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Frontiers in Education (FIE) Conference

作者： Duo Li Elizabeth Milonas Qiping Zhang Department of Big Data Management and Application Shenyang Institute of Technology Fushun China Computer Systems Technology New York City College of Technology (CUNY) Brooklyn USA Palmer School of Library and Information Science Long Island University Brookville USA

Numerous educational institutions, including those specializing in computer science, engineering, business, and science, now offer graduate programs in data science. However, it remains uncertain whether the data science programs provided by universities with different rankings deliver similar or distinct data science competencies. This study aims to compare the competencies of graduate data science programs offered in the United States. It addressed the research question of “Are there any differences in graduate data science program competencies based on their university rankings?” The program competencies of 228 data science graduate programs as well as their U.S. News university rankings were collected and compared. The findings indicate that regardless of their national rankings, all universities offer most of the competencies required for data science programs, except for Machine Learning and Sensor Networks. Forty-six percent of data science programs are offered in universities that hold national rankings within the top 75, focusing on majors such as data science and business analytics. Furthermore, the top-ranked universities provide data science programs across all departments or schools, with the most prominent ones being Business, Computer Science, Information Science & technology, and Math/Statistics. The primary research contribution of this study revolves around conducting a comprehensive analysis of data science program competencies within the United States. The implications of the findings have significant relevance to computing education. The study provides valuable guidelines for educational practices in the field of data science: 1) naming the new majors as either “data Science” or “Business Analytics”; 2) housing the new data science programs within existing departments or schools specializing in Business, Computer Science, Information Science & technology, and Math/Statistics; 3) incorporating all ACM data Science competencies into the curriculum

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Excess mortality associated with high ozone exposure:A national cohort study in China

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Environmental Science and Ecotechnology 2023年第3期15卷 98-105页

作者： Yang Yuan Kai Wang Haitong Zhe Sun Yu Zhan Zhiming Yang Kejia Hu Yunquan Zhang Institute of Social Development and Health Management Hubei Province Key Laboratory of Occupational Hazard Identification and ControlSchool of Public HealthWuhan University of Science and TechnologyWuhan430065China Centre for Atmospheric Science Yusuf Hamied Department of ChemistryUniversity of CambridgeCambridgeCB21EWUK Department of Earth Sciences University of CambridgeCambridgeCB23EQUK Department of Environmental Science and Engineering Sichuan UniversityChengduSichuan610065China School of Economics and Management University of Science and Technology BeijingBeijing100083China Department of Big Data in Health Science School of Public HealthZhejiang UniversityHangzhou310058China

Emerging epidemiological studies suggest that long-term ozone(O_(3))exposure may increase the risk of mortality,while pre-existing evidence is mixed and has been generated predominantly in North America and *** this study,we investigated the impact of long-term O_(3)exposure on all-cause mortality in a national cohort in China.A dynamic cohort of 20882 participants aged40 years was recruited between 2011 and 2018 from four waves of the China Health and Retirement Longitudinal Study.A Cox proportional hazard regression model with time-varying exposures on an annual scale was used to estimate the mortality risk associated with warm-season(Aprile September)O_(3)*** annual average level of participant exposure to warm-season O_(3)concentrations was 100 mg m^(-3)(range:61 e142 mg m^(-3)).An increase of 10 mg m^(-3)in O_(3)was associated with a hazard ratio(HR)of 1.18(95%confidence interval[CI]:1.13e1.23)for all-cause *** with the first exposure quartile of O_(3),HRs of mortality associated with the second,third,and highest exposure quartiles were 1.09(95%CI:0.95e1.25),1.02(95%CI:0.88e1.19),and 1.56(95%CI:1.34e1.82),respectively.A J-shaped concentration eresponse associationwas observed,revealing a non-significant increase in risk below a concentration of approximately 110 mg m^(-3).Low-temperature-exposure residents had a higher risk of mortality associated with long-term O_(3)*** study expands current epidemiological evidence from China and reveals that high-concentration O_(3)exposure curtails the long-term survival of middle-aged and older adults.

关键词： Ozone Long-term exposure All-cause mortality Cohort study

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PISketch: Finding Persistent and Infrequent Flows 3

PISketch: Finding Persistent and Infrequent Flows

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3rd ACM SIGCOMM 2022 Workshop on Formal Foundations and Security of Programmable Network Infrastructures, FFSPIN 2022

作者： Fan, Zhuochen Hu, Zhoujing Wu, Yuhan Guo, Jiarui Liu, Wenrui Yang, Tong Wang, Hengrui Xu, Yifei Uhlig, Steve Tu, Yaofeng School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China Computer Science Department University of California Los Angeles United States School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom ZTE Corporation China

ISBN: (纸本)9781450393294

Finding persistent and inactive activity periods is very helpful in practice, for example to detect intrusion activities. Most of the literature focuses on finding persistent flows or frequent flows. No previous work is able to find persistent and infrequent flows. In this paper, we propose a novel sketch data structure, PISketch, to find persistent and infrequent flows in real time. The key idea of PISketch is to define a weight and its Reward and Penalty System for each flow to combine and balance the information of both persistency and infrequency, and to keep high-weighted flows in a limited space through a strategy. We implement PISketch on P4 and CPU platforms, and compare the performance of PISketch with two strawman solutions (On-Off + CM sketch, and PIE + CM sketch), in terms of finding persistent and infrequent flows. Our experimental results demonstrate the advantage of PISketch, by comparing it to two strawman solutions: 1) The F1 Score of PISketch is around 22.1% and 57.6% higher than two strawman solutions, respectively;2) The Average Relative Error (ARE) of PISketch is around 820.9 (up to 1188.8) and 126.2 (up to 265.6) times lower than two strawman solutions, respectively;3) The insertion throughput of PISketch is around 1.23 and 16.5 times higher than two strawman solutions, respectively. Moreover, we implement two concrete cases of PISketch through end-to-end experiments. All of our codes are available at GitHub. © 2022 ACM.

关键词： infrequent items persistent items sketch weight fusion

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Zero-shot Cross-lingual Conversational Semantic Role Labeling

Zero-shot Cross-lingual Conversational Semantic Role Labelin...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Wu, Han Tan, Haochen Xu, Kun Liu, Shuqi Wu, Lianwei Song, Linqi City University of Hong Kong Shenzhen Research Institute Hong Kong Department of Computer Science City University of Hong Kong Hong Kong Huawei China Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China

ISBN: (纸本)9781955917766

While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. To avoid expensive data collection and error-propagation of translation-based methods, we present a simple but effective approach to perform zero-shot cross-lingual CSRL. Our model implicitly learns language-agnostic, conversational structure-aware and semantically rich representations with the hierarchical encoders and elaborately designed pre-training objectives. Experimental results show that our model outperforms all baselines by large margins on two newly collected English CSRL test sets. More importantly, we confirm the usefulness of CSRL to non-Chinese conversational tasks such as the question-in-context rewriting task in English and the multi-turn dialogue response generation tasks in English, German and Japanese by incorporating the CSRL information into the downstream conversation-based models. We believe this finding is significant and will facilitate the research of non-Chinese dialogue tasks which suffer the problems of ellipsis and anaphora. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Semantics

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Face Detection and Tracking Based on Neural Network 3

Face Detection and Tracking Based on Neural Network

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3rd International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2022

作者： Li, Jiaojun Yang, Chuan Yang, Fan Huang, Jie Wei, Wei Zhang, Sujuan Zuo, Xun Zhang, Shilong Chongqing University of Technology Chongqing400054 China China Telecom Group Chongqing Co.Ltd. Cloud Network Development Department Chongqing400042 China Chongqing Branch of China Telecom Co. Ltd. Big Data and Ai Center Department 401121 China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

Face detection and tracking technology is used in transportation, security, military fields. In view of the traditional face detection and tracking technology is easy to be affected by light, which leads to low detection accuracy, this paper uses Retinaface and Camshift algorithm to face detection, and realizes real time face detection and tracking by P control steering gear in PID control. Through tests in different environments, the detection accuracy of the Retinaface algorithm and the Camshift algorithm is above 99%. The camera is rotated through P to ensure that the face can be captured by the camera, and the camera response time can reach 0.1s. © 2022 IEEE.

关键词： Cameras

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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Graph Convolution Synthetic Transformer for Chronic Kidney Disease Onset Prediction 9th

Graph Convolution Synthetic Transformer for Chronic Kidney ...

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19th International Conference on Advanced data Mining and applications, ADMA 2023

作者： Zhu, Di Liu, Yi Chen, Weitong Wang, Yanda Huang, Yefan Wang, Xiaoli Cai, Ken Li, Bohan Nanjing University of Aeronautics and Astronautics Nanjing211106 China Adelaide University AdelaideSA 5005 Australia Nanjing Normal University Nanjing210023 China Xiamen University Xiamen361005 China Zhongkai University of Agriculture and Engineering Guangzhou510225 China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing211106 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xian710072 China

ISBN: (纸本)9783031466700

Effective disease prediction based on electronic health records (EHR) is an important topic in health informatics. The current methods usually use common deep-learning models for disease prediction. However, it is difficult to fully learn the graphic encounter structure of EHR to improve prediction performance. Moreover, in prediction tasks, chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and complex comorbidities. Therefore, we propose a CKD onset prediction model called Graph Convolution Synthetic Transformer (GCST) based on EHR, using Fusion Attention Mechanism to solve these challenges. By modifying Transformer, GCST uses Factorized Dense Attention and Medical Local Attention to learn global and local attention, generating Synthetic Attention to learn the potential encounter structure and meaningful medical knowledge of EHR. In addition, we also propose a transfer learning strategy based on sample weighted correction to guide the prediction of GCST in specific low-resource EHR. We conduct sufficient experiments on three datasets to test the performance of GCST. Experiments show that GCST has significant improvement over state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model

arXiv

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arXiv 2024年

作者： Xing, Yinghui Qu, Litao Zhang, Shizhou Zhang, Kai Zhang, Yanning Ran, Lingyan Zhang, Xiuwei Yin, Hanlin The Shaanxi Provincial Key Laboratory of Speech and Image Information Processing The National Engineering Laboratory for Integrated Aerospace-GroundOcean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China The Research Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Fusion of a panchromatic (PAN) image and corresponding multispectral (MS) image is also known as pansharpening, which aims to combine abundant spatial details of PAN and spectral information of MS. Due to the absence of high-resolution MS images, available deep-learning-based methods usually follow the paradigm of training at reduced resolution and testing at both reduced and full resolution. When taking original MS and PAN images as inputs, they always obtain sub-optimal results due to the scale variation. In this paper, we propose to explore the self-supervised representation of pansharpening by designing a cross-predictive diffusion model, named CrossDiff. It has two-stage training. In the first stage, we introduce a cross-predictive pretext task to pre-train the UNet structure based on conditional DDPM, while in the second stage, the encoders of the UNets are frozen to directly extract spatial and spectral features from PAN and MS, and only the fusion head is trained to adapt for pansharpening task. Extensive experiments show the effectiveness and superiority of the proposed model compared with state-of-the-art supervised and unsupervised methods. Besides, the cross-sensor experiments also verify the generalization ability of proposed self-supervised representation learners for other satellite’s datasets. We will release our code for reproducibility. © 2024, CC BY.

关键词： Deep learning

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Exploring the Non-linear Relationship between E-scooter Ridership and the Built Environment 6

Exploring the Non-linear Relationship between E-scooter Ride...

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6th International Conference on Transportation Information and Safety, ICTIS 2021

作者： Qiu, Zhongquan Yuan, Hongxia Wang, Chao Dai, Shideng Huo, Yamin Wen, Yi Southwest Jiaotong University Hi-Tech Industrial Development Zone School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Chengdu611756 China Southwest Jiaotong University Hi-Tech Industrial Development Zone School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Transportation Chengdu611756 China University of Tennessee Department of Civil and Environmental Engineering KnoxvilleTN37996 United States

ISBN: (纸本)9781665497138

Shared e-scooter has become a popular mode of transportation in the United States. Despite the increasing attention paid to the e-scooter patterns and the influencing factors of e-scooter trips, few studies have explored the relative importance of the built environment and its threshold effect on e-scooters. Using the trip records of all the shared e-scooters in Austin, Texas, USA from August 2018 to March 2021, the origins and destinations of which are recorded by census tract, this study employs gradient boosting decision trees (GBDT) to examine the non-linear effects of the built environment on the e-scooter ridership. The results show that the relative importance of all built environment factors, with a contribution of 70.45%, is higher than that of socio-demographics (29.54%), and the distance from census tract to city center and bike lane density are the two most essential attributes to e-scooter ridership. Besides, all built environment factors have salient non-linear and threshold effects on the e-scooter ridership. For example, distance to the city center has a significant negative impact on e-scooter trips with an effective range of 6.5 kilometers, and the bike lane density is positively associated with the e-scooter ridership in a certain range. By quantifying each built environment factor's relative importance and threshold effect on e-scooters, this paper can provide e-scooter operators with fleet deployment and operation management suggestions and guide planning departments to build an e-scooter-friendly environment. © 2021 IEEE.

关键词： Decision trees

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Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Chen, Ziyang Ye, Yiwen Pan, Yongsheng Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China Ningbo Institute of Northwestern Polytechnical University Ningbo China

Although recent years have witnessed significant advancements in medical image segmentation, the pervasive issue of domain shift among medical images from diverse centres hinders the effective deployment of pre-trained models. Many Test-time Adaptation (TTA) methods have been proposed to address this issue by fine-tuning pre-trained models with test data during inference. These methods, however, often suffer from less-satisfactory optimization due to suboptimal optimization direction (dictated by the gradient) and fixed step-size (predicated on the learning rate). In this paper, we propose the Gradient alignment-based Test-time adaptation (GraTa) method to improve both the gradient direction and learning rate in the optimization procedure. Unlike conventional TTA methods, which primarily optimize the pseudo gradient derived from a self-supervised objective, our method incorporates an auxiliary gradient with the pseudo one to facilitate gradient alignment. Such gradient alignment enables the model to excavate the similarities between different gradients and correct the gradient direction to approximate the empirical gradient related to the current segmentation task. Additionally, we design a dynamic learning rate based on the cosine similarity between the pseudo and auxiliary gradients, thereby empowering the adaptive fine-tuning of pre-trained models on diverse test data. Extensive experiments establish the effectiveness of the proposed gradient alignment and dynamic learning rate and substantiate the superiority of our GraTa method over other state-of-the-art TTA methods on a benchmark medical image segmentation task. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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