Reviewerly/exHarmony huggingface dataset:
AdaReNet: Adaptive Reweighted Semi-supervised Active Learning to Accelerate Label Acquisition

Data scarcity and quality pose significant challenges to supervised learning. The process of generating informative annotations can be time-consuming and often requires high domain expertise. Active and semi-supervised learning methods can reduce labeling effort by either automatically expanding the training set or by selecting the most informative examples to request domain expert annotation. As most selection methods are heuristic, the performance varies widely across datasets and tasks. Bootstrapping approaches such as self-training can result in negative effects due to the addition of incorrectly pseudo-labeled instances. In this work, we take a holistic approach to label acquisition and consider the expansion of clean and pseudo-labeled subsets jointly. To address the challenge of producing high-quality pseudo-labels, we introduce a collaborative teacher-student framework, where the teacher, termed AdaReNet, learns a data-driven curriculum. Experimental results on several natural language processing (NLP) tasks demonstrate that the proposed framework outperforms baselines.
Fault Tolerance Framework for Composite Web Services

A composite Web service combines multiple, logically interrelated services for creating more common services meeting complex requirements from users. The services participating in a composition coordinate the actions of distributed activity using Web services protocols to reach consistent agreement on the outcome of joint operation. However, as services run over unreliable protocols, there is a great chance that services fail due to the failure of protocols. However, current protocol standards provide fault-tolerance but are limited to backward recovery using expensive compensation and roll-back strategies. This paper gives an extension of the existing Web services business activity (WS-BA) protocol to deal with failures using forward recovery approach. A set of common failure types affecting the execution of component services is identified, and recovery solutions for each identified failure are also presented. The fault-handling extension of the WS-BA protocol implements recovery solutions for each of the identified failures to handle failures at runtime. Another important aspect about which the WS-BA protocol specification is unclear is reaching and notifying consistent outcome on the completion of joint work. This study extends the WS-BA protocol to notify consistent outcome reached by all participating services. The implementation and testing of the framework are performed using the model-checking and verification tool UPPAAL. A well-known application example supports the study. The key properties of the framework, like the execution of corresponding recovery actions in cases of failures and reaching a consistent agreement on the outcome of joint operation, are verified.
G Suite Configuration for Education and Teaching Mechatronics

Due to the Covid19 pandemic, the educational system had to adapt to online teaching. To increase the quality of our educational system, we used Google Scripts for our school database. As a result, usernames, groups, courses on Google Classroom, the school timetable have been created and our teachers and students were automatically added to their courses. We had to use the scripts because our school has over 1600 students, 103 teachers and a lot of students who commute to school, so we needed an efficient and fast way to update our online teaching system. The paper presents how we configured the G Suite for Education and how we used the Google environment for teaching the mechatronics subjects.
Augmented Reality to Improve Public Awareness and Safety at The Beach

Rip currents pose a threat to the safety of bathers at most beaches around the world. The proposed research includes the specification of a platform to empower the user to become active beach-safety agents. To achieve this strategic goal, a service design approach was implemented to conceptualize a solution for collecting data about potential hazards, including information about the status of active rip currents. The innovation is settled over the combination of immersive technologies (e.g., Augmented Reality) with visual data analytics and Artificial Intelligence (AI). Mobile Augmented Reality mixed with a gamification strategy is also considered in the architectural design to specify a mobile tool that provides an interactive and gamified environment for promoting a safe beach experience. The presented approach also addresses a strategy to create a social movement challenging citizen to integrate and benefit from the services and informational artifacts provided by the Beach Safety Community (BSC).
A Machine Learning Approach in Data Perturbation for Privacy-Preserving Data Mining

Data mining is a process where we can extract relevant information or patterns from the collection of data. In this era of big data, every organization aims to handle huge amounts of data and perform data mining techniques in order to extract pieces of information or patterns for various work and decision making. To protect privileged data and leakage of private information, the clients use different privacy-preserving techniques such as perturbation that protects client's data from revealing private information. The job of perturbing data on the client side is a herculean task, and it gets more difficult with the increase in the size of data. In this paper, we proposed a machine learning regression model that has been trained in such a way that it predicts the perturb data from original data and it even contains a comparative study of different regression models and their accuracy in perturbing the data.
Exploring the Possibility of Maker Pedagogy in Home Economics Education

In the age of artificial intelligence, maker education is attracting attention as an alternative to educational innovation through making. Instead of a teaching method applicable to a specific subject, maker education based on constructivism and experiential learning can be considered as pedagogy that emphasizes the process of solving problems through production and improvement centering on the learning subject. Maker education can utilize various learning models, such as TMI model, design thinking model, and maker-centered learning depending on the content or teaching and learning situation. Maker pedagogy is education centered on the ‘maker’ as an active participant rather than the making activity itself, and it emphasizes the process of cooperation and sharing of makers rather than the output from the making activity. In addition, rather than a space that uses digital materials or tools, maker pedagogy emphasizes a space where learners can learn through play and imagination. Considering the characteristics of home economics education emphasizing on the self-reliance capacity, ability to solve problems in an integrated way through problem solving in learners
Formation of a culture of a healthy lifestyle in students during the pandemic period

The article focuses on the need to introduce health-saving technologies in the process of distance learning for students. The definition of the culture of a healthy lifestyle of students is given, the results of a pilot experiment aimed at the formation of a culture of a healthy lifestyle of students are presented. Attention is drawn to the idea of а healthy lifestyle among students during the spread of coronavirus infection. It is proved that during the period of self-isolation, the majority of students think about a healthy lifestyle, and students begin to actively implement the ideas of a healthy lifestyle in order to support the body's resources.
Graph Drawing Approaches for Petri Net Visualisation and Representation

Petri net structures can benefit from being modelled using graph drawing approaches. This work presents some advanced graph drawing approaches that can be used for visualising Petri net models. These are i) topological shape metrics, ii) visibility drawing, iii) orthogonalization, iv) hierarchical and v) bi-graph partitioning. The models show that this can be successfully done and the Petri nets that are generated in this work are suitable for visualisation. Graph visualisation is an important topic and the results show that there is a large potential to apply this approach to drawing Petri Nets in novel ways.
S2S Translator by Natural Language Processing

Correspondence by means of motions is a visual language that is used by in need of a hearing-aided people as their local language. Not in the least like acoustically passed on sound models, signal-based correspondence uses non-verbal correspondence and manual correspondence to easily pass on the contemplations of a person. It is practiced by simultaneously uniting hand shapes, heading and advancement of the hands, arms or body, and outward appearances. In this paper, we will endeavor to develop a programmed model which changes over voice to motion-based correspondence and besides correspondence by means of motions to voice/text. We may be using talk-to-message programming interface (Python modules or Google programming interface) and a while later using the semantics of natural language processing to breakdown the substance into more diminutive sensible pieces which requires AI as a segment. Instructive assortments of predefined motion-based correspondence are used as the information with the objective that the item can use manmade mental ability to show the changed over sound into the signal-based correspondence.
Restrictions of Integer Parameters for Generating Attractive BLS Subfamilies of Pairing-Friendly Elliptic Curves with Specific Embedding Degrees

Pairings are widely used for innovative protocols such as ID-based encryption and group signature authentication. According to the recent works, the Barreto-Lynn-Scott (BLS) family of pairing-friendly elliptic curves is suggested for the pairings at the various security levels. One of the important facts is that the BLS family has fixed polynomial parameters of a field characteristic and group order in terms of an integer x_0. For practical pairing-based protocols, we have to carefully find x_0 which leads to efficient pairings, however, this search of x_0 is typically complicated. Thus, it is desired some convenient ways of finding x_0 which have advantageous for the pairings. For this reason, Costello et al. proposed simple restrictions for finding x_0 that generates the specific BLS subfamilies of curves with embedding degree k = 24 having one of the best field and curve constructions for the pairings. Since there are demands of such restrictions for the other cases of the embedding degrees, the authors extend their work and provide these for the cases of k = 2^m 3 and 3^n with arbitrary integers m, n>0 in this paper. The results will help to find new parameters which lead to one of the best performing pairings with the BLS family of curves with various k. The results also allow us to respond to change in the security levels of the pairings flexibly according to the progress in the security analyses in the future.
Development of a Modular Structure for a Digital Passport of an Electronic Product at Instrument-Making Industry Enterprises

The development of a modular structure of an electronic product's digital passport at the enterprises of the instrument-making industry is proposed. For this, the fundamental principles of creating a knowledge base ontological model, the method of forming and using a passport are outlined. For their implementation, functions have been defined that are combined into modules for generating design solutions, visualizing the user interface, interacting with the ontological model and with systems. The results obtained are combined into the structure of a digital passport, which can be used to develop signatures and semantics of applied unified services.
Design of a Flexible Template Approach for Characterising Health Activity Habits Using Step Count Data

This paper describes a template-driven pragmatic approach to characterising habitual ambulatory movements in constrained physical settings, using step count data from consumer wearable devices. A generic design process was undertaken using design science principles to establish the main structural elements of the template. The template was then applied to data collection and analysis for a case study in a typical office building environment. The associated activity habit characterisation results for three participants across two different simulated activities are presented.
Mechanism of Valence Change in Mandailing Language: Linguistics Typology

This paper described the mechanism of valence change in Mandailing language. The concept of valence refers to the number of arguments a verb that serves as a predicate in a clause can present. Changing the valence of verbs in Mandailing through a causative process causes the verbs to experience an increase in valence. This study was included a qualitative descriptive study, in which the method is a study of language typology and the data analyzed based on the problems discussed using the method Agih. The results of the data analysis of this study are presented using an informal method in the form of explanation or exposure using ordinary words or verbal language. The determinants used by this method are the language elements in the Mandailing language. The purpose of this study was to find what types of verbs in the Mandailing language can experience changes in valence, symptoms, and their process of change, and the semantic role of these verbs. The data analysis from this study was obtained through the observation method and the proficient method with informants; based on the results of the analysis, it can be concluded that changes in the valence of the Mandailing language occur mainly in intransitive, transitive, transitive verbs, which are grouped into symptoms that cause valence reduction and valence expansion. This study is also expected to inspire some researchers interested in the mechanism of valence change in Mandailing language, either micro or macro.
Dealing with IoT Defiant Components

A defiant IoT component is a smart device of an IoT system-of-systems (SoS) that should adapt its local behaviour to accomplish the new global requirements of the SoS only in exceptional scenarios. There are several self-adaptive approaches for system-of-systems in the literature. However, to handle defiant components, there is currently only one solution, called cautious adaptation, which relies on the use of AOP-based wrappers. That approach may not be appropriate for IoT systems since the components expose their communication API. Therefore, this paper proposes a new cautious adaptation approach for IoT SoS that adds three external components, the Configurator, the Observer and the Effector, that realize the MAPE control loop to apply behaviour adaptation. The solution is evaluated through a proof of concept using two types of IoT device communication mechanisms that demonstrated that our proposal helps the SoS to achieve its global goals.
Problem of Network Traffic Classification in Multiprovider Cloud Infrastructures Based on Machine Learning Methods

The development of new services, VoIP, video calls, multimedia streaming, streaming of high-quality content, and the Internet of Things (IoT) technology is forcing communication providers to rebuild cloud infrastructures in accordance with new requirements. Analysis of traffic data allows effectively reconfiguring the structure of a multiprovider network, taking into account the user's network portrait. In first part of this work an overview of the tasks solved in multiprovider cloud infrastructures by the classification of network traffic is showed. Then authors describe classical machine learning algorithms (Naive Bayes classifier, SVM, kNN, decision tree) that can be used to solve the assigned tasks, and revealed their advantages and disadvantages. In experimental part the problem of traffic classification of various types of applications generated by the D-ITG tool is decided, and a comparative analysis of machine learning algorithms is carried out. The main comparison metrics are precision, recall, F1, accuracy, complexity and time of training and testing. According obtained results decision tree method is chosen as optimal method that shows the maximum accuracy and fastest time of learning.
Deep Learning Demodulation of Amplitude Noise Shift Keying Spread Spectrum Signals

Digital communications techniques based on random, chaotic, or noisy carriers are well known and successfully used in a number of applications. Simple on-off or amplitude shift noise keying modulation schemes are among the most popular. In this paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , we propose to use a classification model based on an artificial dense neural network and a deep learning approach for software-defined demodulation of amplitude noise shift keying spread spectrum signals. The main challenge with processing of such signals is that statistical properties of signal and interference are very similar. The aim of the research is to proof the feasibility of the proposed technique and to obtain the noise-immunity metrics. The methodology of the research is to evaluate the deep learning demodulation model pre-trained on the artificially synthesized dataset. The dataset contains the automatically labeled mixtures of noise carrier signals with additive white Gaussian interferences. The average signal-to-noise ratios in the dataset range from -30 dB to 0 dB. The numerical results from simulations are used to evaluate the demodulation performance. We present the demodulation performance as symbol and bit error rates and compare results with other well-known approaches. The paper reports noise immunity greater than immunity of “power reception” method at least for 3.5 dB. In addition, we assessed the time complexity and proved real-time processing capability.
Multi-brightness layers with a genetic optimization algorithm for stereo matching under dramatic illumination changes

Stereo matching under dramatic illumination changes is a big challenge in imbalanced binocular vision, self-driving cars, and the remote sensing image field. A novel, to the best of our knowledge, multi-brightness layer mechanism with a genetic optimization algorithm is proposed in this paper. The mechanism of multi-brightness layers transforms the two images with dramatic illumination changes into a series of matched pairs with similar brightness by the stretching function and histogram matching principle. Therefore, the large illumination variations are reduced greatly. Moreover, the initial disparities as first generation of genetic optimization approach are generated from matched pairs using fast segmentation local stereo matching to increase the efficiency and accuracy. For further improving the accuracy of disparity, an enhanced genetic optimization algorithm for stereo matching is designed to have more inliers and continuity. The experimental results comparing with state-of-the-art stereo matching methods demonstrate that the proposed method has better performance in accuracy and stability.
Examining the Cultural Differences of Users’ Characteristics Between the United States and Japan Related to User Interface Design

This study aims to grasp the differences in user characteristics related to user interface (UI) design from the perspective of cultural differences and examine how to approach UI design. This study mainly focused on the cultural differences between the United States and Japan to examine UI design that takes cultural differences into account. Based on the following six cultural differences between the United States and Japan, differences in user characteristics related to UI design were examined, and how to approach UI design considering cultural differences was examined: (1) short-term or long-term orientation, (2) individualism or totalism, (3) having control of the environment or acceptance of a given environment, (4) preferring change or being risk-averse and seeking for stability, (5) emphasizing the value of time or the value of human relation, and (6) low- or high-context culture.
A survey on spatio-temporal data mining

With vast usage of Spatio-temporal data usage in various domains and capturing data as remote sensing, GPS, and ST data. STDM is critical for various domains such as transportation, public safety, medical and environmental statistics. In this article, ST characteristics are explained in seven different types and followed by models for STDM in different domains including medical, mobile usage, sensors, industrial area, weather analysis, data querying, traffic control. Finally, the research and future direction.
Penerapan Metode Naive Bayes Classifier (Nbc) Untuk Klasifikasi Kondisi Internal Program Studi

The low interest of prospective students in study programs at universities could be influenced by internal factors within the study program. These factors become the main variables in assessing the condition of the study program. For this reason, it is necessary to classify the internal conditions of the study program. A good method is needed in terms of accuracy and minimal misclassification to obtain the final classification results of the assessment. The purpose of this research is to classify the internal conditions of the study program. Classification of the internal conditions of the study program was carried out using the Naive Bayes Classifier (NBC) method which is a simple form of Bayesian Network with the assumption that all features are independent of each other. The NBC method shows an overall superior performance in terms of accuracy and misclassification rate. The NBC method can be used to determine the internal conditions of the study program, which could help identify factors that need to be addressed to increase the interest of prospective students enrolling in the study program.
Design and Development of Soft Computing Toolbox Using Metaheuristics

In recent years, various soft computing optimization techniques have been developed metaheuristic optimization methods are one of them. This paper presents a MATLAB based optimization toolbox for experimentation and study of metaheuristic techniques for solving optimization problems. The toolbox provides a graphical user interface to perform the experimentation, which shows convergence curve, box plot, and corresponding optimal solutions along with standard deviation and mean. The toolbox helps to find efficient algorithm for given optimization problem.
AR MOOC’s (Augmented Reality Massive Open Online Course) to Support the Implementation of Distance Learning

Abstract This study aims to reveal (1). Development of Augmented Reality (ARMOOC’s) coupled with MOOC’s to improve distance learning; (2). Fraction of the concept of All Education Blended which shows learning constantly updated; (3). The results of this study are the MOOC’s concept which is integrated with the actual Augmented Reality, MOOC’s as a place for student access through an online system that can be accessed anywhere and anytime, while Augmented Reality as a supplement of content that supports student activities to find out about learning. The results of the development of AR MOOC’s can be concluded as follows: (1) The development of AR MOOC’s (Augmented Reality Massive Open Online Courses) has the value of efficiency and effectiveness at the usability test stage of 88.9% which shows that the level of user usability is very high in AR MOOC’s ; (2). The application of AR MOOC’s as a supporter of distance education is quite successful, this is evidenced by 81.2% of users giving very good responses to universal use and the remaining 8.8% stated that it was sufficient; (3). The results of quantitative data indicate the significance of an increase in interest and ease of learning&gt; 0.05, so it can be proven that the development of AR MOOC’s can be generalized to distance learning.