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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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A Learning Approach for Topic-Aware Influence Maximization 3rd

A Learning Approach for Topic-Aware Influence Maximization

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3rd APWeb and WAIM Joint conference on Web and Big Data, APWeb-WAIM 2019

作者： Tian, Shan Zhang, Ping Mo, Songsong Wang, Liwei Peng, Zhiyong School of Computer Science Wuhan University WuhanHubei China Huawei Shenzhen China

ISBN: (纸本)9783030260712

Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics. This problem has been proved to be NP-hard and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we encode the feature of each node by a vector and introduce a deep learning model, called deep-influence-evaluation-model (DIEM), to evaluate users' influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework. © 2019, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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An acute kidney injury prediction model based on ensemble learning algorithm 10

An acute kidney injury prediction model based on ensemble le...

引用

10th International conference on Information Technology in Medicine and Education, ITME 2019

作者： Wang, Yuan Wei, Yake Wu, Qin Yang, Hao Li, Jingwei College of Computer Science and Information Engineering Tianjin University of Science and Technology China Ltd China Center for Cyber Security University of Electronic Science and Technology of China China Department of Critical Care Medicine West China Hospital Sichuan University China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China

ISBN: (纸本)9781728139173

Acute Kidney Injury (AKI), a common disease in Intensive Care Unit (ICU) patients, is related to high cost, morbidity and mortality. The early prediction of AKI is critical for improving patients' outcome. However, sparse clinical data and highly imbalanced dataset bring great challenges to AKI prediction. Among existed machine learning algorithms, ensemble learning often stands out with its good performance on complex classification problems. Boosting algorithm is one of the best ensemble learning algorithms. Therefore, we develop a prediction model based on it aiming to forecast AKI ahead 24 hours and 48 hours. We also adopt the way that is analogous to text modeling transforming heterogeneous time series reflecting patients' medication information into multidimensional vector to overcome problem brought by sparse data. Additionally, since the imbalanced dataset would affect predictive performance, we artificially construct a more balanced dataset based on the original dataset to initialize the model. According to the experimental results, our model works well on the ICU patients dataset (AUC 24h ahead: 0.80 48h ahead 0.77). We also verify that medication information improves model performance (24h ahead: AUC 0.75 to 0.80 48h ahead: AUC 0.75 to 0.77) and find the optimal ratio of number between classes when initializing model for AKI prediction. © 2019 IEEE.

关键词： Forecasting

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Execution details and assessment results of a summer bridge program for engineering freshmen 126

Execution details and assessment results of a summer bridge ...

引用

126th ASEE Annual conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

作者： Nazempour, Rezvan Darabi, Houshang Nelson, Peter C. Revelo, Renata A. Siow, Yeow Abiade, Jeremiah University of Illinois Chicago United States

This paper reports the execution details and the summary assessment of a Summer Bridge program (SBP) that is a part of an ongoing National Science Foundation (NSF) Scholarships in Science, Technology, Engineering, and Math (S-STEM) project in the College of Engineering at the University of Illinois at Chicago. The project supports 18 Scholars (academically-talented, low-income engineering students). The primary goals of the SBP are facilitating Scholars' transition to their first-year and improving their academic success. To achieve these goals, the SBP is implemented as a two-week on-campus intensive experience that occurs in the summer before the student's first year. The first round of the SBP was completed in Summer 2018 and the current paper offers the details, lessons learned, and a brief evaluation of the SBP. Based on the assessment data, it is concluded that the SBP was successful in achieving its stated goals. The evaluation results and the lessons learned from the SBP execution can be used to build a sustainable Summer Bridge program for all first-year engineering students in the future. © American Society for Engineering Education, 2019

关键词： Students

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Data-Driven Student Support for Academic Success by Developing Student Skill Profiles

Data-Driven Student Support for Academic Success by Developi...

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International Multidisciplinary Information Technology and Engineering conference (IMITEC)

作者： Ritesh Ajoodha Shalini Dukhan Ashwini Jadhav School of Computer Science and Applied Mathematics The University of the Witwatersrand Johannesburg South Africa School of Animal Plant and Environmental Sciences The University of the Witwatersrand Johannesburg South Africa Faculty of Science The University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9781728195209

ISBN: (纸本)9781728195216

In this paper, we attempt to provide a data-driven solution to the data-congested environment of attributes related to student success and contribute towards preventing the increased dropout rates at South African higher education institutions. One of the most significant discussions in higher education is student attrition in their first year of study. Student career guidance is an area that requires investigation in light of high attrition rates at university. Recent developments in data analytics, and the analysis of large data sets have enabled the production of powerful predictive models. This paper highlights how a predictive model can assist students, with an interest in Science to develop a skill profile required to be successful in their undergraduate Science programme. This is achieved by identifying the difference between the necessary skills required to be successful in a science programme (derived using data driven approaches) from the current learner's skill profile (derived from the learners' performance in assessments). The learners' skill profile is used to predict success in four Science streamlines. Based on the prediction results, we gauge the improvement in skills required to succeed in that programme. We provide the following contributions: (a) a trained classifier able to calculate the distribution over learners' success in Science streamlines focused around the notion of skill profiles; (b) a ranking of these skill profiles according to their information gain (entropy); and (c) an interactive program to calculate the posterior probability over these skill profiles given learner's pre-university observations. We argue that it is crucial that students gauge the focus areas of skill improvement prior to enrolling for their degree so that they can consider streams in Science degrees that are suited to their academic strengths.

关键词： Engineering profession Education Production Predictive models Probability Entropy Information technology

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Extensible cross-modal hashing 28

Extensible cross-modal hashing

引用

28th International Joint conference on Artificial Intelligence, IJCAI 2019

作者： Tian-Yi, Chen Zhang, Lan Shi-Cong, Zhang Zi-Long, Li Bai-Chuan, Huang School of Computer Science and Technology University of Science and Technology of China China School of Data Science University of Science and Technology of China China School of Information Science and Engineering Northeastern University China Department of Physics University of California Berkeley United States

ISBN: (纸本)9780999241141

Cross-modal hashing (CMH) models are introduced to significantly reduce the cost of large-scale cross-modal data retrieval systems. In many real-world applications, however, data of new categories arrive continuously, which requires the model has good extensibility. That is the model should be updated to accommodate data of new categories but still retain good performance for the old categories with minimum computation cost. Unfortunately, existing CMH methods fail to satisfy the extensibility requirements. In this work, we propose a novel extensible cross-modal hashing (ECMH) to enable highly efficient and low-cost model extension. Our proposed ECMH has several desired features: 1) it has good forward compatibility, so there is no need to update old hash codes;2) the ECMH model is extended to support new data categories using only new data by a well-designed "weak constraint incremental learning" algorithm, which saves up to 91% time cost comparing with retraining the model with both new and old data;3) the extended model achieves high precision and recall on both old and new tasks. Our extensive experiments show the effectiveness of our design. © 2019 International Joint conferences on Artificial Intelligence. All rights reserved.

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The Implementation of a Power Efficient BCNN-based Object Detection Acceleration on a Xilinx FPGA-SoC 12

The Implementation of a Power Efficient BCNN-based Object De...

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IEEE Int Congr on Cybermat / 12th IEEE Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th IEEE Int Conf on Green Computing and Communications (GreenCom) / 12th IEEE Int Conf on Internet of Things (iThings) / 5th IEEE Int Conf on Smart Data

作者： Kim, Heekyung Choi, Ken IIT Dept Elect & Comp Engn Chicago IL 60616 USA

ISBN: (纸本)9781728129808

This paper focuses on the power efficient design on the FPGA SoC for the object detection system based on Binary Convolutional Neural Network (BCNN). Especially, for the small IoT devices, such as an intelligent dash-cam, computer vision system installed on an unmanned aerial vehicle, the power consumption could be a significant factor of the performance and scalability. However, the optimized FPGA design has limitations to reduce the overall power consumption amount. We focus on the design of the FPGA Accelerator as well as the effective design of the peripherals including CPU. In our proposed FPGA-SoC design, it supports not only FPGA but also CPU and the peripheral component can be supported by additional virtual memory system for reducing the processing time. Overall customization including customized BCNN, virtual memories for CPU and FPGA part allows our testbed to achieve low power consumption without speed degradation. Our testbed is based on customized YOLOv2 which consists of applied binary and half precision convolution, and pipeline-based architecture with accelerated hardware design on the target device. The target device used in this paper is the Xilinx ZYNQ-SoC based PYNQ Z-1 board. Our proposed system achieves 15.15 frames per second (FPS) and 1.45 watts of power dissipation. Our result shows that our design technique is effective for real-time object detection and low power system.

关键词： component Low Power Power Efficient high-performanace Convolutional Neural Network Accelerator FPGA SoC Design PYNQ Overlay Real-time Object Detection

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LOW DATA REQUIREMENTS FRAMEWORK for LANDSLIDE SUSCEPTIBILITY PREDICTION USING 3D ALOS PALSAR IMAGES and NEURAL NETWORKS 6

LOW DATA REQUIREMENTS FRAMEWORK for LANDSLIDE SUSCEPTIBILITY...

引用

6th International conference on Geomatics and Geospatial Technology, GGT 2019

作者： Abujayyab, S.K.M. Karaş, I.R. Dept. of Computer Engineering Karabuk University Demir Celik Campus Karabuk78050 Turkey

Development of landslides susceptibility (LS) predictors based on 3D data is an active area of research in the recent years. Predicting landslides susceptibility maps help to secure human lives and maintaining infrastructures from this risk. Several advanced frameworks proposed with high input data to improve the predictors. The aim of this paper is to develop low data requirement framework for LS predictors development. This framework is only using one input 3D ALOS PALSAR image. The framework has three stages. (A) data pre-processing, (B) deriving explanatory factors, and (C) neural networks training and testing. Exactly. 22 input spatial factors were extracted from ALOS PALSAR image. Extracted factors were utilized to develop the FFNN predictor. The structure of the predictor is 22 factors (input layer) × 150 neurons (hidden layer) × 1 (output layer). Furthermore, 5829 sample points utilized during the training stage, while 745810 points sent to the trained predictor to create LS map. Based on confusion matrix metric, performance accuracy (89.3% training and 82.3 testing), While (95.22% training and 84.7% testing) based on Receiver Operating Characteristic curve. Out of the study area in Karabuk, 3.53 km2 (3.03%) were located in very high susceptibility category. Lastly, the application of the proposed framework showed that it is capable develop low data requirement predictors with high accuracy. Framework provide guideline data for future development in taxing topographic circumstances and large scale of data coverage. In addition, the framework handled the inconsistency in data quality and data updating problem. © 2019 S. K. M. Abujayyab.

关键词： Neural networks

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Distributed Analysis of the BMC Kind: Making It Fit the Tornado Supercomputer 4th

Distributed Analysis of the BMC Kind: Making It Fit the Torn...

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4th International conference on Tools and Methods of program Analysis (TMPA)

作者： Abdullin, Azat Stepanov, Daniil Akhin, Marat Peter Great St Petersburg Polytech Univ St Petersburg Russia

ISBN: (纸本)9783319717340;9783319717333

Software analysis is becoming increasingly important as a way of software quality assurance. Most works in this area focus their attention on a single machine scenario, when the analysis is run and implemented on a single processing node, as it seems to be a good fit for the current software development methodologies. We argue that in some cases it is reasonable to employ high performance computing (HPC) to do software analysis, if the performance impact is worth the increase in computational requirements. In this paper we present our experience with the implementation of a HPC version of the bounded model checker Borealis, major problems we encountered together with their solutions, and the evaluation results on a number of different real-world projects.

关键词： Bounded model checking high performance computing Distributed computing Supercomputing

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USING PYTHON FOR MAPPING MOLECULAR DYNAMICS SIMULATION OPENMM ONTO THE XEON PHI ARCHITECTURE

USING PYTHON FOR MAPPING MOLECULAR DYNAMICS SIMULATION OPENM...

引用

IEEE SoutheastCon conference

作者： Morris, Omar A. Abed, Khalid H. Jackson State Univ Dept Elect & Comp Engn Jackson MS 39217 USA

ISBN: (纸本)9781538661338

In seeking to accelerate the mathematically intense engineering and scientific applications, parallel computing is used to bypass the physical limitations of traditional stand-alone CPU based systems. This has led to the creation of new hardware and software paradigms developed in tandem for future performance gains. The thrust of this paper is to investigate the performance of applications and code run in two distinct hardware configurations: standalone 16-Core 2.3 GHz Intel Xeon E5-2968 CPU and the Xeon E5 coupled with dual Xeon Phi 7120 61-Core 1.25GHz Intel Many Integrated Core (MIC) Architecture. The hardware configurations were implemented under different software environments: standard Python, Intel's distribution of Python and both Python versions with Automatic Offloading enabled. The results revealed a significant increase in application performance when implemented under Intel's distribution of Python combined with the offloading of computationally demanding portions of the code to the host co-processors.

关键词： Python Mathematical model Hardware Computational modeling Libraries computer architecture

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Prediction of Motor and Somatosensory Function from Human ECoG 6

Prediction of Motor and Somatosensory Function from Human EC...

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6th International Winter conference on Brain-computer Interface (BCI)

作者： Ryun, Seokyun Kim, June Sic Lee, Donghyuk Chung, Chun Kee Seoul Natl Univ Interdisciplinary Program Neurosci Seoul South Korea Seoul Natl Univ Dept Brain & Cognit Sci Seoul South Korea

ISBN: (纸本)9781538625743

One of the most challenging issues in recent BCI research is not only achieving high performance, but also creating a sense of ownership of artificial devices. To investigate this issue, sensory-motor integrated BMI system should be considered. In this study, we attempted to predict the somatosensory property of tactile stimulus as well as the movement trajectory and type using elctrocorticography (ECoG) signals. We showed that 1) single-trial 3-D movement trajectory can be estimated from low-frequency ECoG signals with relatively high performance, 2) high-gamma activity can be a robust feature for movement type classification, and 3) the location of pressure stimulation can be classified by macro ECoG signals from sensory-related cortical areas. These results might be applied to the closed-loop BMBI systems which simultaneously encode sensory information during movement decoding.

关键词： primary motor cortex primary somatosensory cortex electrocorticography brain-machine interface

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