Reviewerly/exHarmony huggingface dataset:
Combining Image Processing Techniques and Mobile Sensor Information for Marker-less Augmented Reality Based Reconstruction

Marker-less Augmented Reality(AR) based recon- struction using mobile devices, is a near impossible task. When considering vision based tracking approaches, it is due to the lack of processing power in mobile devices and when considering mobile sensor based tracking approaches, it is due to the lack of accuracy in mobile Global Positioning System(GPS). In order to address this problem this research presents a novel approach which combines image processing techniques and mobile sensor information which can be used to perform precise position localization in order to perform augmented reality based reconstruction using mobile devices. The core of this proposed methodology is tightly bound with the image processing technique which is used to identify the object scale in a given image, which is taken from the user’s mobile device. Use of mobile sensor information was to classify the most optimal locations for a given particular user location. This proposed methodology has been evaluated against the results obtained using 10cm accurate Real-Time Kinematic(RTK) device and against the results obtained using only the Assisted Global Positioning System(A-GPS) chips in mobile devices. Though this proposed methodology require more processing time than A-GPS chips, the accuracy level of this proposed methodology outperforms that of A-GPS chips and the results of the experiments carried out further convince that this proposed methodology facilitates improving the accuracy of position local- ization for augmented reality based reconstruction using mobile devices under certain limitations.
The Use of the Modified Semi-bounded Plug-in Algorithm to Compare Neural and Bayesian Classifiers Stability

Despite of the widespread use of the neural networks in the industrial applications, their mathematical formulation remains difficult to analyze. This explains a limited amount of work that formally models their classification volatility. Referring to the statistical point of view, we attempt in this work to evaluate the classical and Bayesian neural networks stability degree compared to the statistical methods stressing their error rate probability densities. The comparison based on this new criterion is performed using the modified semi-bounded Plug-in algorithm.
Methodological Contributions to Educational Research on the Formation of Social Thought

The main purpose of social science education is the formation of social thought for the understanding of reality from its critical and creative dimensions and for the intervention and democratic participation of a responsible and committed citizenship; that is, the formation of citizens capable of living democratically with each other and of participating in the social, labor, cultural, and political life of their world, and trying to improve it [...].
Implementation and Evaluation of Using Digital Book Media with Contextual Approach to Learning Citizenship Education Material in University

The objective of this research is to examine the procedure of advancement, feasibility, amount of accuracy, and medium efficacy of an online book with CTL (Contextual Teaching and Learning) to increase university students' problem-solving capabilities in civic education courses. The R&amp;D research method was used, along with the ADDIE development model. The subject of the research are college students of civic education at Universitas PGRI Semarang, USM and IVET University in the academic year 2019/2020. After qualified media and material validation, this product was tested for gaining the research hypothesis using a one-party t-test, which revealed tcount = 6.320 and ttable = 2.004; if tcount &gt; ttable, 6,324 &gt; 2,004, then Ho is rejected. As a result, it can be reached the conclusion that students who use media digital books with the CTL learning model are effective in resolving citizenship education problems than students who use traditional learning. So that in the second year, more universities in Semarang will use this digital citizenship book, and it is hoped that more than 90% of students and lecturers will be interested in using it.
Assessing Heterogeneity of Two-Part Model via Bayesian Model-Based Clustering with Its Application to Cocaine Use Data

The purpose of this chapter is to provide an introduction to the model-based clustering within the Bayesian framework and apply it to asses the heterogeneity of fractional data via finite mixture two-part regression model. The problems related to the number of clusters and the configuration of observations are addressed via Markov Chains Monte Carlo (MCMC) sampling method. Gibbs sampler is implemented to draw observations from the related full conditionals. As a concrete example, the cocaine use data are analyzed to illustrate the merits of the proposed methodology.
Service Learning in Preschool Institutions and Their Impact on Civic Competencies of Children

Service-learning (S-L) aims to promote the quality of education as well as improve local development in the community. In this approach, a child learns theories in the preschool classroom, but practices work in the local community. The best example of service-learning is described in the Concept of education for the 21st century, where Delors(1996) states: “Learn for learning, learn to work, learn to be, learn to live together with others.“From this concept, one can notice lifelong learning that has a specific social intervention based on solidarity and realization of true needs in the community. The research was conducted on a sample of 17 kindergartens in Zenica-Doboj Canton that applied Service Learning in their work. The purpose of this study is to investigate whether Service Learning can influence the development of civic competencies in preschool children. According to the research data and statements made by a preschool teacher, children had excellent experiences during the realization of Service-Learning ( S-L). In addition, parents and community members had positive attitudes and readiness to work in partnership with kindergartens. Namely, Service-learning as a project method in kindergarten is a great example of how the youngest can set a community in motion but also encourage its development through civic participation. The acquired data suggest that Service learning had been recognized as activity “civic education” in an area that enables them to act and educate the future generation of socially responsible citizens.
Knowledge Enhancement and Understanding of Diversity

In many societies, diversity has been neglected. The importance of diversity in the society for knowledge generation has become increasingly apparent in recent years. In order to achieve diversity, we need to take into account people from various backgrounds and backgrounds, and to understand that every individual is unique. Differentiating people can be based on race or ethnicity, nationality, age, gender, sexual orientation, economic background, and disabilities. We must recognize that each of these differences results in different experiences for individuals, and that all of these experiences can be synthesized to generate knowledge that is both more inclusive and more comprehensive. Diverse initiatives can be used to promote diversity in educational institutions, such as affirmative action and diversity programs. As diversity enhances receptivity to learning from others’ experiences, it is essential for creativity and innovation. Furthermore, diversity promotes productivity and performance as a team can draw on the experience of each individual to find solutions to problems. Individuals must be aware of different opportunities and possibilities in order to grow as individuals, and diversity allows this to happen. As diversity prepares individuals for a diverse world, it is also important to students’ learning. Ultimately, diversity contributes significantly to the creation of new knowledge due to the fact that it helps people draw on their own experiences and the experiences of others in order to discover new approaches to problems.
Time analysis of regional structure of large-scale particle using an interactive visual system

N-body numerical simulation is an important tool in astronomy. Scientists used this method to simulate the formation of structure of the universe, which is key to understanding how the universe formed. As research on this subject further develops, astronomers require a more precise method that enables expansion of the simulation and an increase in the number of simulation particles. However, retaining all temporal information is infeasible due to a lack of computer storage. In the circumstances, astronomers reserve temporal data at intervals, merging rough and baffling animations of universal evolution. In this study, we propose a deep-learning-assisted interpolation application to analyze the structure formation of the universe. First, we evaluate the feasibility of applying interpolation to generate an animation of the universal evolution through an experiment. Then, we demonstrate the superiority of deep convolutional neural network (DCNN) method by comparing its quality and performance with the actual results together with the results generated by other popular interpolation algorithms. In addition, we present PRSVis, an interactive visual analytics system that supports global volume rendering, local area magnification, and temporal animation generation. PRSVis allows users to visualize a global volume rendering, interactively select one cubic region from the rendering and intelligently produce a time-series animation of the high-resolution region using the deep-learning-assisted method. In summary, we propose an interactive visual system, integrated with the DCNN interpolation method that is validated through experiments, to help scientists easily understand the evolution of the particle region structure.
The Design of a Cracker Dough Cutting Machine Using an Effective And Efficient Roll Cutting Setter For Home Industry Scale

The design aims to produce mass cracker production on a homa scale industry that is effective and efficient i order to meet consumption needs. The cutting machine has on a 50cm length of aluminium strip plate, a roll knife made of stainless stell, 1,5mm thick, 45mm diameter, 5mm middle axle hole, with letter L shape support. With 12 knives the roll has a distance of 4cm. Testing on a thick dough of 1-1,5mm width 46cm square with the help of a 10mm thick pvc mattress 50cm wide square using a stopper right and left in the shape of an angle of 3mm thick iron plate the work process of scratching a roll knife into the dough on the mat with a reference to the stopper of longitudinal and transverse movements for effective and efficient results, testing five times gets an average time of 00:49 seconds in the previous test while in the renewable machines it gets an average time of 00:22seconds. So the results of the analysis can be said to be effective and have more efficiency than previous machines.
A feature selection method for classification based on ensemble of penalized logistic models

Classification accuracy of any classifier can be enhanced by performing the classification on selected informative features. Features selection methods are generally approached for the purpose. Combining multiple models known as ensemble method emerged as prominent method for achieving classification accuracy. These techniques can be considered for variable selection for accuracy gain and interpretability of a classifier. we propose an ensemble of penalized logistic models (EPLM) for feature selection. EPLM employs Lasso, adaptive Lasso and elastic net for feature selection. EPLM is applied to high dimensional microarray data sets and simulated data sets. State-of-the-art classifiers, Lasso, Random Forest (RF), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN) are employed for assessment of EPLM. Experimental comparisons reveal that significant improvement in classification accuracy is achieved for all the classifiers considered. The proposed method is also compared to the Ensemble of subset of KNN classifiers (ESKNN). In comparison to ESKNN EPLM achieves better performance accuracy for all the classifiers on simulated and microarray data sets. Moreover, it is also observed that EPLM has selected significantly smaller number of features as compared to the full feature set.
Development of mathematical giftedness in the conditions of distance learning

Background . The article examines the factors of the development of mathematical giftedness in the context of distance learning, depending on the choice of computer mathematical packages and digital platforms. Mathematical giftedness is considered as one of the types of special intellectual giftedness associated with mathematical thinking. The aim . To identify the links between the cognitive structures and types of the mathematical thinking that affect the development of mathematical giftedness, with the specifics of the use of digital resources in distance learning. Materials and methods . The analysis of the features of distance learning and its influence on the development of mathematical giftedness is performed; a comparative study of the relationship between the child’s productive informational activity and the implemented distance learning tools was carried out; methods of selection of digital resources, different in the presented forms and levels of activity of distance work, which contribute to the development of mathematical giftedness of students, have been investigated. Results . The following factors were assigned to the development of mathematical giftedness by means of digital resources: the formation of a child’s productive informational activity; implementation of innovative approaches to teaching; implementation of the methodology for the selection of digital resources. It was found that the implementation of mathematical abstractions by digital means of visualization improves the quality of assimilation of concepts, forms a stable interest in the subject, and contributes to the development of topological thinking. The work identifies specific psychological problems arising in the process of implementing distance learning mediated by computer technologies, the resolution of which affects the possibility of developing mathematical giftedness, in particular: the problems of emotional saturation and the construction of interpersonal relationships. As specific factors, contributing to the solution of these problems, the following are proposed, in particular: increasing motivation, designing group tasks, special systems of tasks, implemented according to the principle of engagement, the solution of which leads to competition and cooperation. The understanding of mathematical abstractions is facilitated by computer applications that implement technologies for rendering graphic components. Conclusions . Based on the analysis of cognitive structures and types of mathematical thinking, conclusions are drawn about the specifics of the use of digital resources in the process of distance learning, contributing to the effective development of student’s mathematical giftedness.
Behavioral Problems and ICTs: Research and analysis in schools in Athens

Behavior is a relative concept that is evaluated, analyzed, and interpreted based on subjective and social factors. Therefore, it is very likely that different people may characterize the same behavior as normal, desirable, problematic, or undesirable. According to international research, behavioral problems in childhood are the most common forms of dysfunction in interpersonal and social relationships and hinder severely a child's ability to adapt to all levels of education. This paper investigates the behavioral problems that occur in the classroom and the role of ICT in controlling them. It aims to define what problematic behavior is, and what its causes are; to document its impact on the learning process and on the interpersonal relationships of students; and finally, to describe the role that the teacher assumes to prevent, to limit and intervene vigorously, in order to help the students solve their problems.
The development of Firefly algorithm with fuzzy logic integration for priority search simulation of flood evacuation routes

Heavy rain in a particular area can cause flooding in both the primary area and the surrounding area. A flood is an event where water is inundated in an area due to increased water volume. Due to high level of water and other hazards arising from flooding, flood victims need to move to a location prepared for evacuation. To get to that location prepared, the victims must get through a safe route. Searching for safe evacuation routes is important to save flood victims and bring them to the evacuation centre safely. Search for evacuation routes related to obstacles on the road to get through. Slippery roads, high puddles of water on the roads, rivers that are located close to the roads that flood victims will have to get through, drainage of waterways and the vulnerability of victims are taken into consideration in choosing a route to get to the evacuation location. There are several problems in choosing a safe route: (1) how to take into account the obstacles on the road to be passed (2) how to choose the priority of the route to be passed with the obstacles encountered. The proposed solution to deal with the problems encountered are (1) to take into account road obstacles by giving the obstacle weights. Fuzzy logic is used to calculate the value of obstacle weights (2) the problem of selecting route priorities will be solved using the firefly algorithm. The firefly algorithm is an algorithm inspired by the social life of fireflies. The priority route for evacuation of flood victims is sought using the method proposed in this study which is the optimal route. The optimal route referred to in this study is the route that has the smallest obstacle weight value. The simulation results show that the fuzzy logic integrated into the firefly algorithm (FuFA) provides a safe route priority, indicated by the smallest obstacle weight value.
User Activity Monitoring System / SPYWARE

Abstract: User activity monitoring (UAM) do the task of monitoring and recording of user actions, in the field of information security or cyber security. Including the use of applications, windows opened, system commands executed, checkboxes clicked, text entered/edited, URLs visited and nearly every everything on-screen event to protect data by ensuring that employees and contractors are performing their assigned tasks and not posing any risk to the organization are all captured and recorded in the system by the UAMS. Video-like playback of user activity and process the videos into user activity logs that keep step-by-step records of user actions that can be searched and analysed is delivered by the User Activity Monitoring System to investigate any out-of-scope activities. Creating a visual record of potentially hazardous user activity are all involved in Visual Forensics. Each user action is logged, and recorded. Once a user session is completed, UAM creates a written record as well as visual record. It can be screen- captures/screenshots or video of exactly what kind of activity a user has done. This written record of our UAMS differs from that of a SIEM or logging tool, because it captures data at a user-level not at a system level –providing plain English logs rather than System Logs (which is originally created for debugging purposes). These textual logs can be used to pair with the corresponding screen- captures/screenshots or video summaries. Using these corresponding logs and images, the visual forensics component of UAM allows for organizations to search for exact user activity in case of a security incident. In the case of a security threat, i.e. a data breach or data leak, visual Forensics are used to show exactly what kind of activity a user has done, and everything leading to the incident. Visual Forensics can also be used to provide evidence to any law enforcement that investigate the intrusion or leak.
Penerapan Asas – Asas Muamalah dan Undang – Undang Nomor 8 Tahun 1999 tentang Perlindungan Konsumen terhadap Jual Beli Kosmetik Preloved

Abstract. The cosmetics and skincare marketplace facebook group is a facebook group that trades preloved cosmetics. Where preloved cosmetics are defined as cosmetics that have been liked by previous users or used cosmetics. The purpose of this study is to find out how the practice of buying and selling preloved cosmetics and the application of the principles of muamalah and law number 8 of 1999 to the sale and purchase of preloved cosmetics in the cosmetics and skincare marketplace facebook group. The research method used is descriptive qualitative with a normative sociological approach. This type of research is field research with data collection techniques, namely observation, interviews and documentation. The results of this study are some of the buying and selling practices that are appropriate and that are not in accordance with the concept of buying and selling, the principles of muamalah and law number 8 of 1999 concerning consumer protection.&#x0D; Abstrak. Grup facebook marketplace kosmetik dan skincare merupakan grup facebook yang memperjual belikan kosmetik preloved. Dimana kosmetik preloved diartikan sebagai kosmetik yang pernah disukai oleh pengguna sebelumnya atau kosmetik seken (bekas). Tujuan dari penelitian ini yaitu untuk mengetahui bagaimana praktik jual beli kosmetik preloved serta penerapan asas – asas muamalah dan undang – undang nomor 8 tahun 1999 terhadap jual beli kosmetik preloved di grup facebook marketplace kosmetik dan skincare. Metode penelitian yang digunakan yaitu kualitatif deskriptif dengan pendekatan sosiologis normatif. Jenis penelitian ini adalah penelitian lapangan dengan teknik pengumpulan data yaitu observasi, wawancara dan dokumentasi. Hasil dari penelitian ini adalah sebagian praktik jual beli ada yang sesuai dan ada yang tidak sesuai dengan konsep jual beli, asas – asas muamalah dan undang – undang nomor 8 tahun 1999 tentang perlindungan konsumen.
How to plan for a post-quantum security ecosystem

Sooner or later, companies will have to adapt to security in an age of quantum computing. Even the slowest of these new machines can perform calculations millions of times faster than the fastest conventional supercomputers, meaning that many of the encryption techniques on which digital security is built could become insecure. This would result in confidential information being revealed and digitally signed documents rendered untrustworthy.
Looking for covariation in heritage Italian in Toronto1

We examine the degree of coherence in Heritage Italian speech to understand the degree of coherence in a community’s linguistic system. Coherence, in this context, is defined as covariation: in a coherent system, innovators in one variable will also be innovators in others, but extended to a language that has been less studied in sociolinguistics. Our findings counter claims from the experimental/acquisition literature that variation among heritage speakers stems from incomplete acquisition. The data are from conversational speech from homeland speakers (in Calabria, Italy), immigrants from Calabria to Toronto, and children of immigrants. Regression models were constructed for six dependent variables (prodrop, VOT (2 contexts), DOM, apocope, speech-rate). Speaker random effect estimates from the best-fitting models constitute a speaker list ranked by rate of use of the innovative form, controlling for the effects of uneven distributions across contexts. We calculate, pairwise, correlation coefficients across these ranked speaker lists. Rarely do the same speakers have the highest usage rates across the variables. We find little correlation to factors expected to affect heritage language speakers, as in a similar analysis of Heritage Cantonese.