Reviewerly/exHarmony huggingface dataset:
The Effective Diffusion of Educational Robotics in Rural Areas: A Case Study in the Sakha Republic of Russia

&lt;p style="text-align:justify"&gt;The purpose of this study was to find the determinants of effective diffusion of educational robotics in rural areas. The study analysed the key components, difficulties, and major lessons of the successful case of the remote northern region of Russia – Sakha Republic. The study used a mixed-method approach consisting of interviews, survey, literature analysis, and participants’ observation. In the survey participated 57 robotics teachers and 113 elementary, middle, and high school students of the Sakha Republic. From survey participants were randomly chosen 30 robotics teachers and 18 middle and high school students for the interview. The literature analysis explored reports and articles on the educational robotics activities in the Sakha Republic for the period of 2011-2018. Based on the findings, the study developed a process model with five principles and ten components that influenced educational robotics diffusion in the Sakha Republic. One of the main determinants of the model was a support system of public-private partnerships and a local community of educators. Another was the need to consider the local area’s constraints, possibilities, and culture when implementing any technology in the rural region's education. In the researched case two major determinants would not be possible and sustainable without strong leadership. The findings showed that strong local leadership could use knowledge of the local area to provide more appropriate solutions and could build the active support of the local community and public-private partnerships that would accelerate the diffusion of technology in the rural region's education.&lt;/p&gt;
A Critical Review on Educational Data Mining Segment: A New Perspective

Nowadays, more attention is taken in data mining for research in various fields like educational sector, disease prediction, customer behavior, fraud detection, etc. Data mining techniques are applied to the huge amount of data generated by different domains to predict the hidden patterns and managerial decisions. Today, more attention is given on the application of data mining techniques to the analyzing of educational data, which is also known as educational data mining. The primary goal of educational data mining is to provide quality education to students by predicting the performance of students and to find the drop-out ratio. Data mining considered as stepping stone to the procedure of information detection in databases. Here, an analysis of the obtainable literature on data mining is provided. The knowledge of data mining as well as its an assortment of methodologies is summarized. Some applications, tasks as well as issues associated with it have also been illustrated.
Stick-Breaking processes, Clumping, and Markov Chain Occupation Laws

We connect the empirical or ‘occupation’ laws of certain discrete space time-inhomogeneous Markov chains, related to simulated annealing, to a novel class of ‘stick-breaking’ processes, a ‘nonexchangeable’ generalization of the Dirichlet process used in nonparametric Bayesian statistics. To make this unexpected correspondence, we examine an intermediate ‘clumped’ structure in both the time-inhomogeneous Markov chains and the stick-breaking processes, perhaps of its own interest, which records the sequence of different states visited and the scaled proportions of time spent on them. By matching the associated intermediate structures, we identify the limits of the empirical measures of the time-inhomogeneous Markov chains as types of stick-breaking processes.
Democracy and Right to Freedom of Expression: A Case Study on the Nigerian Youth Protest on Police Brutality

It is stated that both under the constitution of Nigeria and under the African Charter on Human People's Rights, persons in any part of Nigeria have the fundamental human right to privately and publicly freely express their disproval or objection over an issue through a protest any time or day.In history, protests have often inspired positive social change and improved protection of human rights, and they continue to help define and protect civic space in all parts of the world.In a democratic Nation like Nigeria, Protests encourage the development of an engaged and informed citizenry and strengthen representative democracy by enabling direct participation in public affairs.They enable individuals and groups to express dissent and grievances, to share views and opinions, to expose flaws in governance and to publicly demand that the authorities and other powerful entities solve problems and are accountable for their actions as seen in the case of the Nigeria Youths protest on police brutality.Yet governments around the world too often treat protests as either an inconvenience to be controlled or a threat to be extinguished.In a democratic Nation, the right to freedom of expression which could come through peaceful protests, involves the exercise of numerous fundamental human rights, and it is essential for securing all human rights, which the citizens should not be denied off by any person in power.
Transfer Building of Multiword Expression Resource from Indonesian to Malay

The scarcity of corpus makes multiword expression (MWE) extraction very difficult for some low-resource languages. The homology and similarity between languages bring a new opportunity about morphological transfer learning from rich-resource languages to low-resource languages. This paper addresses the issue of MWE extraction for relatively low-resource Malay, and proposes a novel transfer building algorithm from rich-resource Indonesian. The experimental results verify the effectiveness of our algorithm to extract Malay MWEs from Indonesian corpus, and also prove a strong computability of morphological transfer learning from Indonesian to Malay owing to the homology and similarity of the same Malay-Polynesian language branch of the Austronesian language family.
LSTM-Based Spatial Encoding: Explainable Path Planning for Time-Variant Multi-Agent Systems

Reinforcement learning-based path planning for multi-agent systems of varying size constitutes a research topic with increasing significance as progress in domains such as urban air mobility and autonomous aerial vehicles continues. Reinforcement learning is used to train a policy network that accommodates desirable path planning behaviors and can be used for time-critical applications. A Long Short-Term Memory module is proposed to encode an unspecified number of states for a varying, indefinite number of agents. Furthermore, a novel gradient-based approach which promotes interpretability and safety within the Long Short-Term Memory structure is presented. The described training strategies and policy architecture lead to a guidance that scales to an infinite number of agents and unlimited physical dimensions, although training takes place at a smaller scale. The guidance is implemented on a low-cost, off-the-shelf onboard computer. The feasibility of the proposed approach is validated by presenting flight test results of up to four drones, autonomously navigating collision-free in a real-world environment.
A Health-Route-Search Model

The concept of a smart city is expected to solve many of the problems of modern cities, including air pollution. This paper presents the first version of a model known as health-route-search model. Health-route-search model is used to implement an electronic service that will offer possible routes through areas with unpolluted air. Such routes would be preferred by the inhabitants of a city and in particular by people with various lung diseases.
Indonesia's Districts Mapping Based on the Scores of the 2017 High School National Examination

The success of student learning in Indonesia is determined based on the National Education Standards Agency's tests in the form of a national examination called Ujian Nasional (UN). UN can also illustrate the central government on the quality of regional education through its result mapping. A simple mapping can be conducted by clustering districts in Indonesia using the clustering method. This study aims to cluster and rank districts in Indonesia using the UN for high school students majoring in Natural Science. In 2017, the UN in high school was held differently compared with previous years, wherein four subjects are evaluated, i.e., Bahasa Indonesia, Mathematics, English, and one elective subject. For Natural Science majors, the elective subject in either Physics, Chemistry, or Biology. Out of 512 districts, 12 did not have scores for Physics and Chemistry. The clustering method requires a data set, so to overcome this problem, the imputation method is used. Accordingly, the districts in Indonesia are classified into three clusters. Based on the ranking, Cluster 3 ranked first with 114 districts, followed by Clusters 1 and 2, with 210 and 188 districts, respectively. This result should be taken into account by the government because the number of districts falls into the best rank is lower than the other two clusters.
A Warp Speed Chain-Code Algorithm Based on Binary Decision Trees

Contours extraction, also known as chain-code extraction, is one of the most common algorithms of binary image processing. Despite being the raster way the most cache friendly and, consequently, fast way to scan an image, most commonly used chain-code algorithms perform contours tracing, and therefore tend to be fairly inefficient. In this paper, we took a rarely used algorithm that extracts contours in raster scan, and optimized its execution time through template functions, look-up tables and decision trees, in order to reduce code branches and the average number of load/store operations required. The result is a very fast solution that outspeeds the state-of-the-art contours extraction algorithm implemented in OpenCV, on a collection of real case datasets. Contribution-This paper significantly improves the performance of existing chain-code algorithms, by smartly introducing decision trees to reduce code branches and the average number of load/store operations required.
Convolutional Neural Network-Based Signal Classification in Real Time

The capability to discriminate signals in the wireless spectrum is significantly crucial in a wide range of practices, such as mesh networks, military, and defense applications. Motivated by these applications, we propose a signal classification scheme deployed in embedded software-defined radio. The proposed work is differentiated by designing the system for over-the-air signals and considering real-life conditions: hardware-constraints and varying channel congestions. The modified Bartlett-based method is unified with a lightweight CNN on a standalone embedded device. Datasets are produced by over-the-air measurements from a WLAN modem that reflects the sparse and dense channel scenarios. The influence of bit resolution and dimension count of the dataset on classification is monitored. We demonstrate the designed system performance across a variety of traffic rates to examine stability. Additionally, several state-of-the-art CNNs are compared with the proposed CNN for accuracy, model complexity, and FLOPS. Our CNN architecture demonstrates immensely nearby accuracy (0.7% – 2.1%) to the rivals despite its exceedingly lightweight architecture.
Evaluation of Graphical Password Schemes in Terms of Attack Resistance and Usability

User Authentication is an important component of security. There are several mechanisms for authentication in use, such as alphanumerical usernames and passwords. However, due to the well-known weaknesses attached to this method, graphics-based passwords were suggested as an alternative. Due to the ability of humans to remember images faster and for a longer period. This study comprises of comprehensive research in the graphical password schemes and evaluates each of the available schemes at two main areas (attack resistance and usability). In the end, it also provides an answer to the question “Are graphical passwords more secure than alphanumerical passwords?”.
Improved Binary Particle Swarm optimization with Evolutionary Population Dynamic for Key Oncogene Selection

Cancer is one of the most deadly diseases in the world, and researchers have been investigating various methods to detect it in the early stages in order to improve the possibility of survival. Unfortunately, cancer data is often very expensive to collect and high-dimensional, where redundant and/or irrelevant features result in the challenges for cancer detection and hide information from useful features or key oncogenes. Therefore, an efficient and effective feature selection algorithm is proposed in this paper to deal with these problems, which based on the classic Particle Swarm optimization (PSO) algorithm, one of the most widely used Evolutionary Computation (EC) techniques. In this paper, the Evolutionary Population Dynamics (EPD) strategy is integrated into PSO to address its limitations and improve its performance on addressing feature selection problems. The proposed algorithm is examined on eight cancer datasets of varying difficulty. Comparisons have been done among the two versions of the proposed approaches, standard binary PSO, and three other feature selection methods in terms of the classification performance, the number of selected features and the convergence behaviors. The results show that in most cases, the proposed EPD mechanism can help PSO to achieve better performance over the compared methods.
Study on Semantic Transparency of Chinese Compounds Based on Word Embedding

Based on word embeddings, this paper proposes an automatic calculation method for the semantic transparency of Chinese compounds and realizes the calculation of the semantic transparency of a large number of compounds. According to the experiment results, we find that the grammatical structure of Chinese compounds and the semantic relationship between compounds and its morphemes are vital factors affecting the semantic transparency of compounds. We prove that the semantic transparency of compounds with parallel grammatical structure is higher than that of compounds with other grammatical structures. There are two important semantic relationships between compounds with high semantic transparency and its internal morphemes: AB≈A≈B; AB≈A+B, by analyzing compounds with higher transparency.
Underwater Depth Estimation based on Water Classification using Monocular Image

The rapid growth of computational and sensor capacities allows the development of image restoration methods that can be applied to underwater images. Due to its high degree of absorption, water becomes a major challenge for robotic perception applications. A fundamental issue for many underwater robot applications is the requirement of a depth map. One of the challenges to obtaining monocular underwater depth image is the lack of large image sets to validate the method, or even training a learning-based method. For the estimation, some methods have been proposed in the state-of-the-art either based on a physical model and on a deep learning approach. Through the analysis of the strengths and weaknesses of each kind of approach, this work aims to obtain the best depth map by classifying the input color image. For this, the water type of each image is evaluated. The results obtained in this work are promising, showing the capability of the classifier to identify the most suitable for each input image.
Predicting Coreference in Abstract Meaning Representations

This work addresses coreference resolution in Abstract Meaning Representation (AMR) graphs, a popular formalism for semantic parsing. We evaluate several current coreference resolution techniques on a recently published AMR coreference corpus, establishing baselines for future work. We also demonstrate that coreference resolution can improve the accuracy of a state-of-the-art semantic parser on this corpus.
Recognition of Offline Handwritten Arabic Words Using a Few Structural Features

Handwriting recognition is one of the most significant problems in pattern recognition, many studies have been proposed to improve this recognition of handwritten text for different languages.Yet, Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts.The present paper suggests a feature extraction technique for offline Arabic handwriting recognition.A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function (RBF) neural networks is proposed.The methods of feature extraction are central to achieve high recognition performance.The proposed methodology relies on a feature extraction technique based on many structural characteristics extracted from the word skeleton (subwords, diacritics, loops, ascenders, and descenders).In order to reach our purpose, we built our own word database and the proposed system has been successfully tested on a handwriting database of Algerian city names (wilayas).Finally, a simple classifier based on the radial basis function neural network is presented to recognize certain words to verify the reliability of the proposed feature extraction.The experiments on some images of the benchmark IFN/ENIT database show that the proposed system improves recognition and the results obtained are indicative of the efficiency of our technique.
Blockchain Integration in House Voting System

Developing a secured electronic democratic framework assures the safety and decency of existing democratic, without compromising in straightforwardness, and flexibility. This has only been a trial for current frameworks. The electronic democratic has risen being a substitution for paper–pen-based democratic to decrease the duplications and irregularities. In this existing current framework, ballot-based voting is available; yet at the same time, there is no framework to stay away from proxy casting and recasting is actualized. We do not have a choice to see our made choice also. There is no security right now. In the proposed framework, a novel blockchain-dependent e-casting ballot framework is provided. This electronic democratic framework is improved from the existing framework by using blockchain structures. The portions of impediments are used to assess the enhancement made from the existing system. In altering the undertaking part, we coordinate aadhar card connected versatile number for OTP age, at exactly that point the voter can make the choice; this framework forestalls throwing and re-throwing of intermediaries.
Voice Conversion of Philippine Spoken Languages using Deep Neural Networks

Most of the voice conversion systems available have only focused on the spectral parameter of the speech such as the spectral envelope. This project developed a voice conversion system that converts not only the spectral parameters but also the prosodic features of speech, specifically the Wavelet modeling of the F0 contour, to improve the voice quality and naturalness. This system can be used in conjunction with text-to-speech systems to introduce personalization and customization to the speech output. The project was implemented not only in the English language but also in the context of Philippine Spoken Languages such as Tagalog, Hiligaynon and Cebuano. Results show that the English voice conversion yielded the highest score in terms of naturalness with 2.7167 Mean-Opinion Score and Cebuano in terms of intelligibility with 3.0875 Mean-Opinion Score. Using the objective metrics, results show that Hiligaynon has the lowest Mel-Cepstral Distortion with 5.5335 while English yielded the lowest F0:RMSE with 20.254. Results also showed that intra-gender voice conversion performs better than inter-gender.
Profiling teaching staff using blended learning in their practices in higher education

This study addresses three issues linked to teaching practices in higher education and the implementation of teaching-learning methods in a changing context: (1) the profile of teachers using virtual platforms and blended learning (according to professional category, gender, teaching experience, and branch of knowledge), (2) the use of blended learning (according to the type of studies, number of students, and activities), and (3) the relationship between teachers’ instruction and the use of blended learning. A quantitative, descriptive-correlational design and a sample of 982 teachers at 35 Spanish public universities have revealed that the profile of the teaching staff using blended learning corresponds to those in the field of social sciences and law, with a stable professional category, who report using virtual means in support of face-to-face teaching. The results confirm the need for teacher training and the design of institutional plans for monitoring and subsequent support.
Effect of Specimen Size on Localization using Digital Image Correlation

Failure of structures made of brittle materials like rock and concrete exhibits deterministic size effect on post-localization mechanical behavior. The application of advanced image-based instrumentation, along with conventional testing can provide full-field strain and its evolution that can be useful for the analysis of localized behavior of brittle materials. In this view, this study collaborates the Digital Image Correlation technique (DIC) with lateral strain-controlled uniaxial compression tests on Hawkesbury sandstone for insights into size effect on localized behavior. Full-field strain obtained from DIC is synchronized with macroscopic responses to investigate the effect of specimen size on strain localization mechanism. Results from this study demonstrate that the increase in specimen size intensifies the local scale deformation, accelerates the localization process, and increases both strain gradients across the shear band and shear band thickness. The shear band evolution analysis in this study also provides rich quantitative data useful for the development of constitutive models to capture size effect on the behavior of rocks.
Time-Series Measurement of Parked Domain Names and Their Malicious Uses

On the Internet, there are lots of unused domain names that are not used for any actual services. Domain parking is a monetization mechanism for displaying online advertisements in such unused domain names. Some domain names used in cyber attacks are known to leverage domain parking services after the attack. However, the temporal relationships between domain parking services and malicious domain names have not been studied well. In this study, we investigated how malicious domain names using domain parking services change over time. We conducted a large-scale measurement study of more than 66.8 million domain names that have used domain parking services in the past 19 months. We reveal the existence of 3,964 domain names that have been malicious after using domain parking. We further identify what types of malicious activities (e.g., phishing and malware) such malicious domain names tend to be used for. We also reveal the existence of 3.02 million domain names that utilized multiple parking services simultaneously or while switching between them. Our study can contribute to the efficient analysis of malicious domain names using domain parking services.