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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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A Digital Psychophysiological Mapping of Primary School Children with/without Learning Disabilities 9th

引用

9th international conference on Cognitive Sciences, Intercognsci 2020

作者： Savchuk, Lyudmila V. Polevaya, Sofia A. Gromov, Kirill N. Fedotchev, Alexander I. Parin, Sergey B. Balandina, Oksana V. Demareva, Valeriia A. Privolzhsky Research Medical University 10/1 Minin and Pozharsky Sq. Nizhny Novgorod603005 Russia National Research Lobachevsky State University of Nizhny Novgorod 23 Gagarin Ave. Nizhny Novgorod603950 Russia Institute of Cell Biophysics Russian Academy of Sciences 3 Institutskaya St. Pushchino Moscow Region142290 Russia

ISBN: (纸本)9783030716363

School entry is a critical stage in children’s development, that is intrinsically related to preparedness to learn as reflected in cognitive and behavioral maturation in terms of psychophysiology. the growing number of cognitive and behavioral disorders makes school maladjustment (SM) a socially relevant issue. Attention-Deficit Hyperactivity Disorder (ADHD) is reported as the most frequent but not the only reason for behavioral deviations, study difficulties and school maladjustment. the current study applies the methodology of digital psychophysiological mapping for primary-school children with and without learning difficulties. the analysis has revealed several quantitative indicators as non-specific indicators of psychopathology that accompany a high level of school maladjustment. Still other ones are specific to clinical populations. the study also revealed specific features characterizing SM and non-SM children’s performance on various sensorimotor tasks. Children with ADHD and mild cognitive impairment (MCI) are characterized by overall power reduction in heart rate spectral variability and inhibition of central regulation activity. In contrast to ADHD-children, children with MCI did not show increased tension of the regulatory systems. Children diagnosed with anxiety (AD) demonstrate a juggled pattern of central regulatory activity induced by cognitive and sensorimotor load;parietal-occipital focus of alpha-rhythm is less pronounced in the right central-parietal area for children with MCI, and children with AD demonstrate the smallest deviation from the pattern that is typically observed and represents the norm. theta/beta relation for the MCI and the ADHD-group is significantly higher than for the control group, and children with AD do not differ from the control group on this measurement type. Our results show that the method of adaptive neuromodulation for optimizing the functional brain status is a highly promising tool for diagnosis of different clinica

关键词： Mapping

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Multiple Speech Source Separation by Using MVDR for B-Format Recordings 2020

Multiple Speech Source Separation by Using MVDR for B-Format...

引用

Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Yitian Jia Maoshen Jia Lu Li Ju Gao Shaoxin Yang Beijing University of Technology Beijing China Beijing-Dublin International College Beijing University of Technology Beijing China

ISBN: (纸本)9781450387835

this paper presents a multiple speech source separation method that adopts the minimum variance distortionless response (MVDR) beamformer to separate speech sources based on the direction-of-arrival (DOA) estimated from the B-format microphone recordings. Unlike conventional microphone arrays, the B-format microphone provides complete spatial information of the pressure gradient and acoustic pressure which is useful to multiple sources localization. therefore, this paper adopts the single-source points detection of the B-format microphone recordings, where only the dominant source is present in the time-frequency domain. A higher accuracy of source localization is achieved by identifying and utilizing only single-source points which contain correct direction cues. According to the DOA estimates, MVDR beamformer is then adopted for recovering the sources separately. the objective evaluation shows that the proposed method outperforms the existing BSS algorithm.

关键词： Blind source separation B-format microphone minimum variance distortionless response direction of arrival

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DATA MINING APPLIED TO FEATURES SELECTIVITY FOR A NON-TECHNICAL LOSS DETECTION MODEL IN RURAL DISTRIBUTION GRIDS

DATA MINING APPLIED TO FEATURES SELECTIVITY FOR A NON-TECHNI...

引用

CIRED 2021 - the 26th international conference and Exhibition on Electricity Distribution

作者： V. Uberti D. P. Bernardon V. Vieira M. Heinke R. De Figueiredo M. Evaldt A. Abaide L. de Chiara J. A. Silva Federal University of Santa Maria Santa Maria - RS Brazil University of Rio dos Sinos Valley São Leopoldo - RS Brazil 3Energy CPFL São Paulo - SP Brazil

With the smart grids growth, much information started to be generated daily by smart meters throughout the power grids. the acquisition of large volume of data enables improvements in the management, control and maintenance of networks. However, processing of this data is a slower, costly and complex task. In this context, the process of discovering patterns in large data sets, called data mining, can be considered as an important ally in the electrical sector with focus on identifying non-technical losses, as it is an important challenge for power utilities, especially in the case of customers in rural grids. thus, the objective of this paper is to present a methodology applying data mining to identify correlations and patterns in the features associated with the rural power consumption, considering meteorological and power consumption data in the irrigation period. Results obtained in a case study where situations of non-technical losses were simulated show great recognition capacity after the application of data mining and reduction of the considered variables. Discrepancies of 10% or higher are detected for all considered rural consumers.

关键词： power engineering computing power consumption data mining smart power grids power distribution electricity supply industry

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Frequent Itemset Mining with Hadamard Response Under Local Differential Privacy

Frequent Itemset Mining with Hadamard Response Under Local D...

引用

international conference on Electronics Information and Emergency Communication

作者： Haijiang Liu Xiangyu Bai Xuebin Ma Lianwei Cui Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Inner Mongolia University Hohhot China Inner Mongolia Big Data Development Authority Hohhot China

ISBN: (数字)9781728163130

ISBN: (纸本)9781728163147

Frequent itemset mining is a basic data mining task and has many applications in other data mining tasks. However, users’ personal privacy information will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. Data mining using local differential privacy involves two major problems. the first one is that the accuracy of the results after mining is low, and the other one is that the user transmits a large amount of data to the server, which results in higher communication costs. In this study, we demonstrate that the Hadamard response (HR) algorithm improves the accuracy of the results and reduces the communication cost from k to log k. Finally, we use the Frequent pattern tree (FP-tree) algorithm for frequent itemset mining to compare the existing algorithms.

关键词： Itemsets Perturbation methods Privacy Task analysis

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Enhanced Competitive Swarm Optimizer for Multi-task Optimization 2020

Enhanced Competitive Swarm Optimizer for Multi-task Optimiza...

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Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Wei Li Junqing Yuan Haonan Luo Zhou Lei Qingzheng Xu School of Computer Science and Engineering Xi'an University of Technology and Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China College of Information and Communication National University of Defense Technology Xi'an China

ISBN: (纸本)9781450387835

In the real world, many problems possess the high degree of similarity. Inspired by multifactorial inheritance model, evolutionary multi-task optimization paradigm is proposed to simultaneously solve multiple optimization problems. However, experimental results have revealed that the performance of multifactorial evolutionary algorithm deteriorates with negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we proposed an enhanced competitive swarm optimizer to explore the generality of the multitasking paradigm. A new velocity update mechanism for losers is proposed to improve the search ability. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm is competitive in comparison with other state-of-the-art multifactorial optimization algorithms.

关键词： Evolutionary multitasking competitive swarm optimizer evolutionary algorithms

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Design Guide for Emotional Evocative Student’s Leadership Program 10th

Design Guide for Emotional Evocative Student’s Leadership P...

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10th international conference on Kansei Engineering and Emotion Research, KEER 2024

作者： Ahmad, Zaiha Ibrahim, Zuraeda Ibrahim, Nordiana Azmi, Nur Nafishah Shahibi, Mohd Sazili Lokman, Anitawati Mohd Faculty of Communication and Media Studies Universiti Teknologi MARA Shah Alam Malaysia Faculty of Accountancy Universiti Teknologi MARA Selangor Puncak Alam Malaysia Faculty of Plantation and Agrotechnology Universiti Teknologi MARA Melaka Jasin Malaysia College of Creative Arts Universiti Teknologi MARA Selangor Puncak Perdana Malaysia College of Computing Informatic and Mathematic Universiti Teknologi MARA Selangor Puncak Perdana Malaysia

ISBN: (纸本)9789819798896

this paper presents an in-depth study on the emotional experiences induced by student leadership programs. Utilizing the Kansei Engineering approach, the research examines three leadership programs, dissecting their design components and corresponding attributes. A comprehensive evaluation process involving 150 students and a Kansei checklist of 28 words was used to synthesize students’ emotions. the collected data was then analyzed using Factor Analysis (FA), Principal Component Analysis (PCA), and Partial Least Squares (PLS) to identify the attributes from the program components that significantly influence emotional experiences. the results reveal that the emotional structure towards student leadership programs is predominantly characterized by two factors: Excitement and Confidence. Further, the PLS analysis discerns the program components and attributes that significantly influence student emotions. Importantly, these findings have led to the creation of a framework for Emotionally Evocative Leadership Program Design. this framework can become invaluable reference for educators, program designers, and leadership coaches, providing clear direction on how to incorporate significant emotional elements into program design to enhance student experience and engagement effectively. By prioritizing elements that foster a sense of excitement and confidence, the framework helps to create more impactful and emotionally resonant leadership programs. Its usability lies in its systematic approach, making it easy to apply across various leadership program contexts. Ultimately, this work underscores the significant role that emotions play in educational settings, contributing to a more holistic approach to developing future leaders. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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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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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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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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