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Synthetic data generation for medical by generative adversarial network

Synthetic data generation for medical by generative adversar...

引用

2022 International Conference on Electronics and Devices, Computational Science, ICEDCS 2022

作者： Liu, Yaoxuan Xu, Hao Tang, Ziling Beijing Jiaotong University School of Computer and Information Technology Beijing China Internet of Things Engineering University College Dublin Beijing Dublin International Collage Dublin Ireland Monash University Faculty of Engineering Department of Mechanical and Aerospace Engineering Melbourne Australia

ISBN: (纸本)9781665455411

As the world develops, medical data privacy is becoming an important issue. Differential privacy can better ensure accuracy and reduce the reflection of personal privacy, while generative adversarial networks can generate data sets. Obviously, the combination of differential privacy and generative adversarial network (GAN) can synthesize sensitive medical data to form new data sets, which not only protects personal privacy but also does not affect medical research. We summarize the current difficulties in medical data, compare the common medical data types with typical DP methods of different GAN, analyze the advantages and disadvantages of each method, and discuss future development. At the same time, we also introduce GAN applied in the image data field. Because this approach is very mature, we only introduce some commonly used GANs, compare their improvements, and introduce their applications in data generation. This may help in the future development of medical data privacy algorithms. © 2022 IEEE.

关键词： Generative adversarial networks

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Accelerating authenticated emergence message propagation to mitigate chain-reaction accidents in highway traffic

Accelerating authenticated emergence message propagation to ...

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2009 4th International Conference on Communications and Networking in China, CHINACOM 2009

作者： Lu, Rongxing Lin, Xiaodong Zhu, Haojin Shen, Xuemin Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada Faculty of Business and Information Technology University of Ontario Institute of Technology Oshawa ON Canada Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai China

ISBN: (纸本)9781424443376

The chain-reaction collision caused by a sudden event such as deer-vehicle collision is a serious accident in highway traffic. By propagating the authenticated emergence message on deer-vehicle collision with vehicle-to-vehicle (V2V) communication, the chain-reaction accident could be mitigated. However, the long delay imposed by traditional signature based authentication may weaken the effectiveness of such message propagation. In this paper, in order to accelerate the propagation, we propose a new online/offline Rabin signature scheme integrated in an authenticated emergence message propagation model. With the proposed signature scheme, the emergence message can be quickly signed and verified, and thus the propagation is accelerated. Extensive simulation results also demonstrate the effectiveness of the proposed scheme.

关键词： Authentication

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Proceedings of Fifth Doctoral Symposium on Computational Intelligence 1

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丛书名： Lecture Notes in Networks and Systems

1000年

作者： Abhishek Swaroop Vineet Kansal Giancarlo Fortino Aboul Ella Hassanien

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Reducing reconfiguration time of reconfigurable computing systems in integrated temporal partitioning and physical design framework 20

Reducing reconfiguration time of reconfigurable computing sy...

引用

20th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2006

作者： Mehdipour, Farhad Zamani, Morteza Saheb Ahmadifar, Hamid Reza Sedighi, Mehdi Murakami, Kazuaki Amirkabir University of Technology IT and Computer Engineering Department #424 Hafez Ave. Tehran Iran Kyushu University Dep. of Informatics Graduate School of Information Science and Electrical Engineering Fukuoka Japan Guilan University Engineering Faculty Rasht Iran

ISBN: (纸本)1424400546

In reconfigurable systems, reconfiguration latency is a very important factor impact the system performance. In this paper, a framework is proposed that integrates the temporal partitioning and physical design phases to perform a static compilation process for reconfigurable computing systems. A temporal partitioning algorithm is proposed which attempts to decrease the time of reconfiguration on a partially reconfigurable hardware, This algorithm attempts to find similar single or pair of operations between subsequent partitions. Considering similar pairs instead of single nodes brings about less complexity for routing process. By using this technique, smaller reconfiguration bit-stream is obtained, which directly decreases the reconfiguration overhead time at the run-time. A complementary algorithm attempts to increase the similarity of subsequent partitions by searching for similar pairs and using a technique called dummy node insertion, An incremental physical design process based on similar configurations produced in the partitioning stage improves the metrics over iterations. © 2006 IEEE.

关键词： computer systems

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Learning orientation detection system and its application to RGB images

Learning orientation detection system and its application to...

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2024 International Conference on Optics, Electronics, and Communication engineering, OECE 2024

作者： Chen, Tianqi Qiu, Zhiyu Hua, Yuxiao Todo, Yuki Tang, Zheng Division of Electrical Engineering and Computer Science Graduate School of Natural Science & Technology Kanazawa University Ishikawa Kanazawa920-1152 Japan Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa920-1192 Japan Faculty of Engineering University of Toyama Toyama930-8555 Japan

ISBN: (纸本)9781510685437

Our previous research showed that emulating dendritic neuron structure effectively addresses orientation detection challenges in learning tasks, reducing both learning time and costs compared to alternative neural network approaches. Our simulation model incorporates an On-Off Response mechanism in bipolar cells (BCs) and horizontal cells (HCs) for processing grayscale input images. To overcome limitations with single-channel input, we introduce color-selective cells. By integrating these cells, we enhance and select outputs of local orientation detection dendritic neurons, generating specific feature maps. These feature maps are generated using a biologically-inspired model that mimics the mechanism of color-selective cells. Additionally, global neurons are used to capture overall image features by aggregating outputs from local dendritic neurons. Our system utilizes backpropagation to update parameters of local orientation detection dendritic neurons. Furthermore, we integrate a learnable orientation detection neural network after the dendritic neuron stage. © 2024 SPIE.

关键词： RGB color model

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Exploiting Deep Learning Architectures for Effective Real-Time Data Analysis in Machine Learning 3

Exploiting Deep Learning Architectures for Effective Real-Ti...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shreenidhi, H.S. Rajarajeswari, S. Bhagwat, Suvarna R. Sharma, Khushboo Seth, Kanika Yadav, Rakesh Kumar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper seeks to discover how deep state-of-the-art architectures can be leveraged for robust actual-time information evaluation in device studying packages. The paper begins by supplying a comprehensive assessment of contemporary the modern-day in deep brand new-based totally facts evaluation technology. It then examines case research state-of-the-art real-time records analysis programs to illustrate the viability and blessings of modern-day existing deep latest architectures. Programs encompass self-driving vehicle generation, laptop vision, natural language processing, and time-collection evaluation. This targeted evaluation is supplemented by assessing ultra-modern capability shortcomings and demanding situations associated with using these architectures in actual-time scenarios. Subsequently, the paper provides a set of modern-day pointers and first-rate practices to help practitioners efficiently leverage deep trendy architectures for actual-time information evaluation. With the aid of combining a comprehensive review of current deep-gaining knowledge state modern-based records evaluation technology, a detailed dialogue present day its utility guiding ideas, and a summary of the latest potential shortcomings and demanding situations, this paper offers a practical manual to the effective of deep learning architectures for real-time data analysis. © 2023 IEEE.

关键词： architectures challenges combining recommendations shortcomings

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Granger-Causality Mining in Atmospheric Visibility Based on Deep Learning 9th

Granger-Causality Mining in Atmospheric Visibility Based on ...

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9th International Conference on Frontier Computing, FC 2019

作者： Liu, Bo He, Xi Li, Jianqiang Qu, Guangzhi Lang, Jianlei Gu, Rentao Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing100124 China Computer Science and Engineering Department Oakland University RochesterMI48309 United States Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811532498

Causal relationship mining of multi-dimensional meteorological time series data can reveal the potential connection between visibility and other influencing factors, and it is meaningful in terms of environmental management, air pollution chasing, and haze control. However, because causality analysis based on statistical methods or traditional machine learning techniques cannot represent the complex relationship between visibility and its influencing factors, as an extension of traditional Granger-causality analysis, we propose a causality mining method based on the seq2seq-LSTM deep learning model. The method can profoundly reveal the hidden relationship between different features and visibility and the regular pattern of bad weather, which can provide theoretical support for air pollution control. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Time series

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