Changes in everyday activities, such as adapting to the new online format due to lockdowns during the COVID-19 pandemic and being far away from family and friends, greatly influenced the emotions and feelings of students and their parents. Assessing the emotions of students’ parents at the higher education level is necessary since their emotional well-being has a direct impact on the emotional well-being of students throughout their distant learning experience. In this article, we held a quantitative study over 8 subsequent weeks from the onset of the COVID-19 pandemic in students and parents from the Mexican institution Tecnologico de Monterrey. Using a questionnaire from Inteligencia Audiencias (Intelligence Hearings), students and their parents could register their feelings and their valence from April 13thto July 20th, 2020. The results indicate that the most predominant emotions in both groups were very unpleasant and unpleasant in nature, being "worried" and "tired" the most common ones. The current study also provides some approaches for addressing the negative repercussions of the COVID-19 pandemic

During the Coronavirus disease (COVID-19) pandemic, students and their parents have unsurprisingly experienced uncertainties as well as concerns regarding the students' sustained education and learning outcomes. Analyzing the emotions of students' parents at higher education level holds significant importance as emotional wellbeing of parents will have a direct impact on that of students throughout their remote learning process. In this study, we held a quantitative study and assessed data collected from students' parents who are currently enrolled in undergraduate and postgraduate programs. We applied an independent sample (Chi-square test of association) statistical technique by utilizing Jamovi software for the data analysis. Our findings suggest that the COVID-19 pandemic impacted the emotion of parents in a considerable manner. The change in the emotions was observed on a weekly basis. A wide majority of the parents were concerned and uncertain about the pandemic situation which resulted in being worried about their children's studies and educational outcomes. The institutional surveys that take a detailed look into the emotional wellbeing of parents lead to possible reforms in educational policies which, in turn, is a key playing factor is creating a positive and effective educational environment for the students.

This systematic review leverages “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) method as a way to evaluate and map the state of the art of gamification strategies that are used in engineering studies. This research will correspond to the following research questions; (1) are there any commonly used techniques of gamification in engineering education? (2) What are the state-of-the-art practices in engineering education in terms of gamification? (3) Is gamification a useful teaching strategy in engineering education? Through answering these questions, the study offers insight to the lecturers on adapting current gamification techniques, efficient and effective way of utilizing this education strategy, and provide further research venues for future academic endowers.

In academia, the teacher-student relationship relies on research collaboration, and this topic is considered important in the field of higher education. In university-offered programs, research collaboration hinges on what teachers and students expect from each other. Teachers expect better learning outcomes from students, while students expect expertise, support, and balance between creativity and criticism during their study period. This literature review-based research study explores teacher-student research collaboration where teachers and students interact with each other. Research articles written in the last five years were accessed from university databases for this study. This research work highlights and provides guidelines to competent authorities of higher educational institutions and research experts on the basic elements of research collaboration among teachers and students. Such guidelines promote better learning outcomes, such as academic and research achievements. The findings of this study mention teacher-student elements that help students in gaining academic achievements and teachers in publishing more work for a successful academic research career. This study sheds light on student expectations of teacher-student research collaboration. They expect expertise, support, creativity, and criticism from the teacher in academic research collaboration.

The imagination of non-existent faces in random patterns, clouds and rock formations is known as facial pareidolia. We show that facial pareidolia also occurs naturally in a standard Convolutional Neural Network (CNN) trained on face recognition. For achieving this we propose a new method to analyse CNNs that combines feature visualisation and dimensionality reduction methods to cluster the hidden neuron activations in convolutional layers into groups with discriminative roles. The main contributions of the present paper are 1.) an approach that uses a CNN trained on human face detection for facial pareidolia simulation without any additional training on a target image set of abstract facial patterns and 2.) a novel way of improving the generalisation capacity of a CNN for cross-depiction recognition and domain adaptation scenarios using features learned by hidden neurons.

While deep learning has achieved state-of-the-art results on many computer vision tasks it is still challenged when interpreting human facial expressions, namely: poor generalisation ability of models across datasets, failure to account for individual differences in similar emotional states, and inability to recognise compound facial expressions and low-intensity or subtle emotional states. This study analyses how the resolution of face images that are input to various Convolutional Neural Network (CNN) models impacts on their ability to recognise compound and low-intensity emotions. Several high-resolution facial expression databases were combined to compile a simple dataset containing high-intensity emotions and a complex data set consisting of compound and low-intensity emotions. In the experiments, standard pre-trained CNN models that were further fine-tuned achieved higher validation accuracies than CNN models that were trained from scratch on the simple data set. However, when tested on the complex data the models trained from scratch generalised better than the refined pre-trained models. Using a technique of output visualisation we could show how our high-resolution CNN models were able to generalise to the complex data where they utilised small facial features that previously were not detectable.

Multispectral imaging allows for analysis of images in multiple spectral bands. Over the past three decades, airborne and satellite multispectral imaging have been the focus of extensive research in remote sensing. In the recent years, ground based multispectral imaging has gained an immense amount of interest in the fields ranging from computer vision and medical imaging to art, archaeology and computational forensics. The rich information content in multispectral images allows forensic experts to examine the chemical composition of forensic traces. Due to its rapid, non-contact and non-destructive characteristics, multispectral imaging is an effective tool for visualization, age estimation, detection and identification of forensic traces in document images. Ink mismatch is a key indicator of forgery in a document. Inks of different materials exhibit different spectral signature even if they have the same color. Multispectral analysis of questioned documents images allows identification and discrimination of visually similar inks. In this paper, an efficient automatic ink mismatch detection technique is proposed which uses Fuzzy C-Means Clustering to divide the spectral responses of ink pixels in handwritten notes into different clusters which relate to the unique inks used in the document. Sauvola's local thresholding technique is employed to efficiently segment foreground text from the document image. Furthermore, feature selection is used to optimize the performance of the proposed method. The presented method provides better ink discrimination results than state-of-the-art methods when tested on publicly available UWA Writing Inks Dataset.

The cognitive network uses cognitive processes to record data transmission rate among nodes and applies self-learning methods to trace data load points for finding optimal transmission path in the distributed computing environment. Several industrial systems, e.g., data centers, smart grids, etc., have adopted this cognitive paradigm and retrieved the least HOP count paths for processing huge datasets with minimum resource consumption. Therefore, this technique works well in transmitting structured data such as `XML', however, if the data is in unstructured format i.e. `RDF', the transmission technique wraps it with the same layout of payload and eventually returns inaccuracy in calculating traces of data load points due to the abnormal payload layout. In this paper, we propose a knowledge-based optimal routing path analyzer (RORP) that resolves the transmission wrapping issue of the payload by introducing a novel RDF-aware payload-layout. The proposed analyzer uses the enhanced payload layout to transmit unstructured RDF triples with an append pheromone (footsteps) value through cognitive nodes towards the semantic reservoir. The grid performs analytics and returns least HOP count path for processing huge RDF datasets in the cognitive network. The simulation results show that the proposed approach effectively returns the least HOP count path, enhances network performance by minimizing the resource consumption at each of the cognitive nodes and reduces traffic congestion through knowledge-based HOP count analytics technique in the cognitive environment of the smart grid.

Hyperspectral imaging helps in identifying patterns and objects in an observed hyperspectral scene on the basis of their unique spectral signatures; such identification is otherwise difficult using regular imaging. Recently, ink mismatch detection analysis based on hyperspectral imaging has shown enormous potential in distinguishing visually similar inks. Such analysis provides significant information to forensic document examiners to determine the authenticity of the questioned documents. However, a major challenge still exists in disproportionate ink mismatch detection because it is inherently an unbalanced clustering problem. The presented approach deals with ink mismatch detection in unbalanced clusters by using hyperspectral unmixing scheme. It identifies the spectral signatures (endmembers) of the inks and their corresponding proportions (abundances). Our results show that HySime outperforms other methods in signal subspace estimation. Hyperspectral unmixing is done by using minimum volume enclosing simplex algorithm. Efficacy of the purposed approach is demonstrated by successfully distinguishing varying disproportionate ink datasets generated from UWA database and results are compared with existing state of the art methods in hyperspectral ink mismatch detection field. We expect that these finding will further encourage the use of hyperspectral imaging in document analysis, particularly towards automated questioned document examination.

This paper presents the implementation details of a proposed solution to the Emotion Recognition in the Wild 2017 Challenge, in the category of group-level emotion recognition. The objective of this sub-challenge is to classify a group's emotion as Positive, Neutral or Negative. Our proposed approach incorporates both image context and facial information extracted from an image for classification. We use Convolutional Neural Networks (CNNs) to predict facial emotions from detected faces present in an image. Predicted facial emotions are combined with scene-context information extracted by another CNN using fully connected neural network layers. Various techniques are explored by combining and training these two Deep Neural Network models in order to perform group-level emotion recognition. We evaluate our approach on the Group Affective Database 2.0 provided with the challenge. Experimental evaluations show promising performance improvements, resulting in approximately 37% improvement over the competition's baseline model on the validation dataset.