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IEEE International Conference on big data

作者： Rongzhe Wei Bo Dong Qinghua Zheng Xulyu Zhu Jianfei Ruan Huan He Qian Xuesen College School of Mathematics and Statisitcs Xi’an Jiaotong University Xi’an China National Engineering Lab of Big Data Analytics School of Distance Education Xi’an Jiaotong University Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China SPKLSTN Lab Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

The identification of tax evasion plays an important role in ensuring tax order, promoting the level of tax collection and management, and reducing tax losses. With the advancements in data mining technology, many machine learning techniques have yielded results in identifying tax evasion. However, to realize satisfactory performance, these models require large amounts of human annotated data. In the tax field, unlabeled tax data are abundant, data annotation in a single region is expensive, and the distributions of characteristics differ among regions; these factors pose substantial difficulties in the development of an identification model. Existing tax evasion detection methods are either trained for single-region tasks, in which case they perform poorly on inter-region tax evasion identification due to the discrepancies in feature distributions, or utilize labeled data from both the target-task field and different but related auxiliary fields to reuse and transfer knowledge of the target domain data, in which case they cannot deal with scenarios in which there are no labeled data in target audit tasks. Although current unsupervised transfer learning techniques can train models in labeled regions for unlabeled regions, large intra-class distribution discrepancies cannot be perfectly minimized in tax evasion detection scenarios. To better address the above challenges, this paper proposes a general architecture, namely, the unsupervised conditional adversarial networks (UCAN) for tax evasion detection, which is the first approach to solve audit tasks in unlabeled target domains via inter-region transfer. Our architecture establishes an adversarial neural network adding label information in the distribution adapter, which can granularly adapt the joint probability distribution (JPD) of the data. We introduce a constraint that is based on the conditional maximum mean discrepancy (CMMD) of the extracted features to align the conditional probability distribution (CPD)

关键词： Finance Feature extraction Task analysis Adaptation models Computer architecture data models data mining

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Equal heartbeat intervals and their effects on the nonlinearity of permutation-based time irreversibility in heart rate

arXiv

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arXiv 2019年

作者： Yao, Wenpo Yao, Wenli Wang, Jun School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing Jiangsu210003 China School of Mines China University of Mining and Technology Xuzhou221116 China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China

The association of equal heartbeat intervals with cardiac conditions and the effect of the equality on permutation-based time irreversibility are investigated in this paper. We measure the distributions of equal heartbeat intervals under three conditions, namely congestive heart failure (CHF), healthy young and elderly, whose time irreversibility of heartbeats is detected by measuring the probabilistic difference between permutations instead of raw vectors. We demonstrate that heartbeats contain high rates of equal states, particularly the CHF with around 20% equalities, and the distributions of equal values discriminate the heartbeats at very short data length. The CHF have more equal values than the healthy young (p−15) and elderly (p−11), and the healthy young have less equalities than the elderly (p−4). Time irreversibility considering equal values is promising to extract nonlinear behaviors of heartbeats, confirming the decreased nonlinear complexity of the diseased and aging heart rates, while that involving no equality leads to erroneous nonlinearity detection. In our contribution, we highlight the pathological or physiological information contained by the distribution of equal heartbeat intervals that might contribute to develop relevant biomarkers in the area of heart analysis, and demonstrate the effectiveness of equality-based time irreversibility in the nonlinearity detection of heartbeats. Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors

arXiv

引用

arXiv 2018年

作者： Li, Zhi Zhao, Hongke Liu, Qi Huang, Zhenya Mei, Tao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China School of Software Engineering University of Science and Technology of China JD AI Research

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers’ interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers’ long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users’ historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Design for intelligent control system of curtain based on arduino 1st

Design for intelligent control system of curtain based on ar...

引用

1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Meng, Sheng-Hui Huang, An-Chi Lee, Chia-Jung Huang, Tian-Jiun Dal, Jian-Nan Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China School of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Kaohsiung Municipal ChungShan Senior High School Kaohsiung Taiwan

In this research, a design for smart home curtains was proposed. The design is based on an Arduino UNO R3 development board equipped with an ESP8266-01 Wi-Fi module that communicates with an Android client application... 详细信息

ISBN: (纸本)9783319707297

关键词： HTTP

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GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS

arXiv

引用

arXiv 2025年

作者： Li, Zhenlong Ning, Huan Gao, Song Janowicz, Krzysztof Li, Wenwen Arundel, Samantha T. Yang, Chaowei Bhaduri, Budhendra Wang, Shaowen Zhu, A. Xing Gahegan, Mark Shekhar, Shashi Ye, Xinyue McKenzie, Grant Cervone, Guido Hodgson, Michael E. Geoinformation and Big Data Research Lab Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of Wisconsin – Madison WI United States STKO Lab Department of Geography and Regional Research University of Vienna Vienna Austria Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University AZ United States Center of Excellence for Geospatial Information Science U.S. Geological Survey VA United States NSF Spatiotemporal Innovation Center Department of Geography & Geoinformation Science George Mason University VA United States TN United States CyberGIS Center for Advanced Digital and Spatial Studies Department of Geography and Geographic Information Science University of Illinois Urbana-Champaign IL United States School of Computer Science University of Auckland New Zealand Department of Computer Science & Engineering University of Minnesota MN United States Department of Landscape Architecture & Urban Planning Center for Geospatial Sciences Applications & Technology Texas A&M University TX United States Platial Analysis Lab Department of Geography McGill University Quebec Canada Institute for Computational and Data Sciences Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of South Carolina SC United States

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five autonomous levels, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modeling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future. © 2025, CC BY.

关键词： Image retrieval

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Enhancing person-Job fit for talent recruitment: An ability-aware neural network approach

arXiv

引用

arXiv 2018年

作者： Qin, Chuan Zhu, Hengshu Xu, Tong Zhu, Chen Jiang, Liang Chen, Enhong Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology an Application China

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person-Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end-to-end Ability-aware Person-Job Fit Neural Network (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, four hierarchical ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large-scale real-world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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The design of RNA biosensors based on nano-gold and magnetic nanoparticles 12th

The design of RNA biosensors based on nano-gold and magnetic...

引用

12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Yang, Jing Yin, Zhi-xiang Cui, Jian-zhong School of Mathematics and Big Data Anhui University of Science and Technology Huainan China School of Electronic and Information Engineering Anhui University of Science and Technology Huainan232001 China

ISBN: (纸本)9789811071782

With the application of biosensors in environmental monitoring, these features of low sample concentrations and the need for real-time monitoring feedback in environmental monitoring, make the sensor requirements also increasing. High sensitivity, short response time and low cost are the environmental monitoring biological sensors goal. RNA has a high affinity capacity and sensitivity, and has better thermal stability after hybridization. Combined with the characteristics of nano-gold and magnetic particles in this paper, improving material and probe of electrode, propose design ideas of several biosensors with LNA, PNA for RNA viruses in water monitoring to improve biosensors in environmental monitoring of practicality. © Springer Nature Singapore Pte Ltd 2017.

关键词： Gold nanoparticles

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Dynamic Harmonic Modeling and Characteristics Analysis of Photovoltaic Converter

Dynamic Harmonic Modeling and Characteristics Analysis of Ph...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ding Wang Yangwu Shen Xun Mao Ao Pi Bing Zeng Chengye Zhang Power grid technology center State Grid Hunan Electric Power Company Limited Research Institute Changsha China Power grid technology center State Grid Anhui Electric Power Co. Ltd. Research Institute Hefei China Big data center state key laboratory of offshore wind power technology and testing XEMC Wind power Co. Ltd. Changsha China School of Electrical & Information Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

Photovoltaic converters whose dynamic characteristics determine the behavior of grid-connected device are important equipment for new energy generation. Therefore, to analyze the harmonic characteristics generated by grid-connected photovoltaic devices, it is necessary to firstly study the mechanism of harmonic generation, then build the dynamic harmonic model of photovoltaic power generation unit, and discuss the influencing factors. In this paper, based on the deep comprehension of harmonic characteristics, a Norton equivalent circuit that described the harmonic characteristics of grid-connected converter of photovoltaic power generation unit is established. Besides, the effects of the power output of photovoltaic power generation unit and the filter structure on converter's harmonic output are investigated. The accuracy of the proposed model is verified by comparing the results between the theoretical calculation and simulation.

关键词： Power harmonic filters Harmonic analysis Modulation Photovoltaic systems

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Bicycle flow dynamics on wide roads: Experiment and modeling

arXiv

引用

arXiv 2019年

作者： Guo, Ning Jiang, Rui Wong, S.C. Hao, Qing-Yi Xue, Shu-Qi Hu, Mao-Bin School of Automotive and Transportation Engineering Hefei University of Technology Hefei230009 China Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Department of Civil Engineering University of Hong Kong Pokfulam Road Hong Kong School of Mathematics and Computational Science Anqing Normal University Anqing246133 China School of Engineering Science University of Science and Technology of China Hefei230026 China

Cycling is a green transportation mode, and is promoted by many governments to mitigate traffic congestion. However, studies concerning the traffic dynamics of bicycle flow are very limited. This study experimentally investigated bicycle flow dynamics on a wide road, modeled using a 3-m-wide track. The results showed that the bicycle flow rate remained nearly constant across a wide range of densities, in marked contrast to single-file bicycle flow, which exhibits a unimodal fundamental diagram. By studying the weight density of the radial locations of cyclists, we argue that this behavior arises from the formation of more lanes with the increase of global density. The extra lanes prevent the longitudinal density from increasing as quickly as in single-file bicycle flow. When the density is larger than 0.5 bicycles/m2, the flow rate begins to decrease, and stop-and-go traffic emerges. A cognitive-science-based model to reproduce bicycle dynamics is proposed, in which cyclists apply simple cognitive procedures to adapt their target directions and desired riding speeds. To incorporate differences in acceleration, deceleration, and turning, different relaxation times are used. The model can reproduce the experimental results acceptably well and may also provide guidance on infrastructure design. Copyright © 2019, The Authors. All rights reserved.

关键词： Traffic congestion

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