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Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.

This paper aims to discuss the utility of eye movement desensitization and reprocessing (EMDR) therapy as a treatment for children with intellectual disabilities (ID) who have experienced trauma.

Relevant National Institute for Health and Care Excellence (NICE) guidance and literature were reviewed to provide support for the use of EMDR as a treatment for trauma in children with ID.

There is a growing body of evidence which demonstrates that EMDR therapy is successful for the treatment of trauma in adults and children. However, for children with ID, the research is limited despite those with ID being more likely than non-disabled peers to experience trauma such as abuse or neglect.

EMDR can only be facilitated by trained mental health nurses, psychiatrists, psychologists (clinical, forensic, counselling or educational) or occupational therapists or social workers with additional training. Finally, general practitioners who are experienced in psychotherapy or psychological trauma and have accreditation. Therefore, this highlights that there may be a lack of trained staff to facilitate this intervention and that those who are generally working with the client closely and long term such as learning disability nurses are not able to conduct this intervention.

This paper presents an account of NICE guidance and evidence of the efficacy of EMDR as a treatment for adults, children and those with ID.

This study sought to understand which food allergen labeling systems (non-directive, semi-directive, and directive) were attended to and preferred by 34 participants with food hypersensitivity and their perceived corporate social responsibility (CSR) and behavioral intention towards a restaurant that identifies food allergens on menus.

This study used an online survey with open-ended and ranking questions, combined with eye-tracking technology, to explore participants' visual attention and design preferences regarding four menus. This study utilized one-way repeated measures analysis of variance (RM-ANOVA) and heat maps to analyze participants' menu-reading behaviors. A content analysis of survey responses and a ranking analysis of menus were conducted to understand the reasons behind consumers' preferred menu designs.

The advisory statement was not much attended to. Participants identified food allergen information significantly quicker with the directive labeling system (icons) than the other two systems, implying they were eye-catching. Semi-directive labeling system (red text) has lower visit count and was more preferred than two other systems; each labeling system has its strengths and limitations. Participants viewed restaurants that disclosed food allergen information on menus as socially responsible, and they would revisit those restaurants in the future.

This study was one of the first to explore, through use of eye-tracking technology, which food allergen labeling systems were attended to by consumers with food hypersensitivity. The use of triangulation methods strengthened the credibility of the results. The study provided empirical data to restauranteurs in the US on the values of food allergen identification on restaurant menus, although it is voluntary.

The prevalence of visually splendid multi-million-dollar cellar doors (CDs) builds an assumption that bricks and mortar create the co-created cellar door experience (CDE). This study aims to determine what attracts the visual attention of staff and customers during a CDE at three visual designs of CD: lively, stylised and simple.

A total of 23 customers and five staff consented to record their CDEs using TobiiPro2 glasses with 35 recordings providing 993 min for analysis with Tobii Pro Lab. Twenty-five areas of interest were used to calculate fixation and visit metrics.

The most attended elements of a co-created CDE were staff and faces. Attention is less influenced by the design of CD, whereas staff significantly influence attention.

The findings are valuable to the industry as they highlight the importance of human resources to a winery business, an increasingly casualised workforce. Future research could focus on staffing needs, including training and performance during experience delivery, with the expectation of increasing profitability.

To the best of the authors’ knowledge, this study is the first to analyse objective recordings of staff and customer visual attention during their experience.

This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques.

Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability.

There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains.

The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers.

As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians.

This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

The present study investigates human behavioral and emotional experiences based on human-built environment interaction with a specific interest in urban graffiti displaying fear and pleasure-inducing facial expressions. Regarding human behavioral and emotional experience, two questions are asked for the outcome of human responses and two hypotheses are formulated. H1 is based on the behavioral experience and posits that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified behavioral fear and pleasure responses. H2 is based on emotional experience and states that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified emotional fear and pleasure responses.

The research design is developed as a multi-method approach, applying a lab-based experimental strategy (N:39). The research equipment includes a mobile electroencephalogram (EEG) and a Virtual Reality (VR) headset. The behavioral and emotional human responses concerning the representational features of urban graffiti are assessed objectively by measuring physiological variables, EEG signals and subjectively by behavioral variables, systematic behavioral observation and self-report variables, Self-assessment Manikin (SAM) questionnaire. Additionally, correlational analyses between behavioral and emotional results are performed.

The findings of behavioral and emotional evaluations and correlational results show that specialized fear and pleasure response patterns occur due to the affective characteristics of the urban graffiti's representational features, supporting our hypotheses. As a result, the characteristics of behavioral fear and pleasure response and emotional fear and pleasure response are identified.

The present paper contributes to the literature on human-built environment interactions by using physiological, behavioral and self-report measurements as indicators of human behavioral and emotional experiences. Additionally, the literature on urban graffiti is expanded by studying the representational features of urban graffiti as a parameter of investigating human experience in the built environment.

Understanding the effect of assortment composition on attentional levels is an essential topic for academic researchers and practitioners. This work has important implications when analyzing the influence of shopping frame time and search effort on the relationship between the reaction to assortment composition and visual attention to stock-keeping units (SKUs) pricing.

Two experimental studies through gauze behavior analysis technology (using eye-tracking equipment) analyze the variable's large assortment, visual attention to SKU pricing, search effort and shopping frame time.

The results suggest that, although it increases the search effort, a large assortment decreases the visual attention to SKU pricing. Further, our results indicate a moderating effect associated with mitigating the negative effect by medium-low levels of search effort and a moderating impact of time in this relation.

Marketing professionals can carefully optimize the in-store experience by managing the assortment and variety and by influencing consumers' visual attention to SKU pricing along the journey as part of the experience. Assortment and SKU pricing strategies need to be aligned with consumer journey design.

Our findings contribute to assortment theory and management by detailing the relationship between consumers' reactions to assortment perception and visual attention to SKU pricing in time flow. We reinforce the importance of considering assortment strategies from the consumer perspective and giving reliable information about in-store behavior.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

This study analyzed the factors affecting visual attention toward sugar-reduction information (SRI) on sugar-reduced beverages (SRBs) and identified the most optimal SRI type and location using eye-tracking. The eye-tracking results were compared with those of a self-reported questionnaire.

An eye-tracking experiment was conducted on 50 Korean people in their 20s and 30s to analyze implicit responses. Subsequently, a self-reported questionnaire was administered to analyze explicit responses, facilitating the investigation of perceptions, attitudes, preferences, intentions to purchase SRBs, and preferred SRI types and positions.

The results were as follows. First, personal trait-, state-, and product-related factors were found to affect eye movement in relation to SRI. Second, eye-tracking revealed that SRI types and locations that drew long-lasting fixation and attracted considerable attention were similar to those preferred in the self-reported questionnaire. Therefore, to efficiently convey information on SRBs, SRI should be combined with a graphic, and not merely a word, and placed in the upper-right corner, exhibiting consistency with the results of two previous experiments.

This study specifically focused on considering personal and product-related traits while conducting an eye-tracking experiment to investigate the factors that attract consumers' attention. Furthermore, this study is the first to investigate the use of SRI labels to promote SRB selection. What is significant is that both explicit and implicit responses were assessed and compared via a self-reported survey and eye-tracking experiments for various SRB categories.