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A Real-Time Recommender System Design Based on Spark Streaming 1

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Zhang, Qihang Yu, Xue Ling, Keke Wang, Tianhao Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

In the big data environment, the personalized recommender system based on offline batch processing has the advantages of accurate calculation and high fault tolerance. However, due to its large amount of calculation, it cannot satisfy the real-time requirement. At present, the real-time recommender system architecture based on the combination of offline layer and online layer also has some disadvantages in summarizing results and maintaining updates, and the recommended result depends on the offline layer. If the user behavior changes in a short time and the result update of the offline layer is lagging, the final recommendation will not reflect the user’s interest change very well. Aiming at these problems, this paper makes use of the Spark Streaming micro-batch data stream processing function to implement a real-time recommendation system. the system realized a real-time recommendation composed entirely of online stream computing based on the consistency of the user positive and negative evaluation of the *** the problem of unstable data flow in the actual application scenario, the message queue Kafka is introduced to ensure the smooth and stable real-time data inflow speed. Finally, the performance of the entire system and the effectiveness of the recommendation are verified by Movielens data set. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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the TaPaSCo Open-Source Toolflow for the Automated Composition of Task-Based Parallel Reconfigurable computing Systems 15th

The TaPaSCo Open-Source Toolflow for the Automated Compositi...

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15th International symposium on Applied Reconfigurable computing

作者： Korinth, Jens Hofmann, Jaco Heinz, Carsten Koch, Andreas Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9783030172268;9783030172275

In this paper we present TaPaSCo - the Task Parallel Systems Composer, an open-source, toolflow and software framework for automated construction of System-on-Chip FPGA designs for task parallel computation. TaPaSCo aims to increase the scalability and portability of FPGA designs by performing the construction of heterogeneous many-core architectures from custom processing elements, and providing a simple, uniform programming interface to utilize spatially parallel computation on FPGAs. A key feature of TaPaSCo's is automated design space exploration, which can be performed in parallel on a computing cluster. this greatly simplifies scaling hardware designs, facilitating iterative growth and portability across FPGA devices and families.

关键词： FPGA Reconfigurable computing Design space exploration System-on-Chip design Design automation high-level synthesis Scalability Portability TaPaSCo Heterogeneous computing Parallel computing

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GPU-Accelerated Similarity Self-Join for Multi-Dimensional Data 15

GPU-Accelerated Similarity Self-Join for Multi-Dimensional D...

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15th International Workshop on Data Management on New Hardware (DaMoN)

作者： Gowanlock, Michael Karsin, Ben No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA Univ Libre Bruxelles Dept Comp Sci Brussels Belgium

ISBN: (纸本)9781450368018

the similarity self-join finds all objects in a dataset that are within a search distance, epsilon, of each other. As such, the self-join is a building block of many algorithms. In high dimensions, indexing structures become increasingly ineffective at pruning the search, making the self-join challenging to compute efficiently. We advance a GPU-accelerated self-join algorithm targeted towards high dimensional data. the massive parallelism afforded by the GPU and high aggregate memory bandwidth makes the architecture well-suited for data-intensive workloads. We leverage a grid-based GPU-tailored index to perform range queries, and propose the following optimizations: (i) a trade-off between candidate set filtering and index search overhead by exploiting properties of the index;(ii) reordering the data based on variance in each dimension to improve the filtering power of the index;and (iii) a pruning method for reducing the number of expensive distance calculations. Our algorithm generally outperforms a parallel CPU state-of-the-art approach.

关键词： GPGPU high-dimensional data Index structure In-memory database Query optimization Self-join

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CryoCore: A Fast and Dense Processor architecture for Cryogenic computing

CryoCore: A Fast and Dense Processor Architecture for Cryoge...

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Annual International symposium on computer architecture, ISCA

作者： Ilkwon Byun Dongmoon Min Gyu-hyeon Lee Seongmin Na Jangwoo Kim Department of Electrical and Computer Engineering Seoul National University

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

Cryogenic computing can achieve high performance and power efficiency by dramatically reducing the device's leakage power and wire resistance at low temperatures. Recent advances towards cryogenic computing focus on developing cryogenic-optimal cache and memory devices to overcome memory capacity, latency, and power walls. However, little research has been conducted to develop a cryogenic-optimal core architecture despite its high potentials in performance, power, and area efficiency. Once a cryogenic-optimal core becomes available, it will also take full advantage of the cryogenic-optimal cache and memory devices, which leads to a cryogenic-optimal *** this paper, we first develop CryoCore-Model (CC-Modet), a cryogenic processor modeling framework which can accurately estimate the maximum clock frequency of processor models running at 77K. Next, driven by the modeling tool, we design CryoCore, a 77K-optimal core microarchitecture to maximize the core's performance and area efficiency while minimizing the cooling cost. the key idea of CryoCore is to architect a core in a way to reduce the size and number of cooling-unfriendly microarchitecture units and maximize the potential of a voltage and frequency scaling at 77K. Finally, we propose two halfsized, but differently voltage-scaled CryoCore designs aiming for either the maximum performance or power efficiency. With both conventional and our design integrated with cryogenic memories, our high-performance CryoCore design achieves 41% higher single-thread performance for the same power budget and 2x higher multi-thread performance for the same die area. Our lowpower CryoCore design reduces the power cost by 38% without sacrificing the single-thread performance.

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third Party CAD Tools for FPGA Design-A Survey of the Current Landscape 15th

Third Party CAD Tools for FPGA Design-A Survey of the Curren...

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15th International symposium on Applied Reconfigurable computing

作者： Nelson, Brent E. Brigham Young Univ Dept Elect & Comp Engn NSF Ctr Space High Performance & Resilient Comp S Provo UT 84602 USA

ISBN: (纸本)9783030172268;9783030172275

the FPGA community is at an exciting juncture in the development of 3rd party CAD tools for FPGA design. Much has been learned in the past decade in the development and use of 3rd party tools such RapidSmith, Torc, and IceStorm. New independent open-source CAD tool projects are emerging which promise to provide alternatives to existing vendor tools. the recent release of the RapidWright tool suggests that Xilinx itself is interested in enabling the user community to develop new use cases and specialized tools for FPGA design. this paper provides a survey of the current landscape, discusses parts of what has been learned over the past decade in the author's work with 3rd party CAD tool development, and provides some thoughts on the future.

关键词： computer-aided design CAD FPGA Digital circuits

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2d-3d Hierarchical Feature Fusion Network For Segmentation Of Bone Structure In Knee Mr Image

2d-3d Hierarchical Feature Fusion Network For Segmentation O...

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IEEE International symposium on Biomedical Imaging

作者： Hui Wang Demin Yao Jiayi Chen Yanjing Liu Wensheng Li Yonghong Shi Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University Shanghai China. Histology and Embryology School of Basic Medical Sciences Fudan University Shanghai China.

ISBN: (纸本)9781665412469;9781665429474

Automatic segmentation of knee bone structures is an important task in orthopedics diagnosis of knee disease based on MRI images. Inspired by doctors' diagnosis of knee in sagittal plane of MR image, we propose to first calculate the sagittal maximum intensity projection (MIP) of MR image, then construct a high precision 2D-3D hierarchical feature fusion network for automatic segmentation of knee based on convolutional encoding and decoding architecture. It includes: 1) A 2D bypass network extracting global features based on MIP; 2) A 3D backbone network calculating local details based on MR volume; 3) A feature fusion module integrating 2D global context and 3D local details hierarchically. Particularly, the global features as anchor points will be fused with the local details at each level of the encoding path to enrich the context of local details and improve the segmentation accuracy. Our method is verified on SKI10 dataset. the average dice coefficients of femur, femoral cartilage, tibia and tibia cartilage are 0.978, 0.848, 0.979 and 0.848, respectively, and the segmentation performance is far better than the state-of-the-art methods.

关键词： Image segmentation three-dimensional displays Image coding Magnetic resonance imaging Semantics Medical services Feature extraction

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the Internet of Renewable Energy: Big Data-Driven Smart Grid Management with the Reliability and Security Analysis 1st

The Internet of Renewable Energy: Big Data-Driven Smart Grid...

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1st International and 4th National Conference on Reliability and Safety Engineering, INCRS 2018

作者： Vaishnavi, P. Deenadayalan, V. Department of Computer Applications Anna University Chennai—BIT Campus Tiruchirappalli India Anna University Tiruchirapalli India

ISBN: (纸本)9789811537455

Renewable energy sources as the solar and wind power are transforming the energy landscape across the world, especially in India. As the government is planning to install 80 GW of solar power generation and 50 GW of wind power generation by 2020, we are presently in an era of increasing solar and wind power generation. the solar and wind energy, along with the associated sensor devices, generate huge amount of data that our traditional warehousing system is unable to store, manage and analyze smart grid, and big data technology provides an answer. Big data is a major research and development direction which provides data storage, retrieval, real-time processing, analytical modeling and visualization. In this paper, we propose the renewable energy architecture along with the integration of big data and power grid. this integration realizes high degree of network connectivity, along with complex security vulnerabilities into the grid. these security challenges, as well as reliability of the system in terms of privacy, integrity authentication and third-party protection have also been discussed in this paper. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data analytics computing (IoT) high performance Renewable energy

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An Empirical Methodological Study of Evaluation Methods Applied to Educational Timetabling Visualizations 15th

An Empirical Methodological Study of Evaluation Methods Appl...

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15th International symposium on Visual computing, ISVC 2020

作者： de Souza Alencar, Wanderley Abdala Rfaei Jradi, Walid Dantas do Nascimento, Hugo Alexandre Felix, Juliana Paula Alves de Melo Nunes Soares, Fabrízzio Alphonsus Instituto de Informática Universidade Federal de Goiás GoiâniaGO Brazil Department of Computer Science Southern Oregon University AshlandOR United States

ISBN: (纸本)9783030645557

the conception, and usage, of methods designed to evaluate information visualizations is a challenge that goes along with the development of these visualizations. In the scientific literature there is a myriad of proposals for such methods. However, none of them was able to pacify the field or establish itself as a de facto standard, due to difficulties like: (a) the complexity of its usage;(b) high financial and time costs;and (c) the need of a large number of raters to guarantee the reliability of the results. One way to circumvent such adversities is the usage of Heuristic Evaluation given its simplicity, low cost of application and the quality of reached results. this article intends to conduct an empirical methodological study about the use of three of such methods (Zuk et al., Forsell & Johansson and Wall et al.) for evaluation of visualizations in the context of Educational Timetabling Problems. Five different visualizations were evaluated using the original methods and versions modified by the current authors (where an importance factor was assigned to each statement being evaluated, as well as the rater’s level of confidence) in order to improve their efficiency when measuring the quality of visualizations. the experimental results demonstrated that for the two first heuristics, only the modification on the importance of the statements proved to be (statistically) relevant. For the third one, both factors did not induce different results. © 2020, Springer Nature Switzerland AG.

关键词： Visualization

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A Scalable Priority-Aware Approach to Managing Data Center Server Power 25

A Scalable Priority-Aware Approach to Managing Data Center S...

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25th IEEE International symposium on high performance computer architecture (HPCA)

作者： Li, Yang Lefurgy, Charles R. Rajamani, Karthick Allen-Ware, Malcolm S. Silva, Guillermo J. Heimsoth, Daniel D. Ghose, Saugata Mutlu, Onur IBM Corp Armonk NY 10504 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781728114446

Power management is a key component of modern data center design. Power managers must (1) ensure the cost- and energy-efficient utilization of the data center infrastructure, (2) maintain availability of the services provided by the center, and (3) address environmental concerns associated with the center's power consumption. While several power management techniques have been proposed and deployed in production data centers, there are still many challenges to comprehensive data center power management. this is particularly true in public cloud environments, where different jobs have different priority levels, and where high availability is critical. One example of the challenges facing public cloud data centers involves power capping. As power delivery must be highly reliable and tolerate wide variation in the load drawn by the data center components, the power infrastructure (e.g., power supplies, circuit breakers, UPS) has high redundancy and overprovisioning. During normal operation (i.e., typical server power demands, and no failures in the center), the power infrastructure is significantly underutilized. Power capping is a common solution to reduce this underutilization, by allowing more servers to be added safely (i.e., without power shortfalls) to the existing power infrastructure, and throttling power consumption in the infrequent cases where the demanded power exceeds the provisioned power capacity to avoid shortfalls. However, state-of-the-art power capping solutions are (1) not directly applicable to the redundant power infrastructure used in highly-available data centers;and (2) oblivious to differing workload priorities across the entire center when power consumption needs to be throttled, which can unnecessarily slow down high prioritywork. To address this need, we develop CapMaestro, a new power management architecture with three key features for public cloud data centers. First, CapMaestro is designed to work with multiple power feeds (i.e., sources

关键词： Servers Power demand Cloud computing Power system management Data center power Feeds

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Band-Pass Filtering for Non-Stationary Noise in Agricultural Images to Pest Control Based On Adaptive Semantic Modeling

Band-Pass Filtering for Non-Stationary Noise in Agricultural...

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International Conference on Semantic computing

作者： Alex B. Bertolla Paulo E. Cruvinel Post-Graduation Program in Computer Science Federal University of São Carlos SP Brazil

ISBN: (数字)9781728188997

ISBN: (纸本)9781728189000

Image analysis has been used in a very large scale for different purposes. When an image is captured by a digital sensor, it is usually affected by some type of noise, even the smoothest ones. therefore, image enhancement and denoising process are important tasks of digital image processing. this paper presents an algorithm to reduce non-stationary noise with the combination of a Low-Pass Filter (LPF) and a high-Pass Filter (HPF), in conjunction with an adaptive semantic model. To simulate the usefulness of such arrangement, a non-stationary Gaussian noise has been applied to an image, which has been splitted into the four quadrants, all of them having the same dimensions. In fact, such a noise with different intensities, has been added to the image in each of its quadrants. the Peak Signal-to-Noise Ratio (PSNR) has been used to measure the best cutoff frequencies for both filters, as well as rules based on semantic concepts have been structured for decision making. Furthermore, for the validation of the algorithm we have taken into account the evaluation of the Mean Squared Error (MSE) using a typical digital image obtained from a crop of maize with the presence of the earwornm (Helicoverpa Zea). Besides, the denoising process demonstrates the efficiency and the satisfactory performance for the non-stationary noise filtering in agricultural images.

关键词： Band-pass filters PSNR Semantics Cutoff frequency Low-pass filters Filtering algorithms Digital filters

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