Pub Date : 2023-11-03DOI: 10.1007/s41060-023-00467-9
Sepehr Omidvar, Thomas Tran
{"title":"Tackling cold-start with deep personalized transfer of user preferences for cross-domain recommendation","authors":"Sepehr Omidvar, Thomas Tran","doi":"10.1007/s41060-023-00467-9","DOIUrl":"https://doi.org/10.1007/s41060-023-00467-9","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"54 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1007/s41060-023-00460-2
Francis Rakotomalala, Aimé Richard Hajalalaina, Manda Vy Ravonimanantsoa Ndaohialy, Anselme Andriavelonera Alexandre, Andriatina H. Ranaivoson
{"title":"FLICs (Facebook Language Informal Corpus): a novel dataset for informal language","authors":"Francis Rakotomalala, Aimé Richard Hajalalaina, Manda Vy Ravonimanantsoa Ndaohialy, Anselme Andriavelonera Alexandre, Andriatina H. Ranaivoson","doi":"10.1007/s41060-023-00460-2","DOIUrl":"https://doi.org/10.1007/s41060-023-00460-2","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"70 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-28DOI: 10.1007/s41060-023-00463-z
Mona Moradi, Mohammad Rahmanimanesh, Ali Shahzadi
{"title":"Learning from streaming data with unsupervised heterogeneous domain adaptation","authors":"Mona Moradi, Mohammad Rahmanimanesh, Ali Shahzadi","doi":"10.1007/s41060-023-00463-z","DOIUrl":"https://doi.org/10.1007/s41060-023-00463-z","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":" 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136158664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1007/s41060-023-00458-w
Sai Ram Aditya Parisineni, Mayukha Pal
{"title":"Enhancing trust and interpretability of complex machine learning models using local interpretable model agnostic shap explanations","authors":"Sai Ram Aditya Parisineni, Mayukha Pal","doi":"10.1007/s41060-023-00458-w","DOIUrl":"https://doi.org/10.1007/s41060-023-00458-w","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"65 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1007/s41060-023-00438-0
Gautier Pialla, Hassan Ismail Fawaz, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller, Christoph Bergmeir, Daniel F. Schmidt, Geoffrey I. Webb, Germain Forestier
Abstract Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this task. For time series, few attacks have yet been proposed. Most that have are adaptations of attacks previously proposed for image classifiers. Although these attacks are effective, they generate perturbations containing clearly discernible patterns such as sawtooth and spikes. Adversarial patterns are not perceptible on images, but the attacks proposed to date are readily perceptible in the case of time series. In order to generate stealthier adversarial attacks for time series, we propose a new attack that produces smoother perturbations. We introduced a function to measure the smoothness for time series. Using it, we find that smooth perturbations are harder to detect both visually, by the naked eye and by deep learning models. We also show two ways of protection against adversarial attacks: the first one by detecting the attacks using a deep model; the second one by using adversarial training to improve the robustness of a model against a specific attack, thus making it less vulnerable.
{"title":"Time series adversarial attacks: an investigation of smooth perturbations and defense approaches","authors":"Gautier Pialla, Hassan Ismail Fawaz, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller, Christoph Bergmeir, Daniel F. Schmidt, Geoffrey I. Webb, Germain Forestier","doi":"10.1007/s41060-023-00438-0","DOIUrl":"https://doi.org/10.1007/s41060-023-00438-0","url":null,"abstract":"Abstract Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this task. For time series, few attacks have yet been proposed. Most that have are adaptations of attacks previously proposed for image classifiers. Although these attacks are effective, they generate perturbations containing clearly discernible patterns such as sawtooth and spikes. Adversarial patterns are not perceptible on images, but the attacks proposed to date are readily perceptible in the case of time series. In order to generate stealthier adversarial attacks for time series, we propose a new attack that produces smoother perturbations. We introduced a function to measure the smoothness for time series. Using it, we find that smooth perturbations are harder to detect both visually, by the naked eye and by deep learning models. We also show two ways of protection against adversarial attacks: the first one by detecting the attacks using a deep model; the second one by using adversarial training to improve the robustness of a model against a specific attack, thus making it less vulnerable.","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"6 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Periodic-confidence: a null-invariant measure to discover partial periodic patterns in non-uniform temporal databases","authors":"Uday Kiran Rage, Vipul Chhabra, Saideep Chennupati, Krishna Reddy Polipalli, Minh-Son Dao, Koji Zettsu","doi":"10.1007/s41060-023-00462-0","DOIUrl":"https://doi.org/10.1007/s41060-023-00462-0","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1007/s41060-023-00456-y
Guoqi Qian, Pei-Yun Sun
Abstract Finding associations between genetic markers and a phenotypic trait such as coronary artery disease (CAD) is of primary interest in genome-wide association studies (GWAS). A major challenge in GWAS is the involved genomic data often contain large number of genetic markers and the underlying genotype-phenotype relationship is mostly complex. Current statistical and machine learning methods lack the power to tackle this challenge with effectiveness and efficiency. In this paper, we develop a stochastic search method to mine the genotype-phenotype associations from GWAS data. The new method generalizes the well-established association rule mining (ARM) framework for searching for the most important genotype-phenotype association rules, where we develop a multinomial Gibbs sampling algorithm and use it together with the Apriori algorithm to overcome the overwhelming computing complexity in ARM in GWAS. Three simulation studies based on synthetic data are used to assess the performance of our developed method, delivering the anticipated results. Finally, we illustrate the use of the developed method through a case study of CAD GWAS.
{"title":"Association rule mining for genome-wide association studies through Gibbs sampling","authors":"Guoqi Qian, Pei-Yun Sun","doi":"10.1007/s41060-023-00456-y","DOIUrl":"https://doi.org/10.1007/s41060-023-00456-y","url":null,"abstract":"Abstract Finding associations between genetic markers and a phenotypic trait such as coronary artery disease (CAD) is of primary interest in genome-wide association studies (GWAS). A major challenge in GWAS is the involved genomic data often contain large number of genetic markers and the underlying genotype-phenotype relationship is mostly complex. Current statistical and machine learning methods lack the power to tackle this challenge with effectiveness and efficiency. In this paper, we develop a stochastic search method to mine the genotype-phenotype associations from GWAS data. The new method generalizes the well-established association rule mining (ARM) framework for searching for the most important genotype-phenotype association rules, where we develop a multinomial Gibbs sampling algorithm and use it together with the Apriori algorithm to overcome the overwhelming computing complexity in ARM in GWAS. Three simulation studies based on synthetic data are used to assess the performance of our developed method, delivering the anticipated results. Finally, we illustrate the use of the developed method through a case study of CAD GWAS.","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136079463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1007/s41060-023-00457-x
Pratik Shrivastava
{"title":"Multiple security policies for classified data items in replicated DRTDBS","authors":"Pratik Shrivastava","doi":"10.1007/s41060-023-00457-x","DOIUrl":"https://doi.org/10.1007/s41060-023-00457-x","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1007/s41060-023-00432-6
Laura Pollacci, Letizia Milli, Tuba Bircan, Giulio Rossetti
Abstract Understanding the careers and movements of highly skilled people plays an ever-increasing role in today’s global knowledge-based economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers’ movements over time. To this end, we define the Yearly Degree of Collaborations Index, which measures the annual tendency of researchers to collaborate intra-nationally, and two scores to measure the mobility in and out of countries, as well as their balance.
在当今的全球知识经济中，了解高技能人才的职业和流动发挥着越来越重要的作用。研究人员和学者是政府和机构创新和发展的源泉。我们的研究使用与科学相关的数据来跟踪职业发展和研究人员的运动。为此，我们定义了年度合作程度指数(annual Degree of collaboration Index)，该指数衡量研究人员在国内合作的年度趋势，以及两个分数来衡量国家内外的流动性及其平衡。
{"title":"Academic mobility from a big data perspective","authors":"Laura Pollacci, Letizia Milli, Tuba Bircan, Giulio Rossetti","doi":"10.1007/s41060-023-00432-6","DOIUrl":"https://doi.org/10.1007/s41060-023-00432-6","url":null,"abstract":"Abstract Understanding the careers and movements of highly skilled people plays an ever-increasing role in today’s global knowledge-based economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers’ movements over time. To this end, we define the Yearly Degree of Collaborations Index, which measures the annual tendency of researchers to collaborate intra-nationally, and two scores to measure the mobility in and out of countries, as well as their balance.","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-05DOI: 10.1007/s41060-023-00461-1
Mukesh Soni, Mohammad Shabaz, Renato R. Maaliw, Ismail Keshta, Rasool Altaee, Sanju Das
{"title":"Cloud-based non-invasive cognitive breath monitoring system for patients in health-care system","authors":"Mukesh Soni, Mohammad Shabaz, Renato R. Maaliw, Ismail Keshta, Rasool Altaee, Sanju Das","doi":"10.1007/s41060-023-00461-1","DOIUrl":"https://doi.org/10.1007/s41060-023-00461-1","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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