2004 |
Murovec, Boštjan; Šuhel, Peter A repairing technique for the local search of the job-shop problem Journal Article In: European Journal of Operational Research, vol. 153, no. 1, pp. 220 - 238, 2004, ISSN: 0377-2217, (Timetabling and Rostering). Abstract | Links | BibTeX | Tags: Job-shop, Local search, Neighborhood function, Scheduling @article{MUROVEC2004220,The local search technique has become a widely used tool for solving many combinatorial optimization problems. In the case of the job-shop the implementation of such a technique is not straightforward at all due to the existence of the technological constraints among the operations that belong to the same job. Their presence renders a certain set of schedules infeasible. Consequently, special attention is required when defining optimization algorithms to prevent the possibility of reaching an infeasible schedule during execution. Traditionally, the problem is tackled on the neighborhood level by using only a limited set of moves for which feasibility inherently holds. This paper proposes an alternative way to avoid infeasibility by incorporating a repairing technique into the mechanism for applying moves to a schedule. Whenever an infeasible move is being applied, a repairing mechanism rearranges the underlying schedule in such a way that the feasibility of the move is restored. The possibility of reaching infeasible solutions is, therefore, eliminated on the lowest possible conceptual level. Consequently, neighborhood functions need not to be constrained to a limited set of feasible moves any more. |
0000 |
Peter Rot Blaz Meden, Philipp Terhorst Privacy-Enhancing Face Biometrics: A Comprehensive Survey Journal Article In: IEEE Transactions on Information Forensics and Security, vol. vol. 16, pp. 4147-4183, 0000. Abstract | Links | BibTeX | Tags: B-PETs, biometrics, DEID, deidentification, face deidentification, face recognition, FaceGEN, overview, privacy, privacy-enhancing techniques, survey @article{TIFS_PrivacySurvey,Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy--enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy--related research in the area of biometrics and review existing work on textit{Biometric Privacy--Enhancing Techniques} (B--PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B--PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future. |
Gan, Chenquan; Zhou, Daitao; Wang, Kexin; Zhu, Qingyi; Jain, Deepak Kumar; Štruc, Vitomir Optimizing ambiguous speech emotion recognition through spatial–temporal parallel network with label correction strategy Journal Article In: Computer Vision and Image Understanding, vol. 260, no. 104483, pp. 1–14, 0000. Abstract | Links | BibTeX | Tags: deep learning, emotion recognition, speech, speech processing, speech technologies @article{CVIU_2025,Speech emotion recognition is of great significance for improving the human–computer interaction experience. However, traditional methods based on hard labels have difficulty dealing with the ambiguity of emotional expression. Existing studies alleviate this problem by redefining labels, but still rely on the subjective emotional expression of annotators and fail to consider the truly ambiguous speech samples without dominant labels fully. To solve the problems of insufficient expression of emotional labels and ignoring ambiguous undominantly labeled speech samples, we propose a label correction strategy that uses a model with exact sample knowledge to modify inappropriate labels for ambiguous speech samples, integrating model training with emotion cognition, and considering the ambiguity without dominant label samples. It is implemented on a spatial–temporal parallel network, which adopts a temporal pyramid pooling (TPP) to process the variable-length features of speech to improve the recognition efficiency of speech emotion. Through experiments, it has been shown that ambiguous speech after label correction has a more promoting effect on the recognition performance of speech emotions. |