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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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An ensemble approach for annotating source code identifiers with part-of-speech tags

arXiv

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arXiv 2021年

作者： Newman, Christian D. Decker, Michael J. Alsuhaibani, Reem S. Peruma, Anthony Mkaouer, Mohamed Wiem Mohapatra, Satyajit Vishnoi, Tejal Zampieri, Marcos Sheldon, Timothy J. Hill, Emily Software Engineering Deptartment Rochester Institute of Techonology RochesterNY United States Software Engineering Department Bowling Green State University Bowling GreenOH United States Computer Science Department Prince Sultan University Riyadh Saudi Arabia Language Technology Group Rochester Institute of Techonology RochesterNY United States Financial Services Sector Risk and Compliance BNY Mellon PittburghPA United States Department of Math and Computer Science Drew University MadisonNJ United States

This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble’s annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75% accuracy at the identifier level and 84-86% accuracy at the word level. This is an increase of +17% points at the identifier level from the closest independent part-of-speech tagger. © 2021, CC BY.

关键词： Natural language processing systems

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Review for Influence of 5G on Industry Internet 1

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16th World Congress on Services, SERVICES 2020, held as part of the Services Conference Federation, SCF 2020

作者： Liu, Yang Zhang, Liang-Jie Xing, Chunxiao Research Institute of Information Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Computer Science and Technology Institute of Internet Industry Tsinghua University Beijing100084 China National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Company Limited Shenzhen518057 China

ISBN: (数字)9783030595951

ISBN: (纸本)9783030595944

In recent year, the downward pressure on the global economy is increasing, and the market is gradually changing. The urgent problem for entrepreneurs is how to keep their companies growing continuously. Industry Internet is a new direction that can help them by promoting business growth of enterprises. And the growth should be realized by exploring innovative business scenarios. The core of industry internet is not the internet, but the traditional industry. It forms the industry value chain through the mutual connection of traditional industries. The enterprises in each link of the value chain will become more powerful because of their value-added data. This paper solves several key problems in the development of enterprises: what economic era are they in? What is industry internet? How does industry Internet empower enterprises? How can 5G related technologies help traditional industries transform into industry internet? © 2020, Springer Nature Switzerland AG.

关键词： 5G mobile communication systems

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Review for Message-Oriented Middleware 1

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5th International Conference on Internet of Things, ICIOT 2020, held as part of the Services Conference Federation, SCF 2020

ISBN: (数字)9783030596156

ISBN: (纸本)9783030596149

With the rapid development of 5G technology and micro-service software developing technology, a growing number of intelligent devices are connected to the Internet, and it has become a trend to provide services to customers in a collaborative way. As the information exchange center of collaborative services, the role of message-oriented middleware becomes more and more important. Firstly, this paper describes the definition, main characteristics, core technology, main products and existing problems of middleware. Then, it takes one of the problems as an example, designs a gateway architecture based on a message middleware, and introduces its application in the Internet of things in detail. © 2020, Springer Nature Switzerland AG.

关键词： Middleware

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Towards adaptive provision of examples during problem solving 27

Towards adaptive provision of examples during problem solvin...

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27th International Conference on computers in Education, ICCE 2019

作者： Tahir, Faiza Mitrovic, Antonija Sotardi, Valerie Intelligent Computer Tutoring Group Department of Computer Science and Software Engineering University of Canterbury New Zealand School of Educational Studies and Leadership University of Canterbury New Zealand

ISBN: (纸本)9789869721431

Intelligent Tutoring Systems (ITSs) are effective in supporting learning, as shown in numerous studies. The goal of our project is to develop an adaptive strategy that would be capable of identifying situations during problem solving in which the student would benefit from worked examples. As a first step towards developing such a strategy, we conducted a pilot study in the context of SQL-Tutor, a mature ITS that teaches database querying. The participant could ask for a worked example whenever he/she wanted during problems solving. After each example, the participant specified whether the example was useful, and whether additional examples were needed. Participants’ facial expressions and eye gaze were recorded. The findings show that the participants generally found examples useful, although in some cases they stated additional examples would be beneficial. The analysis of the eye gaze shows that students compared provided examples to their own solutions. Affect analysis shows that negative emotions reduced while engagement increased when participants viewed examples, and immediately after examples. © 2019 International Conference on computers in Education, *** right reserved.

关键词： Eye tracking

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Using gamification to effect learning behaviors in intelligent tutoring system 27

Using gamification to effect learning behaviors in intellige...

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27th International Conference on computers in Education, ICCE 2019

ISBN: (纸本)9789869721448

Engagement and motivation is always a challenge in online learning environments. The benefits of learning environments have proven its history for many years, but effectively engaging users with these environments and motivating them is an active and important research problem. In this work, I will investigate the potential of gamification on motivation and user engagement in an intelligent tutoring system SQL-Tutor. This work is inspired by the growing trend of gamification and its positive effects in various domains. © ICCE 2019 - 27th International Conference on computers in Education, Proceedings. All rights reserved.

关键词： computer aided instruction

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Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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TWIST-GAN: Towards wavelet transform and transferred GAN for spatio-temporal single image super resolution

arXiv

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arXiv 2021年

作者： Dharejo, Fayaz Ali Deeba, Farah Zhou, Yuanchun Das, Bhagwan Jatoi, Munsif Ali Zawish, Muhammad Du, Yi Wang, Xuezhi Computer Network Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences Bejing100190 China Department of Electronic Engineering Quaid-e-Awam University Engineering Science and Technology Nawabshah Pakistan Department of Biomedical Engineering Salim Habib University Karachi Pakistan Telecommunication Software and Systems Group Waterford Institute of Technology Rono-Hills Waterford Ireland

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). However, the generated image still suffers from undesirable artifacts such as, the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN).We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using theWT, whereas, the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merceed Landsat remote sensing dataset and Set14 with each image size of 256x256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers. © 2021, CC BY.

关键词： Wavelet transforms

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern

Classification of Awareness in Disorders of Consciousness Us...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Chaewon Lee Sunho Lee Jitka Annen Ji-Hoon Jeong Marcello Massimini Silvia Casarotto Melanie Boly Olivier Bodart Aurore Thibaut Mario Rosanova Steven Laureys Olivia Gosseries Minji Lee Dept. Data Science The Catholic University of Korea Bucheon Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Republic of Korea Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent Ghent Belgium Dept. Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical and Clinical Sciences “L. Sacco” University of Milan Milan Italy Dept. Psychiatry Wisconsin Institute for Sleep and Consciousness University of Wisconsin Madison USA Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future.

关键词： Deep learning Accuracy Biological system modeling Pattern classification Focusing Signal processing Brain modeling Particle measurements Electroencephalography Robustness

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TEA: Temporal Excitation and Aggregation for Action Recognition

TEA: Temporal Excitation and Aggregation for Action Recognit...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yan Li Bin Ji Xintian Shi Jianguo Zhang Bin Kang Limin Wang Platform and Content Group (PCG) Tencent State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Temporal modeling is key for action recognition in videos. It normally considers both short-range motions and long-range aggregations. In this paper, we propose a Temporal Excitation and Aggregation (TEA) block, including a motion excitation (ME) module and a multiple temporal aggregation (MTA) module, specifically designed to capture both short- and long-range temporal evolution. In particular, for short-range motion modeling, the ME module calculates the feature-level temporal differences from spatiotemporal features. It then utilizes the differences to excite the motion-sensitive channels of the features. The long-range temporal aggregations in previous works are typically achieved by stacking a large number of local temporal convolutions. Each convolution processes a local temporal window at a time. In contrast, the MTA module proposes to deform the local convolution to a group of sub-convolutions, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-convolutions, and each frame could complete multiple temporal aggregations with neighborhoods. The final equivalent receptive field of temporal dimension is accordingly enlarged, which is capable of modeling the long-range temporal relationship over distant frames. The two components of the TEA block are complementary in temporal modeling. Finally, our approach achieves impressive results at low FLOPs on several action recognition benchmarks, such as Kinetics, Something-Something, HMDB51, and UCF101, which confirms its effectiveness and efficiency.

关键词： Two dimensional displays Videos Spatiotemporal phenomena Optical imaging Feature extraction Three-dimensional displays Stacking

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