parEC can significantly reduce the encoding time (i.e., by 74.06%-96.86%) and energy consumption (i.e., by 73.57%-96.86%), and Demand-based Workload Assignment (DBWA) algorithm can a high system utilization (i.e., 95.23%).

关键词： Encoding Multicore processing Parallel processing Servers Testing Software systems

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A Peer-to-Peer Based Framework of InterLibrary Cooperation for Digital Libraries

A Peer-to-Peer Based Framework of InterLibrary Cooperation f...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Wen-Chung Shih Chao-Tung Yang Shian-Syong Tseng Chun-Jen Chen National Chi Nan University Nantou Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Department of Information Science and Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan

Traditionally, interlibrary cooperation is an important interaction between libraries. Nevertheless, the emerging digital library architecture has not explicitly supported this requirement. In this paper, we proposed an interlibrary cooperation framework for digital libraries using P2P technology. An application of this framework to Faculty Publication Sharing System was presented. Besides, a reputation model based on data mining is utilized to provide libraries with incentives to join this framework

关键词： Peer to peer computing Software libraries computer science Books computer architecture Data mining Client-server systems Chaos laboratories Knowledge engineering

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates United Arab Emirates University Emirates Center for Mobility Research United Arab Emirates National Yang Ming Chiao Tung University College of AI and Green Energy Taiwan University at Albany - State University of New York NY United States National Yang Ming Chiao Tung University Department of Computer Science Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. © 2023 IEEE.

关键词： Object detection

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A novel PUF based on cell error rate distribution of STT-RAM

A novel PUF based on cell error rate distribution of STT-RAM

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Asia and South Pacific Design Automation Conference

作者： Xian Zhang Guangyu Sun Yaojun Zhang Yiran Chen Hai Li Wujie Wen Jia Di Center for Energy-Efficient Computing and Applications Peking University Beijing China Collaborative Innovation Center of High Performance Computing NUDT Changsha China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Florida International University Miami FL USA Computer Science and Computer Engineering Department University of Arkansas Fayetteville AR USA

ISBN: (纸本)9781467395700

Physical Unclonable Functions (PUFs) have been widely proposed as security primitives to provide device identification and authentication. Recently, PUFs based on Non-volatile Memory (NVM) are widely proposed since the promise of NVMs' wide application. In addition, NVM-based PUFs are considered to be more immune to invasive attack and simulation attack than CMOS-based PUFs. However, the existing NVM-based PUF either shows the unreliability under environmental variations or need extra modifications to the IC manufacturing process. In this work, we propose err-PUF, a novel PUF design based on the cell error rate distribution of STT-RAM. Instead of using the distribution directly, we generate a stable fingerprint based on a novel concept called Error-rate Differential Pair (EDP) without modifications to the read/write circuits. Comprehensive results demonstrate that err-PUF can achieve sufficient reliability under environmental variations, which can significantly impact the cell error rates. Moreover, compared with existing approaches, err-PUF has a higher speed and lower power consumption with negligible overhead.

关键词： Nonvolatile memory Bit error rate Robustness

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A novel data distortion approach via selective SSVD for privacy protection

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International Journal of Information and computer Security 2008年第1期2卷 48-70页

作者： Wang, Jie Zhang, Jun Xu, Shuting Zhong, Weijun Laboratory for High Performance Scientific Computing and Computer Simulation Department of Computer Science University of Kentucky Lexington KY 40506-0046 United States Department of Computer Information Systems Virginia State University Petersburg VA 23806 United States Department of Management Sciences and Engineering Southeast University Nanjing 210096 China

Data privacy preservation has become one of the major concerns in the design of practical data-mining applications. In this paper, a novel data distortion approach based on structural partition and Sparsified Singular Value Decomposition (SSVD) technique is proposed. Three schemes are designed to balance privacy protection in centralised datasets and mining accuracy. Some metrics are used to evaluate the performance of the proposed new strategies. Data utility of the three proposed schemes is examined by a binary classification based on the support vector machine. Furthermore, we examine three sparsification strategies. The effect of method parameters on data distortion level and utility is also studied experimentally. Our experimental results on synthetic and real datasets indicate that, in comparison with standard data distortion techniques, the proposed schemes are efficient in balancing data distortion level and data utility. They afford a feasible solution with a good promise for mining accuracy and a significant reduction in the computational cost from SVD. Copyright © 2008 Inderscience Enterprises Ltd.

关键词： Data privacy

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Cluster computing and Grid 2005 works in progress

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IEEE Distributed Systems Online 2005年第9期6卷

作者： Wang, Frank Helian, Na Wu, Sining Deng, Yuhui Zhou, Ke Guo, Yike Thompson, Steve Johnson, Ian Milward, Dave Maddock, Robert Khoo, Benjamin Bharadwaj, Veeravalli Cambridge-Cranfield High Performance Computing Facility London Metropolitan University Cranfield University Imperial College Xyratex Department of Grid Computing and E-science Centre for Grid Computing Data Mining at the London Metropolitan University Centre for Grid Computing Cranfield University London E-science Centre Parallel Computing Centre Imperial College National University of Singapore Department of Electrical and Computer Engineering National University of Singapore

A grid-oriented storage (GOS) project, which aims to support advanced data bank services and data reservoirs for sharing data on the gird, is discussed. The GOS appliance is a disk array that connects directly to the grid using a Globus XIO interface and fits in the thin server categorization. The security model of the GOS is based on the Grid security Infrastructure (GSI). A dynamic estimation scheme for fault-free scheduling in grid systems is also discussed. Most of the grid falls into one of the two categories, planned and negotiated, and dynamic and voluntary. This discrepancy is the main reason behind the complexity in designing robust and fault tolerant scheduling algorithms for both resource categories.

关键词： Data storage equipment

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Search-in-Memory (SiM): Reliable, Versatile, and Efficient Data Matching in SSD's NAND Flash Memory Chip for Data Indexing Acceleration

arXiv

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arXiv 2024年

作者： Chen, Yun-Chih Chang, Yuan-Hao Kuo, Tei-Wei The Department of Computer Science TU Dortmund Germany The Institute of Information Science Academia Sinica Taipei115 Taiwan Department of Computer Science and Information Engineering High Performance and Scientific Computing Center Center of Data Intelligence: Technologies Applications and Systems of National Taiwan University Taiwan

To index the increasing volume of data, modern data indexes are typically stored on SSDs and cached in DRAM. However, searching such an index has resulted in significant I/O traffic due to limited access locality and inefficient cache utilization. At the heart of index searching is the operation of filtering through vast data spans to isolate a small, relevant subset, which involves basic equality tests rather than the complex arithmetic provided by modern CPUs. We introduce the Search-in-Memory (SiM) chip, which demonstrates the feasibility of performing data filtering directly within a NAND flash memory chip, transmitting only relevant search results rather than complete pages. Instead of adding complex circuits, we propose repurposing existing circuitry for efficient and accurate bitwise parallel matching. We demonstrate how different data structures can use our flexible SIMD command interface to offload index searches. This strategy not only frees up the CPU for more computationally demanding tasks, but it also optimizes DRAM usage for write buffering, significantly lowering energy consumption associated with I/O transmission between the CPU and DRAM. Extensive testing across a wide range of workloads reveals up to a 9X speedup in write-heavy workloads and up to 45% energy savings due to reduced read and write I/O. Furthermore, we achieve significant reductions in median and tail read latencies of up to 89% and 85% respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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