Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition

The electroencephalogram (EEG) has great attraction in emotion recognition studies due to its resistance to deceptive actions of humans. This is one of the most significant advantages of brain signals in comparison to visual or speech signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that EEG recordings exhibit varying distributions for different people as well as for the same person at different time instances. This nonstationary nature of EEG limits the accuracy of it when subject independency is the priority. The aim of this study is to increase the subject-independent recognition accuracy by exploiting pretrained state-of-the-art Convolutional Neural Network (CNN) architectures. Unlike similar studies that extract spectral band power features from the EEG readings, raw EEG data is used in our study after applying windowing, pre-adjustments and normalization. Removing manual feature extraction from the training system overcomes the risk of eliminating hidden features in the raw data and helps leverage the deep neural network’s power in uncovering unknown features. To improve the classification accuracy further, a median filter is used to eliminate the false detections along a prediction interval of emotions. This method yields a mean cross-subject accuracy of 86.56% and 78.34% on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED) for two and three emotion classes, respectively. It also yields a mean cross-subject accuracy of 72.81% on the Database for Emotion Analysis using Physiological Signals (DEAP) and 81.8% on the Loughborough University Multimodal Emotion Dataset (LUMED) for two emotion classes. Furthermore, the recognition model that has been trained using the SEED dataset was tested with the DEAP dataset, which yields a mean prediction accuracy of 58.1% across all subjects and emotion classes. Results show that in terms of classification accuracy, the proposed approach is superior to, or on par with, the reference subject-independent EEG emotion recognition studies identified in literature and has limited complexity due to the elimination of the need for feature extraction.<br

Socially-distant fasting: information practices of young Muslims during pandemic

The COVID-19 pandemic has forced people to reimagine how they engage in spiritual and religious activities. This paper presents an analysis of the information practices of young Muslims during Ramadan, with a focus on their social, spiritual and COVID-related needs and strategies. Our qualitative approach entailed semi-structured interviews with 22 self-identified Muslims from across the Muslim spectrum. They were asked about their experiences with completing Ramadan under pandemic, including the nature of information accessed and shared as part of the fasting rituals. Interviews were transcribed, and open coding was used to categorize the data into themes. The thematic analysis was conducted through an iterative process. Our findings pointed to the differing affective states of the young Muslims who observed the fast under COVID. Participants also hinted at the loss of communal practices and rituals and the emergence of new habits and coping strategies (many informational in nature). Social and emotional support were particularly critical to overcoming the challenges. This study contributes to a better understanding of the intersection between information activities and spiritual/religious practices. The findings also have theoretical and practical implications for the role of information and technology in times of crisis.Peer Reviewe

Virtual transcendence experiences: Exploring technical and design challenges in multi-sensory environments

In this paper 1, we introduce the concept of Virtual Transcendence Experience (VTE) as a response to the interactions of several users sharing several immersive experiences through different media channels. For that, we review the current body of knowledge that has led to the development of a VTE system. This is followed by a discussion of current technical and design challenges that could support the implementation of this concept. This discussion has informed the VTE framework (VTEf), which integrates different layers of experiences, including the role of each user and the technical challenges involved. We conclude this paper with suggestions for two scenarios and recommendations for the implementation of a system that could support VTEs

Quality-aware adaptive delivery of multi-view video

Advances in video coding and networking technologies have paved the way for the Multi-View Video (MVV) streaming. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D viewing experience may be degraded signifi- cantly, unless quality-aware adaptation methods are deployed. There is no research work to discuss the MVV adaptation of decision strategy or provide a detailed analysis of a dynamic network environment. This work addresses the mentioned issues for MVV streaming over HTTP for emerging multi-view displays. In this research work, the effect of various adaptations of decision strategies are evaluated and, as a result, a new quality-aware adaptation method is designed. The proposed method is benefiting from layer based video coding in such a way that high Quality of Experience (QoE) is maintained in a cost-effective manner. The conducted experimental results on MVV streaming using the proposed strategy are showing that the perceptual 3D video quality, under adverse network conditions, is enhanced significantly as a result of the proposed quality-aware adaptation

Prevention of medication related osteonecrosis of the jaw after dentoalveolar surgery : an institution's experience

Dentoalveolar surgery is a predisposing factor for medication related osteonecrosis of the jaw (MRONJ). The aim of our study was to evaluate the described surgical procedures to prevent the development of MRONJ after dentoalveolar surgery in patients receiving bisphosphonates. In this retrospective study, sixty-three dentoalveolar surgeries were performed on 44 patients taking bisphosphonate in accordance with the treatment procedures we described. The following procedures were applied to patients 1) use of antibiotics 2) performed dentoalveolar surgical procedures 3) fill the socket with leukocyte- and platelet-rich fibrin (L-PRF) 4) post-operative application of low level laser therapy through Nd: YAG laser 5) sutures were removed on post-op 14th day 6) long-term results were evaluated. Healing of all patients was uneventful. Complete mucosal healing was achieved in all patients at 1 month. There is no failure was observed in long-term follow-up. Because of the pathophysiology of MRONJ is not fully understood and has many risk factors, definitive protocols on prevention and treatment have not been established yet. Personal risk assessment is required for the prevention and treatment of MRONJ. The described surgical protocol may be considered to reduce the risk of developing MRONJ after dentoalveolar surgery due to its high success rate

Multi-view video coding via virtual view generation

In this paper, a multi-view video coding method via generation of virtual picture sequences is proposed. Pictures are synthesized for the sake of better exploitation of the redundancies between neighbouring views in a multi-view sequence. Pictures are synthesized through a 3D warping method to estimate certain views in a multi-view set. Depth map and associated colour video sequences are used for view generation and tests. H. 264/AVC coding standard based MVC draft software is used for coding colour videos and depth maps as well as certain views which are predicted from the virtually generated views. Results for coding these views with the proposed method are compared against the reference H. 264/AVC simulcast method under some low delay coding scenarios. The rate-distortion performance of the proposed method outperforms that of the reference method at all bit-rates

Predicting head trajectories in 360° virtual reality videos

In this paper a fixation prediction based saliency algorithm is used in order to predict the head movements of viewers watching virtual reality (VR) videos, by modelling the relationship between fixation predictions and recorded head movements. The saliency algorithm is applied to viewings faithfully recreated from recorded head movements. Spherical cross-correlation analysis is performed between predicted attention centres and actual viewing centres in order to try and identify prevalent lengths of predictable attention and how early they can be predicted. The results show that fixation prediction based saliency analysis correlates with head movements only for limited durations. Therefore, further classification of durations where saliency analysis is predictive is required

No-reference depth map quality evaluation model based on depth map edge confidence measurement in immersive video applications

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance

Adaptive delivery of immersive 3D multi-view video over the Internet

The increase in Internet bandwidth and the developments in 3D video technology have paved the way for the delivery of 3D Multi-View Video (MVV) over the Internet. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D video experience may well be degraded unless content-aware precautionary mechanisms and adaptation methods are deployed. In this work, a novel adaptive MVV streaming method is introduced which addresses the future generation 3D immersive MVV experiences with multi-view displays. When the user experiences network congestion, making it necessary to perform adaptation, the rate-distortion optimum set of views that are pre-determined by the server, are truncated from the delivered MVV streams. In order to maintain high Quality of Experience (QoE) service during the frequent network congestion, the proposed method involves the calculation of low-overhead additional metadata that is delivered to the client. The proposed adaptive 3D MVV streaming solution is tested using the MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Both extensive objective and subjective evaluations are presented, showing that the proposed method provides significant quality enhancement under the adverse network conditions

Analysis of pixel-mapping rounding on geometric distortion as a prediction for view synthesis distortion

We analyze the performance of the geometric distortion, incurred when coding depth maps in 3D Video, as an estimator of the distortion of synthesized views. Our analysis is motivated by the need of reducing the computational complexity required for the computation of synthesis distortion in 3D video encoders. We propose several geometric distortion models that capture (i) the geometric distortion caused by the depth coding error, and (ii) the pixel-mapping precision in view synthesis. Our analysis starts with the evaluation of the correlation of geometric distortion values obtained with these models and the actual distortion on synthesized views. Then, the different geometric distortion models are employed in the rate-distortion optimization cycle of depth map coding, in order to assess the results obtained by the correlation analysis. Results show that one of the geometric distortion models is performing consistently better than the other models in all tests. Therefore, it can be used as a reasonable estimator of the synthesis distortion in low complexity depth encoders