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A data Value Matrix: Linking FAIR data with Business models 17th

A Data Value Matrix: Linking FAIR Data with Business Models

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17th international conference on Research Challenges in Information Sciences, RCIS 2023

作者： Hellmanzik, Ben Sandkuhl, Kurt University of Rostock Rostock Germany

ISBN: (纸本)9783031330797

data is the raw material of digitization, but its economic use and potential economic benefits are not always clear. Therefore, we would like to show that the processing of data, especially according to FAIR principles, plays an enormous role in enabling business models and improving existing business models. This is being tested within the EU funded project Marispace-X, part of the Gaia-X initiative. The maritime domain in particular currently still suffers from a lack of digitization: while as much data is being collected as ever before, this data is often kept in silos and hardly reused or even shared. This work therefore involved linking the FAIR principles to a data value chain, which together with business model dimensions form a data value matrix. This application of this matrix was carried out together with practice partners using the example of maritime data processing in the use case "offshore wind" and can be used and adapted as an analysis tool for data-driven business models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Offshore oil well production

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A Machine Learning Approach to Steel Sheet Production Surface Quality

A Machine Learning Approach to Steel Sheet Production Surfac...

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international conference on Computer Science and Engineering (UBMK)

作者： Asena Öztürk Mehmet Nafiz Aydin Dept. of Management Information system Kadir Has University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

This study aims to develop a machine learning approach for defect evaluation in steel sheet production. The primary objective is to improve the defect decision process by integrating human knowledge with technical data. The paper uses a case study with data from 2020 and reviews the literature on steel surface defects, decision support systems, classification algorithms, and text mining. The study focuses on the detection and repair of defects, aiming to eliminate defects in production and optimize decisions related to defect detection and repair. The methodology of the study involves comparing different classification techniques and enhancing these results with text processing applications. The study concludes that the existence of text data improves the performance of the classification algorithms.

关键词： Computational modeling Knowledge based systems Production Machine learning Maintenance engineering data models Classification algorithms Steel Surface treatment Research and development

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Research on Feature Optimization Scheme Based on data Feature Enhancement 21

Research on Feature Optimization Scheme Based on Data Featur...

引用

21st IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Deng, Zhi Shi, Zhao Wang, Zhenxin Liu, Tao Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Anhui Polytech Univ Coll Comp & Informat Wuhu Peoples R China

ISBN: (纸本)9781665478366

Based on the common knowledge in the field of feature engineering that "data and features determine the upper limit of the model, and models and algorithms just approach the upper limit", this article believes that the knowledge of feature engineering can be used to extract features from the original data to the maximum. Used by algorithms and models to improve the accuracy of algorithms and models. Based on the above considerations, this paper proposes a feature optimization scheme based on data feature enhancement (DFET) to improve the performance of deep learning models in natural language processing. The DFET feature optimization scheme uses machine learning classification algorithms to weaken noise features and adopts a sliding window data enhancement is performed in the form of a window, the ignored features are selected through the form of windows, and low-dimensional data with significant features are generated. This part of the data is added to the original model training to fine-tune the model training, and then optimize the model to meet the requirements of improving accuracy. The paper examines the security bug report prediction problem in the field of natural language processing to verify the impact of data feature optimization on the performance optimization of deep learning models. Experiments show that compared with the basic deep learning model, the model after the DFET feature optimization scheme can show more excellent feature learning ability in text classification and prediction.

关键词： Deep Learning Feature Engineering Natural Language processing Security Bug Report Prediction

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Real-Time Deep Learning-Based Anomaly Detection Approach for Multivariate data Streams with Apache Flink 1st

Real-Time Deep Learning-Based Anomaly Detection Approach for...

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21st international conference on Web Engineering (ICWE)

作者： Ha, Tae Wook Kang, Jung Mo Kim, Myoung Ho Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9783030922313;9783030922306

For detecting anomalies which are unexpected behaviors in complex systems, deep learning-based anomaly detection algorithms for multivariate time series have gained a lot of attention recently. While many anomaly detection algorithms have been widely proposed, there has been no work on how to perform these detection algorithms for multivariate data streams with a stream processing framework. To address this issue, we present a real-time deep learning-based anomaly detection approach for multivariate data streams with Apache Flink. We train a LSTM encoder-decoder model to reconstruct a multivariate input sequence and develop a detection algorithm that uses reconstruction error between the input sequence and the reconstructed sequence. We show that our anomaly detection algorithm can provide promising performance on a real-world dataset. Then, we develop a Flink program by implementing three operators which process and transform multivariate data streams in a specific order. The Flink program outputs anomaly detection results in real time, making system experts can easily receive notices of critical issues and resolve the issues by appropriate actions to maintain the health of the systems.

关键词： Multivariate data streams Anomaly detection LSTM encoder-decoder Stream processing Apache Flink

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FAST LEARNING-BASED SPLIT TYPE PREDICTION ALGORITHM FOR VVC 30

FAST LEARNING-BASED SPLIT TYPE PREDICTION ALGORITHM FOR VVC

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30th IEEE international conference on Image processing (ICIP)

作者： Wang, Dayong Chen, Liulin Lu, Xin Dufaux, Frederic Li, Weisheng Zhu, Ce Chongqing Univ Posts & Telecommun Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China De Montfort Univ Fac Comp Engn & Media CEM Leicester Leics England Univ Paris Saclay Cent Supelec Lab Singnaux & Syst CNRS Paris France Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781728198354

As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding complexity, which seriously hinders its widespread application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split type (ST) prediction algorithm for VVC using a deep learning approach. We first construct a large-scale database containing sufficient STs with diverse video resolution and content. Next, since the ST distributions of coding units (CUs) of different sizes are significantly distinct, so we separately design neural networks for all different CU sizes. Then, we merge ambiguous STs into four merged classes (MCs) to train models to obtain probabilities of MCs and skip unlikely ones. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 67.53% with 1.89% increase in Bjontegaard delta bit-rate (BDBR) on average.

关键词： VVC Split type Deep learning

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Adaptation of Machine Translation models with Back-Translated data Using Transductive data Selection Methods 1

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20th international conference on Computational Linguistics and Intelligent Text processing, CICLing 2019

作者： Poncelas, Alberto Wenniger, Gideon Maillette de Buy Way, Andy ADAPT Centre School of Computing Dublin City University Dublin Ireland

ISBN: (数字)9783031243370

ISBN: (纸本)9783031243363

data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produced by the popular back-translation technique, raises the question if data selection could also be useful for synthetic data? In this work we use Infrequent n-gram Recovery (INR) and Feature Decay algorithms (FDA), two transductive data selection methods to obtain subsets of sentences from synthetic data. These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it. Performing data selection on back-translated data creates new challenges as the source-side may contain noise originated by the model used in the back-translation. Hence, finding n-grams present in the test set become more difficult. Despite that, in our work we show that adapting a model with a selection of synthetic data is an useful approach. © 2023, Springer Nature Switzerland AG.

关键词： Computer aided language translation

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Freevc: Towards High-Quality Text-Free One-Shot Voice Conversion 48

Freevc: Towards High-Quality Text-Free One-Shot Voice Conver...

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48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Jingyi Tu, Weiping Xiao, Li Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Hubei Luojia Laboratory China Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China

ISBN: (纸本)9781728163277

Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information. However, current approaches normally either extract dirty content information with speaker information leaked in, or demand a large amount of annotated data for training. Besides, the quality of reconstructed waveform can be degraded by the mismatch between conversion model and vocoder. In this paper, we adopt the end-to-end framework of VITS for high-quality waveform reconstruction, and propose strategies for clean content information extraction without text annotation. We disentangle content information by imposing an information bottleneck to WavLM features, and propose the spectrogram-resize based data augmentation to improve the purity of extracted content information. Experimental results show that the proposed method outperforms the latest VC models trained with annotated data and has greater robustness. © 2023 IEEE.

关键词： data augmentation information bottleneck self-supervised learning voice conversion

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Competitive Sports data Analyzing Model Based on Machine Learning 3

Competitive Sports Data Analyzing Model Based on Machine Lea...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Luo, Junyao Wang, Zihan Zhang, Junyi Franklin & Marshall College 17603 United States School of Engineering The University of Mississippi Oxford38655 United States University of Electronic Science and Technology of China 610000 China

ISBN: (纸本)9781510661301

Machine learning is an important research area belonging to artificial intelligence technology. Machine learning has potent data processing and prediction ability. This paper uses machine learning to build a data analysis model and validate it with the NBA. This paper consists of two parts in total. The first part uses a clustering algorithm to analyze all teams' high-order data from 2000 to 2021 and classify the teams' styles. The main effect of using the k-means algorithm is to put the data in very different clusters, and in this paper the different clusters are considered to be the different team styles. At the same time, we can determine whether the style of the team has changed. We used the Euclidean distance as the distance formula for k-means. By the test of different k numbers, the result showed that between the numbers 2 to 9, the Silhouette Coefficient performed best when and second when. This paper used to measure the method that can be used to evaluate the influence of different algorithms or operating modes of algorithms on clustering results based on the same original data. For example, when k=2 and k=4. The experimental results show that the team styles in the NBA can be broadly classified into several categories over a long development period. The second part constructs a win prediction model based on the Elo method for the matchup data from 2020 to 2022. To sum up, in this paper, Elo ranking system, the Logistic regression model and three data sets are used to get a detailed prediction of the winning rate of NBA teams in 21-22 seasons. © 2023 SPIE.

关键词： Machine learning

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Next POI Recommendation with Neighbor and Location Popularity 29th

Next POI Recommendation with Neighbor and Location Popularit...

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29th international conference on Neural Information processing

作者： Li, Xianxian Liu, Tianran Wang, Li-E Sun, Zhigang Zeng, Huachang Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Guangxi Normal Univ Sch Comp Sci & Engn Guilin 541004 Peoples R China

ISBN: (纸本)9783031301070;9783031301087

Next point-of-interest (POI) recommendation aims to predict the next destination for users. In the past, most POI recommendation models were based on the user's historical check-in trajectory to achieve recommendations. However, when these models are trained with sparse historical trajectory data, the learned user's sequence patterns are unstable, which is difficult to obtain good recommendations. In view of the above problem, we propose the next POI recommendation approach that combines neighbor information with location popularity to alleviate the sparsity of data. Specifically, we construct User-POI graph and POI-POI graph, and use graph neural networks (GNN) to capture neighbor information of effective users on these two graphs. In addition, considering that location popularity is influenced by different times and distances, we design a dynamic method to measure the impact of location popularity on the user's check-in preferences. In evaluating the experimental performance of two real-world datasets, our approach outperforms several classical next POI recommendation approaches.

关键词： POI Recommendation Graph Neural Network data Fusion Location Popularity

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ENHANCING RISK ASSESSMENT AND MONITORING FOR CULTURAL HERITAGE SITES THROUGH data CUBES: A MULTIDIMENSIONAL APPROACH 9

ENHANCING RISK ASSESSMENT AND MONITORING FOR CULTURAL HERITA...

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9th international conference on Remote Sensing and Geoinformation of the Environment, RSCy 2023

作者： Leventis, Georgios Argyriou, Athanasios V. Cerra, Daniele Hadjimitsis, Diofantos ERATOSTHENES Centre of Excellence 82 Franklin Roosevelt Limassol3012 Cyprus German Aerospace Center Remote Sensing Technology Institute Oberpfaffenhofen Weßling Germany

ISBN: (纸本)9781510668225

The Eastern Mediterranean, Middle East, and North Africa (EMMENA) regions are rich in Cultural Heritage (CH) sites that have been subject to various threats, including conflicts, natural disasters, and urban development. Effective risk assessment and monitoring are essential to preserve and protect these assets. Towards that direction novel technologies and their integration can be valuable for a holistic framework of managing diverse datasets and providing a robust safeguarding of CH assets. A data cube is a multidimensional representation of data that allows for efficient and flexible analysis, designed to support online analytical processing (OLAP) and data mining. data cubes can be regarded as a threedimensional structure, with each cell representing a unique combination of values from the different dimensions. By creating a data cube that includes several satellite and geospatial data sources, organizations can gain a more holistic understanding of the risks and opportunities associated with CH sites as well as to identify patterns and trends that might not be apparent in individual data sets. Within this context, it becomes apparent that data cubes allow for a multidimensional view of the risk landscape and can be used to create data-driven predictive models forecasting risks and opportunities for CH assets, - in order for them to be preserved and protected for future generations. The risk assessment and monitoring framework used in this study can be easily transferred, in order to monitor CH sites in any sensitive region and can be adapted to include data from other sources and monitor different types of threats, including climate change related, environmental, and social risks. © 2023 SPIE.

关键词： Risk assessment

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