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A Method for Generating Medical Text Data by Integrating Spatiotemporal Attributes and Implicit State Transitions

A Method for Generating Medical Text Data by Integrating Spa...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Xu Yang Lijuan Sun Yutong Gao Sanchuan Guo Xu Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Beijing China Intellectual Property Information Service Center Beihang University Beijing China School of Information Engineering Minzu University of China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

this study aims to generate medical text data that integrates spatiotemporal attributes to solve the problems of small size and low availability of open source text datasets due to the privacy of medical data. this paper optimizes the LeakGAN model by using a multi-layer LSTM structure and increasing the number of hidden and embedding layers to help generate networks that capture long-distance dependencies in data. In addition, we designed a spatiotemporal data generation method based on random algorithms and Markov chains to generate the spatiotemporal attributes of electronic medical records. In order to comprehensively evaluate the generation effect of the model, this paper evaluates from similarity-based indicators and likelihood-based indicators. the results show that the method designed in this article is 2.038 better than other models on NLL-oracle, 0.016 on NLL-test, and 0.0069 on EmbSim. the method proposed in this article has good ability to fit real data and can avoid the high cost and privacy leakage problems faced when manually constructing electronic medical record data sets by generating realistic medical electronic medical record synthetic data. this method also provides new solutions to the problem of insufficient data sets faced by more downstream applications (such as data auditing, anomaly detection, etc.).

关键词： Data privacy Cyberspace Data collection Data science Data models Spatiotemporal phenomena Electronic medical records Usability Long short term memory Synthetic data

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Decode the Dark Side of the Language: Applications of LLMs in the Dark Web

Decode the Dark Side of the Language: Applications of LLMs i...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Hongfan Chen Yigang Diao Hui Xiang Yixuan Huo Xiaqing Xie Jiapeng Zhao Xuebin Wang Yanwei Sun Jinqiao Shi School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Cyberspace Administration of China Data and Technology Support Center Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Previous studies have discovered that Transformers pre-trained on large scale datasets have a great advantage over traditional methods and can achieve human-level performance in many NLP tasks. Various encoder-only models like BERT and RoBERTa are used in the researches of the language in the Dark Web and most of them have achieved state-of-the-art performance. However, decoder-only models are not frequently used as they were considered to be unsuitable for NLU tasks until recent advancements in large language models. In this paper, we discuss the application of large scale decoder-only models in NLP tasks of real-world Dark Web research. We explore the capabilities of these models with the most realistic datasets in this specific domain. Surprisingly, results show that decoder-only models can achieve similar or better performance compared with state-of-the-art encoder-only model in some cases and show their own unique advantages in such field. We also discuss the limitations of these models and the challenges associated with their application in such field. Our study unveils the potential of decoder-only models in NLP tasks of real-world Dark Web research.

关键词： Dark Web Large language models Text categorization Zero shot learning Cyberspace Data science Transformers Natural language processing Data models Encoding

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FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL NETWORKS 9

FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL N...

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9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Khokhriakova, Anastasiia Grishkin, Valery Saint Petersburg State University 7-9 Universitetskaya emb. Saint Petersburg199034 Russia

In some regions, mainly occupied by agriculture and cattle breeding, irreversible soil changes, e.g. desertification, have appeared, which can lead to serious environmental and economic problems. this paper considers the application of neural networks for prediction and assessment of desertification-prone lands using satellite images. An autoencoder type of the neural network is applied for these purposes. Datasets were generated for training from the Sentinel-2 satellite open database. the first network was used for prediction. the second network is responsible for segmentation of the image into classes using NDVI index. In this paper we explain the method, the architecture of the network and present some experimental results. the presented method allows making a qualitative and quantitative assessment of possible changes, which can be useful for planning preventive works. © 2021 CEUR-WS. All rights reserved.

关键词： Forecasting

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End-to-End Performance Optimization for Training Streaming Convolutional Neural Networks using Billion-Pixel Whole-Slide Images

End-to-End Performance Optimization for Training Streaming C...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Tao, Liang-Wei Hwu, An-Fong Huang, Yu-Jen Chen, Chi-Chung Yeh, Chao-Yuan Hung, Shih-Hao Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan aetherAI Co Ltd Taipei Taiwan

ISBN: (纸本)9781665439022

the combination of digital pathology and artificial intelligence attracts more and more attention. In order to take into account the global and detailed textural information, recent works proposed to use whole-slide images (WSI) with more than 10 million pixels to train artificial neural networks in pursuit of a high-precision model. However, such an approach faces new technical challenges. When researchers conduct neural network training on a graphics processing unit (GPU), the extremely high spatial resolution of WSI and the large amount of intermediate data generated during the training process exceed the memory capacity of the GPU. therefore, streaming convolutional neural network (SCNN) was proposed to disassemble a large image into multiple smaller patches that can be forward and backward propagated under the limitation of GPU memory during the training process. However, in a multi-GPU high-performance computing (HPC) system, when the central processing unit (CPU) performs data preprocessing for the distributed training of multiple GPUs, decoding large images and data augmentation consume a lot of memory and CPU time, resulting in a performance bottleneck. this paper first presents a multithreaded image decoder optimized for multi-core CPUs to speedup WSI loading. We also propose a patch-level data augmentation algorithm and implement it on a dedicated GPU to distribute the augmented data to other GPUs for training the SCNN. We use the above method to train the ResNet-50 SCNN model by learning from the TCGA-LUAD and TCGA-LUSC datasets published by the Cancer Genome Atlas. In comparison to training the SCNN on multiple GPUs using the regular distributed data parallelism, our method can save up to 92.8% of memory and increase the training speed by up to 242.5% when training with 8 A100 GPUs on the NVIDIA DGX-A100 system.

关键词： Machine learning distributed computing Data preprocessing Performance analysis

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NATIONAL RESEARCH and EDUCATION NETWORK of RUSSIA: DIRECTIONS of DEVELOPMENT in the CONTEXT of EXPANDING of international COOPERATION 9

NATIONAL RESEARCH and EDUCATION NETWORK of RUSSIA: DIRECTION...

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9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Abramov, Alexey Evseev, Anton Gonchar, Andrey St. Petersburg Branch Joint Supercomputer Center The Russian Academy of Sciences nab. 14a Smolenka river Saint Petersburg199034 Russia Joint Supercomputer Center The Russian Academy of Sciences 32a Leninsky pr. Moscow119334 Russia

the paper is devoted to certain aspects of the current functioning and development of National Research Computer Network (NIKS) in the status of National Research and Education Network (NREN) of Russia. the main emphasis is made on the discussion of foreign network connectivity of NIKS, the planned technological modernization and expansion of international cooperation with NRENs of the EAEU countries, the intensification of network and service interaction with leading European research and education consortia GÉANT and NORDUnet. © 2021 CEUR-WS. All rights reserved.

关键词： international cooperation

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Russian DATA LAKE PROTOTYPE AS AN APPROACH towards NATIONAL FEDERATED STORAGE for MEGASCIENCE 9

Russian DATA LAKE PROTOTYPE AS AN APPROACH towards NATIONAL ...

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9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Alekseev, Aleksandr Kiryanov, Andrey Klimentov, Aleksey Korchuganova, Tatyana Oleynik, Danila Zarochentsev, Anderey NRC "Kurchatov Institute" 1 Akademika Kurchatova sq. Moscow123182 Russia Petersburg Nuclear Physics Institute of NRC "Kurchatov Institute" 1 Orlova Rocha Gatchina 188300 Russia Saint Petersburg State University 7-9 Universitetskaya emb. Saint Petersburg199034 Russia Brookhaven National Laboratory UptonNY United States Ivannikov Institute for System Programming RAS 25 Alexander Solzhenitsyn st. Moscow109004 Russia University Andres Bello Santiago Chile Plekhanov Russian University of Economics 36 Stremyanny lane Moscow117997 Russia Joint Institute for Nuclear Research 6 Joliot-Curie st. Dubna 141980 Russia

A substantial data volume growth will appear with the start of the HL-LHC era. It is not well covered by the current LHC computing model, even taking into account the hardware evolution. the WLCG DOMA project was established to provide data management and storage researches. National data lake r&d's, as a part of the DOMA project, should address the study of possible technology solutions for the organization of intelligent distributed federated storage. this talk will present the current status of the Russian Scientific Data lake prototype and the methodology, which is used for the validation and functional testing of deployed infrastructure. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Lakes

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Analysis of the Influence of Social Media Marketing on Purchase Intention with Customer Engagement in Industrial Automotive Sales: Case Study: CV Sri Mobilindo Motor

Analysis of the Influence of Social Media Marketing on Purch...

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Information Technology and Digital Applications (ICITDA), IEEE international conference on

作者： Quini Cendikiawaty Harijanto Pangestu Enggal Sriwardiningsih Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Binus Business School Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

the growth of technology and internet users in Indonesia has significantly impacted marketing strategies, with digital marketing, particularly social media, emerging as the most effective tool. Social media's simplicity and connectivity enhance customer interaction, leading to stronger customer relationships and increased purchase propensity. Businesses utilizing digital marketing tools can quickly offer goods and services, create strong client relationships, reduce costs, and potentially improve sales volume. this research aims to explore marketing literature from a Social Cognitive theory (SCT) perspective, providing theoretical and empirical directions for future research, and offering a clear understanding of SCT in marketing. Emphasizing the importance of analyzing consumer needs and behavior, the study investigates social media marketing, platform credibility, content quality, source credibility, consumer engagement, and purchase intention within CV Sri Mobilindo Motor, an automotive manufacturer. the study seeks to optimize research outcomes applicable for businesses’ long-term objectives and to identify relevant implications and theoretical confirmations between the studied factors. An online questionnaire was distributed to 400 respondents, who are prospective customers of CV Sri Mobilindo Motor and have interacted with content or viewed advertisements on various social media platforms. the responses were analyzed using Smart PLS 3, applying the Structural Equation Modeling-Partial Least Squares (SEM-PLS) approach to determine the influence and correlation of each variable. the primary goal is to ascertain the impact of digital marketing, particularly social media, on customer engagement and how this engagement affects purchase intention. By understanding these dynamics, businesses can better tailor their digital marketing strategies to enhance customer interaction and drive sales. this study contributes to the theoretical and practical understanding of dig

关键词： Costs Correlation Social networking (online) Motors Mathematical models Reliability Object recognition Vehicle dynamics Information technology Business

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Fake News Detection Using Deep Learning and Natural Language Processing

Fake News Detection Using Deep Learning and Natural Language...

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international conference on Soft computing and Machine Intelligence (ISCMI)

作者： Anand Matheven Burra Venkata Durga Kumar School of Computing & Data Science Xiamen University Malaysia Sepang Selangor Darul Ehsan Malaysia

the rise of social media has brought the rise of fake news and this fake news comes with negative consequences. With fake news being such a huge issue, efforts should be made to identify any forms of fake news however it is not so simple. Manually identifying fake news can be extremely subjective as determining the accuracy of the information in a story is complex and difficult to perform, even for experts. On the other hand, an automated solution would require a good understanding of NLP which is also complex and may have difficulties producing an accurate output. therefore, the main problem focused on this project is the viability of developing a system that can effectively and accurately detect and identify fake news. Finding a solution would be a significant benefit to the media industry, particularly the social media industry as this is where a large proportion of fake news is published and spread. In order to find a solution to this problem, this project proposed the development of a fake news identification system using deep learning and natural language processing. the system was developed using a Word2vec model combined with a Long Short-Term Memory model in order to showcase the compatibility of the two models in a whole system. this system was trained and tested using two different dataset collections that each consisted of one real news dataset and one fake news dataset. Furthermore, three independent variables were chosen which were the number of training cycles, data diversity and vector size to analyze the relationship between these variables and the accuracy levels of the system. It was found that these three variables did have a significant effect on the accuracy of the system. From this, the system was then trained and tested with the optimal variables and was able to achieve the minimum expected accuracy level of 90%. the achieving of this accuracy levels confirms the compatibility of the LSTM and Word2vec model and their capability to be synergiz

关键词： Industries Deep learning Training Social networking (online) Natural language processing Fake news Machine intelligence

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HPC WORKLOAD BALANCING ALGORIthM for CO-SCHEDULING ENVIRONMENTS 9

HPC WORKLOAD BALANCING ALGORITHM for CO-SCHEDULING ENVIRONME...

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9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Kuchumov, Ruslan I. Korkhov, Vladimir V. Saint Petersburg State University 7/9 Universitetskaya nab. St. Petersburg199034 Russia

the goal of this research work is to reduce wait time of HPC (high performance computing) applications in schedulers queue by applying a co-scheduling strategy. this strategy allows the execution of more than one task with different non-overlapping requirements for computational resources simultaneously. Co-scheduling strategy reduces task queue wait time and improves utilization of cluster resources when compared to the scheduling strategies that do not allow for parallel task execution on the same machine. We have proposed a method for measuring application processing speed in its run-time, which can be used as a feedback for scheduling strategies. In this work, we have formalized the co-scheduling problem and proposed strategies for solving it. For some strategies we have shown analytically the upper bounds values of their competitive ratios. Besides that for the proposed scheduling strategies we ran numerical experiments using imitation models to show how they compare to the optimal strategy. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Scheduling

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RECONSTRUCTION of ORDINARY DIFFERENTIAL EQUATIONS from IRREGULARLY distributed TIME-SERIES DATA 9

RECONSTRUCTION of ORDINARY DIFFERENTIAL EQUATIONS from IRREG...

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9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Golovkina, Anna Kozynchenko, Vladimir Kulabukhova, Natalia Saint Petersburg State University 7/9 Universitetskaya emb. Saint Petersburg199034 Russia

the present paper aims to develop a reconstruction method for the right side of a system of ODEs in polynomial form from sparse and irregularly distributed time-series data. this method doesn't require any additional knowledge about the system and has several steps. the scarcity of the data through the trajectory length is compensated by the artificially generated points using approximating trigonometrical polynomials. then, we get uniformly spread data points with the step conditioned by the desired accuracy of derivatives approximation in ODEs. this let to further use conventional reconstruction algorithms described in the literature. We test the proposed method on time series data generated from known ODE models in a two-dimensional system. We quantify the accuracy of the reconstruction for the system of ODEs as a function of the amount of data used by the method. Further, we solve the reconstructed system of ODEs and compare the solution to the original time series data. the method developed and validated here can now be applied to large data sets for physical and biological systems for which there is no known system of ODEs. © 2021 CEUR-WS. All rights reserved.

关键词： Ordinary differential equations

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