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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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the Use of Features to Enhance the Capability of Deep Reinforcement Learning for Investment Portfolio Management

The Use of Features to Enhance the Capability of Deep Reinfo...

引用

IEEE international conference on Big Data Analysis (ICBDA)

作者： Xiaotian Ren Zhengyong Jiang Jionglong Su Xi'an Jiaotong-Liverpool University Suzhou P. R. China School of AI and Advanced Computing XJTLU Entrepreneur College (Taicang) Xi‘an Jiaotong-Liverpool University Suzhou P. R. China

Reinforcement learning algorithms and neural networks have been widely used in stock market forecasting, image recognition processing and many other fields. In this research, we adopt features based on asset prices and transaction volume to have better description of the current state. this research aims to input new features combination into the neural network to assist agent in analyzing the market environment for 11 cryptocurrencies. the experimental data contains historical data of the price and transaction volume of the sample from 30 days before backtest sets, as well as features (the volume, the rate of change, the moving average, the stochastic oscillator, the Eliot oscillator and the On Balance Volume) calculated using the historical data. the efficacy of our strategy is comparing to that of nine traditional strategies, i.e., Aniticor, Online Moving Average Reversion, Passive Aggressive Mean Reversion, Confidence Weighted Mean Reversion, Robust Median Reversion, Online Newton Step, Universal Portfolios and Exponential Gradient. the experiments result shows the use of features can increase the final profit, which is about 10% more profitable compare with strategy established by Jiang et al. in Jul. 2017. Furthermore, the best combination of our features outperforms all other traditional strategies by at least 7.6%.

关键词： Profitability Network topology Neural networks Reinforcement learning Feature extraction Cryptography Stock markets

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Simulation Study of Routing Mechanism in the computing-aware Network 2021

Simulation Study of Routing Mechanism in the Computing-aware...

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2021 the 10th international conference on Networks, Communication and computing

作者： Yuhan Zhao Zheng Chong Xueying Han Zongpeng Du Ke Yu Xiaohong Huang School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications China Infrastructure Network Technology Research Department China Mobile Research Institute China School of Artificial Intelligence Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450385848

Traditional routing algorithms select the path based on the cost, hop, or other network-related metrics. However, with cloud computing and edge computing development, it is essential to develop a computing-aware routing mechanism to distribute the task to the computing nodes with better capabilities. In order to achieve the combined scheduling of computing resources and network resources, computing-aware Network (CAN) is proposed. the solution aims to comprehensively consider the computing resources and network resources existing when routing and forwarding user requests, which is essential to improve the current situation of uneven utilization of edge computing resources. As many theories and structures are proposed, the requirements for experimental verification of CAN are gradually increasing. However, there is still a lack of experimental simulation prototypes available for study. In this work, motivated by the gap, we propose a simulation prototype based on the idea of the CAN, where routing nodes can incorporate both the computing status and network status into routing and forward packets to an edge server according to the computing demand service type. We verified the effectiveness of the CAN prototype from simulation experiments on a cross-domain topology. From the results, we can conclude that the utilization rate of computing resources has been effectively improved compared to not considering computing status.

关键词： utilization rate simulation prototype computing resources computing-aware Network

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Retrieving Large-scale Product Knowledge by Collaborative computing of CPUs and GPUs 2021

Retrieving Large-scale Product Knowledge by Collaborative Co...

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2021 10th international conference on Internet computing for Science and Engineering

作者： Hao Song Chuangxin Fang Zhengguo Huang Yuming Lin Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology China

ISBN: (纸本)9781450384957

the knowledge graph is the essential infrastructure of plenty of intelligent Web applications. there are various types of knowledge graphs designed and deployed to make the applications smarter during the past decade. However, the increasing amount of product data brings new challenges to the query and retrieval of products. In this work, we present a product knowledge representation method that converts text into IDs to reduce the data transmission time. In order to decrease the retrieval time, we transform the join operation in the relational database into a matrix operation. We propose a pipeline query optimization strategy under GPUs to speed up the execution of the query. To evaluate the performance, we compare our method with the state-of-the-art RDF engine RDF-3X and gStore on the large-scale product datasets. the experimental results show that our approach can significantly improve the efficiency of the SPARQL query.

关键词： College students Chinese sex education Correlation analysis for sex issues Sex-related knowledge

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Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification

Outlier Detection in Logistic Regression: A Quest for Reliab...

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IEEE international conference on Data Mining Workshops

作者： Abdul Nurunnabi Geoff West Department of Spatial Sciences Curtin University

ISBN: (纸本)9781467351645

Logistic regression is well known to the data mining research community as a tool for modeling and classification. the presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge discovery from the underlying databases. Most of the existing outlier detection methods in regression analysis are based on the single case deletion approach that is inefficient in the presence of multiple outliers because of the well known masking and swamping effects. To avoid these effects the multiple case deletion approach has been introduced. We propose a group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression. At the same time we introduce a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations. this paper has two objectives. First, it investigates the problems of outlier detection in logistic regression, proposes a new method that can find multiple influential observations, and classifies the types of outlier. Secondly, it shows the necessity for proper identification of outliers and influential observations as a prelude for reliable knowledge discovery from modeling and classification via logistic regression. We demonstrate the efficiency of our method, compare the performance with the existing popular diagnostic methods, and explore the necessity of outlier detection for reliability and robustness in modeling and classification by using real datasets.

关键词： Data mining High leverge point Influential observation Knowledge discovery Outlier pattern recognition Regression Reliability Statistical computing

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Benchmarking integration pattern implementations 16

Benchmarking integration pattern implementations

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Proceedings of the 10th ACM international conference on Distributed and Event-based Systems

作者： Daniel Ritter Norman May Kai Sachs Stefanie Rinderle-Ma SAP SE Dietmar-Hopp-Allee Walldorf Germany University of Vienna Vienna Austria

ISBN: (纸本)9781450340212

the integration of a growing number of distributed, heterogeneous applications is one of the main challenges of enterprise data management. through the advent of cloud and mobile application integration, higher volumes of messages have to be processed, compared to common enterprise computing scenarios, while guaranteeing high throughput. However, no previous study has analyzed the impact on message throughput for Enterprise Integration patterns (EIPs) (e. g., channel creation, routing and transformation).Acknowledging this void, we propose EIPBench, a comprehensive micro-benchmark design for evaluating the message throughput of frequently implemented EIPs and message delivery semantics in productive cloud scenarios. For that, these scenarios are collected and described in a process-driven, TPC-C-like taxonomy, from which the most relevant patterns, message formats, and scale factors are derived as foundation for the benchmark. To prove its applicability, we describe an EIPBench reference implementation and discuss the results of its application to an open source integration system that implements the selected patterns.

关键词： data-aware processing integration patterns benchmark

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A pattern for Features on a Hierarchical Spatial Grid 21

A Pattern for Features on a Hierarchical Spatial Grid

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Proceedings of the 10th international Joint conference on Knowledge Graphs

作者： Cogan Shimizu Rui Zhu Gengchen Mai Colby Fisher Ling Cai Mark Schildhauer Krzysztof Janowicz Pascal Hitzler Lu Zhou Shirly Stephen Kansas State University United States University of California United States Stanford University United States Hydronos Labs LLC United States UC Santa Barbara United States National Center Ecological Analysis & Synthesis United States Arizona State University United States

ISBN: (纸本)9781450395656

the integration of data along a common spatial component remains an obstacle in many problem spaces. One promising method for integrating data in such a way is through the use of a common, underlying spatial reference system, such as a Discrete Global Grid (e.g., the S2 Grid System), and pre-computing spatial relations between features and the constituent components at a spatial resolution appropriate for the data and use case. that is, by emphasizing the notion of the cell, we can examine what is in a cell, predict contents of its parent and child cells, and quickly get an overview of spatially co-located features and regions of interest without having to directly compute spatial interactions. this paper provides an ontology design pattern, to be used as a structural template, for modeling how features or regions map onto a hierarchical grid system and addresses how the attributes of these features may be inherited upwards or downwards through the hierarchy. We furthermore provide a motivating example and implementation.

关键词： ontology engineering geoinformation science ontology design pattern

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Design and Implementation of Distributed Video Live-Streaming System Based on SRS

Design and Implementation of Distributed Video Live-Streamin...

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international conference on Educational and Information Technology (ICEIT)

作者： Qiang Wang Bo She Jingru Cui Zunying Qin Guodong Li Network Information Center Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781665422956

ISBN: (纸本)9781665446471

Since the COVID-19 pandemic, video live-streaming technologies that ensure learning continuity have evolved rapidly. With the epidemic under control in China, a growing number of colleges classroom teaching will be online live simultaneously. Traditional offline teaching will gradually transform to the combination of online and offline mode. With the demand for the normality high-definition online live-streaming, the traditional live broadcast system faces the problems of limited scalability, poor fault tolerant ability and poor user experience. In this paper, a distributed online live-streaming system cross multiple campuses which based on SRS is designed and realized. through dynamic distributed push, upstream and downstream traffic separation, and multi-level load balancing mechanism, the system can satisfy the need of the automatic and unattended normal video live-streaming teaching of large-scale classrooms. In this loosely coupled system, all components are distributed on each campus and communicate via message queues and HTPP API. the system has been applied to Xi'an Jiaotong University daily online and offline teaching for 700 classrooms spread over three campuses.

关键词： Concurrent computing Scalability Education Transforms Streaming media Load management User experience

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Design and System Realization of the Evaluation Index of Learners’ Core Literacy Ability Based on STEAM Education

Design and System Realization of the Evaluation Index of Lea...

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international conference on Educational and Information Technology (ICEIT)

作者： Benyue Su Qimei Zhao Shulin Cheng Wei Liu Hongmei Chen Tongling University Tongling China The University Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China School of Computer and Information Anqing Normal University Anqing China College of Mechanical and Electrical Engineering Anhui Jianzhu University Hefei China Anqing Road No. 3 Primary School Hefei China

ISBN: (数字)9781665422956

ISBN: (纸本)9781665446471

STEAM education has attracted worldwide attention. However, the comprehensive assessment of learners' core learning literacy ability in STEAM education lacks in-depth discussion. Based on this fact, this research conducted an in-depth analysis of learners' scientific, technical, engineering, artistic and mathematical literacy. In addition to that, this work sought the objective evaluation path, method of the five types of literacy and their corresponding observation points. the evaluation index system for learners' core literacy capabilities in STEAM education was constructed after two rounds of expert opinion consultation by Delphi method and the determination of the evaluation index weights. On this basis, the learner's core literacy ability evaluation system based on STEAM education was further designed. this evaluation system not only helps learners to find their own strengths and weaknesses and make timely adjustments, but also helps teachers to conduct targeted teaching and guidance.

关键词： System realization Education Indexes Information technology

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Subject-Transfer Approach based on Convolutional Neural Network for the SSSEP-BCIs

Subject-Transfer Approach based on Convolutional Neural Netw...

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international Winter Workshop on Brain-Computer Interface (BCI)

作者： Keun-Tae Kim Junhyuk Choi Hyungmin Kim Song Joo Lee Center for Bionics Biomedical Research Institute Korea Institute of Science and Technology Seoul Republic of Korea Division of Bio-Medical Science & Technology University of Science and Technology KIST School Seoul Republic of Korea

ISBN: (数字)9781728184852

ISBN: (纸本)9781728184869

Nowadays, pattern recognition or machine learning-based various approaches have been applied to the steady-state somatosensory evoked potential (SSSEP)-based brain-computer interfaces (BCIs) for improving the accuracy of classifying the user's selective attention to vibration stimulation. However, the accuracy still needs to be improved for applying to a real-world environment. In this paper, the subject-transfer strategy based on the convolutional neural network (CNN) was investigated, if it can improve classification accuracy within SSSEP-based BCIs. In our experiment, for the subject-transferring, the supportive subject's data and target subject's data were used to training the common spatial pattern (CSP) for feature extraction and CNN for classification. the SSSEPs were acquired by selective attention to one of the vibration motors (left-hand and right-hand). the experimental results based on 10-fold cross-validation indicate that the subject-transfer method showed approximately 5% higher accuracies than the traditional self-training method, which is training the CSP and CNN using only the target subject's data. Based on these results, we can confirm that the subject-transfer strategy can contribute to improving the accuracy of the SSSEP-based BCIs for real-world applications.

关键词： Vibrations Training Feature extraction Brain-computer interfaces Steady-state pattern recognition Convolutional neural networks

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Skeleton-based Human Action recognition by the Integration of Euclidean distance 2021

Skeleton-based Human Action Recognition by the Integration o...

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2021 the 9th international conference on Information Technology: IoT and Smart City

作者： Yi Gao Zhaokun Liu Xinmeng Wu Guangyuan Wu Jiahui Zhao Xiaofan Zhao College of Intelligence and Computing Tianjin University China School of Information Technology and Cyber Security People's Public Security University of China China

ISBN: (纸本)9781450384971

With the growing popularity of somatosensory interaction devices, human action recognition becomes more and more attractive in many application scenarios. Nowadays, it takes a long time for some behavior recognition methods to train a model. To solve the problem, this paper proposes a human skeleton model for human action recognition. the joint coordinates are extracted using OpenPose and the thermodynamic diagram, and then we extract partial features and overall features, as well as Euclidean distances between joints. All the data collected above makes up the motion features of independent video frames. What's more, we leverage Multilayer Perceptron (MLP) classifier to classify all the motion features. On the Kth and ICPR datasets, we test the accuracy of the mode verified by changing several parameters. When the image resolution is 320*240, weight is 12 and stride is 1, the highest accuracy rate of single-person behavior recognition is 0.821. When weight is 10 and stride is 1, the highest accuracy rate of multi-person behavior recognition is 0.812, and the high running speed enables the mode to be real-time.

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