Yang Liu, Luke Shaw, Haotian Liu, Kuan Hong Wang, Feng Vankee-Lin, Guoying Zhao
{"title":"Cross-Species Behavioral Analysis via Multidimensional Knowledge Transfer.","authors":"Yang Liu, Luke Shaw, Haotian Liu, Kuan Hong Wang, Feng Vankee-Lin, Guoying Zhao","doi":"10.1109/mis.2026.3692032","DOIUrl":"10.1109/mis.2026.3692032","url":null,"abstract":"<p><p>Computer vision tools can provide objective, reproducible, and scalable measurements for understanding animal behaviors, which is a fundamental challenge in cross-species research. Existing approaches primarily emphasize body detection and tracking, with limited advancement in integrative behavioral pattern analysis due to sparse annotations and substantial variability in species' physical and cognitive traits. In this article, we conduct two pilot studies for cross-species behavioral analysis within primates using multidimensional transfer learning. First, prior knowledge of physiological similarities between humans and monkeys is introduced to adjust the representation learning for macaque facial expression classification under limited labeled data. Second, we improve our approach by leveraging the foundation model and self-supervised strategy to mitigate appearance variations across primate species for downstream marmoset engagement level estimation. Experiments on public and private cross-primates datasets demonstrate the effectiveness of our framework, highlighting the potential of knowledge transfer to bridge the gap between species-specific behavioral analysis and generalizable models.</p>","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"41 4","pages":"98-107"},"PeriodicalIF":4.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13521619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148840099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring the inequitable impact of data missingness on fairness in machine learning.","authors":"Sitao Min, Hafiz Asif, Jaideep Vaidya","doi":"10.1109/mis.2025.3549484","DOIUrl":"10.1109/mis.2025.3549484","url":null,"abstract":"<p><p>Today, data-driven models and artificial intelligence / machine learning underlie decision making in almost all aspects of society. However, significant concerns have been raised over the fairness of such models. While various aspects of algorithmic fairness have been studied, the effect of missing data on fairness remains understudied. This is a significant problem since data in real-world settings is almost never complete, and may often suffer from systemic missingness. This article systematically evaluates how missing data, particularly when correlated with protected classes and outcome variables, affects the fairness of classifiers. Utilizing a comprehensive framework covering various missing data patterns, rates, and mitigation methods, we analyze 150 experimental dataset variants derived from real-world scenarios, and find that missing data correlated with sensitive attributes and outcomes can exacerbate disparities, even for little missingness, making it crucial to address missingness in fairness evaluations.</p>","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"40 3","pages":"28-38"},"PeriodicalIF":4.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144527668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arun Narayanan, Evangelos Pournaras, Pedro H. J. Nardelli
{"title":"Large-scale Package Deliveries with Unmanned Aerial Vehicles using Collective Learning","authors":"Arun Narayanan, Evangelos Pournaras, Pedro H. J. Nardelli","doi":"10.1109/mis.2024.3439535","DOIUrl":"https://doi.org/10.1109/mis.2024.3439535","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"40 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222363","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":"Do Large Language Models Bias Human Evaluations?","authors":"Daniel E. O’Leary","doi":"10.1109/mis.2024.3415208","DOIUrl":"https://doi.org/10.1109/mis.2024.3415208","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872567","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}