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The rapid development of computer science has led to the rapid development of new media technology. Among them, VR technology is sought after by the vast market for its multidimensional experience and immersive model. VR technology plays an important role in today's art exhibition and presentation. However, in the process of transforming 2D images into 3D models, traditional VR technology has some problems, such as long loading time, low model decision-making efficiency, and chaotic operation process, which often lead to visual interference when viewers watch art. Based on this background and the interactive page algorithm based on traditional VR technology, this paper studies and improves the visual interference in immersive multimedia art design by using task technology perception matching model (TTF). The results show that the improved task technology perception matching model reduces the visual and auditory interference by 10% in the process of virtual reality art display and viewing, and the model layout is more reasonable, and the utilization rate is higher. [ABSTRACT FROM AUTHOR]

In the past, the traditional small-scale 3D reconstruction technology developed maturely and mainly relied on manual handheld devices for data acquisition. Due to multiple factors, inaccuracy, and other problems, data will be omitted, and sometimes staff will face risks directly. Nowadays, with the development of computer science and technology, three-dimensional reconstruction technology has been innovated continuously, and it has also made a breakthrough in the application of large-scale reconstruction of dangerous areas. In this paper, the expensive sensors in the past are abandoned, and the cheaper and more efficient lidar is used instead. The laser point cloud and image are combined to describe the mining heritage scene in depth, and the academic achievements are transformed into productivity. The results show that (1) comparing the performance of the algorithm, the overall error of the proposed method is 5.86, and the average time consumption is about 10.23 ms. This filtering algorithm can restore the landscape of mining heritage to a great extent and optimize the problems of noise and outliers. (2) Our designed model has a very low loss value, and the point cloud accuracy is as high as 92.9%. Compared with other model methods, the model has excellent performance. (3) After the completion of the model, the overall satisfaction effect is above 70%, which can well restore the mining heritage style. Finally, the experimental effect of 3D reconstruction is good, which is more conducive to the research of reconstruction protection. There are still some work details and performance problems that need to be optimized and solved. [ABSTRACT FROM AUTHOR]

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) connects a number of intelligent devices with minimum human interference that can interact with one another. IoT is rapidly emerging in the areas of computer science. However, new security problems are posed by the cross-cutting design of the multidisciplinary elements and IoT systems involved in deploying such schemes. Ineffective is the implementation of security protocols, i.e., authentication, encryption, application security, and access network for IoT systems and their essential weaknesses in security. Current security approaches can also be improved to protect the IoT environment effectively. In recent years, deep learning (DL)/machine learning (ML) has progressed significantly in various critical implementations. Therefore, DL/ML methods are essential to turn IoT system protection from simply enabling safe contact between IoT systems to intelligence systems in security. This review aims to include an extensive analysis of ML systems and state-of-the-art developments in DL methods to improve enhanced IoT device protection methods. On the other hand, various new insights in machine and deep learning for IoT securities illustrate how it could help future research. IoT protection risks relating to emerging or essential threats are identified, as well as future IoT device attacks and possible threats associated with each surface. We then carefully analyze DL and ML IoT protection approaches and present each approach's benefits, possibilities, and weaknesses. This review discusses a number of potential challenges and limitations. The future works, recommendations, and suggestions of DL/ML in IoT security are also included. [ABSTRACT FROM AUTHOR]

Facial expression is one of the most significant elements which can tell us about the mental state of any person. A human can convey approximately 55% of information nonverbally and the remaining almost 45% through verbal communication. Automatic facial expression recognition is presently one of the most difficult tasks in the computer science field. Applications of facial expression recognition (FER) are not just limited to understanding human behavior and monitoring person's mood and the mental state of humans. It is also penetrating into other fields such as criminology, holographic, smart healthcare systems, security systems, education, robotics, entertainment, and stress detection. Currently, facial expressions are playing an important role in medical sciences, particularly helping the patients with bipolar disease, whose mood changes very frequently. In this study, an algorithm, automated framework for facial detection using a convolutional neural network (FD-CNN) is proposed with four convolution layers and two hidden layers to improve accuracy. An extended Cohn-Kanade (CK+) dataset is used that includes facial images of different males and females with expressions such as anger, fear, disgust, contempt, neutral, happy, sad, and surprise. In this study, FD-CNN is performed in three major steps that include preprocessing, feature extraction, and classification. By using this proposed method, an accuracy of 94% is obtained in FER. In order to validate the proposed algorithm, K-fold cross-validation is performed. After validation, sensitivity and specificity are calculated which are 94.02% and 99.14%, respectively. Furthermore, the f1 score, recall, and precision are calculated to validate the quality of the model which is 84.07%, 78.22%, and 94.09%, respectively. [ABSTRACT FROM AUTHOR]