This study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI.

Abstract In this work, MineStudio is introduced, a novel AI filmmaking framework designed to facilitate future creative collaborative networks. MineStudio uses a hybrid digitization approach that involves reconstructing 3D digital environments, capturing 2D live‐action performances, employing AI tools to generate synthetic images and videos, and compositing with AI assistance. This method effectively addresses the main challenges in the current AI video generation, including consistency, directability, and issues with human actions and interactions. MineStudio has been utilized to create pioneering AI films, such as the love story “Next Stop Paris” and the sci‐fi short film “Message in a Bot”, and has been recognized as a trailblazer in the AI filmmaking industry.

Domain generalization (DG) approaches intend to extract domain invariant features that can lead to a more robust deep learning model. In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains. While it is one of the state-of-the-art methods, prior works on style augmentation have either disregarded the interdependence amongst distinct feature channels or have solely constrained style augmentation to linear interpolation. To address these research gaps, in this work, we introduce a novel augmentation approach, named Correlated Style Uncertainty (CSU), surpassing the limitations of linear interpolation in style statistic space and simultaneously preserving vital correlation information. Our method's efficacy is established through extensive experimentation on diverse cross-domain computer vision and medical imaging classification tasks: PACS, Office-Home, and Camelyon17 datasets, and the Duke-Market1501 instance retrieval task. The results showcase a remarkable improvement margin over existing state-of-the-art techniques. The source code is available https://github.com/freshman97/CSU.

In this study, the development of electric motor design optimization methods and algorithms for electric vehicles, which have become widespread as a result of energy policies, is discussed. The rapidly increasing need for micro transportation within the scope of small cities has increased the interest in short-range transportation vehicles such as electric bicycles and electric scooters. Therefore, an electric scooter model is considered and the desired motor requirements are determined by analyzing its dynamic model. Then, IPM topologies are investigated and the appropriate topology is decided. IPM design parameters are dealt with in the ANSYS RMXprt environment, and all design combinations by selecting the appropriate test matrix in Taguchi's experiment design method are modeled in ANSYS RMXprt and logged in the appropriate file format together with the obtained results. The motor design models of all experiments are saved as the. png format in the aspect format to be determined. Then, the labeled pictures with the obtained results in the experimental design are trained in MATLAB on a neural network model with appropriate input and output. Thereafter, the trained neural network derives the appropriate motor geometry in terms of the design requirements. The derived motor geometry is converted into a 2D technical drawing format with the help of a package program (Img2CAD) and uploaded to the ANSYS Maxwell environment. To assess the motor performance are performed in ANSYS Maxwell. The proposed methodology shows that the results of parameter estimation and geometry generation in solution space with the trained neural network give sufficient performance.

This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022). OCFR-2022 attracted a total of 3 participating teams, from academia. Eventually, six valid submissions were submitted and then evaluated by the organizers. The competition was held to address the challenge of face recognition in the presence of severe face occlusions. The participants were free to use any training data and the testing data was built by the organisers by synthetically occluding parts of the face images using a well-known dataset. The submitted solutions presented innovations and performed very competitively with the considered baseline. A major output of this competition is a challenging, realistic, and diverse, and publicly available occluded face recognition benchmark with well defined evaluation protocols.

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

The existing research in action recognition is mostly focused on high-quality videos where the action is distinctly visible. In real-world surveillance environments, the actions in videos are captured at a wide range of resolutions. Most activities occur at a distance with a small resolution and recognizing such activities is a challenging problem. In this work, we focus on recognizing tiny actions in videos. We introduce a benchmark dataset, Tiny VIRAT, which contains natural low-resolution activities. The actions in Tiny VIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging. We propose a novel method for recognizing tiny actions in videos which utilizes a progressive generative approach to improve the quality of low-resolution actions. The proposed method also consists of a weakly trained attention mechanism which helps in focusing on the activity regions in the video. We perform extensive experiments to benchmark the proposed Tiny VIRAT dataset and observe that the proposed method significantly improves the action recognition performance over baselines. We also evaluate the proposed approach on synthetically resized action recognition datasets and achieve state-of-the-art results when compared with existing methods. The dataset and code are publicly available at https://github.com/UgurDemir/Tiny-VIRAT.

Activity detection in security videos is a difficult problem due to multiple factors such as large field of view, presence of multiple activities, varying scales and viewpoints, and its untrimmed nature. The existing research in activity detection is mainly focused on datasets, such as UCF-101, JHMDB, THUMOS, and AVA, which partially address these issues. The requirement of processing security videos in real-time makes this even more challenging. In this work, we propose Gabriella, a real-time online system to perform activity detection on untrimmed security videos. The proposed method consists of three stages: tubelet extraction, activity classification, and online tubelet merging. For tubelet extraction, we propose a localization network which takes a video clip as input and spatio-temporally detects potential foreground regions at multiple scales to generate action tubelets. We propose a novel Patch-Dice loss to handle large variations in actor size. Our online processing of videos at a clip level drastically reduces the computation time in detecting activities. The detected tubelets are assigned activity class scores by the classification network and merged together using our proposed Tubelet-Merge Action-Split (TMAS) algorithm to form the final action detections. The TMAS algorithm efficiently connects the tubelets in an online fashion to generate action detections which are robust against varying length activities. We perform our experiments on the VIRAT and MEVA (Multiview Extended Video with Activities) datasets and demonstrate the effectiveness of the proposed approach in terms of speed (~100 fps) and performance with state-of-the-art results. More details about this work are available on our project webpage11https://www.crcv.ucf.edu/research/projects/gabriella-an-online-system-for-real-time-activity-detection-in-untrimmed-security-videos.