Reviewerly/exHarmony huggingface dataset:
Playfulness as a strategy to strengthen reading comprehension, motivation and social interaction

The José Acevedo y Gómez Educational Institution, located in the Cajambre River, rural area, south of the district of Buenaventura, covers from (0º) to 11th grade. The population served, is characterized by learning problems, evidenced both in internal and external tests (Saber), mainly in reading comprehension, which shows that the teaching-learning process does not respond to the proposed educational quality, therefore the general objective was raised in "Strengthening reading comprehension in ninth grade students", instruments were used to collect information such as: structured interview for parents and teachers, diagnostic test to students, non-participant observation to teachers, participant observation to students and the final test. This study was established from the constructivist paradigm, in search of achieving the objectives through the qualitative approach, the methodology was action research. The first results found were: absence of reading habits, little or no parental support, inadequate pedagogical practices, poorly conditioned physical spaces and a low level of inferential reading in most of the students. The use of the pedagogical proposal produced attitudinal changes in the students, such as integration, satisfaction, communication, and slight individual and group improvement in inferential reading. It is concluded that the use of games modified the attitude of the students, which explains the slight difference between the diagnostic test and the final test. It is worth mentioning that the categories Motivation and social interaction of the general objective were fully met, while reading comprehension only partially improved in the subjects investigated
High-Accuracy Model-Based Reinforcement Learning, a Survey

Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems from game playing and robotics have been solved with deep model-free methods. Unfortunately, the sample complexity of model-free methods is often high. To reduce the number of environment samples, model-based reinforcement learning creates an explicit model of the environment dynamics. Achieving high model accuracy is a challenge in high-dimensional problems. In recent years, a diverse landscape of model-based methods has been introduced to improve model accuracy, using methods such as uncertainty modeling, model-predictive control, latent models, and end-to-end learning and planning. Some of these methods succeed in achieving high accuracy at low sample complexity, most do so either in a robotics or in a games context. In this paper, we survey these methods; we explain in detail how they work and what their strengths and weaknesses are. We conclude with a research agenda for future work to make the methods more robust and more widely applicable to other applications.
Hand Gesture Recognition

Abstract: This work presents a computer-vision-based application for recognizing hand gestures. A live video feed is captured by a camera, and a still image is extracted from that feed with the aid of an interface. At least once per count hand gesture (one, two, three, four, and five), the system is trained. After that, the system is given a test gesture to see if it can identify it. Several algorithms that are capable of distinguishing a hand gesture were studied. It was determined that the highest rate of accuracy was achieved by using the computational neural network known as the Alexnet algorithm. Traditionally, systems have used data gloves or markers as a means of input. We are free to use the system however we like. In this way, the user can make natural hand gestures in front of the camera. The system implemented serves as an extendable basis for future work toward a fully robust hand gesture recognition system, which is still the subject of intensive research and development.
De-Autonomy of Post-Human Imagination: New Directions in the Theory of Art

The article is devoted to the problem of interpretation of new media art.It is proposed to consider the new phenomena of art through the concepts of de-autonomy and posthuman imagination.De-autonomy is affirmed as an essential feature of a work of contemporary art.This concept captures the fundamental "non-autonomy" of the existence of art, the situational nature of actions, the interdependence of contexts, the interpenetration and interconnection of environments.The embodiment of digital reality objects in the physical environment, both screen images and paintings, installations, sculptures and other complex objects, requires an updated theoretical approach.The interconnection of a complex ecosystem of constant material transformation of objects becomes obvious.Man has "delegated" many abilities to machines, including "intelligence" -neural networks.The camera's gaze is no longer tied to the subject's point of view.It is the gaze, but the gaze of an "object" that internalizes into our own subjectivity, becoming our vision, so much so that no overseer is required to maintain this system.This is the process of developing of a posthuman imagination.Posthuman imagination is based on corporeality, which has the experience of interacting with the emerging techno-genic visuality and digital materiality, when the experience of thinking machines becomes a human aesthetic experience.
Change of the National English Curricula in Korea and Considerations for the Next Curriculum

This study investigates the national English curriculum, social and academic culture, roles and positions of (English) teachers and students, and their changes in Korean history. Based on this exploration, the author discusses considerations to advance the current Korean English curriculum and where the next curriculum is to be headed in the era of the fourth industrial revolution (4IR). Given that the 4IR welcomes people who have high qualities in complex problem-solving, critical thinking, creativity, management, collaboration, decision-making and negotiation, significant changes in teacher and student roles and teaching practice are needed. The Korean pedagogical background of teacher-led practice, text- or grammar-based learning, test-preparation lessons and pursuit of competition in English education should not be obstructions for these changes. Thus, the author suggests the application of AI programmes and problem-based learning for the realisation of more learner-centred, democratic, and constructive learning. This study will provide educators in East Asian countries as well as in Korea with several rationales to deliberate for their next curriculum design.
Sistem Pakar Diagnosa Penyakit pada Tanaman Padi Kecamatan Satarmese Kabupaten Manggarai Provinsi Nusa Tenggara Timur Berbasis Web dengan Metode Forward Chaining

Rice diseases are adverse conditions in rice caused by environmental factors, humans, and microorganisms such as viruses, bacteria, and fungi. Farmers do not understand how to determine the nature of disease and how to deal with diseases that affect rice. Farmers suffer a lot of losses due to late diagnosis, and have trouble meeting advice because they move around rather than stay in specific advice places, confusing yields. A frequently used method in expert systems is the forward chain method. This approach was chosen because it is an evolution process that begins by selecting a collection of data or facts and draws conclusions based on the selected facts. Based on the results of the system design, users of the program can diagnose rice diseases by inputting data and facts as symptoms occurring in rice, and obtain results that enable them to determine the type of disease and preventive measures increase a disease affecting rice.
Semi-Supervised Multi-Label Dimensionality Reduction Learning by Instance and Label Correlations

The label learning mechanism is challenging to integrate into the training model of the multi-label feature space dimensionality reduction problem, making the current multi-label dimensionality reduction methods primarily supervision modes. Many methods only focus attention on label correlations and ignore the instance interrelations between the original feature space and low dimensional space. Additionally, very few techniques consider how to constrain the projection matrix to identify specific and common features in the feature space. In this paper, we propose a new approach of semi-supervised multi-label dimensionality reduction learning by instance and label correlations (SMDR-IC, in short). Firstly, we reformulate MDDM which incorporates label correlations as a least-squares problem so that the label propagation mechanism can be effectively embedded into the model. Secondly, we investigate instance correlations using the k-nearest neighbor technique, and then present the l1-norm and l2,1-norm regularization terms to identify the specific and common features of the feature space. Experiments on the massive public multi-label data sets show that SMDR-IC has better performance than other related multi-label dimensionality reduction methods.
Graph-Assisted Bayesian Node Classifiers

Many datasets can be represented by attributed graphs on which classification methods may be of interest. The problem of node classification has attracted the attention of scholars due to its wide range of applications. The problem consists of predicting nodes’ labels based on their intrinsic features, features of their neighboring nodes and the graph structure. Graph Neural Networks (GNN) have been widely used to tackle this task. Thanks to the graph structure and the node features, they are able to propagate information over the graph and aggregate it to improve the classification performance. Their performance is however sensitive to the graph topology, especially its degree of impurity, a measure of the proportion of connected nodes belonging to different classes. Here, we propose a new Graph-Assisted Bayesian (GAB) classifier, which is designed for the problem of node classification. By using the Bayesian theorem, GAB takes into consideration the degree of impurity of the graph when classifying the nodes. We show that the proposed classifier is less sensitive to graph impurity, and less complex than GNN-based classifiers.
GRAMMATICAL ALLIANCE AND PIVOT SYSTEM OF BATAK SIMALUNGUN LANGUAGE: A SYNTACTIC TYPOLOGY STUDY

Syntax, morphology, and phonology can be used as a reference for language classification. This research aims to determine the grammatical alliance of the Simalungun language syntactically and its typology. It is a qualitative study with the typology approach. The data are phrases and clauses of the Simalungun language collected from informants and books – Simalungun language syntax books, by interviews and notes. It found that a transitive verb formulates the intransitive clause of the Simalungun Batak language as a predicate. The predicative verb appears with affixes. In general, the Simalungun language contains verbs that, in their presence, correspond to the subject. Simalungun language has a grammatical alliance system that treats subject (S) the same as agent (A), and a different treatment is given to predicate (P). Based on grammatical relations, the Simalungun language treats S the same as A at the syntactic level. Thus, it can be typologically grouped into syntactical accusatives. Based on the Pivot test, it also belongs to the accusative language group.
Prevalence of ‘Atoms of Confusion’ in Open Source Java Systems: An Empirical Study

Atoms of confusion, or simply “atoms,” are pieces of code that lead to misunderstanding while being interpreted. Previous research has shown that the presence of atoms has an effect on code readability. Aside from simple misunderstanding in lab setting, atoms of confusion are common and meaningful in open source C and C++ projects, and are thus removed by bug-fix commits. However, due to syntactical differences between language paradigms, the prevalence of atoms may vary in projects written in other languages (e.g. Java), which is yet to be explored. In this study, the first step is taken towards investigating the prevalence of 12 different atoms in the 13 most popular open-source Java projects. The relationship between the presence of atoms and aspects of code maintainability is also studied. Results show that, atoms are 4.7 time more prevalent in Java projects compared to open source C/C++ projects based on occurrence per line. For a total of 1085223 atoms in our corpus, they occur once every 4.8 lines. Some atoms are very obscure (e.g. the Logic As Control Flow atom which occurs once in 440060 lines). Some atoms are frequently occurring (e.g. the Infix Operator Precedence atom which occurs once in 6.4 lines). Impact of the presence of atoms on code maintainability is also explored. Besides, correlation between atoms are investigated. Results indicate that object oriented metrics contribute less in atom prevalence, whereas fine grained code-metrics have relatively better association.
QUBO Decision Tree: Annealing Machine Extends Decision Tree Splitting

This paper proposes an extension of regression trees by quadratic unconstrained binary optimization (QUBO). Regression trees are very popular prediction models that are trainable with tabular datasets, but their accuracy is insufficient because the decision rules are too simple. The proposed method extends the decision rules in decision trees to multi-dimensional boundaries. Such an extension is generally unimplementable because of computational limitations, however, the proposed method transforms the training process to QUBO, which enables an annealing machine to solve this problem.
Keystroke Presentation Attack: Generative Adversarial Networks for replacing user behaviour

Digital security has become crucial in this new era of technology and biometry is becoming a natural and reliable authentication system. In recent years, keystroke dynamics, a type of behavioral biometric, has been used for user authentication and attack detection. In this study, we pursue a new approach to keystroke dynamics data generation focused on the impersonation of a user at the identification stage using Conditional Generative Adversarial Networks (cGAN). To that aim, three different architectures have been designed, implemented, and validated: a Vanilla-cGAN based on simple Neural Networks (NN), an LSTM-cGAN based on Recurrent Neural Networks using Long Short-Term Memory units (LSTM), and a CNN-cGAN based on Convolutional Neural Networks. These models have been validated in two different conditions, one in which the attacker knows exactly the order of the typed words for replicating the behavior and the other in which the order is unknown. To validate the data generated by these models, beyond the internal discriminator’s accuracy, a pre-trained Siamese Network has been used to detect whether two keystroke sequences belong to the same or not. This study suggests that the keystroke dynamics of a user can be successfully imitated via keystroke dynamics data generation using cGANs with different architectures.
Lattice-Based Authentication Scheme to Prevent Quantum Attack in Public Cloud Environment

Public cloud computing provides a variety of services to consumers via high-speed internet.The consumer can access these services anytime and anywhere on a balanced service cost.Many traditional authentication protocols are proposed to secure public cloud computing.However, the rapid development of high-speed internet and organizations' race to develop quantum computers is a nightmare for existing authentication schemes.These traditional authentication protocols are based on factorization or discrete logarithm problems.As a result, traditional authentication protocols are vulnerable in the quantum computing era.Therefore, in this article, we have proposed an authentication protocol based on the lattice technique for public cloud computing to resist quantum attacks and prevent all known traditional security attacks.The proposed lattice-based authentication protocol is provably secure under the Real-Or-Random (ROR) model.At the same time, the result obtained during the experiments proved that our protocol is lightweight compared to the existing lattice-based authentication protocols, as listed in the performance analysis section.The comparative analysis shows that the protocol is suitable for practical implementation in a quantum-based environment.
Sign Language Recognition System Using Customized Convolution Neural Network

With the rapid development of computer vision and its expansion of applications, the demand for interaction between human and machine is becoming more and more extensive. Sign language recognition, which is a part of language technology, involves evaluating human hand signs and gestures through machine learning algorithms. Vision-oriented hardware like camera is used to convert these sign languages into meaningful form like text or sound. We have implemented a system that captures the hand sign or gesture of the user and predicts the equivalent meaning of the language. A lot of work has been done in the field of sign language recognition; however, a smaller amount of work done where, kids can learn and practice sign language as well as learn basic mathematical operations using sign language. This paper mainly focuses on making a system that can be used by children with hearing or vocal disabilities to learn basic alphabets, numbers and some words in sign language. Deep learning-based customized convolution neural network is trained on the dataset size 2400 * 44, where seven convolution layers are defined for sign language prediction with the accuracy of 99.92%.
Is Bitcoin A Blessing or A Curse?

In this article, several perspectives of bitcoin are briefly stated. First, there is an introduction for bitcoin, such as the concepts and the process of its launch, in order to have a general idea. Then, I borrow a few main ideas from Adam Smith, which help to support that bitcoin is a blessing. Based on six behavioral motivations from Adam Smith, I tried to define the property of bitcoin and find out the development of bitcoin in the future. In the other hand, bitcoin is limited, which can not satisfy all the consumers and do not have a supervision department.
Co-designing the Embodiment of a Minimalist Social Robot to Encourage Hand Hygiene Practises Among Children in India

We conducted an empirical study to co-design a social robot with children to bring about long-term behavioural changes. As a case study, we focused our efforts to create a social robot to promote handwashing in community settings while adhering to minimalistic design principles. Since cultural views influence design preferences and technology acceptance, we selected forty children from different socio-economic backgrounds across India as informants for our design study. We asked the children to design paper mock-ups using pre-cut geometrical shapes to understand their mental models of such a robot. The children also shared their feedback on the eight resulting different conceptual designs of minimalistic caricatured social robots. Our findings show that children had varied expectations of the robot’s emotional intelligence, interactions, and social roles even though it was being designed for a specific context of use. The children unequivocally liked and trusted anthropomorphized caricatured designs of everyday objects for the robot’s morphology. Based on these findings, we present our recommendations for the physical and interaction features of a minimalist social robot assimilating the children’s inputs and social robot design principles grounded in prior research. Future studies will examine the children’s interactions with a built prototype.
“In the Wild” Video Content as a Special Case of User Generated Content and a System for Its Recognition

In the five years between 2017 and 2022, IP video traffic tripled, according to Cisco. User-Generated Content (UGC) is mainly responsible for user-generated IP video traffic. The development of widely accessible knowledge and affordable equipment makes it possible to produce UGCs of quality that is practically indistinguishable from professional content, although at the beginning of UGC creation, this content was frequently characterized by amateur acquisition conditions and unprofessional processing. In this research, we focus only on UGC content, whose quality is obviously different from that of professional content. For the purpose of this paper, we refer to "in the wild" as a closely related idea to the general idea of UGC, which is its particular case. Studies on UGC recognition are scarce. According to research in the literature, there are currently no real operational algorithms that distinguish UGC content from other content. In this study, we demonstrate that the XGBoost machine learning algorithm (Extreme Gradient Boosting) can be used to develop a novel objective "in the wild" video content recognition model. The final model is trained and tested using video sequence databases with professional content and "in the wild" content. We have achieved a 0.916 accuracy value for our model. Due to the comparatively high accuracy of the model operation, a free version of its implementation is made accessible to the research community. It is provided via an easy-to-use Python package installable with Pip Installs Packages (pip).
Axiological priorities of subject-subject interaction in higher education from the perspective of the cognitive paradigm

The relevance of the article is due to the need to determine the role of the cognitive paradigm in increasing the effectiveness of subject-subject interaction in the educational process of higher education, this interaction contributing to the development of the educational process's subjects with axiological priorities. The authors solve the problem of revealing empathic reserves and mechanisms of interaction between a teacher and students that allow the educational process to be carried out in an atmosphere of comfort, trust, openness, which determines humanistic semantic formations and value-based transformations of the individual. In the article, on the basis of participation seen as the opposite of domination, coercion and authoritarianism, and as a reserve for creating a favorable psychological climate for cooperation, the authors compare the basic and advanced participatory skills of the teacher. The authors define the importance of the cognitive component of empathy as a connecting link in the development of value-based awareness, understanding and regulation of educational subjects' cognitions and emotions that affect the success of their interaction. Empathy is revealed as a form of rational-emotional-intuitive reflection of another person, which predicts one's behavior in subject-subject interaction. The authors define the position of the teacher in subject-subject interaction from the perspective of the cognitive paradigm, which, in the logic of empathic conditioning of this interaction, is transformed into the role of a guide. Such a position of the teacher is formed on the basis of the development of a step-by-step algorithm for the development of the teacher's emotional identification as a structural component of empathy. Experimental results of the study of empathic reserves and mechanisms of interaction between a teacher and students are presented on the basis of a survey of 48 second-year students of two groups (Gr.07-911 and Gr.07-911k) of the Chemical Education specialty, the Institute of Chemistry named after I.M. Butlerov of Kazan Federal University, and 41 people of two groups (Gr. 3A and Gr. 3B) in the Art of Ballet and Art of Dance specialties of the final year of the Kazan Choreographic School. The survey used the method of diagnosing the level of empathic abilities of V.V. Boyko. The selected diagnostics made it possible not only to obtain the qualitative characteristics of each channel of empathy of the students and their identification, but also, most importantly, to obtain personality-oriented information about the attitudes that promote or hinder empathy in subject-subject interaction for each surveyed student.