International Journal of Data Science and Analytics最新文献_第7页

Optimizing network lifespan through energy harvesting in low-power lossy wireless networks 在低功耗无线网络中通过能量收集优化网络寿命

International Journal of Data Science and Analytics Pub Date : 2023-11-06 DOI: 10.1007/s41060-023-00471-z

Syed Haider Ali, Syed Ashraf Ali, Inam Ullah, Ijaz Khan, Yazeed Yasin Ghadi, Yuning Tao, Muhammad Abbas Khan, Dashdondov Khongorzul

引用次数: 0

Bayes optimal estimation and its approximation algorithm for difference with and without treatment under IRSLC model IRSLC模型下处理前后差异的Bayes最优估计及其逼近算法

International Journal of Data Science and Analytics Pub Date : 2023-11-03 DOI: 10.1007/s41060-023-00468-8

Taisuke Ishiwatari, Shota Saito, Yuta Nakahara, Yuji Iikubo, Toshiyasu Matsushima

引用次数: 0

Tackling cold-start with deep personalized transfer of user preferences for cross-domain recommendation 通过深度个性化的用户偏好转移进行跨域推荐来解决冷启动问题

International Journal of Data Science and Analytics Pub Date : 2023-11-03 DOI: 10.1007/s41060-023-00467-9

Sepehr Omidvar, Thomas Tran

引用次数: 0

FLICs (Facebook Language Informal Corpus): a novel dataset for informal language FLICs (Facebook语言非正式语料库):一个新的非正式语言数据集

International Journal of Data Science and Analytics Pub Date : 2023-11-01 DOI: 10.1007/s41060-023-00460-2

Francis Rakotomalala, Aimé Richard Hajalalaina, Manda Vy Ravonimanantsoa Ndaohialy, Anselme Andriavelonera Alexandre, Andriatina H. Ranaivoson

引用次数: 0

Learning from streaming data with unsupervised heterogeneous domain adaptation 通过无监督异构域自适应从流数据中学习

International Journal of Data Science and Analytics Pub Date : 2023-10-28 DOI: 10.1007/s41060-023-00463-z

Mona Moradi, Mohammad Rahmanimanesh, Ali Shahzadi

引用次数: 0

Enhancing trust and interpretability of complex machine learning models using local interpretable model agnostic shap explanations 使用局部可解释模型不可知的形状解释增强复杂机器学习模型的信任和可解释性

International Journal of Data Science and Analytics Pub Date : 2023-10-25 DOI: 10.1007/s41060-023-00458-w

Sai Ram Aditya Parisineni, Mayukha Pal

引用次数: 1

Time series adversarial attacks: an investigation of smooth perturbations and defense approaches 时间序列对抗性攻击:平滑扰动和防御方法的研究

International Journal of Data Science and Analytics Pub Date : 2023-10-24 DOI: 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

{"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":null,"pages":null},"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}

引用次数: 0

Periodic-confidence: a null-invariant measure to discover partial periodic patterns in non-uniform temporal databases 周期置信度:在非均匀时态数据库中发现部分周期模式的一种零不变度量

International Journal of Data Science and Analytics Pub Date : 2023-10-19 DOI: 10.1007/s41060-023-00462-0

Uday Kiran Rage, Vipul Chhabra, Saideep Chennupati, Krishna Reddy Polipalli, Minh-Son Dao, Koji Zettsu

引用次数: 0

Association rule mining for genome-wide association studies through Gibbs sampling 基于Gibbs抽样的全基因组关联研究关联规则挖掘

International Journal of Data Science and Analytics Pub Date : 2023-10-16 DOI: 10.1007/s41060-023-00456-y

Guoqi Qian, Pei-Yun Sun

{"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":null,"pages":null},"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}

引用次数: 0

Multiple security policies for classified data items in replicated DRTDBS 复制DRTDBS中分类数据项的多个安全策略

International Journal of Data Science and Analytics Pub Date : 2023-10-16 DOI: 10.1007/s41060-023-00457-x

Pratik Shrivastava

引用次数: 0