Reviewerly/exHarmony huggingface dataset:
Embodied Attention in Word-Object Mapping: A Developmental Cognitive Robotics Model

Developmental Robotics models provide useful tools to study and understand the language learning process in infants and robots. These models allow us to describe key mechanisms of language development, such as statistical learning, the role of embodiment, and the impact of the attention payed to an object while learning its name. Robots can be particularly well suited for this type of problems, because they cover both a physical manipulation of the environment and mathematical modeling of the temporal changes of the learned concepts. In this work we present a computational representation of the impact of embodiment and attention on word learning, relying on sensory data collected with a real robotic agent in a real world scenario. Results show that the cognitive architecture designed for this scenario is able to capture the changes underlying the moving object in the field of view of the robot. The architecture successfully handles the temporal relationship in moving items and manages to show the effects of the embodied attention on word-object mapping.
Human Skin Tone Detection

Human skin tone variation can be easily seen from person to person within the same ethnic group or different ethnic group. The skin tone is analysed by melanosome size, significant and progressive variation in size and ethnicity. In face recognition the shape of the face gets identified but background used in real time is usually complex with varying lighting conditions. Automatic skin detection has been intensively studied for human-related recognition systems. The Gaussian model for skin detection is with its generality but they lack in their accuracy. Hence we suggest a new statistical colour model for skin detection called an elliptical boundary model. Detection and tracking of human face and hands are used for gesture recognition and to handle the domain transfer problem. The elliptical boundary model overcomes the limitations of the Gaussian model with better performance based on six chrominance spaces on the face, giving a much higher correct detection ratio with faster speed. For that, we are processing with two main domains that are Full stack development which handles the frontend and backend of the application and Machine Learning algorithms which handles the detection of the skin tone. Here we are dealing with image processing techniques for input and the output will find the skin tone of that particular image that we are processing. The applications that involve skin tone detection are face detection, gesture recognition and image filtering. Hence they overcome the different illumination conditions as different domains of skin pixels. The objective of the project is to detect the skin tone from a human face image with various tone characteristics. We are detecting the basic tone differentiation (Fair, Mild or Dark).
Noisy label learning based on self-supervised pre-training and data augmentation strategies

We propose a two-stage label noise learning framework. Classification by deep learning with datasets with noisy labels. Filtering noise samples based on the loss of the warm-up stage is a common method, but it is impossible to judge the optimal warm-up length and thus suffers from memory effects. This paper is inspired by the use of contrastive learning self-supervision as pre-training to replace the warm-up part, and pseudo-labels for subsequent loss calculations, which can avoid the influence of noisy labels on pre-training. The second part is a semi-supervised algorithm that applies data augmentation. The adopted boosting strategy is to use weak boosting for any loss analysis task and strong boosting for gradient descent. Learn a more accurate representation for each image. Through these two steps, the performance of the network can be improved. Experiments show that our framework is more robust to data classification problems with noisy labels, and also works well in datasets with high noise rates. At the same time, since it is a two-step framework, it is also easier to split and apply in combination with other noisy label learning algorithms.
Research on the impact of specialized STEM education on later education choices

The influence of specialized training on the decision of future study direction is taken into account in the work. The underlying presumption is that pupils should be exposed to mathematical and natural sciences subjects early on, with a focus on mathematics. It will encourage kids to pursue STEM studies in the future. The problem of learning motivation is a crucial factor in future professional choice, development, and formation. It is crucial to note that students in modern times are rapidly losing their motivation to learn. Nearly a third of individuals who select the right study profile experience conflicts between their desire for professional autonomy and the availability of the information required for the profession, as well as between their desire to attend a higher education institution and their ability to do so. The paradox necessitates the deliberate formulation of a deliberate choice of future actions. The results of the research reveal that the study of natural and mathematical disciplines using contemporary teaching technologies and the planning of extra lesson systems based on the anticipated teaching techniques form the foundation of the motivational component of the choice of STEM-learning. It satisfies the expanding demand for education, intellect, and motivational beliefs to comprehend the subtleties of the upcoming vocation.
Mechatronic Design and Path planning optimization for an Omni wheeled mobile robot for indoor applications

Omni-wheeled mobile robots (OWMRs) are commonly used in indoor navigation applications like surveillance, search and rescue, and autonomous transportation. They are characterized by their versatility, mobility, and high payload. This paper presents the mechatronic design, kinematics, and low-level control of an indoor Omni wheeled mobile robot. The robot mechatronic system is presented, showing the selection of wheels, actuators, and the technical specifications of the robot. The robot kinematics are calculated and discussed to pave the way for controlling the robot movement. The control system of the robot is presented while showing the main hardware components and the low-level velocity control of the robot’s main motors. The robot is manufactured and tested, showing the validity of its mechanical and velocity control systems. A path planning algorithm is implemented experimentally and its generated path is optimized using genetic algorithm technique. Both of the generated paths are compared, showing the validity of the optimization process.
A NEW APPROACH FOR TEXT RECOGNITION ON A VIDEO CARD

An important task is to develop a computer system that can automatically read text content from images or videos with a complex background. Due to a large number of calculations, it is quite difficult to apply them in real-time. Therefore, the use of parallel and distributed computing in the development of real-time or near real-time systems is relevant. The latter is especially relevant in such areas as automation of video recording of traffic violations, text recognition, machine vision, fingerprint recognition, speech, and more. The paper proposes a new approach to text recognition on a video card. A parallel algorithm for processing a group of images and a video sequence has been developed and tested. Parallelization on the video-core is provided by the OpenCL framework and CUDA technology. Without reducing the generality, the problem of processing images on which there are vehicles, which allowed to obtain text from the license plate. A system was developed that was tested for the processing speed of a group of images and videos while achieving an average processing speed of 207 frames per second. As for the execution time of the parallel algorithm, for 50 images and video in 63 frames, image preprocessing took 0.4 seconds, which is sufficient for real-time or near real-time systems. The maximum acceleration of image processing is obtained up to 8 times, and the video sequence – up to 12. The general tendency to increase the acceleration with increasing dimensionality of the processed image is preserved, which indicates the relevance of parallel calculations in solving the problem.
VIRTUAL EXPERT GUIDED SERIOUS ROLE-PLAYING GAME FOR LEARNING COMMUNICATION WITH AUTISTIC PEOPLE

Role-play games have been developed to help individuals with autism spectrum disorders to learn social interactions with others.However, there is a lack of role-playing games that teach people without autism the necessary communication skills to engage and interact effectively with autistic people.This research aims to harness the latest technological advances in mobile computing, serious games and human-computer teaming technologies to create a virtual expert guided serious role-playing game to help people learn how to communicate with autistic people.It provides a virtual expert guided experiential and cognitive learning tool, consisting of instruction, play, review, and discussion, that helps achieve enhanced learning outcomes while enabling an independent-learning workflow.By making it available for both iOS and Android devices, it has the potential to help spread the awareness of autism and help create a more inclusive environment for autistic people at schools, universities, and communities.
Subject position as a predictor for the integration of future teachers into the digital learning environment

Introduction. In the context of digitalisation, the vocational education system is forced to respond flexibly to the challenges and requirements of the developing digital society. The focus of the pedagogical community is shifting to addressing issues of a prognostic nature – preparing future teachers for high-quality professional activities in digital formats, achieving high professional results in the future, and quickly adapting to a dynamically changing education environment. The aim of the article is to comprehend the subjective position of students as a meaning-forming concept (predictor) for the successful entry of a young teacher into professional activities in the context of digitalisation of education. Materials and methods. The study was based on system-activity, subject-personal and environmental approaches, according to which the subject position of the future teacher is considered as an integrative developing quality in the context of personal and professional characteristics, as well as manifested and revealed in the conditions of digital education on the basis of interaction with elements of the digital learning environment. An online questionnaire was chosen as a diagnostic material. The respondents were first-year undergraduates from several Russian universities (N=100). To analyse the obtained data, the methods of quantitative analysis, descriptive statistics, correlation analysis, clustering (Ward's hierarchical agglomerative method) were used. Results. It was revealed that for future teachers the most significant are the personality-developing (average value 4.0), value-target (4.0) and activity-regulatory (3.9) components of the subject position in context of digital education. Clustering has shown that it is advisable to pay special attention to the development of information-behavioural, communication-emotional and research components: to expand the digital tools for mastering and transforming content and interaction; develop the skills of network cooperation and dissemination of scientific results (for example, correlations “network community-dissemination of results” r=0.61 were revealed). For each component, a prospective strategy for solving the problem of personal and professional self-development is shown, which should be taken into account when implementing the professional training of future teachers. Conclusion. The subject position of a future teacher will be the basis for successful integration into the digital education environment if there is an awareness of oneself as a subject of prospective professional activity based on constant self-development, focused on quality results, and promising trends.
Modified Marine Predators Algorithm hybridized with teaching-learning mechanism for solving optimization problems

Marine Predators Algorithm (MPA) is a newly nature-inspired meta-heuristic algorithm, which is proposed based on the Lévy flight and Brownian motion of ocean predators. Since the MPA was proposed, it has been successfully applied in many fields. However, it includes several shortcomings, such as falling into local optimum easily and precocious convergence. To balance the exploitation and exploration ability of MPA, a modified marine predators algorithm hybridized with teaching-learning mechanism is proposed in this paper, namely MTLMPA. Compared with MPA, the proposed MTLMPA has two highlights. Firstly, a kind of teaching mechanism is introduced in the first phase of MPA to improve the global searching ability. Secondly, a novel learning mechanism is introduced in the third phase of MPA to enhance the chance encounter rate between predator and prey and to avoid premature convergence. MTLMPA is verified by 23 benchmark numerical testing functions and 29 CEC-2017 testing functions. Experimental results reveal that the MTLMPA is more competitive compared with several state-of-the-art heuristic optimization algorithms.
Current Status, Challenges and Strategies of Artificial Intelligence and E-learning the UAE Military Education System

Recently,UAE has adoptedArtificial Intelligence(AI)and e-learning system in several fields including education. Together with the institutions running on conventional educational system, military colleges have also embrassed this new technology. This study assessed the current status of adoption, challenges and strategies of using AI based e-learning system in the UAE military based colleges. The study was carried out based on the perception of the teachers and students of Joint Command and Staff College (JCSC). A set of the questions was presented to the 50 teachers and 157 students for highlighting the level of agreement for each question. The questions were devided into three sections as level of adoption, challenges, and strategies in using AI based e-learning education system. The study found that for current status, AI and e-learning have got siginificant popularity in the education system due to high level of flexibility. While the challenges, the lack of human relations between teacher and student is reported as the major challenge faced in adoption of AI based e-learning system. Finally, for the strategies, the respondents indicated that the UAE is trying to develop plans and alternatives to address the discrepancy between traditional teaching and artificial intelligence methods adapted in the military college. This study provides a valuable source of information for devicing strategies to promote the uses of these modern tools and also motivate the students to get the maximum benefits of AI and e-learning technologies.
PENGGUNAAN MEDIA VLOG (VIDEO BLOG) UNTUK MENUMBUHKAN KEMAMPUAN PEMAHAMAN KONSEP SPLDV PESERTA DIDIK

AbstractThe study was conducted to investigate the relationship between the use of vlog media in learning and the concept understanding ability. The results of daily test of students who can achieve or exceed the Minimum Completeness Criteria are only 31.4% illustrating the lack of the understanding of students concepts. Using vlogs can be a choice of interactive and interesting learning media to help increase concept understanding of students. This type of research is explanatory research with selected research subjects are students of the 2019/2020 school year class VIII F according to the teacher's considerations. The research instrument was an essay test of concept understanding ability and a questionnaire of the use of vlog media in learning. The research analysis includes a description of the achievement of concept understanding, the feasibility of vlog learning media, and the Spearman Rank (ρ) test. The analysis results shows that the use of vlog media in learning has a relationship to the concepts understanding ability with a known correlation value of 0.410 which indicates the use of good vlogs as a learning medium sufficiently supportive to increase the ability to understand concepts.Keywords: Concept Understanding Ability, Learning Media, Vlog
Self-Training Based Semi-Supervised and Semi-Paired Hashing Cross-Modal Retrieval

The aim of cross-modal retrieval is to search for flexible results across different types of multimedia data. However, the labeled data is usually limited and not well paired with different modalities in practical applications. These issues are not well addressed in the existing works, which cannot consider the semantic information about unlabeled and unpaired data, synchronously. Self-training is a well-known strategy to handle semi-supervised problems. Motivated by the self-training, this paper proposes a self-training-based cross-modal hashing framework (STCH) to tackle the semi-supervised and semi-paired challenges. In the framework, graph neural networks are used to capture potential intra-modality and inter-modality similarities to produce pseudo labels. Then the inconsistent pseudo labels of different modalities are refined with a heuristic filter to enhance the model robustness. To train STCH, we propose an alternating learning strategy to conduct the self-train by predicting pseudo labels during the training procedure, which can be seamlessly incorporated into semi-supervised and supervised learning. In this way, the proposed method can leverage sufficient semantic information to enhance the semi-supervised effect and address the semi-paired problem. Experiments on the real-world datasets demonstrate that our approach outperforms related methods on hash cross-modal retrieval.
Digital Competencies Required by Teachers when Integrating ICT and Using E-Books in the midst of COVID - 19 Education

The integration of information and communication technology (ICT) and usage of E-Books in schools remains a mammoth task for teachers in a number of countries in Sub-Saharan Africa.Teachers lack the necessary skills to integrate ICT and use E-Books.ICT integration thematic experts are trying to find out which digital competencies are required by teachers to integrate ICT and use E-Books in digital education efforts to meet the COVID-19 challenges.This paper reports findings from a study that collected both qualitative and quantitative data on this subject.Qualitative data analysis and exploratory factor analysis of the collected data indicated 11 digital competencies required of teachers to integrate ICT and use E-Books.The study found that the teachers are highly proficient in 4 of these competencies.The study established that there is a need to train in-service teachers on how to integrate ICT in the classroom.The professional development of teachers should focus on developing teachers' competencies in desktop publishing / graphic designing; web quest designing; accessing the internet; multimedia skills; saving a document; composing questions; encrypting; participation in an ICT community of practice; and hyper-linking.This will enable teachers to access, produce, annotate, store, and also publish E-Books.
The Interface of English Language Teaching and Remote Educational Technology

Adapting to the “New Normal” is the prime issue that educational institutions are facing nowadays, thus, adjustments in teaching and learning has to be done to reach all students despite the limitations in the face to face interaction. This paper has been initiated to share the voices of the teachers and students’ circumstances and struggles in the transition due to COVID-19 pandemic. Through this paper, the perception on the psychological, and pedagogical concerns shall be given overview and the interface of English Language Teaching and Remote Educational Technology shall be given light to provide beneficial concept for both teachers and students This shall pave way for the continuous support and development in educational institutions and fronliners
Metode Karyawisata dalam Meningkatkan Kemampuan Menulis Karangan Deskripsi

The application of the field trip method aims as an alternative means for teachers to help students' interest in improving the ability to write essays that were previously a barrier to students in developing writing skills for each student. The field trip method also functions as an activity to eliminate the boredom of each student in learning, which teaches students to provide knowledge outside the classroom.&#x0D; &#x0D;
Attuning Adaptation Rules via a Rule-Specific Neural Network

There have been a number of approaches to employing neural networks (NNs) in self-adaptive systems; in many cases, generic NNs/deep learning are utilized for this purpose. When this approach is to be applied to improve an adaptation process initially driven by logical adaptation rules, the problem is that (1) these rules represent a significant and tested body of domain knowledge, which may be lost if they are replaced by an NN, and (2) the learning process is inherently demanding given the black-box nature and the number of weights in generic NNs to be trained. In this paper, we introduce the rule-specific Neural Network (rsNN) method that makes it possible to transform the guard of an adaptation rule into an rsNN, the composition of which is driven by the structure of the logical predicates in the guard. Our experiments confirmed that the black box effect is eliminated, the number of weights is significantly reduced, and much faster learning is achieved while the accuracy is preserved.
The Detection of Mirai Botnet Attack on the Internet of Things (IoT) Device Using Support Vector Machine (SVM) Model

There are various types of attacks on IoT devices, one of which is Mirai botnet that attacks a number of IoT devices such as web cameras, security cameras, and routers. Even it has caused internet downtime for the users in Europe and North America. Hence, it is deemed necessary to have a system to detect Mirai botnet attack using the Support Vector Machine (SVM) model. This model becomes a solution to detect Mirai botnet attacks for having good generalization performance when the parameter is properly configured in modeling the training set (training dataset) and getting good classification results without a lot of training data. The results of the analysis from the use of the SVM model showed the accuracy of 92.91% with a linear kernel and max_iter 10000. Also, the performance of the Support Vector Machine (SVM) model based on the precision parameter obtained a value of 94.13% with max_iter 10000 and the recall parameter resulted in a value of 91.10% with max_iter 10000.
Improved Indirect Virtual Objects Selection Methods for Cluttered Augmented Reality Environments on Mobile Devices

The problem of selecting virtual objects within augmented reality on handheld devices has been tackled multiple times. However, evaluations were carried out on purely synthetic tasks with uniformly placed homogeneous objects, often located on a plane and with none or low occlusions. This paper presents two novel approaches to indirect object selection dealing with highly occluded objects with large spatial distribution variability and heterogeneous size and appearance. The methods are de-signed to enable long-term usage with a tablet-like device. One method is based on a spatially anchored hierarchy menu, and the other utilizes a crosshair and a side menu that shows candidate objects according to a custom-developed metric. The proposed approaches are compared with direct touch in the context of spatial visual programming of collaborative robots problem, on a realistic workplace and a common robotic task. The preliminary evaluation indicates that the main benefit of the proposed indirect methods could be their higher precision and higher selection confidence for the user.