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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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Quantum neural networks with data re-uploading for urban traffic time series forecasting

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Scientific reports 2025年第1期15卷 19400页

作者： Nikolaos Schetakis Paolo Bonfini Negin Alisoltani Konstantinos Blazakis Symeon I Tsintzos Alexis Askitopoulos Davit Aghamalyan Panagiotis Fafoutellis Eleni I Vlahogianni Computational Mechanics and Optimization Laboratory School of Production Engineering and Management Technical University of Crete Chania 73100 Greece. nischetakis@tuc.gr. Quantum Innovation Pc Chania 73100 Greece. nischetakis@tuc.gr. Alma-Sistemi Srl Guidonia 00012 Italy. Univ Gustave Eiffel COSYS GRETTIA Paris F-77454 France. Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion 71410 Greece. QUBITECH Pc Athens 15231 Greece. Institute of High Performance Computing A*STAR (Agency for Science Technology and Research) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore. Department of Transportation Planning and Engineering School of Civil Engineering National Technical University of Athens 5 Iroon Polytechniou Str Zografou Campus 157 73 Athens Greece.

Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), it has emerged a new paradigm possessing the potential to enhance predictive capabilities beyond what classical machine learning models can achieve. In the present work we pursue a heuristic approach to explore the potential of QML, and focus on a specific transport issue. In particular, as a case study we investigate a traffic forecast task for a major urban area in Athens (Greece), for which we possess high-resolution data. In this endeavor we explore the application of Quantum Neural Networks (QNN), and, notably, we present the first application of quantum data re-uploading in the context of transport forecasting. This technique allows quantum models to better capture complex patterns, such as traffic dynamics, by repeatedly encoding classical data into a quantum state. Aside from providing a prediction model, we spend considerable effort in comparing the performance of our hybrid quantum-classical neural networks with classical deep learning approaches. We observe that, in fully connected network settings, hybrid quantum-classical models consistently underperform, with median scores approximately 10% worse than their purely classical counterparts across different configurations. In contrast, recursive architectures with data re-uploading show the opposite trend: hybrid models achieved up to 5% better median scores under comparable complexity settings. Additionally, these hybrid models converged in fewer training epochs, indicating improved training efficiency. Our results show that hybrid models achieve competitive accuracy with state-of-the-art classical methods, especially when the number of qubits and re-uploading blocks is increased. While the classical models demonstrate l

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RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Li, Heng Li, Haojin Chen, Jianyu Fu, Huazhu Liu, Jiang The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Institute of High Performance Computing Agency for Science Technology and Research Singapore

Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced in clinical settings due to the scarcity of annotated data resulting from the professional and private nature of medical data. Despite the existence of decent solutions, many of them are hindered in clinical settings due to limitations in data collection and computational complexity. To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain. A frequency filter-based data augmentation strategy is first proposed to promote domain variability within a single-source domain by introducing variations in frequency space and blending homologous samples. Then Gaussian filter-based structural saliency is also leveraged to learn robust representations across augmented samples, further facilitating the training of generalizable segmentation models. To validate the effectiveness of RaffeSDG, we conducted extensive experiments involving out-of-domain inference on segmentation tasks for three human tissues imaged by four diverse modalities. Through thorough investigations and comparisons, compelling evidence was observed in these experiments, demonstrating the potential and generalizability of RaffeSDG. The code is available at https://***/liamheng/NonIID_Medical_Image_Segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Inference engines

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Improving Software Defined Network Controllers in a Multi-Vendor Environment

SSRN

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SSRN 2023年

作者： Deneke, Bereketeab Berhanu Beyene, Asrat Mulatu Haile, Endalew Ayenew Department of Computer Science School of Graduate Studies St. Mary’s University Addis Ababa Ethiopia High Performance Computing & Big Data Analytics CoE Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Adama Science and Technology University School of Electrical Engineering and Computing Electrical Power and Control Engineering Adama Ethiopia

Software Defined Networking (SDN) is an emerging networking paradigm with the potential to foster innovation through programmable networks. SDNs are characterized by the separation of control and data planes where, in a logically centralized controller, it’s possible to make routing decisions on behalf of forwarding elements. For this, there are different protocols used like Open Flow, Forward and Control Element Separation (FORCES), Path Computation Element Protocol (PCEP), Network Configuration Protocol (NETCONF), and Interface to Routing System (IRS). Border Gateway Protocol (BGP) is one such protocol that can enable the network programmability promised by SDNs. The controllers used in BGP as control plane protocol leverages RESTCONF (RFC 8040) as management plane protocol to interact with physical routers, switches, and network service firewalls. Therefore, the aim of this work is to improve the performance of SDN controllers using BGP enabling a multi-vendor environment without requiring infrastructure upgrade showcasing how interoperability can be achieved. The proposed system is implemented using OpenDaylight controllers. The performance of the prototype is evaluated using Latency, Throughput, CPU consumption, and TTL (Time to Live). Based on the simulation results it is observed that the SDN controllers that use BGP show improvements in terms of Latency and Throughput. © 2023, The Authors. All rights reserved.

关键词： Controllers

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An environment for importance sampling based on stochastic activity networks

An environment for importance sampling based on stochastic a...

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Reliable Distributed Systems

作者： W.D. Obal W.H. Sanders Department of Electrical and Computer Engineering University of Arizona Tucson Tucson AZ USA Center for Reliable and High-Performance Computing Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana IL USA

Model-based evaluation of reliable distributed and parallel systems is difficult due to the complexity of these systems and the nature of the dependability measures of interest. The complexity creates problems for analytical model solution techniques, and the fact that reliability and availability measures are based on rare events makes traditional simulation methods inefficient. Importance sampling is a well-known technique for improving the efficiency of rare event simulations. However, finding an importance sampling strategy that works well in general is a difficult problem. The best strategy for importance sampling depends on the characteristics of the system and the dependability measure of interest. This fact motivated the development of an environment for importance sampling that would support the wide variety of model characteristics and interesting measures. The environment is based on stochastic activity networks, and importance sampling strategies are specified using the new concept of the importance sampling governor. The governor supports dynamic importance sampling strategies by allowing the stochastic elements of the model to be redefined based on the evolution of the simulation. The utility of the new environment is demonstrated by evaluating the unreliability of a highly dependable fault-tolerant unit used in the well-known MARS architecture. The model is non-Markovian, with Weibull distributed failure times and uniformly distributed repair times.< >

关键词： Monte Carlo methods Stochastic processes Analytical models Discrete event simulation Availability Sampling methods Stochastic systems computer network reliability computer networks Concurrent computing

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Automatic classification of node types in switch-level descriptions

Automatic classification of node types in switch-level descr...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： D.T. Blaauw P. Banerjee J.A. Abraham Center for Reliable an High Performance Computing Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana IL USA Department of Electrical & Computer Engineering University of Texas Austin Austin TX USA

In switch-level simulation, nodes carry a charge on their parasitic capacitance from one evaluation to the next, which gives them a memory quality. A node is classified as temporary if its memory aspect is lost and cannot affect the circuit operation, whereas a node is classified as a memory node if the memory of the node is maintained and can affect the circuit operation. Accurate classification of nodes into temporary and memory nodes increases the performance of compiled simulators and high-level model generators. An approach for reliable automatic classification of nodes in a switch-level description is introduced. Both an exhaustive, exponential-time algorithm and a polynomial-time heuristic are presented. The heuristic was implemented and tested for several large circuits, including a commercial microprocessor. For this processor, the proposed heuristics identified an average of 92% of all nodes as temporary nodes. The heuristic was applied in a high-level model generator and significantly increased its performance.< >

关键词： Circuit simulation Parasitic capacitance Circuit testing Maintenance Polynomials Switching circuits Switched capacitor circuits Joining processes Capacitance measurement Size measurement

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