Reviewerly/exHarmony huggingface dataset:
Writing Skills Text Report Observation Results At XI Grade Students At SMA Negeri 2 Tondano

The objectives to be achieved in this study are: (1) To describe the learning steps of the communicative approach in writing descriptive text of the report on the observations of class XI students of SMA Negeri 2 Tondano; (2) To describe the writing skills of the description text of the observation report of the XI grade students of SMA Negeri 2 Tondano by using a communicative approach. The method used is action research according to its type. The subjects of this study were students of grade XI in SMA Negeri 2 Tondano. The source of the data is 20 students and teachers. To collect data the techniques used are tests and observations. Acting in two rounds, the results showed that, in the implementation of the first round of Class XI Language Skills of SMA Negeri 2 Tondano in Writing Text Description of Observation Reports using the communicative approach was still in the range of percentage values of 66.20%, categorized as less able. Based on the unsatisfactory results, the second round was carried out, and the results were in the successful category because they were in the able range of 84.40%. These results indicate that the use of a communicative approach in learning to write descriptive text of the observation report has an impact or can improve the skills of 20 high school students of SMA Negeri 2 Tondano
Identifying catheter-related events through sentence classification

Infections caused by Central Venous Catheter (CVC) use is a serious and under-reported problem in healthcare. The CVC is almost ubiquitous in critical care because it enables fast circulatory monitoring and central administration of medication and nutrition. Explicit documentation of normal CVC usage and exposure is sparse and indirect in the health record. To capture evidence about CVC-related risk of infections and complications, we have developed methods for learning classifiers for statements about CVC-related events occurring in the textual health record. We find that even with limited data it is possible to build reasonably accurate sentence classifiers for the most important events. We also find that making use of document meta information helps improve classification quality by providing additional context to a sentence. Finally, we outline some strategies on using our results for future analysis and reasoning about CVC usage intervals and CVC exposure over individual patient trajectories.
Algorithm for determining the optimum number of clusters on the basis of the Kohonen neural network

The article discusses the possibilities of using artificial intelligence systems to solve clustering problems. The value of the optimality criterion for various combinations of the number of clusters and the number of neurons of the output network layer is determined. Self-organizing maps (SOM, Self Organizing Maps), developed by T. Kohonen and representing a powerful tool combining two important paradigms of data analysis - clustering and projecting, visualization of multidimensional data on a plane are considered. An example of the location of cluster nuclei after training the Kohonen neural network for different values of the number of neurons in the source layer is given. Comparing the speed of modern computers with the speed of the Kohonen neural network, with other types of neural networks, allows you to conduct a large number of network exercises in a short time, so you can use one of many methods to determine the maximum value of the function. The results of experimental studies to determine the criterion of optimality are presented in the article for different combinations of the number of clusters and the number of neurons in the original layer of the network. According to the method at the initial stage, a set of input vectors is formed, each of which includes three values. A general sequence of actions is formulated to calculate the optimal number of neurons in the output layer of the Kohonen network. The methodology presented in the article is a further development of teaching methods without a teacher. The technique proposed in the article avoids the need to specify the number of outputs of the Kohonen neural network and can be widely used both in solving data mining problems and in recognizing new unknown classes and situations in different fields.
Mapping of Waste Water Sources in the City of Manado and Alternative Solutions

The problem of wastewater is a serious problem to overcome. Pollution of rivers, waterways, wells, and the sea is increasing due to the increase in community activities. This condition is a serious challenge in the context of developing Manado as a world tourism city. This study aims to identify the location of waste water accumulation, by tracing the source of waste. This information is traced through the RTRW map and the Manado City masterplan. The study of wastewater management in the city of Manado is based on population density maps. The results of the mapping show the level of waste sources starting from a density level of less than 100 people / ha (Model A), a density level of 100-300 People / Ha (Model B), and a density level of more than 300 People / Ha (Model C). The alternative conclusions for solving the problem of Area A wastewater treatment are in place while the waste from the bathroom, kitchen and washing water may be disposed of without treatment. Area B of other household wastes must be processed by processing on-site to a certain degree of processing, which is to reach a BOD concentration of 60 mg / liter. Area C of all household wastewater will be processed in a centralized manner, namely the riolization system to a certain degree of processing so that the BOD concentration that exits is maximum of 30 mg / liter
Application of Non-Additive Entropy in Questionnaire Theory

In present communication We proposed two new measure of average charge for miscellaneous set of questions under non-additivity and studied their properties These measures includes the earlier studied measures as limiting and particular cases .A relation between these measures have been established. it shows that the average charges are upper bounds of non-additive mean value entropies.
Improving Collision Avoidance Behavior of a Target-Searching Algorithm for Kilobots

Collision avoidance in the area of swarm robotics is very important. The lacking ability of such collision avoidance is mentioned as one important reason for the sparse distribution of the small test robots named Kilobots. In this research paper, two new algorithms providing a collision avoidance strategy are presented and compared with previous research results. The first algorithm uses randomness to decide which one of several approaching Kilobots are stopped for a defined time before starting to move again. The second algorithm tries to determine the assumed position of approaching Kilobots based on its radio signal strength and then to move away in the opposite direction by rotation. The results, especially of the second algorithm, are promising as the number of collisions can be significantly reduced.
A Tag And Social Network Based Recommender System

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.
Identification and Contextual Semantic Retrieval of Polysemy Words

Polysemy is a spectacle where an individual word is associated with two or more different meanings. The distinction between polysemy and monosemy is that in monosemy, there is no presence of semantic ambiguity in language that is a word will have only one meaning. Polysemy escalates in natural language: pragmatically every word is polysemy up to a certain extent. This paper attempts to disambiguate a polysemy word and classify it to its correct sense based on its use in particular context. By taking into account the words which surround the polysemy word, the article tries to get the right sense out of it. Oxford dictionary is used to fetch the meaning of polysemy word. In this article, we use the information rich phrase or sentence as a key to identify polysemy word and illuminate it to its appropriate sense. The article is a novel approach to identify a polysemy word just like how hearers effortlessly reach at the contextually appropriate sense of word on a given occasion of use.
An effective shape descriptor for content based image retrieval

In this work, we present a modified version of the Generic Fourier Descriptor (GFD) that operates on edge information within natural images from the COREL image database for the purpose of shape-based image retrieval. By incorporating an edge-texture characterization (ETC) measure, we reduced the complexity inherent in oversensitive edge maps typical of most gradient-based detectors that otherwise tend to contaminate the shape feature description. We find that the proposed techniques not only improve overall retrieval in terms of shape, but more importantly, provide a more accurate similarity ranking measure of retrieved results, demonstrating the need for greater consideration for dominant internal and external shape details. A feature database combined by color moments, color histograms, Gabor wavelet and shape features is applied in our image retrieval system. Relevance feedback has also been considered, bridging the gap between the high level concepts and the low level visual features. The experimental results indicate that dynamically updating weights associated with feature compenents by users' feedback greatly improves retrieval performance.
Comparing Original and Encrypted Sizes of Colorful Images by Steganography

In today's digital world the secure and confidential data transmission by the various ways is quite vital.In order to avoid attacks which aim to receive data unauthorizedly, many different techniques have been developed by the years.In this paper one of these techniques called "image steganography" is analyzed.Ten colourful basic and popular images of image processing were used for the experiments.Also an image to hide in these ten images is chosen.The filtering algorithms is "mean filter algorithm".This filtered hidden image also has been hidden in these ten images.After that, the physical sizes of images were compared after encryption process.MATLAB program has been used for all these issues.
Improving Students’ Reading Skills through Jeopardy Games

This study is a literature study that reviews several papers in studying the advantages of the Jeopardy game. This study intended at analyzing the use of the Jeopardy game in improving students’ reading skills. The Jeopardy game helps students to read a lot and feel relaxed while learning reading. This game also fulfills five game design criteria, namely fun, educational, promoting English, interesting, and easy to use. The findings of this study showed that the Jeopardy game can be used as a fun way to improve students’ reading skills. The Jeopardy game provides a fun and active learning environment. The findings of this study are expected to provide knowledge to teachers regarding the use of the Jeopardy game which is beneficial for teachers and students in the teaching and learning process of reading.
Feature models as service contracts in service oriented architecture

The service contract is one of the fundamental design principles in the service oriented architecture (SOA). Its goal is to express the features (e.g., services and capabilities) of service providers (SPs) so service consumers (SCs) can identify them to communicate correctly. The two most known service contracts in the literature are: WSDL for SOAP and WADL for REST. We identify that these service contracts suffer from several problems (e.g., they only allow expressing a limited set of features). Also, we notice from the literature a lack of service contracts dedicated for SC. In order to overcome these problems, we propose two features models (FMs) based on SOA design patterns that can be considered as generic and formal service contracts for SP and SC. We propose extensions to the feature metamodel (FMM) and semantic constraints that ensure generating fully functional, valid, customised and consistent SPs and SCs from the proposed FMs.
Augmented Reality and Human Factors Applications for the Neurosurgical Operating Room

The virtual overlay of patient-specific anatomies onto a surgical site through Augmented Reality (AR) technologies has been thought to be a potentially ideal neuronavigational system for use in neurosurgery. Although impressive and futuristic, there are many design considerations that must be taken into account, including surgeon reception, perceived utility, intuitive control and manipulation design, and overall system accuracy during surgery. To implement AR into the neurosurgical Operating Room (OR), a gradual approach of evolutionary design to ensure widespread adoption may be considered. This thesis presents a potential pathway for the introduction of AR technologies into the neurosurgical OR. The thesis is divided into three parts: incorporation of AR features into existing platforms for improved functionality and introduction of AR concepts to surgical environments, observation and evaluation of surgeon perception of AR overlays and AR headsets to inform display methods and designs, and quantification of virtual object placement accuracy in a clinical environment. The findings presented show that AR integrated systems improve OR workflow when conventional tracked tools are unavailable, user preference of AR overlays onto the surgical site change depending on operator experience level, and the placement accuracy of state-of-the-art AR head mounted displays are suitable for presurgical planning and very close to accuracy needed for surgical guidance. These three elements are key to developing a pathway for adoption of AR technologies in the OR, and help to inform designs for future headsets to assist surgeons and improve patient care.
GAME ADVENTURE UNTUK PEMBELAJARAN PENGGUNAAN HATSUON PADA MATA KULIAH HANASHIKATA

Nowadays technology is increasingly developing so that everyone must be able to use various technologies to meet their needs, especially in midwives education. As a lecturer, it is expected to be able to keep abreast of rapid technological developments, sometimes even as students, it feels to be more professional in using technology today. So many students who feel less motivated by learning that is not interesting. Therefore this research is in the form of an Android-based Japanese educational game application that will be applied to one of the Japanese language majors, namely speaking or Hanashikata. This research uses a game that will make it easier for students to learn Japanese. The adventure game is an adventure-based game that is given an educational element. The education included is learning how to pronounce Japanese. This game is a solution to overcome the obstacles of teaching methods that are less fun, while Japanese language enthusiasts more and more. In this study, we discussed how to apply this adventure game application as a learning medium in one course, where students will learn learning material not only with the help of lecturers or modules but with the context in the game. Based on the design of the game made it can be felt this game will provide high motivation to learn for students and provoke their curiosity and creativity.
Design of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Application

Shown symptoms in digestive diseases might be similar, resulting in patient’s suspected diseases before and after diagnosis attempt might turn out to be different. This paper aims to build a design of an expert system for digestive disease identification using Naïve Bayes methodology for iOS-based applications. The result from this paper helps medical interns to increase the accuracy in predicting patient’s suspected digestive disease. A precise prediction in suspected disease identification can minimalize unnecessary diagnosis attempts, which saves time and reduces cost. Naïve Bayes is chosen because it has a higher accuracy level than other classification methods. This research includes collecting data through literature reviews on digestive diseases and their symptoms, processing the data to be turned into a knowledge base for the expert system, conducting data training using Naïve Bayes by the designed expert system application through this research. The result from the conducted data training using Naïve Bayes methodology shows that the expert system application has a higher accuracy level, which is 84%.
Friagem Event in Central Amazon and its Influence on Micrometeorological Variables and Atmospheric Chemistry

The manuscript studies a Friagem event during July 9 -11, 2014 in the central Amazon region and its influences on the micrometeorology variables, local circulation, as well as the trace gas concentrations.The investigation of a cold front in the central Amazon is a relevant subject for research in current days.Using the reanalysis and the satellite data, the manuscript demonstrates the propagation of the cold front and the convection on Jul 11, 2014.The second main component of the paper is to understand the event mechanistically and its influences with the local circulation by simulating the cold front.The third component is to explore the influences C1
Exploring the Extreme Learning Machine for Classification of Brain MRIs

Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper.
PENGARUH NORMA SUBJEKTIF DAN MOTIVASI TERHADAP MINAT SERTIFIKASI AKUNTANSI

The purpose of this research is to understand about accounting students' perception on subjective norm, career, economic, social, academic title and knowledge motivation in influence of intention to take accounting certification.Accounting students (S1) in Economic and Business Faculty, Jenderal Sedirman University, who know about accounting certification was become the criterion in samples choosing of this research.The results of research are: (1) parents' influence don't become students' consideration to take accounting certification; (2) the higer family's influence, the higher students' intention to take accounting certification; (3) friends' view or suggestion didn't affect students' intention to take accounting certification; (4) the higher lecturers/professors' influence, the higher students' intention to take accounting certification; totally, subjective norm have positive and significant influence to students' intention to take accounting certification; (5) the higher career motivation, the higher students' intention to take accounting certification; (6) the higher economic motivation, the lower students' intention to take accounting certification; (7) the higher social motivation, the higher students' intention to take accounting certification; (8) academic title motivation doesn't affect students' intention to take accounting certification; (9) the higher knowledge motivation, the higher students' intention to take accounting certification.
An Accident Analysis and Prevention Model based on Heterogeneous Convolution Kernel

In order to reduce the incidence of safety accidents in factories, an accident analysis and prevention model based on heterogeneous convolution kernel is proposed.On the basis of the framework of convolutional neural network, the traditional fixed convolutional kernel optimization network was replaced by constructing heterogeneous convolutional kernel, which was applied to the industrial workshop to monitor the age and gender of workers in the workshop in real time, so as to reduce the calculation amount and the number of parameters while maintaining the efficiency of characterization.Through the comparative experiment in the standard database of Adience and SCUT-FBP 5500, the experimental results of various methods are analyzed.The results show that the model proposed in this paper can achieve 92% accuracy in gender classification of characters and 67% accuracy in age prediction.It can monitor the gender and age of workers in the workshop, real-time warn the potential safety hazards, and reduce the safety of the factory to a certain extent the occurrence rate of all accidents.
LEARNING OUTCOME EFFECTIVENESS: OPEN-SOURCE VERSUS COMMERCIALLY LICENSED DATA MINING SOFTWARE TOOLS

Data Analytics, Data Mining, and Machine-Learning have become increasingly prevalent in higher-education, in both on-ground and online curricula.However, few studies have thoroughly and objectively studied the effectiveness of specific Data Mining software (both commercially-licensed and open-source), on learning outcomes.The current study used the Independent Samples T-Test to objectively compare two popular data mining software tools.Specifically, undergraduate, and graduate Information Systems students enrolled in an Introduction to Data Mining course were tested on the use of an open-source data mining tool (i.e., R Programming Language), and on a commercially-licensed data mining tool (i.e., XLMiner from Frontline Solvers).The current study assessed the impact of both tools on undergraduate and graduate student learning outcomes.The findings of this study will be of interest to higher-education faculty who are using, or who may consider using, data mining and machine-learning software in their undergraduate or graduate Information Systems Curriculum.
Review of Jurányi and Weller, “One year of refractive index measurement from a coastal Antarctic site”

Aerosol refractive indices determined from two different size distributions measured at a coastal Antarctic site are described in this manuscript.The authors have done a commendable job of deriving these values under difficult measurement conditions and have attempted to account for calibrations and measurement losses with minimal supplies and creative means.Their results suggest that the refractive index was very stable over the period of the study, reflecting the low relative variability of aerosol composition at the site.The results are useful for understanding aerosol optical properties in extremely remote and clean regions, as well as method dependent variability in terms of sampling duration and sampling losses.The authors do not discuss size distribution information in this paper (perhaps this is the topic of a later analysis) but it would be interesting to understand size distribution variability, especially considering the scatter-C1