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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

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice full paper compares data science education strategies in China and the United States, exploring whether different approaches can achieve similar educational outcomes. In the U.S., data science programs are typically developed by individual schools to meet labor market demands, integrating data science as an interdisciplinary subject. In contrast, Chinese universities follow a uniform plan set by the education department, resulting in specialized fields like ‘big data Management and Application’ and ‘data science and big data technology.’ This research collected data on course offerings and university rankings in China and the U.S., analyzing curriculum content and program characteristics. major findings indicate that in the U.S., data science and Business Analytics programs focus on technical skills and are primarily found in top-ranking universities, while in China, data science and big data Management programs emphasize management and are more widely distributed across different ranking groups. Despite these differences, both countries show similar geographic concentration patterns in data science programs. The study concludes that China and the U.S. adopt different educational strategies but achieve comparable effectiveness in data science education.

关键词： Economics Industries Technological innovation Analytical models Education data science big data Market research data models Business

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Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module 4

Improving Event Coreference Resolution Using Prompt Learning...

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4th International Conference on Computer science and Blockchain, CCSB 2024

作者： Zhang, Mengyao Long, Jun Huang, Wenti School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (纸本)9798350356502

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments. © 2024 IEEE.

关键词： Semantics

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Behaviour profile on social media: Tik Tok and QQ

Behaviour profile on social media: Tik Tok and QQ

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2021 International Conference on Computer Application and Information Security, ICCAIS 2021

作者： Hassan, Abdiaziz Omar Wang, Lili Hasan, Abdulkadir Abdulahi Latif, Sohaib Department of Mathematics and Big Data Anhui University of Science and Technology Huainan232000 China

ISBN: (纸本)9781510655218

This research seeks to investigate the behaviour profile on social media platforms, using the case of Tik Tok and QQ. Social media platforms are used for many purposes, including the requirements and gratifications, as well as to satisfy various social media needs. As such, these factors were utilised as the independent variables of this study, while social media adoption was treated as a dependent variable. Primary data was collected by using a questionnaire survey to obtain quantitative information. A survey method including self-administered questions was used to collect the study's data. This study used random sampling to identify individuals who would likely use media sites like as Tik Tok and QQ. Based on the findings of this study, uses and gratifications, social media needs, and social media technology have a positive effect on social media adoption. Owing to the increasing number of social media platforms as a result of technological changes, there is need for researchers to continuously investigate the key factors influencing the adoption of different social media platforms. © The Authors.

关键词： Surveys

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Design and Implementation of Intelligent Tour Guide Application System 10

Design and Implementation of Intelligent Tour Guide Applicat...

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10th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2022

作者： Liu, Yizhen Wang, Yanming Lei, Kai Zhang, Jinghui School of Sugon Big Data Liaoning Institute of Science and Technology Liaoning Benxi China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

University campuses generally cover a large area. School office buildings, secondary college buildings, teaching buildings, libraries, staff and student canteens, supermarkets, baths and so on are distributed in different places on the campus, which brings relative inconvenience and difficulties to those who are unfamiliar with the campus environment, such as, the newly admitted college students, teachers and students from outside the school for training, and foreign office personnel. Based on the Hybrid App development model, with the support of Cordova project structure deployment which is compatible with Android and IOS system platforms, and the design of a micro-service architecture that combines distributed deployment and synchronization management, we create an efficient, fast, practical, intelligent tour guide APP with high-security, high-performance, which is to design and open a unified standard API interfaces for system management on the mobile phone, through three parallel tour guide modes - intelligent positioning voice playback, text push, and QR code- the access and sharing of data and information on the university campus is realized. © 2022 IEEE.

关键词： Students

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College Students 9th

Ensemble Learning Based Employment Recommendation Under Inte...

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

作者： Zhu, Haiping Zhao, Yifei Wu, Yuchen Chen, Yan Li, Wenhao Zheng, Qinghua Tian, Feng Department of Computer Science and Technology Xi’an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9783031466632

Recommendation systems play a crucial role in helping college students find job opportunities. However, the sparsity of interactions in employment recommendation for college students poses a challenge for models based on historical user preferences. To address this issue, we propose a novel model called Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students (EERIS). The model comprises two components: a similarity information component that uses pooled users to determine the nearest neighbor in user similarity measurement, and a global interaction component that uses interaction vectors of user groups to enhance interactions. To evaluate the missing interactions, we propose a loss function called CellLoss. These components are combined based on ensemble learning to improve the model’s generalization and scalability. Our experiments on two real-world datasets demonstrate the superior performance of the EERIS model. Ablation experiments further confirm that each component positively contributes to the model’s performance. Additionally, we design a revised metric for better model testing. Overall, the proposed EERIS model effectively addresses the interaction sparsity in employment recommendation for college students and provides satisfactory recommendations to students. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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SFFSlib:A Python library for optimizing attribute layouts from micro to macro scales in network visualization

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Chinese Physics B 2025年第5期34卷 124-138页

作者： Ke-Chao Zhang Sheng-Yue Jiang Jing Xiao College of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230026China College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China

Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these ***,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global *** limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual *** address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature ***,we have enhanced the visualization of pivotal details at different scales across diverse network *** visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout *** experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.

关键词： complex network visualization layout algorithm signed network fuzzy community structure social bot network

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Real is not True: Backdoor Attacks Against Deepfake Detection 9

Real is not True: Backdoor Attacks Against Deepfake Detectio...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Sun, Hong Li, Ziqiang Liu, Lei Li, Bin University of Science and Technology of China Hefei230027 China Laboratory for Big Data and Decision China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9798350330076

The proliferation of malicious deepfake applications has ignited substantial public apprehension, casting a shadow of doubt upon the integrity of digital media. Despite the development of proficient deepfake detection mechanisms, they persistently demonstrate pronounced vulnerability to an array of attacks. It is noteworthy that the pre-existing repertoire of attacks predominantly comprises adversarial example attack, predominantly manifesting during the testing phase. In the present study, we introduce a pioneering paradigm denominated as "Bad-Deepfake,"which represents a novel foray into the realm of backdoor attacks levied against deepfake detectors. Our approach hinges upon the strategic manipulation of a delimited subset of the training data, enabling us to wield disproportionate influence over the operational characteristics of a trained model. This manipulation leverages inherent frailties inherent to deepfake detectors, affording us the capacity to engineer triggers and judiciously select the most efficacious samples for the construction of the poisoned set. Through the synergistic amalgamation of these sophisticated techniques, we achieve an remarkable performance - a 100% attack success rate (ASR) against extensively employed deepfake detectors. © 2023 IEEE.

关键词： Deep neural networks

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Efficient Prediction of Heart Disease Using Cross Machine Learning Techniques

Efficient Prediction of Heart Disease Using Cross Machine Le...

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2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Mistry, Sunit Wang, Lili Anhui University of Science and Technology College of Mathematics and Big Data Huainan232000 China

ISBN: (纸本)9781665409025

Cardiovascular malady is top of the foremost specifics causes of deaths within the all over the world nowadays. Prediction of heart disease infection may be a basic face within the range of scientific information investigation. Mechanism knowledge has been appeared to be helpful in making choices and expectations from the expansive amount of unprocessed form prepared by the health department industry. In this study, machine learning (ML) methodologies and strategies being utilized in later advancements in different ranges of the Web of Things (WoT). Different ponders deliver as it were a see into anticipating heart disease with ML methods. In this paper, a novel methodologies that points for finding particularity methods is proposed, which implementing ML methods coming about in making strides the precision within the forecasting of cardiovascular malady. The forecast demonstrate is presented with diverse collection of highlights and several known classification strategies. Furthermore, it is investigated to create an improved execution level with an accuracy level of 89% through the forecast show for cardiovascular ailment. © 2022 IEEE.

关键词： Diseases

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An Overview Of Multimodal Brain Tumor MR Image Segmentation Methods 3

An Overview Of Multimodal Brain Tumor MR Image Segmentation ...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Jiao, Chunxia Yang, Tiejun School of Information Science and Engineering Henan University of Technology Zhengzhou China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Magnetic resonance imaging (MRI) is one of the most common imaging methods in the diagnosis and treatment of brain tumors. Multimodal brain tumor MRI automatic segmentation is significant for assisting the diagnosis and treatment of brain tumor diseases. In this paper, we first introduce the background and status quo of multimodal brain tumor MRI segmentation. Then, we divide the multimodal brain tumor MRI segmentation methods in recent years into three ways according to the network architecture. The basic theory, development process, research status, advantages and disadvantages of these three network architectures are also summarized respectively. In addition, we also introduce several publicly available datasets and evaluation metrics of brain tumor segmentation. Finally, we summarize and prospect the segmentation methods of multimodal brain tumor MRI. This article mainly introduces the recent research progress of multimodal brain tumor MRI segmentation, which will provide some help for researchers and practitioners in this domain. © 2023 SPIE.

关键词： Image segmentation

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