Data Intelligence最新文献

{"title":"Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh","authors":"M. Raihan, Sami Azam, L. Akter, Md. Mehedi Hassan, Ryana Quadir, Asif Karim, Saikat Mondal, Arun More","doi":"10.3724/2096-7004.di.2024.0002","DOIUrl":"https://doi.org/10.3724/2096-7004.di.2024.0002","url":null,"abstract":"","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AuToGen: Automated Tool Learning Data Generation with Domain-specific Structured Data","authors":"Daojian Zeng, Lin Zhou, Zhiheng Zhang, Lincheng Jiang","doi":"10.3724/2096-7004.di.2024.0005","DOIUrl":"https://doi.org/10.3724/2096-7004.di.2024.0005","url":null,"abstract":"","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable Connectivity in a Community Repository","authors":"Ted Habermann","doi":"10.1162/dint_a_00252","DOIUrl":"https://doi.org/10.1162/dint_a_00252","url":null,"abstract":"\u0000 Persistent identifiers for research objects, researchers, organizations, and funders are the key to creating unambiguous and persistent connections across the global research infrastructure (GRI). Many repositories are implementing mechanisms to collect and integrate these identifiers into their submission and record curation processes. This bodes well for a well-connected future, but metadata for existing resources submitted in the past are missing these identifiers, thus missing the connections required for inclusion in the connected infrastructure. Re-curation of these metadata is required to make these connections. This paper introduces the global research infrastructure and demonstrates how repositories, and their user communities, can contribute to and benefit from connections to the global research infrastructure.\u0000 The Dryad Data Repository has existed since 2008 and has successfully re-curated the repository metadata several times, adding identifiers for research organizations, funders, and researchers. Understanding and quantifying these successes depends on measuring repository and identifier connectivity. Metrics are described and applied to the entire repository here.\u0000 Identifiers (Digital Object Identifiers, DOIs) for papers connected to datasets in Dryad have long been a critical part of the Dryad metadata creation and curation processes. Since 2019, the portion of datasets with connected papers has decreased from 100% to less than 40%. This decrease has significant ramifications for the re-curation efforts described above as connected papers have been an important source of metadata. In addition, missing connections to papers make understanding and re-using datasets more difficult.\u0000 Connections between datasets and papers can be difficult to make because of time lags between submission and publication, lack of clear mechanisms for citing datasets and other research objects from papers, changing focus of researchers, and other obstacles. The Dryad community of members, i.e. users, research institutions, publishers, and funders have vested interests in identifying these connections and critical roles in the curation and re-curation efforts. Their engagement will be critical in building on the successes Dryad has already achieved and ensuring sustainable connectivity in the future.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Intelligent Screening Service Platform (ISSP) to improve the screening process of clinical trial subjects during COVID-19 pandemic: an experimental study","authors":"Bin Li, Runfang Guo, Huan Zhou, Yuanyuan Liu, Xiaolei Zhang, Qian Zhang","doi":"10.1162/dint_a_00253","DOIUrl":"https://doi.org/10.1162/dint_a_00253","url":null,"abstract":"\u0000 Background: During the COVID-19 pandemic, clinical trial recruitment cannot be carried out due to travel restrictions, transmission risks and other factors, resulting in the stagnation of a large number of ongoing or upcoming clinical trials.\u0000 Objective: An intelligent screening app was developed using artificial intelligence technology to rapidly pre-screen potential patients for phase I solid tumor drug clinical trials.\u0000 Methods: A total of 429 screening process records were collected from 27 phase I solid tumor drug clinical trials at the First Affiliated Hospital of Bengbu Medical College from April 2018 to May 2021. Features of the experimental data were analyzed, and the collinearity (principal component analysis) and strong correlation (χ2 test) among features were eliminated. XGBoost, Random Forest, and Naive Bayes were used to sort the weight importance of features. Finally, the pre-screening models were constructed using classification machine learning algorithm, and the optimal model was selected.\u0000 Results: Among the 429 screening records, 33 were data generated by repeated subject participation in different clinical trials, and of the remaining 396 screening records, 246 (62.12%) were screened successfully. The gold standard for subject screening success is the final judgment made by the principal investigator (PI) based on the clinical trial protocol. A Venn diagram was used to identify the important feature intersections of machine learning algorithms. After intersecting the top 15 characteristic variables of different feature screening models, 9 common variables were obtained: age, sex, distance from residence to the central institution, tumor histology, tumor stage, tumorectomy, the interval from diagnosis/postoperative to screening, chemotherapy, and ECOG (Eastern Cooperative Oncology Group, ECOG) score. To select the optimal subset, the 9 important feature variables were expanded to 12 and 15 feature subsets, and the performance of different feature subsets under different machine learning models was validated. The results showed that optimal performance, accuracy and practicability were achieved using XGBoost with the 12 feature subset. The final model could accurately predict the screening success rates in both internal (AUC =3D 0.895) and external (AUC =3D 0.796) validation, and has been transformed into a convenient tool to facilitate its application in the clinical settings. Subjects with a probability exceeding or equaling to the threshold in the final model had a higher probability to be successfully screened.\u0000 Conclusion: Based on the optimal model, we created an online prediction calculator and visualization app -- ISSP (Intelligent Screening Service Platform), which can rapidly screen patients for phase I solid tumor drug clinical trials. ISSP can effectively solve the problem of space and time interval. On the mobile terminal, it realizes the matching between clinical trial projects and patients, and completes the r","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLaMA-LoRA Neural Prompt Engineering: A Deep Tuning Framework for Automatically Generating Chinese Text Logical Reasoning Thinking Chains","authors":"Songlin Chen, Weicheng Wang, Xiaoliang Chen, Peng Lu, Zaiyan Yang, Yajun Du","doi":"10.1162/dint_a_00251","DOIUrl":"https://doi.org/10.1162/dint_a_00251","url":null,"abstract":"\u0000 The exption of Chinese natural language processing (NLP) has stimulated research in the broader NLP domain. However, existing large language models have limitations in comprehending and reasoning in Chinese. This paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource requirements. We propose LLaMA-LoRA, a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation(LoRA) of Large Language Models technique for refinement. Chain-of-Thought(CoT) are crucial for generating intermediate reasoning chains in language models, but their effectiveness can be limited by isolated language patterns. Erroneous reasoning resulting from conventional prompts negatively impacts model performance. Automatic prompts are introduced to encourage reasoning chain generation and accurate answer inference. Training the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning abilities. The LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks, surpassing benchmark performance achieved by related language models such as GPT-3.5, Chat-GLM, and OpenAssistant, delivering accurate, comprehensive, and professional answers. The availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Limitations and Ethical Considerations of ChatGPT","authors":"Shangying Hua, Shuangci Jin, Shengyi Jiang","doi":"10.1162/dint_a_00243","DOIUrl":"https://doi.org/10.1162/dint_a_00243","url":null,"abstract":"\u0000 With the advancements of artificial intelligence technology, ChatGPT, a new practice of artificial intelligence, holds immense potential across multiple fields. Its user-friendly human-machine interface, rapid response capabilities, and delivery of high-quality answers have attracted considerable attention and widespread usage. Regarded by many as a groundbreaking advancement in AI, ChatGPT represents a new milestone in the field. However, as with any technological evolution, the emergence of ChatGPT brings not only benefits, but also inevitable security risks and ethical issues. This paper provides specific information about ChatGPT, including its technology, limitations, ethical issues, governance paths and future directions. Specifically, we firstly offered a thorough exploration of the technical implementation details of GPT series models. Next, we provided an intricate analysis elucidating the reasons for limitations and scrutinized the consequential impacts, such as malicious misuse, privacy violation, and so on. Finally, we explore diverse governance paths to mitigate the impacts of ChatGPT and present future directions. This review aims to equip users with crucial knowledge, facilitating well-informed decision-making, effectively handling of potential challenges in employing ChatGPT, and staying abreast with the rapidly evolving landscape of this technology.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138949820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Attentive Siamese LSTM for Low-Resource Text Plagiarism Detection","authors":"Wei Bao, Jian Dong, Yang Xu, Yuanyuan Yang, Xiaoke Qi","doi":"10.1162/dint_a_00242","DOIUrl":"https://doi.org/10.1162/dint_a_00242","url":null,"abstract":"Low-resource text plagiarism detection faces a significant challenge due to the limited availability of labeled data for training. This task requires the development of sophisticated algorithms capable of identifying similarities and differences in texts, particularly in the realm of semantic rewriting and translation-based plagiarism detection. In this paper, we present an enhanced attentive Siamese Long Short-Term Memory (LSTM) network designed for Tibetan-Chinese plagiarism detection. Our approach begins with the introduction of translation-based data augmentation, aimed at expanding the bilingual training dataset. Subsequently, we propose a pre-detection method leveraging abstract document vectors to enhance detection efficiency. Finally, we introduce an improved attentive Siamese LSTM network tailored for Tibetan-Chinese plagiarism detection. We conduct comprehensive experiments to showcase the effectiveness of our proposed plagiarism detection framework.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139174828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BIKAS: Bio-Inspired Knowledge Acquisition and Simulacrum—A Knowledge Database to Support Multifunctional Design Concept Generation","authors":"Pavan Tejaswi Velivela, Yaoyao Fiona Zhao","doi":"10.1162/dint_a_00240","DOIUrl":"https://doi.org/10.1162/dint_a_00240","url":null,"abstract":"A detailed acquisition, analysis, and representation of biological systems exhibiting different functions is required to develop unique bio-inspired multifunctional conceptual designs and methods. This paper presents BIKAS: Bio-inspired Knowledge Acquisition and Simulacrum, a knowledge database of biological systems exhibiting various functionalities, developed based on case-based bio-inspired examples from literature. The knowledge database represents the biological features, their characteristics, and the function exhibited by the biological feature as a combination of its integrated structure and structural strategy. Furthermore, this knowledge database is utilized by the Expandable Domain Integrated Design (xDID) model that works on classifying, mapping, and representing biological features into their respective geometric designations called Domains. The combination of features from the Domains results in the generation of multifunctional conceptual designs. In addition, Meta-level design factors are proposed to aid designers in filtering the biological features and their respective functions having a similar structural strategy, thus aiding designers in rapidly selecting and emulating biological functions.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rule Mining Trends from 1987 to 2022: A Bibliometric Analysis and Visualization","authors":"Shiqi Zhou, Sheng Bi, Guilin Qi","doi":"10.1162/dint_a_00239","DOIUrl":"https://doi.org/10.1162/dint_a_00239","url":null,"abstract":"\u0000 Rule mining has emerged as a crucial technique in data mining and knowledge discovery, enabling the extraction of valuable insights and patterns from vast datasets. This has garnered significant attention from both academic and industrial communities. However, there is a lack of bibliometric and visualization research on rule mining, leading to an unclear delineation of research topics and trends in the field. To fill this gap, this paper provides a comprehensive and up-to-date bibliometric analysis of rule mining, covering 4524 publications published between 1987 and 2022. Using various metrics and visualization techniques, we examine the patterns, trends, and evolution of rule mining. The results show a sustained growth in rule mining research, with a significant increase in publication output in recent years, and its rapid expansion into new areas such as explainable artificial intelligence and privacy protection. While the majority of publications come from Asia, the National Natural Science Foundation of China emerges as the top funding agency in the field. We also identify highly productive authors and significant members of co-authorship networks, as well as the most influential publications and citation bursts. The need for international collaboration and the integration of diverse research perspectives is highlighted. Despite the progress in rule mining, several challenges still require further research, including scalability and efficiency, explainability, network security and privacy protection, and personalized and user-centered design. Overall, this paper provides a valuable roadmap for researchers, policymakers, and practitioners interested in rule-mining research.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138963544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification and quantification of timestamp data quality issues and its impact on data quality outcome","authors":"Rex Ambe","doi":"10.1162/dint_a_00238","DOIUrl":"https://doi.org/10.1162/dint_a_00238","url":null,"abstract":"\u0000 Timestamps play a key role in process mining because it determines the chronology of which events occurred and subsequently how they are ordered in process modelling. The timestamp in process mining gives an insight on process performance, conformance, and modelling. This therefore means problems with the timestamp will result in misrepresentations of the mined process. A few articles have been published on the quantification of data quality problems but just one of the articles at the time of this paper is based on the quantification of timestamp quality problems. This article evaluates the quality of timestamps in event log across two axes using eleven quality dimensions and four levels of potential data quality problems. The eleven data quality dimensions were obtained by doing a thorough literature review of more than fifty process mining articles which focus on quality dimensions. This evaluation resulted in twelve data quality quantification metrics and the metrics were applied to the MIMIC-III dataset as an illustration. The outcome of the timestamp quality quantification using the proposed typology enabled the user to appreciate the quality of the event log and thus makes it possible to evaluate the risk of carrying out specific data cleaning measures to improve the process mining outcome.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}