Reviewerly/exHarmony huggingface dataset:
A Correlational Study on the Teaching Methodologies and the Competencies of Graduates in a Private University in the Philippines

Teaching and learning methodologies are significant factors in building students’ competency after college life, making them a good contender in the labor market. The study determined the impact of the varied methodologies in teaching and learning on the graduates' competency. The 181 graduate respondents participated in the survey on a snowball method in data gathering. Frequency and simple percentage, weighted mean, Chi-Square Test of Independence, and One-way ANOVA were used to treat and interpret the data. The findings revealed that, in a pervasive way, the teaching and learning methodologies among faculties embodied in the flipped classroom, project-based learning, cooperative learning, problem-based learning, and competency-based learning in the Department were perceived by the graduates. By this instance, further findings revealed a significant relationship with adopting these varied methodologies and its influence on the graduates' competency in oral/written communications, teamwork/ collaboration, information/ technology application, leadership, and professionalism/ work ethic. The study concluded that a more substantial imposition of teaching and learning methodologies to the students' could greatly emphasize graduates' professionalism, leadership, communication, collaboration, and knowledge in information technology. Furthermore, the influence of flipped classrooms, project-based learning, cooperative learning, problem-based learning, and competency-based learning in the teaching and learning experiences in the Department provides a good impact on their competency as a graduate. These practices indicate a strong alignment between the institution's interests which focuses on producing competent and innovative graduates that are efficient and effective in the labor market.
Branching-time logics and fairness, revisited

Abstract Emerson and Halpern (1986, Journal of the Association for Computing Machinery 33 , 151–178) prove that the Computation Tree Logic ( CTL ) cannot express the existence of a path on which a proposition holds infinitely often (fairness for short). The scope is widened from CTL to a general branching-time logic. A path quantifier is followed by a language with temporal descriptions. In this extended setting, the said inexpressiveness is strengthened in two aspects. First, universal path quantifiers are unrestricted. In this way, they are relieved of any temporal quantifiers such as of those in $\mathtt{AU}$ and $\mathtt{AR}$ from CTL . Second, existential path quantifiers are allowed with any countable language. Instances are the temporal quantifiers in $\mathtt{EU}$ and $\mathtt{ER}$ from CTL . By contrast, the fairness statement is an existential path quantifier with an uncountable language. Both aspects indicate that this inexpressiveness is optimal with respect to the polarity of path quantifiers and to the cardinality of their languages.
Air-Ground Integrated Federated Learning: An Experimental Implementation

With the envision of sixth generation (6G) networks technology, diverse artificial intelligence (AI) services are gradually developed from the network center to the edge, which enables unmanned aerial vehicle (UAV) as a hot spot to provide auxiliary services of machine learning (ML). Due to the sensitive privacy and limited resources, traditional centralized ML may not be used directly in such networks. As a promising distributed collaborative ML, federated learning (FL) is more suitable. However, the current research on air-ground integrated federal learning (AGIFL) mostly focuses on architecture design and network simulation. In this paper, we mainly verify the feasibility of deploying FL between UAV and terrestrial users through practical platform. Extensive experiments demonstrate that the training accuracy is higher when the training nodes is close to the server, and the accuracy is improved from 69.48&#x0025; to 83.52&#x0025; when distance is reduced from 20 meters to 5 meters.
Grammar Learning Strategies Practice: An Investigation of Strategies-Based Instruction Effect on Grammatical Competence

Grammar competence as an essential part in learning language has to be mastered by every language learner. Students, especially in higher education, need to possess the knowledge of grammar rules to produce academic products that maintain the grammatical aspects in every task. A new notion in learning grammar by employing grammar learning strategies becomes an alternative way for students in mastering grammar. Since many students are not aware of grammar learning strategies, the need to conduct strategies training in the classroom context is a solution. Empowered by styles and strategies-based instruction, this study aimed to determine the effect of grammar learning strategies on the grammatical competence of second semester students of Universitas Muslim Indonesia. Quasi-experimental design under quantitative research consisting of pre-test, eight meetings of treatment, and post-test was employed to compare the groups’ performance in grammar test. The data were obtained by using two kinds of research instruments: grammar test and questionnaire. In analyzing the data, this study employed the independent sample t-test and frequency of questionnaire. The result of statistical analysis showed that independent t-test which compared the students’ score on post-test revealed a value of p&gt;0.05. It means that there was no significant difference between students taught with and without grammar learning strategies. The result yields some implications of having grammar learning strategies in classroom context. The students’ personal traits and experiences put some influences on the effect of strategies training. The duration of training also put contribution toward the students’ skills in employing the appropriate strategies in solving grammar problems.
Efficient Optimization of Control Libraries

A popular approach to high dimensional control problems in robotics uses a library of candidate “maneuvers” or “trajectories”. The library is either evaluated on a fixed number of candidate choices at runtime (e.g. path set selection for planning) or by iterating through a sequence of feasible choices until success is achieved (e.g. grasp selection). The performance of the library relies heavily on the content and order of the sequence of candidates. We propose a provably efficient method to optimize such libraries, leveraging recent advances in optimizing submodular functions of sequences. This approach is demonstrated on two important problems: mobile robot navigation and manipulator grasp set selection. In the first case, performance can be improved by choosing a subset of candidates which optimizes the metric under consideration (cost of traversal). In the second case, performance can be optimized by minimizing the depth in the list that is searched before a successful candidate is found. Our method can be used in both on-line and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.
KUALA MUDA HISTORICAL SITE IN FULFILL REQUIREMENT OF TOURISM INDUSTRY IN KEDAH

Archaeological research in the district of Kuala Muda since the 1980s until now have enabled several historical sites to be identified and studies conducted to complete the historical chronology that has occurred in this district. As a result, 52 archeological sites of early civilizations and 26 historical sites have been successfully classified, identified and developed such as the Sungai Batu Archaeological Complex, Pengkalan Bujang Archaeological Complex and Bukit Batu Pahat Archaeological Complex. Due to the need for the development of the district tourism sector, there are seven tourism packages such as Package 1: Pusat Interpretasi Pelancongan Kuala Muda (PIPKM), Package 2: Puncak Jerai, Package 3: Rekreasi Air Terjun Kuala Muda, Package 4: Mineral Kuala Muda, Package 5: Kedah Tua, Package 6: Bakau Sungai Merbok and Package 7: Galeri Tsunami dan Pasar Bisik were established to strengthen the tourism sector in Kuala Muda district with the Kuala Muda Tourism Interpretation Center (PIPKM) being made a one-stop tourism center for the district.
An arbitrable multi‐replica data auditing scheme based on smart contracts

Summary In order to improve the availability and persistence of data, lightweight cloud users want to store multiple‐replicas of the original file on the server with less local computing and storage overhead. Meanwhile, to ensure the integrity of the remote storage data, some schemes have been designed to allow public verification. However, most existing schemes only focus on malicious cloud service providers and ignore the possibility that dishonest users cheat for profit. This article implements an arbitrable data auditing scheme under multi‐replica storage. The scheme adopts a new arbitration mechanism under multi‐replica storage, makes use of the non‐tampering characteristics of smart contracts, carries out reliable verification through miners, and realizes the timely detection and punishment of any fraudulent entity. In addition, the scheme also designs a multi‐replica storage model based on the B* tree, realizes the batch verification of replica blocks, enables the fraud behavior of malicious users to be identified after data update, and improves the space utilization efficiency. The article also gives detailed security proof of the proposed scheme. The evaluation result shows our scheme not only realizes a more practical and fairer audit scheme but also has lower computational overhead than current state‐of‐the‐art multi‐replica arbitrable schemes.
Overview of the Trajectories of an Omnidirectional Mobile Robot based on a Single Motion Command

The main benefit of using an omnidirectional motion system in a robot is that it provides three degrees of freedom, allowing motion in any direction while changing the orientation. This paper describes the omnidirectional motion system used in a real three-wheeled assistance mobile robot. This paper presents the ideal simulation of the basic trajectories that this robot can perform with a single motion command. These basic trajectories are the basis of complex trajectories defined by sequences of single motion commands. In the future, the results of the simulations will be applied to improve the path-planning of a real robot in order to generate smooth trajectories and improve social acceptability of mobile robots operating in environments shared with humans.
Real Time FPGA-Based CNN Training and Recognition of Signals

Training a machine learning model requires many resources and a fast, efficient processing system. This work proposes an FPGA based machine learning model for fast and efficient signal recognition, allowing for a mobile application of a training model. This is composed of several processes and steps. First is receiving the signal and running it against the current model. Second is applying several filtering methods, as well as noise, to change how the signal is represented. Finally, training the current model based on the data generated from the original signal, updating the list of recognized items. This is run on an FPGA using python on a Linux environment, utilizing a CPU based training algorithm.
Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction

Auction in Indonesia is carried out by the Office of State Assets and Auction Services (KPKNL). Goods auctioned at KPKNL are quite diverse including land, wood, inventory, vehicles, and other goods. However, not all of the items auctioned were sold. Because not a few items have been auctioned but no one has made an offer. The Purpose of this study is to compare two classification methods, C4.5 and C5.0 algorithm and to determine which items were successfully auctioned with those that did not and its factors. The methods that used were comparing the classification tree C4.5 algorithm and C5.0 algorithm with cross validation. From the results of the comparison of the two methods, it was found that the C5.0 Algorithm method was rated better than the C4.5 algorithm in classifying the auction results with an accuracy of 96.43% and 92.86% respectively. In this case, C5.0 has a higher precision than C4.5.
Internationalization of educational activities of the Russian universities: the Far-Eastern development vector

The article is devoted to the problem of attracting foreign students to the Russian universities. Particular attention is paid to the ways of forming a positive image and factors of attractiveness of educational organizations of higher education in the Far East for potential students. The article considers several areas of development of the indicated problem. First of all, internationalization of education and increase in the number of foreign students is the effective fulfillment of modern request for socio-economic development for the highly professional personnel. Currently, the most urgent tasks are to create necessary conditions to prevent the outflow of foreign students from Russia, thereby preserving their contingent by maintaining the high quality of education with the development and widespread introduction of new learning formats into the educational process: online technologies, hybrid forms of education. The article notes that for Russia, which has a powerful export potential of education system, finding the ways to increase the competitiveness of Russian universities in the world educational space is a current priority. The article pays special attention to the issue of attracting foreign students to the universities in the Far East, especially from the PRC. Close proximity of the Far-Eastern region to China and the need to identify resources that ensure competitive positions for the regional universities in attracting Chinese students justify the prospects for sociological developments in this area. Based on the surveys of foreign students, graduates and potential applicants, the authors of this article identified priority areas in the development of educational potential of the Far-Eastern universities and increasing their attractiveness for the foreign students, refuted stereotypical ideas that foreign students from the low-income families and having a low level of education enter the Russian Far-Eastern universities. The conclusions to which the authors come are addressed to the university leaders, teachers, as well as officials of the government and business bodies interested in the development of the Far-Eastern territories.
Resource optimized Region Based Image Encryption Using Chaotic Maps

Securing medical images becomes a major concern to avoid leaking confidential data. This problem motivated the development of many low computational complexity methods to encrypt these medical images. In this research work, block cipher-based region of interest medical image encryption with multiple maps is proposed. Primarily, region of interest (ROI) regions and region of background (ROB) are extracted with the help of Laplacian edge detection operator. Further, important ROI regions are permuted in a circular fashion with the help of Arnold cat map and angle value. Then permuted ROI part is encrypted using the duffling system and unimportant regions are unchanged. The advantage of the work is that it encrypts only selected/important parts and that will achieve fast execution speed and reduction in computation complexity. The approach presented here enables the storage and transmission of medical image data within an open network. These results show that the security in the proposed method is much better than many chaotic encryption algorithms proposed in the recent times.
Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification

Breast cancer is the most common cancer in women worldwide. While the early diagnosis and treatment can significantly reduce the mortality rate, it is a challenging task for pathologists to accurately estimate the cancerous cells and tissues. Therefore, machine learning techniques are playing a significant role in assisting pathologists and improving the diagnosis results. This paper proposes a hybrid architecture that combines: three of the most recent deep learning techniques for feature extraction (DenseNet_201, Inception_V3, and MobileNet_V2) and random forest to classify breast cancer histological images over the BreakHis dataset with its four magnification factors: 40X, 100X, 200X and 400X. The study evaluated and compared: (1) the developed random forest models with their base learners, (2) the designed random forest models with the same architecture but with a different number of trees, (3) the decision tree classifiers with the best random forest models and (4) the best random forest models of each feature extractor. The empirical evaluations used: four classification performance criteria (accuracy, sensitivity, precision and F1-score), 5-fold cross-validation, Scott Knott statistical test, and Borda Count voting method. The best random forest model achieved an accuracy mean value of 85.88%, and was constructed using 9 trees, 200X as a magnification factor, and Inception_V3 as a feature extractor. The experimental results demonstrated that combining random forest with deep learning models is effective for the automatic classification of malignant and benign tumors using histopathological images of breast cancer.
Hate Speech, Fake News, and the Moderation Problem

While attacks against reputation (Chap. 5 ) are a huge practical issue on Facebook, they do not normally result in the platform’s liability (Chap. 6 ). Nevertheless, the two legal issues of hate speech and misinformation differ on this point, as the influence of hate speech and fake news on democratic societies provokes much greater pressure from legislators and courts to regulate these issues. Unlike attacks against reputation, which are harmful to individuals, hate speech and fake news threaten society itself, in the sense that massive dissemination of such content on social networks induces in average users the false realization that their extreme political movements are strongly supported by the general population. This eventually leads to real life violence and/or to a modification of the political equilibrium in Member States. Even if this phenomenon mainly concerns hate speech, fake news is closely associated with it too, as it is in practice often produced in combination with hate speech.
How Students’ Quality Circles Are Evolving in the World?

The concepts of TQM and Quality Control Circles were developed in Japan and promoted to other parts of the world by Japanese Union of Scientists and Engineers (JUSE). Jagdish Gandhi, CMS, Lucknow, India, brought the concept and tools and techniques of QC Circle and applied among students at his school, and Students’ Quality Control Circle (SQCC) was born. World Council for Total Quality and Excellence in Education (WCTQEE) was formed with the participation international quality experts and educationists in 1999. The author developed Students’ Quality Circles (SQC) as an adapted version of QC Circle suitable for students. The purpose of applying QC Circle with students is just to develop personality which match with the need of the society. Students practices SQC with its own improvised principles, purpose, approach, tools, and techniques at educational institutions to develop their pro-social personality. A non-governmental organization named QUEST-Nepal is established in Nepal in 2006 to promote Students’ Quality Circles all around the nation. It organizes national and international conventions on Students’ Quality Circles (SQC) since 2005 on various parts of the country. Moreover, a two-dimensional conceptual model of total quality person index has also been developed to measure the effectiveness of SQC.
A Stacking Recommender System Based on Contextual Information for Fashion Retails

The recent success of distinct e-commerce systems has driven many fashion companies into the online marketplace, allowing consumers to quickly and easily access a worldwide network of brands. However, to succeed in this scenario, it is necessary to provide a tailored, personalized, and reliable fashion shopping experience. Moreover, unfortunately, current solutions on marketing have provided a general approach to push and suggest the most popular or purchased items in most cases. Thus, this paper proposes a new ensemble recommendation system based on the stacking of classical approaches associated with contextual information about customers and products. Our idea is to incorporate user preferences and item characteristics to ensure a desirable level of personalization to commonly applied methods. Our method is a Neural Collaborative Filtering algorithm that can combine any recommendation system with contextual domain information. The results are promising, showing significant gains of up to 80% MRR, 70% NDCG, and 108% Hits when compared to popular baselines for this same scenario.
Proposal of VAE-Based Deep Learning Anomaly Detection Model for Industrial Products

The anomaly detection of images using deep learning has a great demand. When building a deep learning model to detect defects in products during the production process in a factory, it is difficult to procure anomaly products for training since most of the products do not have defects. Therefore, it is desirable to use unsupervised learning rather than supervised learning. Hence, generative models that can be learned unsupervised such as Variational AutoEncoder (VAE) or Generative Adversarial Network (GAN) are often used for anomaly detection. In this research, the authors built an anomaly detection model named Adaptive Weighted Loss (AWL) VAE, based on VAE. AWL VAE is focused on the two terms of the loss function in the general VAE and changes each weight multiplied by each term depending on certain conditions as training progresses. The results of several experiments show that AWL VAE can detect with better accuracy than conventional VAE for an image dataset of industrial products.
Designing an Augmented Reality-based Educational Board Game Integrated with Dual-Scaffolding Framework for High school History Course: The Evaluation of Learning Performance and Flow State

The study aimed to design an augmented reality-based educational board game with the dual-scaffolding framework integrated peer-scaffolding and cognitive-scaffolding. The teaching objectives is to guide students to learn and understand the history of their hometown. To evaluate the game, a total of 29 senior school students were recruited to participate in the current research. The results shown that students&#x0027; learning performance has been significantly improved, and students all have a high degree of flow state and technology acceptance.
Formation of the readiness of students-instrumentalists for the artistic interpretation of a musical work

The article deals with the problems of forming the readiness of students-instrumentalists to create an artistic interpretation of a musical work, defines the most important competencies (style analysis, the ability to figurative perception, dialogue, variable thinking, etc.). The purpose of forming this readiness is to develop the ability to convincing interpretation, to historical authenticity. The formation of the interpretation abilities of students-instrumentalists determines the approach to the organization of training, based on the principles of individualization, personality-oriented orientation on the development of stylistic features in the process of working on a musical work. The scientific novelty of this direction in music pedagogy is determined by the significance of purposeful selection of funds, taking into account the specifics and peculiarities of the professional activity of students-instrumentalists. As a result, students form the necessary interpretation skills that allow them to acquire an individual style for creative self-realization.