Reviewerly/exHarmony huggingface dataset:
The Trends of Verbal Bullying among University Students: A Sociolinguistic Analysis

Bullying remains a pervasive problem within the educational institutions of Pakistan. Students, especially the ones with any kind of disability are constant victims of bullying. Among the many forms of bullying, verbal bullying is the most common one. The objectives of the present study are to understand the trends of verbal bullying behavior within Pakistani Universities and to identify practical bullying prevention strategies. The study is delimited to studying only the verbal bullying that relates to the use of language to persecute, oppress or victimize people. The researcher has delimited the study to the undergraduate students of three major universities in Islamabad Pakistan: FAST-NUCES, NUST, and NUML. The present study uses questionnaires as a method of data collection. The study highlights the effects of verbal bullying on the health, well-being and also on educational career of students. This study concluded that bullying culture is common among the universities of Pakistan which is mainly reflected in the language students use. Keeping in mind the findings of the study, better strategies can be suggested to the educational institutions to deal with the bullying culture in their institutes so that students do not suffer at the hands of the bullies.
Causal Factors of Forensic Investigators Professional Traits to Expedite The Investigation Process

Financial crimes have become dominant in the contemporary business environment.It is one of the most serious crimes endangering a country's national security and public safety.It is directly associated with legal, social, human rights, political and development issues.This research identifies the causal factors of the professional traits of forensic investigators in investigation process expeditions based on Malaysian evidence.Data for this study is collected through a questionnaire survey.A sample was driven from two enforcement agencies in Malaysia: National Audit Department (NAD) and the Malaysian Anti-Corruption Commission (MACC).A total of 83 questionnaires were distributed, collected and usable.Statistical techniques such as descriptive statistics, correlation, and regression analysis were used to analyze the data.Three hypotheses were developed, and the findings reveal a significant relationship between investigative technique and knowledge of forensic investigators and investigation process expedition.In contrast, the skills of forensic investigators show an insignificant relationship with the investigation process expedition.Issues on process expeditions are significant as the disruption in the process expedition will delay the case solving.Hence, this research provides insights into the forensic investigations process which would be useful for the Malaysian government to incite interest in forensic accounting for monitoring and investigation of suspected financial crime cases.
Weakly Supervised Video Anomaly Detection Combining Deep Features with Shallow Neural Networks

Deep features have outgrown hand-sketched features in many applications. The availability of pre-trained deep feature extractors helps to overcome one of the deep learning main drawbacks, which is the need for large volumes of data for training. Multiple Instance Learning (MIL) has become an attractive solution for video surveillance literature once it allows working with weakly labeled bases. This work evaluates a video anomaly detection approach based on the MIL paradigm combining deep features with shallow Neural Networks. For computational efficiency, we apply Principal Component Analysis (PCA) for dimensionality reduction before classification. We performed the experiments from a set of I3D (Inflated 3D) features, which corresponds to the ShanghaiTech benchmark dataset, and the MLP and SVM shallow classifiers achieved competitive results.
Coherence of probabilistic constraints on Nash equilibria

In this work, we first deal with the modeling of game situations that reach one of possibly many Nash equilibria. Before an instance of such a game starts, an external observer does not know, a priori, what is the exact profile of actions -- constituting an equilibrium -- that will occur; thus, he assigns subjective probabilities to players' actions. Such scenario is formalized as an observable game, which is a newly introduced structure for that purpose. Then, we study the decision problem of determining if a given set of probabilistic constraints assigned a priori by the observer to a given game is coherent, called the PCE-Coherence problem. We show several results concerning algorithms and complexity for PCE-Coherence when pure Nash equilibria and specific classes of games, called GNP-classes, are considered. In this context, we also study the computation of maximal and minimal probabilistic constraints on actions that preserves coherence. Finally, we study these problems when mixed Nash equilibria are allowed in GNP-classes of 2-player games.
An Attendance System Based on Face Recognition and Detection Using CNN

Face recognition is a biological tool that may be used to recognize people by their distinctive facial features. Numerous applications, including security, enforcement agencies, and consumer electronics, have made use of this technology. Face detection and recognition system works by analyzing a person's individual facial features to a database of recognized faces. The system can recognize the person if there is a match. We employ a variety of already trained models, such as those for image processing, pattern recognition, and artificial neural network method (ANN). The accuracy of facial recognition has been considerably improved by the advent of deep learning technologies. CNNs (Convolutional Neural Networks) with deep learning have significantly improved picture categorization. The Computer Vision and Machine Learning Group's deep learning model, DeepID, has achieved recognition rate of 98% on the ORL database for attendance systems. Key Words: Face Recognition, Biometric, Image processing, Pattern Recognition, Artificial Neural Network
Game-theoretical Approaches to Cyber-crime Monitoring

Our society's diploma of reliance on IT and our online world is developing daily. Cyberspace, the call given to the worldwide and dynamic domain, composed of the infrastructure of the statistics era consisting of the net networks and statistics and telecommunications structures has supplied extraordinary globalization that gives new opportunities, but additionally includes new challenges, risks, and threats. Knowledge of its threats, dealing with the risks, and constructing suitable prevention, defense, detection, evaluation, investigation, and recuperation is essential. Given the present-day assessment of the statistics safety and intrusion detection, there's without a doubt a want for a choice and manipulation framework to cope with problems like assault modeling, evaluation of detected threats, and choice of reaction actions. We look at the goals of designing a mathematical version for gamified cybercrime tracking in a community environment.
Active methodologies and ICT to prevent bullying. Main study background and contributions

Bullying is a widespread problem that has caused great social alarm in recent years because of its serious effects on children. In the educational context it is one of the most common expressions of peer violence along with cyber-bullying. The aim of this research has been to analyse the scientific production about the prevention of school bullying through active methodologies and the use of ICTs in formal educational contexts. This study derives from a research project financed through the Ministry of Science, Innovation and Universities (FPU18/00676) and is the result of a bibliometric study, in which various current articles have been analysed through the application of different indicators. The results show that scientific productivity on this subject has proliferated in recent years, which gives a glimpse of the relevance given to these issues in the scientific community. Among the conclusions of this work, the advantages of the use of gamification and the use of digital applications such as Kahoot or digital comics, which allow students to simulate experiences that would occur in the real world in order to develop new skills, critical self-reflection and greater awareness of the problem, should be highlighted. In the same way, it is essential that teachers, through the use of these teaching strategies, promote the students' protagonism, their expectations towards learning, as well as the prevention of bullying behaviour among peers.
PEMILIHAN GENTENG RUMAH MENGGUNAKAN METODE MULTIDIMENSIONAL SCALING

Tile is one of the most important building materials. Tile has the main function as a roof covering. Tile is more often used in the construction of houses. The tile protects the occupants of the house from the hot sun and rain. Selection of the right tile needs to be considered for the comfort of the occupants of the house. In this study, the determining factors in the selection of roof tiles are price, product quality, and product availability. The types of tiles used in this research are ceramic tiles, clay tiles, Alderon tiles, asbestos tiles, zinc tiles, and metal roof tiles. The method used in this study is the multidimensional scaling method. The multidimensional scaling method is one of the multivariate analysis methods used to determine the similarity value based on the proximity seen on the perceptual map. Based on the calculation results, it is determined that the types of tiles that have similarities are ceramic tiles and Alderon tiles with a distance value of 0.2062. Furthermore, the best roof tiles based on respondents' perceptions are Asbestos tiles because the variables of product quality and product availability have advantages
The Hausdorff and packing measure of some digital expansions

We compute the exact Hausdorff and packing measure of sets of real numbers whose digital expansions in a given base are missing the digits beyond a given threshold.
Mineral Mapping on Hyperspectral Imageries Using Cohesion-based Self Merging Algorithm

Recently, hybrid clustering algorithms gained much research attention due to better clustering results and are computationally efficient. Hyperspectral image classification studies should be no exception, including mineral mapping. This study aims to tackle the biggest challenge of mapping the mineralogy of drill core samples, which consumes a lot of time. In this paper, we present the investigation using a hybrid clustering algorithm, cohesion-based self-merging (CSM), for mineral mapping to determine the number and location of minerals that formed the rock. The CSM clustering performance was then compared to its classical counterpart, K-means plus-plus (K-means++). We conducted experiments using hyperspectral images from multiple rock samples to understand how well the clustering algorithm segmented minerals that exist in the rock. The samples in this study contain minerals with identical absorption features in certain locations that increase the complexity. The elbow method and silhouette analysis did not perform well in deciding the optimum cluster size due to slight variance and high dimensionality of the datasets. Thus, iterations to the various numbers of k-clusters and m-subclusters of each rock were performed to get the mineral cluster. Both algorithms were able to distinguish slight variations of absorption features of any mineral. The spectral variation within a single mineral found by our algorithm might be studied further to understand any possible unidentified group of clusters. The spatial consideration of the CSM algorithm induced several misclassified pixels. Hence, the mineral maps produced in this study are not expected to be precisely similar to ground truths.
IMPLEMENTASI ALGORITMA WELCH-POWELL TERHADAP PENGATURAN LALU LINTAS PERSIMPANGAN JALAN DALAM MENGATASI KEMACETAN

The traffic is one of the problems that are often in big cities in Indonesia, where this also happened on Gatot Subroto Sei Sikambing street, Medan city. The traffic light is a tool that is made with the aim of being an assistant in regulating traffic so that it runs more orderly, but it has not been able to run optimally due to the determination of which traffic flow is green or red and also the duration of the traffic light. The solusition that can be given to solve this problem is to use a welch-powell algorithm. The welch-powell algorithm is doing coloring activity by producing a chromatic number. This is study, the chromatic number obtained was 4 and the average duration of the red light has decreased by or 116.51 seconds so that its negative and the average duration of the green light has increased by or 119.63 seconds so its positive
PERBANDINGAN METODE CENTROID DAN WARD DALAM PENGELOMPOKKAN TINGKAT PENYELESAIAN PENDIDIKAN DI INDONESIA

Education is foundation in building the nation and state, besides that education is a right for all citizens. Educational achievements in Indonesia can be seen from the achievement of educational graduation rates for all levels of education from elementary to high school. The government needs to pay more attention to education in Indonesia, so there needs to be a grouping of provinces according to similar characteristics, so that the government can focus more on preparing programs and activities to improve the quality of education. The right method for grouping provinces is the best method from the results of cluster analysis by comparing the centroid and ward methods as seen from the value of the cophenetic correlation coefficient. Based on the research, the best method was obtained, namely the centroid method with cluster profiling: cluster 1 is a cluster with a moderate level of completion of education consisting of 31 provinces, cluster 2 with a level of completion of higher education consists of 2 provinces, and cluster 3 with a low level of completion of education consists of 1 province
Mobile Use Pattern of Sugarcane Growers in Kamareddy District, Telangana State

This study tries to fi gure out an understanding of mobile use pattern of sugarcane growers in Kamareddy District, Telangana state in terms of their socioeconomic profi le, information needs through mobile app, perceived benefi ts of information delivery on sugarcane through mobile app and the possible constraints involved by interviewing 60 growers from the command area of Gayathri Sugars, Telengana. Demographic profi le indicated that they were mostly middle to old-aged with secondary to graduate level of education, had a rich farming experience of more than 25 years and majority owned 3-5 hectares of arable land. Over three-fourth of the respondents had mobile phones with internet facilities and most of them had owned mobile phones for the last 5-10 years. Majority of them got mobile phones to communicate with their family members, relatives and friends, wherein they were using mobile phones to disseminate information related to family matters etc. and hardly use mobile phones to share information on agriculture. The most important information need for the sample farmers was on fertigation schedule, maintenance of drip irrigation system and mechanization to overcome labor problem. All the respondents perceived the benefi ts of mobile app as access to information any time, time saving as we can get the information at home itself, information can be stored in mobile and referred as and when needed, easy to store information in the mobile phone and can repeatedly browse the information. All the respondents felt the necessity to carry the mobiles always, has to be charged quite often as it would run out of battery, fear of theft of mobile sets and loss of all the information stored if mobile phone is lost. When it comes to using mobile apps for educational purpose, the major constraints experienced by all the respondents are the need for internet connection for using mobile apps, slow internet connectivity and have to recharge for internet connection.
Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry

The variational optical flow model is used in this work to investigate a subgrid-scale optimization approach for modeling complex fluid flows in image sequences and estimating their two-dimensional velocity fields. To solve the problem of lack of sub-grid small-scale structure information in variational optical flow estimation, we combine the motion laws of incompressible fluids. Introducing the idea of large eddy simulation, the instantaneous motion can be decomposed into large-scale motion and a small-scale turbulence in the data term. The Smagorinsky model is used to model and solve the small-scale turbulence. The improved subgrid scale Horn-Schunck (SGS-HS) optical flow algorithm provides better results in velocity field estimation of turbulent image sequences than the traditional Farneback dense optical flow algorithm. To make the SGS-HS algorithm equally competent for the open channel flow measurement task, a velocity gradient constraint is chosen for the canonical term of the model, which is used to improve the accuracy of the SGS-HS algorithm in velocimetric experiments in the case of the relatively uniform flow direction of the open channel flow field. The experimental results show that our algorithm has better performance in open channel velocimetry compared with the conventional algorithm.
The Best of Both Worlds: A Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: (A) train standard SR networks on synthetic low-resolution-high-resolution (LR-HR) pairs or (B) predict the degradations of an LR image and then use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information and followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network. We show that a single lightweight metadata insertion block together with a degradation prediction mechanism can allow non-blind SR architectures to rival or outperform state-of-the-art dedicated blind SR networks. We implement various contrastive and iterative degradation prediction schemes and show they are readily compatible with high-performance SR networks such as RCAN and HAN within our framework. Furthermore, we demonstrate our framework's robustness by successfully performing blind SR on images degraded with blurring, noise and compression. This represents the first explicit combined blind prediction and SR of images degraded with such a complex pipeline, acting as a baseline for further advancements.
Laravel-Based Web Application for Alumni Books

Alumni are an inseparable unit in the world of education. Alumni have a close relationship with the annual album book which is used to store alumni data. The alumni data at the Banyuwangi State Polytechnic is still kept in the form of an alumni album book. Data entered in printed form has many shortcomings, including the possibility of being damaged, lost, or not being able to be changed periodically. To overcome the above problems, making a Website-Based Alumni Book Application with the Laravel Framework is the right way to deal with these problems. With this alumni book application, users can store information data digitally and the data stored in this application can be updated regularly. The alumni book application provides a search feature that is used to quickly search alumni data and print alumni albums to print their own albums. In this alumni book application, users are given various kinds of information related to alumni. In addition, users can also view details of alumni annual albums from different graduating generations. Based on software testing carried out using the black box method, the Website-Based Alumni Book Application with the Laravel Framework which was tested at the Banyuwangi State Polytechnic has been in accordance with user needs and resulting the average for each page around 7MB.
Ability to solve mathematics questions with cooperative integrated reading and composition learning model

This study aims to determine the ability to solve math story problems for class VIII students of SMPN 23 Simbang by using the Cooperative Reading Comprehension and Composition (CIRC) cooperative learning model. This type of research is an experiment using a quantitative approach. The design in this study uses True Experimental Design in the Posttest-Only Control Design category. The sampling technique used is simple random sampling. In this study the sample chosen was class VIIIA as the experimental class (using the CIRC cooperative learning model) and class VIIIB as the control class (using conventional learning models in learning). The data collection used includes observation sheets and tests of student learning outcomes that have been validated by experts. The data in this study used prerequisite analysis and hypothesis testing, namely the independent t-test. Based on the analysis of hypothesis testing, it shows that the ability to solve math story problems for class VIII students of SMPN 23 Simbang by using the Cooperative Reading Comprehension and Composition (CIRC) cooperative learning model is better than the conventional learning model.
Differences of Horizontal Brushing Method And Combination Method Towards Students' Score Plaque For Class V SDN 22/IV Jambi City

Problem statement:. This tooth brushing technique consists of several methods that can be used. Its success also depends on the brushing method used. Tooth brushing methods include Bass, Stillman, Fones, Horizontal, Vertical, Roll, and Combination. The behavior of brushing teeth in Jambi Province is 96.4% who brush their teeth every day and 0.69% when brushing their teeth properly.Objectives: 1) To find out the average plaque score for elementary school students who brush their teeth using the horizontal method and the combination method as measured by PHP-M. 2) Knowing the difference between horizontal brushing and the combination method on plaque scores in elementary school students in Jambi City.Method: The method used in this research is to use the waterfall method. The data will be analyzed analytically (bivariate). After getting the data, then the data is tested for normality, when the data is declared more than 0.05 then the Independent T Test is used, which is a type of parametric test to find out whether there is a difference from the estimated value with the value of the statistical calculation.Scientific finding results, and a short conclusion : The obtained significance is 0.000 less than the significant level (α) = 0.05, which means that there is a significant difference between the average plaque scores after the combination method group treatment with the horizontal method. In table t obtained t count -6.119, which means that the average plaque score after the combination method group treatment is lower than the horizontal group.
Hypercycle of Geoscience, Nonlinear Whole Geoscience and Possible Entropy Decrease

First, based on the many levels and their cycles in geoscience, we research the hypercycle of geoscience. This is the hypercycle as a tool of self-organization applied to geoscience. It may form from a level to other higher levels. These levels influence each other and the co-evolution. Second, we discuss some possible mathematical methods, which include graph, vector, matrix, some equations, similar theories, etc. This method can be developed and perfected. Third, we propose the nonlinear whole geoscience and its three basic laws. Fourth, we discuss thermodynamics of geoscience, and in which possible entropy decrease under some sates, such as evolution and cycles of Earth, etc. Sustainable development of society must study the mode from high entropy to low entropy. Various cycles in geoscience cannot all be entropy increases, and cannot all be originated from the external interactions.