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26th International Conference on Extending database Technology, EDBT 2023

作者： Jørgensen, Jakob Rødsgaard Assent, Ira Department of Computer Science Aarhus University Aarhus Denmark Department of Computer Science DIGIT Aarhus University Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus Denmark

ISBN: (纸本)9783893180882

Clustering by synchronization (SynC) is a clustering method that is motivated by the natural phenomena of synchronization and is based on the Kuramoto model. The idea is to iteratively drag similar objects closer to each other until they have synchronized. SynC has been adapted to solve several well-known data mining tasks such as subspace clustering, hierarchical clustering, and streaming clustering. This shows that the SynC model is very versatile. Sadly, SynC has an O(T × n2 × d) complexity, which makes it impractical for larger datasets. E.g., Chen et al. [8] show runtimes of more than 10 hours for just n = 70, 000 data points, but improve this to just above one hour by using R-Trees in their method FSynC. Both are still impractical in real-life scenarios. Furthermore, SynC uses a termination criterion that brings no guarantees that the points have synchronized but instead just stops when most points are close to synchronizing. In this paper, our contributions are manifold. We propose a new termination criterion that guarantees that all points have synchronized. To achieve a much-needed reduction in runtime, we propose a strategy to summarize partitions of the data into a grid structure, a GPU-friendly grid structure to support this and neighborhood queries, and a GPU-parallelized algorithm for clustering by synchronization (EGG-SynC) that utilize these ideas. Furthermore, we provide an extensive evaluation against state-of-the-art showing 2 to 3 orders of magnitude speedup compared to SynC and FSynC. © 2023 ***. All rights reserved.

关键词： Synchronization

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A Time Series Sales data Forecasting Model Based on TCN and Its Three Variants 5

A Time Series Sales Data Forecasting Model Based on TCN and ...

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5th International Conference on Industrial IoT, big data and Supply Chain, IIoTBDSC 2024

作者： Chen, Sian Department of Data Science and Big Data Technology Brunel London School North China University of Technology Beijing China

ISBN: (纸本)9798331539818

The forecasting of sales data is a widely studied issue in the fields of artificial intelligence and time series forecasting. TCN (Temporal Convolutional Network) is the most adept at managing time series data among all classical models due to its distinctive architecture. In this paper, we design a TCN-based model for time series forecasting of sales data and propose three variants: a combined model of TCN with RNN, LSTM and GRU to enhance the performance of time series forecasting. Experimental results show that these models perform well in handling the sales data forecasting task, especially the combination of TCN with LSTM performs best in several metrics. © 2024 IEEE.

关键词： Spatio-temporal data

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Research on one-stop traditional Chinese medicine service system based on character recognition 4

Research on one-stop traditional Chinese medicine service sy...

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4th IEEE International Conference on Information Technology, big data and Artificial Intelligence, ICIBA 2024

作者： Wang, Beile Yang, Qingqiu Kong, Huafeng Wuhan Business University Department of Information Engineering Wuhan China Major in Data Science and Big Data Technology China

ISBN: (纸本)9798350363586

In recent years, China has introduced numerous policies aimed at fostering the growth of the traditional Chinese medicine (TCM) industry. Concurrently, the enrichment of TCM healthcare resources has s purred an increase in demand for TCM services. To address the needs of TCM practitioners and to offer the public more high-quality, precise, and intelligent TCM services, we have designed a comprehensive WeChat applet for TCM services that incorporates text recognition technology. This applet leverages text recognition to analyze the ingredients listed in prescriptions and employs a cold extraction method to prepare Chinese patent medicines, thereby reducing the cost of TCM treatments for consumers. Developed using the WeChat developer tool, the applet boasts a user-friendly interface and a suite of integrated functionalities, presenting a valuable service model for the TCM sector. © 2024 IEEE.

关键词： Chinese patent medicines Intelligent TCM services Text recognition technology Traditional Chinese Medicine WeChat applet

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Analysis on DeepFake dataset, Manipulation and Detection Techniques: A Review

Analysis on DeepFake Dataset, Manipulation and Detection Tec...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Rasool, Aale Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Delhi Technological University New Delhi India

ISBN: (纸本)9789819959730

The ability to spot false news is becoming increasingly important in today’s environment. Modern, rapid communication and easy access to all kinds of information were made possible by social networks and internet media. On the other hand, it opens up new avenues for malicious people to exploit vast amounts of publicly available data. Fake images and videos may now be predicted with greater accuracy because of advances in machine learning, particularly deep learning. Many studies have been conducted in recent years to better understand how deepfakes work, and several deep learning-based algorithms to detect deepfake videos or images have been developed. This study provides a comprehensive summary of the most recent manipulation techniques and datasets utilized in this subject. In addition, we reviewed the recent work of other academics on the subject and provided a roadmap for future study to aid scholars in combating the issue of deep fakes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Convolutional neural networks

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Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers:A Life Course Perspective

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China CDC weekly 2024年第11期6卷 219-224,I0014-I0017页

作者： Cedric Zhang Bo Lua Yajie Gao Jinming Li Xingqi Cao Xinwei Lyu Yinuo Tu Shuyi Jin Zuyun Liu Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Healththe Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceZhejiang University School of MedicineHangzhou CityZhejiang ProvinceChina Institute of Epidemiology and Health Care University College LondonLondonUK College of Chemical and Biological Engineering Zhejiang UniversityHangzhou CityZhejiang ProvinceChina

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of *** this study,we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English *** studies have predominantly used single biomarkers,such as C-reactive protein,or focused on specific life course stages and factors such as socioeconomic status,mental health,educational levels,and unhealthy *** summarizing the progress in this field,our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

关键词： such insight directions

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Domain Reductions After Preprocessing: Effects on Dynamic Variable Ordering Heuristics in Constraint Satisfaction Search 23rd

Domain Reductions After Preprocessing: Effects on Dynamic V...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Wallace, Richard J. Insight Centre for Data Analytics and Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9783031734991

We still do not have an adequate understanding of heuristic methods used for solving constraint satisfaction problems (CSPs). An example involves the effects of preprocessing, an essential means of improving CSP search. The traditional explanation for its beneficial effect is that the resulting "problem reduction" leaves fewer possibilities to explore. Recently, however, it was shown that when dynamic variable ordering heuristics are used, other factors related to domain size reduction are much more important. This paper extends this analysis and explores some implications of this new perspective. The key idea is that pattern of domain reductions produced by preprocessing transmits information that guides heuristic decisions. Treating domain reduction as a code and the set of reductions as a message of length n transmitted to the search algorithm, we distinguish two factors, message discriminability and code quality, and show that greater problem reduction enhances both. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constraint satisfaction problems

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Unraveling the intricacies of EEG seizure detection: A comprehensive exploration of machine learning model performance, interpretability, and clinical insights

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Multimedia Tools and Applications 2024年第41期83卷 89217-89247页

作者： Mridha, Krishna Priyok, Masrur Ahsan Shukla, Madhu Department of Computer Engineering Marwadi University Rajkot India Department of Software Engineering Tianjin University Tianjin China Department of Computer Engineering Artificial Intelligence and Big Data Analytics Marwadi University Gujarat Rajkot India

In neurology, it is critical to promptly and precisely identify epileptic episodes using EEG data. Interpretability and thorough model evaluation are still crucial to guarantee reliability, even though machine learning provides sophisticated tools for automated EEG analysis. We undertook a thorough investigation of EEG seizure classification in this work, with equal emphasis on statistical validation, interpretability, and model performance. Following thorough preprocessing, we used statistical significance tests in conjunction with Chernoff and Bhattacharyya Bounds to select features. Top performers were found to be the Random Forest which had accuracies of 96%. A paired t-test and bootstrapped 95% confidence intervals were used to statistically validate the performance robustness, demonstrating the large variation in model performances. Going beyond simple performance measures, we set out to decipher the nuances of model choices by utilizing methods devoted to model interpretability and shedding light on the importance of distinct aspects. This helped neurologists grasp the primary EEG indicators predictive of seizures and opened the door for future clinical insights. It also provided transparency in diagnostic predictions. Our efforts highlight the mutually reinforcing importance of predictability and model interpretability in the context of medical machine-learning applications, hence intensifying the demand for the creation and use of trustworthy and transparent neurology diagnostic instruments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neurology

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Ontology-Based Decision-Making Support in Real-Time Strategy Game 4

Ontology-Based Decision-Making Support in Real-Time Strategy...

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4th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Wang, Yaochu Yang, Jian Yang, Guoli School of Automation Science and Engineering South China University of Technology Guangzhou China Advanced Institute of Big Data Department of Big Data Intelligence Beijing China

ISBN: (纸本)9798350384062

Real-Time Strategy (RTS) games have attracted millions of players due to their characteristics of diverse scenes and flexible decision-making mechanisms. However, the mechanisms, contents, and operations of RTS games could be quite complex for beginners. The frustration and poor beginner experience caused by the high level of difficulty in getting started may lead novice players to give up on a specific RTS game due to a lack of sense of achievement. To address this issue and enhance the gaming experiences of beginners, this article proposes to assist beginners in familiarizing themselves with the mechanisms of the games and evaluating the opponents' abilities based on ontology. Firstly, an ontology will be built according to the properties of game units, which can provide beginners with a clear and comprehensive conceptual framework to understand game mechanisms and operations. Secondly, a knowledge-based reasoning method will be proposed to help beginners evaluate their opponents' abilities. Using a specific RTS game as a demonstration, the ontology is built and a reasoning example is proposed. By analyzing and reasoning about the abilities of their opponents' game units, this reasoning method can help beginners develop more effective strategies and tactics to enhance their experience in the game. © 2024 IEEE.

关键词： Ontology

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The Controversies and Challenges of Developing Beneficial Artificial Intelligence 2nd

The Controversies and Challenges of Developing Beneficial Ar...

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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Golding, Louis Polding, Robert Department of Data and Business Analytics School of Science and Technology IE University Madrid Spain

ISBN: (纸本)9783031569494

Artificial Intelligence (AI) technology is constantly advancing. Intelligent hardware and software that reason like human beings is beginning to affect many critical areas of human life. However, there are many challenges accompanying these innovations. The challenges that need to be vitally considered to get broader advantages of AI are technical and ethical. Learning, adoption, and retention are essential in the technical challenges that we face. To learn humans’ goals, AI must not merely reason about what we do, but also about why we do it. Humans carry out this task without realizing how complicated it is for computing systems to do. It is thus hard for AI systems to strictly digest our motives, but it could be conceivably argued that if intelligent systems are provided with huge amounts of data, they would possibly be able to come up with reasonable conclusions. With regards to adoption, even if AI systems can absorb our goals, there would be no assurance they can inevitably adopt them. To discuss retention, let’s assume an AI system that can learn and adopt our goals is constructed. Can these systems maintain these goals regardless of the endless enhancements these systems endure? When AI systems continue to be more intelligent, it is likely their potential to fulfill our goals will increase. However, their comprehension of the nature of these goals may progress ahead of our knowledge. Ethical challenges deal with expressing morals that AI systems should meet. In this case, it is hard to envision the types of morals that need to be encoded in AI systems, the people who will identify them, their accomplishments on an international scale, and employing consistent laws and morals in different countries and cultures. As a result, there is a great demand for sensible solutions to these requirements. For AI to be beneficial, these solutions should be sought promptly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent systems

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Knowledge Pyramid Perspective of the Political data Ecosystem: A Case Study of Bhutan 16

Knowledge Pyramid Perspective of the Political Data Ecosyste...

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16th International Conference on Knowledge Management and Information Systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Namgay, Phub Wangdi, Pema Department of Informatics and Media Uppsala University Sweden School of Data Science and Data Analytics Sherubtse College Royal University of Bhutan Bhutan

ISBN: (纸本)9789897587160

This study examines the dynamics of data management and knowledge flow in the political data ecosystem through the lens of the Knowledge Pyramid. We used open-government electoral documents and polling data for granular insights into how data, information, knowledge, and wisdom (DIKW) are managed in Bhutan’s political data ecosystem. Bhutan’s electoral stakeholders and political parties manage and use DIKW of varying types, sizes, and complexities. In particular, political parties use information systems, websites, and social media to manage data and construct and use knowledge for political activities. Democracy is still young and gaining a foothold in Bhutan. The political parties do not employ complex data technologies and rich human resources to manage DIKW emanating from the political data ecosystem. Thus, scope exists for electoral stakeholders and political actors to explore and adopt effective and efficient knowledge management infrastructures to deal with DIKW elements in the political arena, namely the complex dynamics of turning raw data into higher elements of the Knowledge Pyramid. In addition to contributing to the knowledge management literature through an in-depth account of the DIKW aspects in the political space, this paper demonstrated the analytical and explanatory power of the Knowledge Pyramid for discourse on the political data ecosystem. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Bhutan Democratic Electoral Process Knowledge Management Knowledge Pyramid Political data Ecosystem

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