Pub Date : 2023-07-27DOI: 10.1007/s11633-023-1469-x
Haotong Qin, Ge-Peng Ji, Salman Khan, Deng-Ping Fan, F. Khan, L. Gool
{"title":"How Good is Google Bard’s Visual Understanding? An Empirical Study on Open Challenges","authors":"Haotong Qin, Ge-Peng Ji, Salman Khan, Deng-Ping Fan, F. Khan, L. Gool","doi":"10.1007/s11633-023-1469-x","DOIUrl":"https://doi.org/10.1007/s11633-023-1469-x","url":null,"abstract":"","PeriodicalId":29727,"journal":{"name":"Machine Intelligence Research","volume":"20 1","pages":"605 - 613"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44455389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-02DOI: 10.1007/s11633-022-1393-5
Yang Zhao, Jiajun Zhang, Chengqing Zong
{"title":"Transformer: A General Framework from Machine Translation to Others","authors":"Yang Zhao, Jiajun Zhang, Chengqing Zong","doi":"10.1007/s11633-022-1393-5","DOIUrl":"https://doi.org/10.1007/s11633-022-1393-5","url":null,"abstract":"","PeriodicalId":29727,"journal":{"name":"Machine Intelligence Research","volume":"20 1","pages":"514 - 538"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41718075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal $$cal{N}cal{P}$$ -complete problem, with the aid of machine learning (ML) techniques. Over the last decade, the machine learning society advances rapidly and surpasses human performance on several tasks. This trend also inspires a number of works that apply machine learning methods for SAT solving. In this survey, we examine the evolving ML SAT solvers from naive classifiers with handcrafted features to emerging end-to-end SAT solvers, as well as recent progress on combinations of existing conflict-driven clause learning (CDCL) and local search solvers with machine learning methods. Overall, solving SAT with machine learning is a promising yet challenging research topic. We conclude the limitations of current works and suggest possible future directions. The collected paper list is available at https://github.com/Thinklab-SJTU/awesome-ml4co .
{"title":"Machine Learning Methods in Solving the Boolean Satisfiability Problem","authors":"Wenxuan Guo, Hui-Ling Zhen, Xijun Li, Wanqian Luo, Mingxuan Yuan, Yaohui Jin, Junchi Yan","doi":"10.1007/s11633-022-1396-2","DOIUrl":"https://doi.org/10.1007/s11633-022-1396-2","url":null,"abstract":"This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal $$cal{N}cal{P}$$ -complete problem, with the aid of machine learning (ML) techniques. Over the last decade, the machine learning society advances rapidly and surpasses human performance on several tasks. This trend also inspires a number of works that apply machine learning methods for SAT solving. In this survey, we examine the evolving ML SAT solvers from naive classifiers with handcrafted features to emerging end-to-end SAT solvers, as well as recent progress on combinations of existing conflict-driven clause learning (CDCL) and local search solvers with machine learning methods. Overall, solving SAT with machine learning is a promising yet challenging research topic. We conclude the limitations of current works and suggest possible future directions. The collected paper list is available at https://github.com/Thinklab-SJTU/awesome-ml4co .","PeriodicalId":29727,"journal":{"name":"Machine Intelligence Research","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135727200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.
{"title":"OTB-morph: One-time Biometrics via Morphing","authors":"Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez, Aythami Morales, Ignacio Serna","doi":"10.1007/s11633-023-1432-x","DOIUrl":"https://doi.org/10.1007/s11633-023-1432-x","url":null,"abstract":"Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.","PeriodicalId":29727,"journal":{"name":"Machine Intelligence Research","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135727202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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