Reviewerly/exHarmony huggingface dataset:
Sistem Pakar Mendiagnosa Penyakit Saraf Pusat Manusia Dengan Metode Certainty Factor

&#x0D; Abstract— Central nervous disease is a group of neurological disorders that affect the structure or function of the brain or spinal cord, which collectively form the central nervous system. Society in general there are still many lay people who do not understand nerve health, so many of them ignore the symptoms experienced and may be a symptom of central nervous disease. This may be due to the high cost of consultation, and not to mention doctors who are difficult to find due to time invoices. In various fields the use of technology has developed rapidly, one of which is in the field of health, one of which is the use of expert systems. Expert systems are computer programs that present and reason with the knowledge of several experts to solve problems or provide solutions. The development of technology such as expert systems is of course also supported by methods of diagnosing, such as the Certainty Factor Method. To create an expert system that can help the community in consultation about the central nervous disease experienced by the Expert System to Diagnose Human Central Nerve Disease Using the Certainty Factor Method.
A STRUCTURAL MODEL OF ENTERPRISE GIS REQUIREMENT FOR JOHOR BAHRU CITY COUNCIL

Abstract. The city council has been using rapid information technology (IT), especially geographical information system (GIS) to improve their performance. Most initiatives in IT applications, including GIS, are limited to individuals of each department rather than supporting a comprehensive operating institution. Even though GIS is now increasingly popular, implementation of an enterprise system is low compared to developed countries. Lack of awareness on the importance of geospatial data, capability of GIS and GIS application are the reasons for the low implementation rate. In this study, the collection of information is based on the requirements of MBJB (Johor Bahru City Council) in the development of the structural model of Enterprise GIS which covers five departments. Enterprise GIS can help the council in their day-to-day management of their activities especially spatial data sharing to provide the better services to the public. The analysis is based on five categories which are data, organisation information, work information, technology information and process. Following that, analysis using four partial least squares structural equation modeling (PLS-SEM) based empirical studies was carried out to examine Enterprise GIS requirement. Data analysis was carried out using Software Packages SmartPLS. Structural model was constructed which showed the value of relationship between independent and dependent variables. The highest value was found to be the organization information.
Content Based Image retrieval system using machine learning

In recent times throughout digitization technology is used. Textual content recognition generally abbreviated to OCR, includes a computer system designed to translate pics of typewritten text (usually captured by using a scanner) into gadget editable textual content or to translate pics of characters into a widespread encoding scheme representing them. OCR started out as a discipline of studies in synthetic intelligence and computational vision. Textual content popularity used in legitimate project wherein the big facts must kind like submit offices, banks, schools etc. In real existence packages in which we need to acquire a few information from text written picture. Human beings want to experiment in a file and have the text of that record available in a .txt or .docx format and additionally desire to translate the document in their very own designated Indian language. Optical person popularity (OCR) is technique by which text characters can be entered to a laptop by imparting the computer with an image. The computer makes use of an OCR Engine--a laptop application with the particular feature of making a bet which letter (recognizable to a computer) a picture (recognizable to a human) represents.
Keakuratan Teks Berita pada Materi Ajar Bahasa Indonesia bagi Siswa Kelas VIII di SMP N 3 Polanharjo

The background of the problem of this research is that there is still a lack of understanding of the teachers in handling the accuracy of the delivery of news texts. This research aims to find out the accuracy of the news text studied based on five categories of accuracy, there are, check and recheck; writing errors; the resources of relevant news; the accuracy of the title with the content; and the accuracy of photos with the content in the material of learning Bahasa Indonesia for VIII students at SMP N 3 Polanharjo. This research is qualitative research type. The results of the news text in the material of learning Bahasa Indonesia for VIII students at SMP N 3 Polanharjo are, as follows: Using the accuracy of Juditha’s content analysis, the result of this research illustrates that the news text in the material of Bahasa Indonesia learning for VIII students at SMP N 3 Polanharjo shows that the teacher lacks of holding on the accuracy in delivering the news texts. It can be seen from the qualitative result studied based on five categories of accuracy, there are, check and recheck; writing errors; the resources of relevant news; the accuracy of the title with the content; and the accuracy of photos with the content. All of the categories indicate that the news text is less accurate.
XLCS: A New Bit-Parallel Longest Common Subsequence Algorithm on Xeon Phi Clusters

Finding the longest common subsequence (LCS) of two strings is a classical problem in bioinformatics. A basic approach to solve this problem is based on dynamic programming. As the biological sequence databases are growing continuously, bit-parallel sequence comparison algorithms are becoming increasingly important. In this paper, we present XLCS, a new parallel implementation to accelerate the LCS algorithm on Xeon Phi clusters by performing bit-wise operations. We have designed an asynchronous IO framework to improve the data transfer efficiency. To make full use of the computing resources of Xeon Phi clusters, we use three levels of parallelism: node-level, thread-level and vector-level. We also propose a segmentation-method to decrease cache misses. Our performance evaluation shows that XLCS achieves a peak performance of 3.61 TCUPS on a single 31S1P card and 8.20 TCUPS on a single 7210 card. Compared to the well-parallelized LCS algorithm OCS implemented on three M2090 cards, XLCS achieves a speedup of 3.6 on a KNC and 8.1 on a KNL, respectively. XLCS further achieves 93.84 TCUPS running 16 compute nodes of the Tianhe-2 supercomputer and 312.30 TCUPS with 32 compute nodes of a KNL cluster. To our knowledge, this is the first reported implementation of the bit-parallel LCS algorithm on Xeon Phi clusters. XLCS is available at https://github.com/wxiaoning/Longest-Common-Subsequence.
Features Of Information Retrieval Activity Of Orphanage Pupils

The article is dedicated to a comparative analysis of the characteristics of information search activity on the Internet for students living in the government run orphanages based on the results of surveys conducted in 2013 and 2018, which were attended by 68 students enrolled in the basic general education program and 62 students enrolled in the adapted basic educational program, at the age of 14-17. Comparison was made by frequency in use of the Internet and contents of the most popular information enquiries. We found out that the frequency in use of the Internet by students enrolled in the basic general educational program has not changed significantly. However, according to 2018 data, proportion of requests related to the preparation for schoolwork assignments has increased significantly. The frequency of using the opportunities provided by the Internet by students enrolled into the adapted basic general education program, according to the data of 2018, is significantly higher compared to the data of 2013. At the same time, the structure of information enquiries in this group of students has not changed. The authors revealed the differences in the use of the Internet by students enrolled in various general educational programs (the basic general and the adapted basic). The paper shows that only students enrolled in adapted basic educational program showed that the frequency in use of the Internet, self-assessment of the ability to find the required information and self-assessment of personal independence were connected at a statistically significant level.
Dismantlability, Connectedness, and Mixing in Relational Structures.

Abstract The Constraint Satisfaction Problem (CSP) and its counting counterpart appears under different guises in many areas of mathematics, computer science, and elsewhere. Its structural and algorithmic properties have been shown to play a crucial role in many of those applications. For instance, in the decision CSPs, structural properties of the relational structures involved—like, for example, dismantlability—and their logical characterizations have been instrumental for determining the complexity and other properties of the problem. Topological properties of the solution set such as connectedness are related to the hardness of CSPs over random structures. Additionally, in approximate counting and statistical physics, where CSPs emerge in the form of spin systems, mixing properties and the uniqueness of Gibbs measures have been heavily exploited for approximating partition functions and free energy. In spite of the great diversity of those features, there are some eerie similarities between them. These were observed and made more precise in the case of graph homomorphisms by Brightwell and Winkler, who showed that dismantlability of the target graph, connectedness of the set of homomorphisms, and good mixing properties of the corresponding spin system are all equivalent. In this paper we go a step further and demonstrate similar connections for arbitrary CSPs. This requires much deeper understanding of dismantling and the structure of the solution space in the case of relational structures, and new refined concepts of mixing introduced by Briceno. In addition, we develop properties related to the study of valid extensions of a given partially defined homomorphism, an approach that turns out to be novel even in the graph case. We also add to the mix the combinatorial property of finite duality and its logic counterpart, FO-definability, studied by Larose, Loten, and Tardif.
Improving the Architecture of Agricultural Knowledge Processing Systems using Design Patterns

Use of Knowledge Processing in agriculture has continuously increased, since the first era of knowledge based systems. Such software systems are used for support in detailed tasks, such as determining the amount of fertilizer in precision farming, as well as on high-level decision support, such as ‘what to plant in the next growing season’. Unfortunately, these software systems often have shortcomings in software quality. Applying design patterns is a recognized means to achieve better systems in terms of efficiency, flexibility, and quality. In this paper, several software design patterns are mapped to the context of knowledge processing systems in agriculture. Furthermore, additional patterns are identified and described. The need for patterns with focus on particular aspects of knowledge processing in agriculture is addressed, and an implementation is introduced as a proof of concept.
Parallelization Strategies for GPU-Based Ant Colony Optimization Applied to TSP

Graphics Processing Units (GPUs) have been widely used to speed up the execution of various meta-heuristics for solving hard optimization problems. In the case of Ant Colony Optimization (ACO), many implementations with very distinct parallelization strategies and speedups have been already proposed and evaluated on the Traveling Salesman Problem (TSP). On the one hand, a coarse-grained strategy applies the parallelization on the ant-level and is the most intuitive and common strategy found in the literature. On the other hand, a fine-grained strategy also parallelizes the internal work of each ant, creating a higher degree of parallelization. Although many parallel implementations of ACO exist, the influence of the algorithm parameters (e.g., the number of ants) and the problem configurations (e.g., the number of nodes in the graph) on the performance of coarse- and fine-grained parallelization strategies has not been investigated so far. Thus, this work performs a series of experiments and provides speedup analyses of two distinct ACO parallelization strategies compared to a sequential implementation for different TSP configurations and colony sizes. The results show that the considered factors can significantly impact the performance of parallelization strategies, particularly for larger problems. Furthermore, we provide a recommendation for the parallelization strategy and colony size to use for a given problem size and some insights for the development of other GPU-based meta-heuristics.
Progressively Training an Enhanced U-Net Model for Segmentation of Kidney Tumors

An enhanced U-Net model with multi-scale inputs and deep supervision are adopted for Kidney tumor segmentation.Focal Tversky Loss is used to train the model, in order to improve the model performance of detecting small tumors.Progressive training is proposed for facilitating model converge.A simple postprocessing method is used to remove segmentation noises.The preliminary results indicate that the proposed model can segment the normal kidney with a satisfactory result; for the tumors with small sizes in low contrast or extreme sizes, there is still a room for improvement.
AIBD-A Data-Driven Model for Ontology Property Alignment

The unprecedented increase in the web data sources provides numerous opportunities to the organizations that require data manipulation in their daily businesses.However, organizations are faced with a serious challenge associated to this rate of increase as a result of distinct data sources.This challenge is known by the community of data science as heterogeneity.Many researchers proposed feasible ways of addressing data heterogeneity using both traditional and classical approaches.Ontology instance based matching is one of popular approach of addressing data heterogeneity and improvement of interoperability between different sources of data.However, one serious issue with instance based approach is the inability to matching all potential property attributes in generating alignments between these data sources using their URIs.This paper addresses this challenge by proposing a property alignment and similarity generation algorithms that effectively used generated training samples to align properties and also generate similarity using both the training samples and the generated alignments.The experimental result obtained on benchmark data sets demonstrates significant improvement of our method over state of the art approaches in minimizing the heterogeneity and generation of complete alignment between interrelated data sources.
Movement Recognition of Sports Teaching and Training Based on Feature Reduction and Gauss Mixture Model

In order to overcome the shortcomings of current sports movement recognition methods and obtain better sports movement recognition effect, a sports movement recognition method based on feature dimensionality reduction and Gauss mixture model is proposed. Firstly, the video images of sports movements are collected, and the feature vectors of sports movements are extracted. Then, the feature vectors are reduced by using the random projection algorithm. Finally, the training samples after dimensionality reduction are learned by using the Gauss mixture model, the recognition model of sports movements is constructed, and the performance is tested by using various sports movement data sets. The results show that the method achieves ideal recognition results of sports movement, and the recognition accuracy is higher than other sports movement recognition methods.
Analisis Pendukung Keputusan Penentuan Pembelian Bahan Pokok Untuk Pembuatan Nasi Kotak Menggunakan Fuzzy Tsukamoto

Determining a decision in a business is quite difficult. Various considerations must be carefully calculated. Capital and the number of products ordered are sources of consideration that must be taken into account. This study discusses the implementation of fuzzy tsukamoto in making decisions on purchasing raw materials or making orders for rice boxes, where the input is capital of Rp. 3,500,000 and the number of orders is 500 boxes while the results of this study are four outputs including purchasing 93 Kg of rice, purchasing 47 chicken meat, purchasing 21 vegetables or fresh vegetables and purchasing 37 Kg of citrus fruit.
THE INFLUENCE MEDIA OF LEARNING BASED MACROMEDIA FLASH TO KNOWLEDGE ABOUT TAX ON STUDENT SMA SIDOARJO

The purpose of this study was to determine the effect of learning media based of macromedia flash on the knowledge of students high school in Sidoarjo Regency. The type of research carried out was quasi-experimental. The study was designed with two treatment classes, namely the experimental class and the control class. The sample used as subjects in this study were two high schools in Sidoarjo Regency, East Java. The research subjects are class XI PGRI High School and Sidoarjo High School. Each school was chosen by two classes which were used as the experimental class and the control class. The research instrument used was a test instrument about tax material. The research data were analyzed using the parametric statistical test with a t-test that aims to determine the average difference of each class of research. The results showed that: there was a significant difference between the knowledge of students who were given learning assisted by interactive media based on macromedia flash tax and conventional learning. Results of t-test analysis show that the significance value is less than alpha 0.05.
Non-Stationary Acceleration Strategies for PageRank Computing

In this work, a non-stationary technique based on the Power method for accelerating the parallel computation of the PageRank vector is proposed and its theoretical convergence analyzed. This iterative non-stationary model, which uses the eigenvector formulation of the PageRank problem, reduces the needed computations for obtaining the PageRank vector by eliminating synchronization points among processes, in such a way that, at each iteration of the Power method, the block of iterate vector assigned to each process can be locally updated more than once, before performing a global synchronization. The parallel implementation of several strategies combining this novel non-stationary approach and the extrapolation methods has been developed using hybrid MPI/OpenMP programming. The experiments have been carried out on a cluster made up of 12 nodes, each one equipped with two Intel Xeon hexacore processors. The behaviour of the proposed parallel algorithms has been studied with realistic datasets, highlighting their performance compared with other parallel techniques for solving the PageRank problem. Concretely, the experimental results show a time reduction of up to 58.4 % in relation to the parallel Power method, when a small number of local updates is performed before each global synchronization, outperforming both the two-stage algorithms and the extrapolation algorithms, more sharply as the number of processes increases.
Development of a combined image recognition model

The object of research is the processes of identification and classification of objects in computer vision tasks. Currently, for the recognition of images, the best results are demonstrated by artificial neural networks. However, learning neural networks is a poorly conditioned task. Poor conditioning means that even a large data set can carry a small amount of information about a problem that is being solved. Therefore, a key role in the synthesis of parameters of a specific mathematical model of a neural network belongs to educational data. Selection of a representative training set is one of the most difficult tasks in machine learning and is not always possible in practice.The new combined model of image recognition using the non-force interaction theory proposed in the paper has the following key features:– designed to handle large amounts of data;– selects useful information from an arbitrary stream;– allows to naturally add new objects;– tolerant of errors and allows to quickly reprogram the behavior of the system.Compared to existing analogues, the recognition accuracy of the proposed model in all experimental studies was higher than the known recognition methods. The average recognition accuracy of the proposed model was 71.3 %; using local binary patterns – 59.9 %; the method of analysis of the main components – 65.2 %; by the method of linear discriminant analysis – 65.6 %. Such recognition accuracy in combination with computational complexity makes this method acceptable for use in systems operating in conditions close to real time. Also, this approach allows to manage the recognition accuracy. This is achieved by adjusting the number of sectors of the histograms of local binary patterns that are used in the description of images and the number of image fragments used in the classification stage by the introformation approach. To a large extent, the number of image fragments affects the time of classification, since in this case, it is necessary to calculate the matching of the system actions in each of the possible directions in pairs.
Efficient Web Page Mining for Dynamic Web Site

A huge portion of the organizations have the sites for their business. A huge portion of the clients of the association register their subtleties as client profiles. These client profiles have the individual subtleties and their fascinating propensities for the client. At the point when the client visits our sites the log record is made in the server. By partner the client profiles and web log record we can discover the much of the time visited clients. From the frequently visited client, we can discover when they are visited by grouping the client profiles with web log records. In our work we disclose how to get "who" the clients were, "what" they took a gander at, and "how their interests changed with time, "when" they visit which are all significant inquiries in Customer Relationship Management (CRM). In our examination we present bunching the client profiles. We additionally depict how they found client profiles can be advanced with unequivocal data.