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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lu Tang Qun Huang Patrick P. C. Lee Department of Computer Science and Engineering The Chinese University of Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Superspreaders (i.e., hosts with numerous distinct connections) remain severe threats to production networks. How to accurately detect superspreaders in real-time at scale remains a non-trivial yet challenging issue. We present SpreadSketch, an invertible sketch data structure for network-wide superspreader detection with the theoretical guarantees on memory space, performance, and accuracy. SpreadSketch tracks candidate super-spreaders and embeds estimated fan-outs in binary hash strings inside small and static memory space, such that multiple SpreadSketch instances can be merged to provide a network-wide measurement view for recovering superspreaders and their estimated fan-outs. We present formal theoretical analysis on SpreadSketch in terms of space and time complexities as well as error bounds. Trace-driven evaluation shows that SpreadSketch achieves higher accuracy and performance over state-of-the-art sketches. Furthermore, we prototype SpreadSketch in P4 and show its feasible deployment in commodity hardware switches.

关键词： Data structures computer crime Real-time systems Hardware Memory management Estimation IP networks

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Big data-oriented energy prosumption service in smart community districts: a multi-case study perspective

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Energy Informatics 2019年第1期2卷 1-26页

作者： Anthony, Bokolo Petersen, Sobah Abbas Ahlers, Dirk Krogstie, John Livik, Klaus Department of Computer Science Norwegian University of Science and Technology NTNU TrondheimNO-7491 Norway Department of Architecture and Planning Norwegian University of Science and Technology Trondheim Norway Powel AS Klæbuveien 194 TrondheimNO-7037 Norway

The smart grid achieves bidirectional information and energy flow between energy consumer and utility grid, aiding energy users not only to utilize energy, but also to produce, sell, and share energy with other consumers or with the utility grid. This type of energy user is referred to as the "prosumer". Thus, prosumer management structures are important within energy market. However, prior studies on energy sustainability has paid little attention on prosumer involvement and management. Likewise, the continuous growth of cities has increased data processing complexity. Consequently, processing and analysis of historical, online, and real-time streaming data from energy sensors and metering devices has become a major issue in smart cities. Therefore, this research aims to present an architecture based on big data to improve energy prosumption in smart community districts by applying enterprise architecture approach grounded on The Open Group architecture Framework (TOGAF). Accordingly, qualitative methodology is adopted to collect data by employing case study by focus group interview from two energy companies in Norway to preliminarily validate the architecture. Findings from the case studies was demonstrated in ArchiMate modeling language to evaluate the applicability of the architecture. Moreover, findings from this study provides practical scenario that energy service providers can refer to in designing their own energy data platforms. Essentially, the architecture can be utilized as a guide to help municipalities and policy makers in creating approach for energy data analytics in smart community districts towards making decisions for future energy prosumption planning. © 2019, The Author(s).

关键词： Big data

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RETRACTED ARTICLE: A research study on the cervical cerclage to deal with cervical insufficiency using machine learning

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Soft Computing 2023年第2期28卷 643-643页

作者： Kaur, Mandeep Khedkar, Ganesh Sakhare, Sachin Rogulj, Katarina Department of Computer Science Savitribai Phule Pune University Pune India Siddhant College of Engineering Pune India Computer Engineering Department Vishwakarma Institute of Information Technology Pune India Faculty of Civil Engineering Architecture and Geodesy University of Split Split Croatia

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Sequence Triggered Hardware Trojan in Neural Network Accelerator

Sequence Triggered Hardware Trojan in Neural Network Acceler...

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VLSI Test Symposium

作者： Zizhen Liu Jing Ye Xing Hu Huawei Li Xiaowei Li Yu Hu State Key Laboratory of Computer Architecture Institution of Computing Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering University of California Santa Barbara

ISBN: (数字)9781728153599

ISBN: (纸本)9781728153605

With the rapid development of deep learning techniques, the security issue for Neural Network (NN) systems has emerged as an urgent and severe problem. Hardware Trojan attack is one of the threatens, which provides attackers backdoors to control the prediction results of NN systems. This paper proposes a sequence triggered hardware Trojan. Normal images but with specific sequence are used to trigger the hardware Trojan and let attackers fully control the prediction results. This kind of trigger is not only robust to image pre-processing, but also unrecognizable by human beings. In comparison with existing hardware Trojan design, it is more practical and less hardware overhead. The experiments on MNIST, CIFAR100, and ISLVRC show that the proposed hardware Trojan is rarely triggered in normal working status while the hardware cost is reduced by 19X.

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Exocentric to Egocentric Image Generation Via Parallel Generative Adversarial Network

Exocentric to Egocentric Image Generation Via Parallel Gener...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Gaowen Liu Hao Tang Hugo Latapie Yan Yan Department of Computer Science Texas State University USA Chief Technology & Architecture Office Cisco USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation. This is a challenging task since egocentric view sometimes is remarkably different from exocentric view. Thus, transforming the appearances across the two views is a nontrivial task. To this end, we propose a novel Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss to learn the shared information for generating egocentric images from exocentric view. We also incorporate a novel contextual feature loss in the learning procedure to capture the contextual information in images. Extensive experiments on the Exo-Ego datasets [1] show that our model outperforms the state-of-the-art approaches.

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Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset

Egok360: A 360 Egocentric Kinetic Human Activity Video Datas...

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IEEE International Conference on Image Processing

作者： Keshav Bhandari Mario A. DeLaGarza Ziliang Zong Hugo Latapie Yan Yan Department of Computer Science Texas State University USA Chief Technology & Architecture Office Cisco USA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Recently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 ° video analysis. However, the lack of 360 ° datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360° Kinetic human activity video dataset (EgoK360). The EgoK360 dataset contains annotations of human activity with different sub-actions, e.g., activity Ping-Pong with four sub-actions which are pickup-ball, hit, bounce-ball and serve. To the best of our knowledge, EgoK360 is the first dataset in the domain of first-person activity recognition with a 360° environmental setup, which will facilitate the egocentric 360 ° video understanding. We provide experimental results and comprehensive analysis of variants of the two-stream network for 360 egocentric activity recognition. The EgoK360 dataset can be downloaded from https://***/.

关键词： Convolution Activity recognition Cameras Three-dimensional displays Kinetic theory Optical distortion Distortion

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Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields

arXiv

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arXiv 2024年

作者： Lian, Haojie Li, Xinhao Qu, Yilin Du, Jing Meng, Zhuxuan Liu, Jie Chen, Leilei Key Laboratory of In-situ Property-improving Mining of Ministry of Education Taiyuan University of Technology Taiyuan100190 China Henan International Joint Laboratory of Structural Mechanics and Computational Simulation College of Architecture and Civil Engineering Huanghuai University Zhumadian463000 China School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China Unmanned Vehicle Innovation Center Ningbo Institute of Northwestern Polytechnical University Ningbo315103 China Key Laboratory of Unmanned Underwater Vehicle Technology of Ministry of Industry and Information Technology Northwestern Polytechnical University Xi’an710072 China Center for Strategic Assessment and Consulting Academy of Military Science Beijing100091 China Computer Engineering Department Taiyuan Institute of Technology Taiyuan030008 China

Neural radiance fields (NeRFs) are a deep learning technique that can generate novel views of 3D scenes using sparse 2D images from different viewing directions and camera poses. As an extension of conventional NeRFs in underwater environment, where light can get absorbed and scattered by water, SeaThru-NeRF was proposed to separate the clean appearance and geometric structure of underwater scene from the effects of the scattering medium. Since the quality of the appearance and structure of underwater scenes is crucial for downstream tasks such as underwater infrastructure inspection, the reliability of the 3D reconstruction model should be considered and evaluated. Nonetheless, owing to the lack of ability to quantify uncertainty in 3D reconstruction of underwater scenes under natural ambient illumination, the practical deployment of NeRFs in unmanned autonomous underwater navigation is limited. To address this issue, we introduce a spatial perturbation field Dω based on Bayes’ rays in SeaThru-NeRF and perform Laplace approximation to obtain a Gaussian distribution N(0, Σ) of the parameters ω, where the diagonal elements of Σ correspond to the uncertainty at each spatial location. We also employ a simple thresholding method to remove artifacts from the rendered results of underwater scenes. Numerical experiments are provided to demonstrate the effectiveness of this approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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A Novel FPGA-Based High Throughput Accelerator for Binary Search Trees

A Novel FPGA-Based High Throughput Accelerator for Binary Se...

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2019 International Conference on High Performance Computing and Simulation, HPCS 2019

作者： Melikoglu, Oyku Ergin, Oguz Salami, Behzad Pavon, Julian Unsal, Osman Cristal, Adrian TOBB University of Economics and Technology Department of Computer Engineering Ankara Turkey Barcelona Supercomputing Center Computer Architecture for Parallel Paradigms Group Barcelona Spain

ISBN: (纸本)9781728144849

This paper presents a deeply pipelined and massively parallel Binary Search Tree (BST) accelerator for Field Programmable Gate Arrays (FPGAs). Our design relies on the extremely parallel on-chip memory, or Block RAMs (BRAMs) architecture of FPGAs. To achieve significant throughput for the search operation on BST, we present several novel mechanisms including tree duplication as well as horizontal, duplicated, and hybrid (horizontal-vertical) tree partitioning. Also, we present efficient techniques to decrease the stalling rates that can occur during the parallel tree search. By combining these techniques and implementations on Xilinx Virtex-7 VC709 platform, we achieve up to 8X throughput improvement gain in comparison to the baseline implementation, i.e., a fully-pipelined FPGA-based accelerator. © 2019 IEEE.

关键词： Forestry

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Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

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Heliyon 2024年第6期10卷 e27747页

作者： Latif, Saima Javaid, Nadeem Aslam, Faheem Aldegheishem, Abdulaziz Alrajeh, Nabil Bouk, Safdar Hussain Department of Management Sciences COMSATS University Islamabad Islamabad 44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad 44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin 64002 Taiwan School of Business Administration (SBA) Al Akhawayn University Ifrane 53003 Morocco Department of Urban Planning College of Architecture and Planning King Saud University Riyadh 11574 Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh 11633 Saudi Arabia Old Dominion University Hampton Blvd Norfolk 4114 United States

Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets' data. Profitable and timely tradi

关键词： Bayesian optimization Contractive autoencoder Ensemble empirical mode decomposition Peephole LSTM Stock market prediction Temporal attention layer Time series Urban planing

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HydraMini: An FPGA-based Affordable Research and Education Platform for Autonomous Driving

HydraMini: An FPGA-based Affordable Research and Education P...

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International Conference on Connected and Autonomous Driving

作者： Tianze Wu Yifan Wang Weisong Shi Joshua Lu SKL of Computer Architecture Institute of Computing Technology CAS Beijing China Department of Computer Science Wayne State University Michigan USA Xilinx Shanghai China

ISBN: (数字)9781728160597

ISBN: (纸本)9781728160603

Autonomous driving has been a hot topic recently, so many industrial and academic groups are putting much engineering and research efforts into this topic. However, it is difficult for most researchers or students to afford a car as a research platform to conduct experiments for autonomous driving. Further, we believe that only when more people have the chance to make contributions will this area be more prosperous. Therefore, in this paper, we present HydraMini, an affordable experimental research and education platform supporting the experiments from hardware systems to vision algorithms, and its high flexibility makes it easily extended and modified. It is equipped with the Xilinx PYNQ-Z2 board as the computing platform, which deploys the Deep Learning Processing Unit (DPU) in FPGA to accelerate the deep learning inference. It also provides useful tools like a simulator for model training and testing in a virtual environment to facilitate the use of HydraMini. Our platform will help researchers and students build and test their own solutions for autonomous driving algorithms and systems easily and efficiently.

关键词： Platform Hardware system Automobile driving Virtual environments Field programmable gate arrays Cars simulator Students Chance Education Model trains motivation

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