Reviewerly/exHarmony huggingface dataset:
Effect of Personality Traits and Stressor Inducers on Users’ Cognitive Load During Interactions with VR Environments

In this paper, HCI-based design criteria focusing on managing the cognitive load of users during their interaction with Virtual Reality (VR) based training environments are presented. The design criteria explored in the paper help lay a foundation for the creation of Human Centric VR environments to train users in two healthcare domains. The first domain is orthopedic surgery, and the second domain is related to the Covid-19 pandemic. The HCI-based perspective presented in the paper investigates criteria such as personality traits and stress inducers and their impact on cognitive load. The paper delineates the implementation of the VR based environments and a set of attributes that guide and influence the content of the environments. Testing and assessment strategy is described and results are also included which provide insights into the impact of such HCI-based criteria on participants’ acquisition of skills and knowledge during interactions with the VR environments.
Visualizing and Processing Information Not Uttered in Spoken Political and Journalistic Data: From Graphical Representations to Knowledge Graphs in an Interactive Application

The present interactive application targets to the processing and direct generation and use of information not uttered, converting it into “visible”, processable information in the form of knowledge graphs and, subsequently, training data, for neural networks and other uses. In-depth understanding and un-biased evaluation of interviews and discussions in spoken political and journalistic texts is targeted, especially when an international public is concerned. Special emphasis is placed on parameters concerning Chinese speakers within the international media and community.
Experience in organizing the subject “Individual Project” in high school

The Federal State Educational Standard of Secondary General Education presupposes the mandatory implementation of the course “Individual Project”, the logical conclusion of which is the protection of one of the selected types of work by the student: project or research. This actu-alizes the expansion of approaches to teaching and learning, the system of accompanying teachers for the effective organization of project and research activities of students in educational organizations at the level of secondary general education. The approach of organizing the subject “Individual Project” in high school is presented. The main stages of organizing project and research activities within the framework of the above subject are identified and clarified, namely, ascertaining – preparing students for independent activity, distinguishing between the concepts of “educational project” and “educational research”, which included two blocks: 1) “immersion” by students in project activities, 2) “immersion” by students in research activities; organizational – stimulation of students' mental and activity processes, consisting of blocks aimed at 3) the development of search and analytical activities, 4) fixing the distinctive characteristics of the object and subject of research, 5) development of planning and goal-setting skills, 6) development of presentation and communication skills; designing – developing skills in working with scientific and educational literature, academic writing; instrumental – acquaintance with the organization and conduct of the experimental part of the study, development of the final product; constructive – finalization of the study/project, taking into account the comments and suggestions, the design of the text of the work, public defense before the commission. A number of practical and laboratory works carried out in the classroom are considered.
A Data Collection and Annotation Tool for Asynchronous Multimodal Data During Human-Computer Interactions

Supervised Learning is a Machine Learning technique where a predictor is trained using a set of labeled data where each data point has a label associated with it. The technique can be used for Affective Computing where a person's physiological signals are measured and used in conjunction with labels to create a predictor for affective states. However, creating this labeled dataset is labor-intensive and prone to human errors. To alleviate some of the costs of manual labeling, a data annotation tool is needed to create labels that can eventually be combined with physiological data to create a fully labeled dataset. The goal of the tool is to streamline the steps from data collection and data annotation to creating an effective computing agent. A use case and initial development of this tool stems from a socially assistive robotics system setting with older adults who have cognitive impairment and dementia. However, the tool was further developed to be adaptive to the needs of any data annotation. In this paper, we present a data annotation tool that can be adapted to any domain for streamlining the process of data annotation.
Multi-Level Feature Aggregation-Based Joint Keypoint Detection and Description

Image keypoint detection and description is a popular method to find pixel-level connections between images, which is a basic and critical step in many computer vision tasks. The existing methods are far from optimal in terms of keypoint positioning accuracy and generation of robust and discriminative descriptors. This paper proposes a new end-to-end self-supervised training deep learning network. The network uses a backbone feature encoder to extract multi-level feature maps, then performs joint image keypoint detection and description in a forward pass. On the one hand, in order to enhance the localization accuracy of keypoints and restore the local shape structure, the detector detects keypoints on feature maps of the same resolution as the original image. On the other hand, in order to enhance the ability to percept local shape details, the network utilizes multi-level features to generate robust feature descriptors with rich local shape information. A detailed comparison with traditional feature-based methods Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and deep learning methods on HPatches proves the effectiveness and robustness of the method proposed in this paper.
Automatic Meme Generation with an Autoregressive Transformer

In this paper we study the way to automate meme generation from textual prompts using autoencoders based on the Transformer architecture. For this we collected a dataset of about 5000 meme images. Then we run an OCR (optical character recognition) library Tesseract on top of these images to get English texts from them. We filtered poorly recognised texts using FastText language identification library to get only images with English texts and scores above 0.9. Then we trained image generation models using our dataset.
State Observer-Based Fuzzy Echo State Network Sliding Mode Control for Uncertain Strict-Feedback Nonlinear Systems Without Backstepping

For uncertain strict-feedback nonlinear systems (SFNSs), the adaptive back stepping control is a popular method, yet this method requires repeatedly differentiating virtual control inputs, which will result in the "explosion of complexity" problem. In this article, an alternative control method for uncertain SFNSs without the backstepping technique is presented. We fifirst translate the uncertain SFNS into a new straightforward normative system whose states are unmeasurable, and then, an observer is designed to estimate the unknown states of the transformed system. A new recurrent neural network, namely fuzzy echo state network (FESN), is constructed to approximate the lumped uncertainty of the normative system. The semi-global stability of the closed-loop system can be guaranteed by the FESN sliding mode controller that only uses one FESN and one adaptation law. Comparative simulations are put forward to verify the theoretical derivation results.
Adoption of a COVID-19 Contact Tracing App Among Older Internet Users in Finland

The outbreak of the COVID-19 pandemic created an unequal need for limiting physical contacts and tracing possible exposures to a novel coronavirus. Smartphone-based contact tracing applications (CTAs) were presented as a vehicle for stopping virus transmission chains and supporting the work of contact tracing teams. In this study, older adults' adoption of a CTA was studied using socioeconomic background factors, satisfaction with health, and the measure of digital activity as predictors. The data were drawn from a larger questionnaire survey targeted at older internet users. A subsample of older Finnish internet users (N = 723) was analyzed using a logistic regression model. Results showed that older internet users had widely adopted the Finnish CTA called Koronavilkku irrespective of demographic background factors, level of education, and self-assessed satisfaction with health. Besides high income and retirement status, digital activity measured through the breadth of mobile phone features used and the use of an online symptom checker increased the likelihood of having the CTA installed on a smartphone. The results of the study lend themselves to be used for future epidemics and other occasions that require a real-time and/or retrospective tracing of people and their physical encounters.
Extension de KLM au Pointage du Regard et Validation Gestuelle dans HoloLens

The advances in mixed reality head-mounted displays led researchers to investigate such systems but the design of mixed reality app could be laborious. Evaluate the effects of the arrangement of these interfaces in the physical world on user performance is complex. In this context, the use of a predictive model allows researchers to estimate, prior to the implementation, the usability issues of such applications. To this end, we propose a new KLM extension to model HMD-based interaction focusing on searching, pointing and validation tasks. First, we define new KLM operators and estimate through two user studies the estimated time for each operator. Then, we validate the model for an ecological task. This final study confirms that the model could be applied for predicting the interaction time on mixed reality interaction.
USE OF MOBILE APPLICATIONS TO IDENTIFY PLANTS

One of the main principles of effective learning is the principle of «corresponding to nature», ie providing the environment in which the child learns should be familiar to him. For the modern child, the environment of gadgets has become a natural environment. That is why the use of mobile applications is a very promising method of learning. Software that can be used during the learning process in the application of STEM technology can be divided into desktop applications, mobile applications, and web-oriented technologies. The paper is devoted to research mobile applications used during the STEM-classes and can be used to identify plants. There are 10 mobile applications that are plant identifiers worldwide. These applications can be classified into three groups, such as plant identifiers that can analyze photos, plant classification provides the possibility to identify plants manually, plants-care apps that remind water of the plant, or change the soil. The following mobile applications were analysed: Flora Incognita, PlantNet, PlantSnap, PictureThis, LeafSnap, Seek, PlantNet regarding ease of use and identification accuracy. PlantNet is the easiest app to install. Also, pretty easy to install are LeafSnap and Flora Incognita. Apps LeafSnap, Flora Incognita, and Seek to have the simplest interface. PlantSnap, PictureThis, and PlantNet are characterized by the most uncomfortable process of identification which can be complicated for teachers. Seek is the interesting application, which provides detailed instructions for students on research. This application also has tools to encourage students and offers participation in international research projects. It has been proven that Flora Incognita and PlantNet have the most user friendly and most informative interface of plant identification programs. Flora Incognita provides correct identification of 71% of plants compared to 55% provided by PlantNet. For comparison, this figure for Google Lens is 92.6%. However, they were significantly less accurate than the Google Lens results. Therefore, Google Lens is the most recommended app to use. Talking to account, results of usability analysis, and quality of analysis, for those students and teachers who do not like Google Lens app, it is possible to use Flora Incognita, but PlantNet can’t be recommended to use due low accuracy which may provide up to half of incorrect analysing results. Although Flora Incognita identifies species of local (aboriginal) flora with higher accuracy. A detailed experimental study of Google Lens and its comparison with other mobile applications allow us to recommend Google Lens for use in the lessons when applying the STEM approach.
Educational Technologies for Multicultural Science Learning

Research on technology-enhanced learning environments in science education has too often sampled participants from overrepresented populations in science, technology, engineering, and mathematics (STEM) degrees and careers more broadly. Conversely, when design studies leveraging technology have sampled from underrepresented populations, research in science education has lacked focus on these students’ sociocultural and communal assets. Such critical design foci encourage historically marginalized students to engage with the sociopolitical nature of science. While there have been recent efforts to confront inequity in science curricula leveraging students’ communities, identities, and cultural assets, these studies often neglect to leverage the technological affordances that many normatively centered populations have benefited from in the broader science education literature. This chapter presents thematic findings from 20 years of exploratory and empirical studies utilizing technology-enhanced learning environments in science education design research. In order to emphasize improvements to curricular innovations for science learning among historically marginalized youth over the past two decades, this chapter foregrounds design research approaches as productive spaces for moving toward more socially just science content learning goals. In the end, research serving majority Black and Brown youth suggest that purposeful design of technology-rich science instruction for students who diverge from the normative center of policy, schools, research, and curriculum is a field in its own right. To that end, we argue that researchers and technology developers should infuse the critical goals that are foundational to multicultural science education into research around innovative learning technologies for learners often relegated to the margins in science education research studies.
Simplified Active Calibration

We present a new mathematical formulation to estimate the intrinsic parameters of a camera in active or robotic platforms. We show that the focal lengths can be estimated using only one point correspondence that relates images taken before and after a degenerate rotation of the camera. The estimated focal lengths are then treated as known parameters to obtain a linear set of equations to calculate the principal point. Assuming that the principal point is close to the image center, the accuracy of the linear equations are increased by integrating the image center into the formulation. We extensively evaluate the formulations on a simulated camera, 3D scenes and real-world images. Our error analysis over simulated and real images indicates that the proposed Simplified Active Calibration method estimates the parameters of a camera with low error rates that can be used as an initial guess for further non-linear refinement procedures. Simplified Active Calibration can be employed in real-time environments for automatic calibrations given the proposed closed-form solutions.
MTMA-DDPG: A Deep Deterministic Policy Gradient Reinforcement Learning for Multi-task Multi-agent Environments

Beyond Multi-Task or Multi-Agent learning, we develop in this work a multi-agent reinforcement learning algorithm to handle a multi-task environments. Our proposed algorithm, Multi-Task Multi-Agent Deep Deterministic Policy gradient, (MTMA-DDPG) (Code available at https://gitlab.com/awadailab/mtmaddpg ), extends its single task counterpart by running multiple tasks on distributed nodes and communicating parameters via pre-determined coefficients across the nodes. Parameter sharing is modulated through temporal decay of the communication coefficients. Training across nodes is parallelized without any centralized controller for different tasks, which opens horizons for flexible leveraging and parallel processing to improve MA learning. Empirically, we design different MA particle environments, where tasks are similar or heterogeneous. We study the performance of MTMA-DDPG in terms of reward, convergence, variance, and communication overhead. We demonstrate the improvement of our algorithm over its single-task counterpart, as well as the importance of a versatile technique to take advantage of parallel computing resources.
Whose Interests and Knowledge are Recognized? An Empirical Study of Teachers’ Reflections on Integrating Digital Technologies in Teaching Activities

The integration of digital technology in teaching activities has shown to be challenging for teachers. To approach this, the article investigated the foundation of teachers' stance about developing their use of digital technology in teaching. The study draws on an action research approach, involving 35 teachers (K-2) in workshops and interaction-based facilitation. Based on content analysis, we identified how the teachers reasoned about their agency or lack of it, and children's knowledge and experiences, as well as guardians' viewpoints. The result is presented in four themes: Exchanging experiences across different school settings, Acknowledging accessibility to digital tools, Teachers' considerations about children's influence, and Teachers' considerations about guardians' influence. The study supports the theoretical framework of funds of knowledge; however, the teachers need to move from 'talking the talk' to 'walking the walk'. They wish to give space for children's experiences, but do not reveal any tools to do so. Hence, the children's home domain is lacking in teachers' discussions and their stance in including children's interests is shadowed by their own needs. The analysis shows that the local context is a foundational support and constitutes a framework for the teachers to step by step becoming digitally competent and, hence, being able to a greater extent give space for children's funds of knowledge – i.e. moving from talk to implementation. The study contributes to a local perspective to the debate on how to integrate digital technology in classrooms and how teachers can be empowered to gradually build digital agency.
NLP-Based Subject with Emotions Joint Analytics for Epidemic Articles

For the last couple years, governments and health authorities worldwide have been focused on addressing the Covid-19 pandemic; for example, governments have implemented countermeasures, such as quarantining, pushing vaccine shots to minimize local spread, investigating and analyzing the virus’ characteristics, and conducting epidemiological investigations through patient management and tracers. Therefore, researchers worldwide require funding to achieve these goals. Furthermore, there is a need for documentation to investigate and trace disease characteristics. However, it is time consuming and resource intensive to work with documents comprising many types of unstructured data. Therefore, in this study, natural language processing technology is used to automatically classify these documents. Currently used statistical methods include data cleansing, query modification, sentiment analysis, and clustering. However, owing to limitations with respect to the data, it is necessary to understand how to perform data analysis suitable for medical documents. To solve this problem, this study proposes a robust in-depth mixed with subject and emotion model comprising three modules. The first is a subject and non-linear emotional module, which extracts topics from the data and supplements them with emotional figures. The second is a subject with singular value decomposition in the emotion model, which is a dimensional decomposition module that uses subject analysis and an emotion model. The third involves embedding with singular value decomposition using an emotion module, which is a dimensional decomposition method that uses emotion learning. The accuracy and other model measurements, such as the F1, area under the curve, and recall are evaluated based on an article on Middle East respiratory syndrome. A high F1 score of approximately 91% is achieved. The proposed joint analysis method is expected to provide a better synergistic effect in the dataset.
Design or Art? Technological Interactivity and Connectivity in Designed Objects in a Speculative Design Approach

Speculative design, as a new approach in design filed, is believed to open new ways of thinking and perception of the future. Through the creation of designed objects, this approach attempts to challenge assumptions and preconceptions about the role that products, services and systems play in everyday life. Products or services or systems can be regarded as fictions that can create stories and scenarios in a participatory and humanized way for the view of future. In this sense, speculative design is a method to discover and discuss technology’s influence on everyday life. Based on the literature review of speculative design, the relationship between technology and design, as well as the longtime discussed question: difference between art and design, the author analyzes the technological interactivity and connectivity in 3 typical speculative design cases. In the end, the author points out the nature of speculative designers are new designers who are very close to artists. Two paths of speculative design are classified. As a conclusion, speculative design work are both design as well as art.
Random Feature Selection Using Random Subspace Logistic Regression

Feature selection becomes a prominent method in the big data era. The logistic regression model is a wrapper method that provides better classification or prediction accuracy but it is computationally expensive. In this study, we propose the random subspace logistic regression where features are randomly selected through bootstrap cycles. The random subspace regression method is applied to both standard and lasso logistic regression models. Using the simulated and empirical data, our proposed random subspace logistic regression shows favorable results and can be a promising alternative for flat feature selection.