Reviewerly/exHarmony huggingface dataset:
FRAUD.MY: a crowdsourcing web application to detect scammers with algolia and google extension plugin / Syed Muhamad Danial Syed Hedzer

There are a lot of people facing scams and frauds each day throughout the year, everywhere around the world. While scam or a fraud can be a civil wrong or even a criminal wrong, a lot of the con artist are still at large. This is because scammers have honed their craft over years and years of trial and error. Currently, there are not enough or well-equipped platform as a place of reference where online shopper can go to when doing online shopping. Therefore, the proposed project have been developed which is FRAUD.MY: A crowdsourcing web application to detect scammer with Algolia and Google Extension Plugin. FRAUD.MY helps in providing online shoppers a place of reference when doing online shopping. With its search capabilities powered by Algolia Search Engine API, users can swiftly search any details regarding a scammer. FRAUD.MY is also equipped with a Google Extension Plugin where users can search without the hassle of going through FRAUD.MY itself. The developed web application received positive feedbacks after the evaluation with the functionality and usability testing was conducted. The network performance of FRAUD.MY was also evaluated in terms of page render time that was within 0.1535 milliseconds when using Digi. FRAUD.MY enables the users to access it anywhere at any time. For future works, it is recommended that FRAUD.MY integrates with the reports from other websites and from the authorities. On the other hand, enabling FRAUD.MY to search the approximate string matching will make it more efficient.
A crowdsourcing web system for curating empirical knowledge in linked open data

Traditional and Empirical knowledge databases are becoming essential for the preservation of a region's culture. Without proper curation this knowledge might be lost over time. The goal of this work is to present a web system to support the co-curation of data derived from empirical knowledge. Our proposal also provides the interconnection between visitors' contributions with data already available in Linked Open Data repositories. The web system was evaluated by specialists from the Rio de Janeiro Botanical Garden, who verified the impact of the system implantation in cultural environments related to medicinal plants.
慈育大学中文系1-4年级综合课教师的表扬方式研究分析 (ANALYSIS OF PRAISE METHOD USED BY LECTURER IN TEACHING CHINESE COMPREHENSIVE SUBJECT IN BUNDA MULIA UNIVERSITY)

Praise is words or movement that use directed at individuals or groups of students in the teaching process in the classroom. Praise from the teacher to students can increase interest in learning, increase student’s confidence or harmonize classroom conditions and strengthen the teacher's relationship with students. This research focuses on praise from lecturers to students in classroom. Data collection methods used were questionnaires, interviews, and observations. Questionnaires are addressed to students, interviews are directed to the lecturers, while observation is used to determine the atmosphere of teaching and learning. The research objectives were students from grade 1-4 and 8 lecturers who taught Chinese Comprehensive subject in Chinese Language Study Program at the Bunda Mulia University. Based on the analysis, it can be concluded that students from grade 1-4 in Chinese Language Study Program at the Bunda Mulia University hope to get praise from the lecturers and lecturers themselves who have given the students the praise as their expected, although not all lecturers always do praise with words, some teacher prefer to praise through actions and movements. Students also feel that lecturer’s praise can harmonize classroom conditions and strengthen the relationship between teachers and students, in addition, students also feel that praise can increase their interest in learning. Therefore, a good praising technique should also be used as an aspect that needs to be considered by lecturers in the learning process.
Relationship between Adaptability and Chemistry Achievement among Students in Kenyan Secondary Schools

The study investigated the relationship between adaptability and chemistry achievement among secondary school students in Rachuonyo South Sub-County, Kenya. The study was informed by the theory of Planned Behavior. The study adopted the Concurrent Triangulation Design. The study targeted all the 4400 Form Three students of the year 2019 in the 80 public secondary schools, 108 chemistry teachers and 80 guidance and counseling teachers in Rachuonyo South Sub-County. The study employed Research Questionnaires, Interview Schedules, achievement test, Focus Group Discussions and Document analysis Guide to gather information from participants. Face, construct and content validities of the questionnaires and interview schedules were determined by presenting and discussing the test items with two experts from Jaramogi Oginga Odinga University of Science and Technology in the Department of Psychology and Educational Foundation. The reliability of the four sub-scales were checked using split half method in which reliability is determined by administering the developed instruments once and scores of each half are recorded separately. Pearson’s product moment correlation was used to calculate the correlation coefficient between the two halves and a reliability co-efficient of r = 0.856 was reported. The study involved gathering both quantitative and qualitative data. Questionnaires and document analysis yielded quantitative data while interview schedules yielded qualitative data. There was a significant moderate positive correlation (n=442; r = 0.617**; p <.05) between Adaptability score and Achieved chemistry exam mean scores. Its recommended that the Ministry of Education should ensure that the study of adaptability skills is made compulsory and time tabled in all secondary schools in Kenya.
Exploring computational geometry of fundamental interactions in static limits

We explore computational geometry, particularly the three-dimensional (3D) contours of the electrostatic potential due to electrical charges with similar and also opposite polarity. We also explore the 3D contours of the gravitational potential by computational geometry. The noticeable outcome of these exercises is that electrostatic interaction affects the geometry of the space as does the gravitational interaction but with the difference that electrostatic interaction affects the geometry of the space differently for charges with different polarity and at different distance scales.
What happens when self-supervision meets Noisy Labels?

The major driving force behind the immense success of deep learning models is the availability of large datasets along with their clean labels. Unfortunately, this is very difficult to obtain, which has motivated research on the training of deep models in the presence of label noise and ways to avoid over-fitting on the noisy labels. In this work, we build upon the seminal work in this area, Co-teaching and propose a simple, yet efficient approach termed mCT-S2R (modified co-teaching with self-supervision and re-labeling) for this task. First, to deal with significant amount of noise in the labels, we propose to use self-supervision to generate robust features without using any labels. Next, using a parallel network architecture, an estimate of the clean labeled portion of the data is obtained. Finally, using this data, a portion of the estimated noisy labeled portion is re-labeled, before resuming the network training with the augmented data. Extensive experiments on three standard datasets show the effectiveness of the proposed framework.
Mammogram Mass Segmentation Using Fractals

A new fractal-based technique for the segmentation of masses from digital mammograms is shown in this paper. This proposed fractal-based approach aims to detect and segment the masses from mammograms after successful suppression of pectoral muscle. This paper identifies and suppresses the pectoral muscle efficiently from the input mammogram and further segments the masses precisely with the help of fractal dimensions and fractal hurst based thresholding oriented computation. This approach is proved to be efficient in getting accurate segmentation results that guarantees the virtue of the projected technique.
Challenges and Prospects of E-Learning for Prison Education in Nigeria

Prison education is principally aimed at improving the inmate’s skills and employability by means of formal, technical and vocational education to reduce recidivism. Sceptics have argued against the need for educating prisoners who are perceived as condemned, particularly education via the internet. This short conceptual article highlights the strengths and weaknesses of educating prisoners and draws forth the prospects and challenges therein and also assesses how e-learning may have influenced the prisoners to become either educated criminals or a reformed and changed personalities. This paper reports some study findings that were globally shared among academic and non-academic scholars concerning e-learning technologies and its integration in general education practices and prison education in particular through a survey of literature and personal observations. The key conclusion is that prison education has contributed more in reducing recidivism and lesser employability of released prisoners better than when formal education initiative was elusive in the prison service world over.
Expert Systems for Marketing: From Decision Aid to Interactive Marketing

Marketing has been one of the most active areas of expert system research and applications. Two major problem types are consulting and data analysis and mining. This reflects the need of many marketing activities to be supported by consulting activities, and the motivation that the existence of enormous amounts of scanner data provided to research and development of expert systems that analyze and mine them for marketing success. The existing two-way home shopping systems have so far been predominantly hypertext and database oriented. Thus, their major role is providing interactive retrieval of text and data about product items. Constraint and Rule Satisfaction Problems (CRSP) consists of a set of variables, an associated domain for each variable, and a set of constraints and rules to serve as relationships among the variables. In UNIK-CRSP, four types of constraints are allowed: value compatibility, algebraic inequality, mandatory equality, and functional assignment.
Pertumbuhan Miselium Bibit F1 Jamur Tiram dan Jamur Kuping Pada Media Alternatif Biji Kacang Hijau dan Kacang Merah dari Bibit F0 Media Ubi Ungu

Green Beans and Red Beans have carbohydrate and protein content that are potential to be used as F1 nursery media for oyster mushrooms and mushrooms. The purpose of this study was to determine the growth of oyster mushroom F1 mushroom mycelium and mushroom on the medium of green beans and kidney beans. The research method used was experimental with Completely Randomized Design (CRD) consisting of 2 factors namely seedling mushroom (J) and treatment of seed media variation (M), carried out in 3 repetitions. The parameters measured were the color, density, and length of the mycelium. Data analysis using descriptive quantitative. The results showed that after 14 days the inoculation of the best fungal mycelium growth in red bean oyster mushrooms was 4.7 cm white, tight and thick mycelium. While the lowest mycelium growth in ear mushrooms in green bean media is 3.6 cm white mycelium and not tight. Based on the research, green beans and red beans can be used as F1 nursery media. The growth of oyster mushroom mycelium and the best ears on red bean media.
Factorization of the Numbers of the Form N + N ^ 2

Factorization of the numbers of the form n + n ^ 2 it can be done with a certain algorithm.
Convergence analysis of a self-stabilizing algorithm for minor component analysis

The Möller algorithm is a self-stabilizing minor component analysis algorithm. This research document involves the study of the convergence and dynamic characteristics of the Möller algorithm using the deterministic discrete time (DDT) methodology. Unlike other analysis methodologies, the DDT methodology is capable of serving the distinct time characteristic and having no constraint conditions. Through analyzing the dynamic characteristics of the weight vector, several convergence conditions are drawn, which are beneficial for its application. The performing computer simulations and real applications demonstrate the correctness of the analysis's conclusions.
Un-Tethered Positioning and Navigation Solution for IoT Applications and Land Vehicles

Several absolute and relative positioning systems have been developed during the last decades with the Global Navigation Satellite System (GNSS) at the core of any navigation system utilized for the Internet of Things (IoT) and car navigation applications. However, in harsh environments such as urban canyons, tunnels, and covered or underground parkades, the overall GNSS positioning accuracy deteriorates due to frequent cycle slips, signal blockage, and attenuations. Dead Reckoning (DR) systems, such as Inertial Navigation Systems (INS) that utilize a full Inertial Measurement Unit (IMU) have been widely used as a complementary system to GNSS to provide a reliable, high-rate navigation solution and bridge GNSS gaps. Low-cost MEMS-based IMUs accumulate errors very rapidly due to bias errors in both the accelerometers and gyroscopes. In addition, these bias errors are random in nature and need to be modeled efficiently using stochastic processes. Thus, proper integration of the measurements from both GNSS and INS systems have provided great value by mitigating each other problems.
Education System Search: A New Population-based Metaheuristic Optimization Algorithm

Optimization algorithms inspired by nature as intelligent optimization methods with classical methods have demonstrated significant success. Some of these techniques are genetic algorithms, inspired by biological evolution of humans and other creatures) ant colony optimization and simulated annealing method (inspired by the refrigeration process metals). The methods for solving optimization problems in many different areas such as determining the optimal course of their work, designing optimal control for industrial processes, solving industrial engineering major issues such as the optimal layout design for industrial units, problem solving, and queuing in the design of intelligent agents have been used. This paper introduces a new algorithm for optimization, which is not a natural phenomenon, but a phenomenon inspired teaching-human. It is entitled Education System algorithm (ESA).   Results demonstrate this method is better than other method in this area.
Analisis Daerah Rawan Longsor di Kecamatan Semarang Barat Menggunakan Sistem Informasi Geografis

Landslides are one of the disasters that often occur in Indonesia, for this reason, serious management from the government is needed to minimize landslides. West Semarang District has a potential for landslide vulnerability is quite large, because part of the area of ​​West Semarang is a former mining area for reclamation of the Semarang coast. The former mining area which is no longer active has been used as a residence for the people until now. Some residents set up residential buildings without looking at security, endangering themselves and others. The purpose of this study is 1) to determine the distribution of the level of landslide vulnerability in the District of West Semarang, 2) to determine the distribution of validation of the previous landslide event against the landslide distribution.The method used is a survey method using area sampling techniques based on each group of land units that have 4 (four) types of similarity in land characteristics namely; land use, slope, soil type and landform. The unit of land that is sampled is the largest land unit of each land unit. Units of land produced as many as 43 units of land that will be used as an analysis unit for the assessment of the level of avalanche vulnerability distribution. There are 6 variables that are used as supporting parameters to determine landslide vulnerability, namely, soil type, geology, slope, rainfall, land use and vegetation density, then overlapping to be made as a map of landslide vulnerability. Distribution analysis of the level of landslide vulnerability is processed using a tiered scaling technique with ArcGis 10.2 software.The results showed that; 1) the research area has four criteria for landslide hazard, namely non-hazard, low vulnerability, moderate vulnerability, and high vulnerability, 2) validation test shows that the location of the most landslides is in the area of ​​moderate hazard as many as 18 points, high hazard 8 points and low vulnerability 3 point
Conflict robust player tracking method for wall-mounted multi-camera systems

Recently, there has been an increase in the use of video and statistical data in sports practice and coaching. A playertracking function plays an important role in order to obtain players’ trajectories which are essential for performing statistical analyses of players. Usually, in video-based tracking systems, sensor cameras are mounted to ceiling to prevent players from overlapping on the image, because the overlapping makes it difficult to detect overlapped further players, which may lead wrong trajectories. So, the ceiling cameras are useful for tracking but it is expensive to mount on ceiling, thus a system using wall-mounted cameras that can be installed at low cost is desired. However, wallmounted cameras cause a problem that players may overlap each other on the image. To overcome this problem which is inevitable for such wall-mounted camera systems, we propose a stable tracking method which determines the position of the player having less overlapping ratio earlier and determines ones having more overlapping ratio later with using previously determined players’ positions. The overlapping ratio is defined by using the positional relation between the target player and one’s surrounding players from all of those images where the target player exists. Experimental results with real basketball scenes show that the positional error is less than 15cm, and the processing time is less than 10ms/frame at 3.0 GHz CPU, which proved that our method has enough performance for using real-time player tracking system.
The Power of Influencers : Instagramanvändares syn på influencers och influencer marketing

It is now increasingly common for companies to communicate with their consumers via influencers and in recent years it has become one of the most successful marketing tools for companies. Influence ...
Penerapan Model Regression Untuk Prediksi Cuaca Wilayah seberang Ulu 1 Palembang

Weather is a short air condition. Therefore, the role of weather forecasting is very much needed considering the number of activities and activities of humans that depend on the weather. Because of this, the processing of weather data must be done quickly and accurately. Therefore, the use of data mining is needed to solve this problem, one of them is a regression model. Regression models are a type of method that predicts predictive data through straight lines as a correlation between 2 variables or more. Linear regression algorithm, used as a technique to learn how to relate variables to the data forecasting process with accurate results. The dataset is taken from AWS LAPAN Palembang. The results of this study are regression models that can later be used as a reference to predict rainy weather or not rain for one day.
Keterampilan Bertanya Guru Biologi SMA Muhammadiyah di Kota Surakarta Semester Genap Tahun Ajaran 2018/2019

The questioning skill is the teacher's speaking skills in the form of verbal sayings asking for responses from students who are subjected to be able to provide responses in the form of knowledge to things of thinking ability. The research objective was to find out the skills to ask the Muhammadiyah High School biology teacher in Surakarta City even semester of the 2018/2019 academic year. This research is a qualitative descriptive study. The technique of collecting data uses observation in 2 Muhammadiyah High Schools. The results of the study show that the teacher's asking skills are in the moderate category. The results of the observations Technique asked overall that is 62.50% (medium). The highest technique in question clarity and question suitability was 91.67% (very good), while the lowest was in enthusiastic students' answers 33.34% (very lacking). observations of the types of questions overall received 25% (very less). teacher A (25%) and teacher B (25%). Thus it can be concluded that the skills asked by Muhammadiyah High School biology teachers in Surakarta City are in the moderate category (62.50%), and the types of questions given by Muhammadiyah High School biology teachers in Surakarta City are very less (25%).
Regression via Kirszbraun Extension

We present a framework for performing regression between two Hilbert spaces. We accomplish this via Kirszbraun's extension theorem -- apparently the first application of this technique to supervised learning -- and analyze its statistical and computational aspects. We begin by formulating the correspondence problem in terms of quadratically constrained quadratic program (QCQP) regression. Then we describe a procedure for smoothing the training data, which amounts to regularizing hypothesis complexity via its Lipschitz constant. The Lipschitz constant is tuned via a Structural Risk Minimization (SRM) procedure, based on the covering-number risk bounds we derive. We apply our technique to learn a transformation between two robotic manipulators with different embodiments, and report promising results.
Improving Software Quality Through Improving Commit Extracting

This paper focuses on the relation between maintenance  and quality. our contribution  to improving commit extracting from the source code to improve software quality.  We suggest an approach used for commit extraction to make detecting the bug easier when receive change request  to improve software quality . Using information retrieval ,code authorship technique and  software clustering and change graph to make the system maintainable to improve the readability of software. Keywords:Information retrival ; code authership ; maintance ; change request DOI : 10.7176/IKM/9-8-01 Publication date :September 30 th 2019
A Formalisation and a Computational Characterisation of ORM Derivation Rules

Object-Role Modelling (ORM) is a framework for modelling a domain using a rich set of constraints with an intuitive diagrammatic representation, not dissimilar to UML class diagrams. ORM is backed by Microsoft with Visual Studio, and it is used to support the design of large database schemas and/or complex software, easing the workflow for all stakeholders and bridging the gap among them, since every constraint of the diagram is encoded in a language which is understandable even by non-IT users. Besides the standard constraints, ORM also supports Derivation Rules that, in a way similar to UML/OCL constraints and SQL triggers, are able to express knowledge which is beyond standard graphic-based ORM capabilities. Despite ORM has its own formalisation in literature, Derivation Rules in ORM lack of this feature. The purpose of this paper is to provide a formalisation for ORM Derivation Rules in order to extend the automated reasoning on diagrams equipped with Derivation Rules. Automated reasoning is useful to check the consistency of diagrams, new inferred knowledge to validate the diagram or to avoid mistakes which could degrade the quality of the system. We provide the formalisation of Derivation Rules with a precise syntax and a semantics grounded on a precise and non-ambiguous encoding in first-order logic. Finally, we also detect an expressive decidable fragment of Derivation Rules by means of an encoding in an expressive Description Logic. A reasoner for this fragment has been implemented in a plugin for Microsoft Visual Studio.