Reviewerly/exHarmony huggingface dataset:
SISTEM MONITORING DAN EVALUASI KEGIATAN BELAJAR MENGAJAR (E-MONEV KBM) BERBASIS WEB

As an organizer of technology-based academic activities, Sumbawa University of Technology wants to utilize technology as the main support for activities on campus. One of the activities that require technological touch is the process of monitoring and evaluation in lecturing activities. Vice Chancellor I in the academic field as the part that oversees the implementation of lectures, so far the system that is carried out in monitoring lectures is still classified as manual, namely by assigning staff to check each lecture room. From the current system, it is considered inefficient because it requires a lot of checking personnel to remember the number of lectures that occur at one time. Therefore the solution used to overcome this is to create a computerized system that can facilitate the process of monitoring and evaluation so as to reduce the need for personnel and time. Therefore the aim of this research is to develop a web-based monitoring and evaluation system of teaching and learning activities (E-Monev). This system was developed using the PHP programming language by using the Codeigniter and Mysql framework as a database and using the waterfall method in application development
MEDICAL IMAGES COMPRESSION BASED ON SPIHT AND BAT INSPIRED ALGORITHMS

There is a significant necessity to compress the medical images for the purposes of communication and storage.Most currently available compression techniques produce an extremely high compression ratio with a high-quality loss. Inmedical applications, the diagnostically significant regions (interest region) should have a high image quality. Therefore, it ispreferable to compress the interest regions by utilizing the Lossless compression techniques, whilst the diagnostically lessersignificant regions (non-interest region) can be compressed by utilizing the Lossy compression techniques. In this paper, a hybridtechnique of Set Partition in Hierarchical Tree (SPIHT) and Bat inspired algorithms have been utilized for Lossless compressionthe interest region, and the non-interest region is loosely compressed with the Discrete Cosine Transform (DCT) technique.The experimental results present that the proposed hybrid technique enhances the compression performance and ratio. Also,the utilization of DCT increases compression performance with low computational complexity.
Peningkatan Hasil Belajar Materi Bangun Datar melalui Model Pembelajaran Index Card Match pada Peserta Didik Kelas II SDN 1 Kebumen Tahun Ajaran 2017/2018

This research is motivated by the low learning outcomes of students in grade II flat material SDN 1 Kebumen. The purpose of this study was to improve mathematics learning outcomes of flat figure material through the Index Card Match (ICM) learning model for grade II students of SDN 1 Kebumen, amounting to 22 students with 13 male students and 9 female students. The research was conducted on April 9, 2018 until May 11, 2018. This study used the Classroom Action Research (CAR) pathway which was conducted in two cycles. Each cycle consists of 2 meetings. Each meeting consists of 4 steps, namely planning, implementation, observation and reflection. Data collection is done by observation and test techniques. Observation sheets were analyzed descriptively, while the learning outcomes tests were analyzed with Minimum completeness criteria (KKM). The results showed an increase in completeness from 23% in pre-cycle, to 45% in cycle I, and 100% in cycle II. The average student learning outcomes increased from 55.90 in the pre-cycle, to 68.54 in the first cycle, and 90.09 in the second cycle. Thus it can be concluded that the application of the Index Card Match learning model can improve student learning outcomes.
PERANCANGAN APLIKASI GO LAND (WEST SUMATERA ISLAND TOUR) BERBASIS ANDROID

Talking about West Sumatra, what will surely be drawn is tourism, it is undeniable that West Sumatra is a tourist province, one of which is island tourism, including Pamusan Island, Cubadak Island, Pasumpahan Island, Pagai Island, Siberut and others. Because it needs the support of supporting applications such as inter-island transportation ordering, it needs the support of technology, one of which is inter-island transportation and also ordering food that can be delivered directly to the address. In order to help make it easier for tourists to visit the island in West Sumatra, an application called the Go Land Application was designed. The Go Land application is an application designed using the Agile system development method with the Android Studio programming language and the UML (Unified Modeling Language) system design tool. This application has several services including: Go_Land, Land_Car, Land_Food, Land_Med, Land_Rent, Land_Auto, in this application Travelers can use facilities on the island including water games, snorkeling, seafood and can be used for formal or informal events. With this application, tourists are expected to be easier, faster and safer to enjoy island tourism in West Sumatra.
Art in the Light of Posthumanism

The article analyzes the condition of art and artistic creation in the context of the development of smart technologies and an advanced computer programs that are used more and more often today in the processes of producing object of art. The author wonders whether in the future the programs and algorithms will allow to cross the limit of seriality and repetitiveness, characteristic for machines’ works of art. The issues of defining art, its intentionality and originality are considered in the situation when the human subject does not stand behind the process of creating art objects. For better distinction, it is proposed to use categories of mass and autonomous art.
Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning

The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort. This is especially true for machine learning problems whose objective function is nicely separable across individual data points, such as classification and regression. In contrast, statistical learning tasks involving pairs (or more generally tuples) of data points—such as metric learning, clustering or ranking—do not lend themselves as easily to data-parallelism and in-memory computing. In this paper, we investigate how to balance between statistical performance and computational efficiency in such distributed tuplewise statistical problems. We first propose a simple strategy based on occasionally repartitioning data across workers between parallel computation stages, where the number of repartitioning steps rules the trade-off between accuracy and runtime. We then present some theoretical results highlighting the benefits brought by the proposed method in terms of variance reduction, and extend our results to design distributed stochastic gradient descent algorithms for tuplewise empirical risk minimization. Our results are supported by numerical experiments in pairwise statistical estimation and learning on synthetic and real-world datasets.
INTERVAL APPROACH TO DETERMINATION OF CALIBRATION CHARACTERISTICS OF INFORMATION TRANSFORMERS

Context. When designing various measuring instruments, the problem arises of constructing the so-called calibration characteristicof a measuring device, i.e. the quantitative dependence of the measurement result on the measured value. This characteristic isinverse to the direct characteristic – the dependence of the measured value on the measurement result. This problem is solved on thebasis of approximate data obtained during the experiment with the measuring instrument. A new method for solving this problem isproposed, based on the apparatus of interval mathematics.Objective. The aim of the work is to develop a completely formalized method for constructing the calibration characteristicof a measuring instrument from approximate data obtained in the experiment with this instrument.Method. The method proposed in this article consists in presenting the function of direct con-version of a measuring device in theform of a linear interval function, determining its interval parameters (coefficients) from experimental data and solving the resultinginterval dependence between the measurement result and the measured quantity with respect to the measured value. The method ofsolving interval equations is used.Result. General formulas are obtained that determine interval calibration characteristic of the measuring instrument on the basis ofdata obtained in the experiment with the instrument. A detailed analysis of formulas is performed. General laws are established thatobey direct and inverse (calibration) characteristics of measuring instrument, as well as the relationship between direct and inversecharacteristics (if the instrument is linear transformer).Conclusions. The article proposes a new approach to the construction of calibration characteristics of measuring instruments,based on use of interval mathematics, for processing data from experiments with instruments. This approach, unlike existing ones,makes it possible to build calibration characteristics of measuring devices and analyze them purely analytically.
Complementary learners for visual tracking

At present, Discriminative Correlation Filter(DCF) based trackers and Deep Learning based trackers are the main methods to achieve visual tracking. Although they have achieved promising performance in many cases, there are still some inherent flaws that they can't overcome. Neither of the two main tracking algorithms can achieve satisfactory tracking performance, and considering that they can complement each other to some extent. In this paper, we focus on combining them together for a better tracking. To this end, we select several Correlation Filter and Deep Learning based tracking methods, then modify them appropriately and take different combinations to get a comprehensive result. To meet the real-time requirements, in the DCF branch, we mainly use hand-crafted features rather than deep features. Finally, we propose a new adaptive fusion approach to improve the tracking robustness and accuracy. Comprehensive experiments are performed on several benchmark datasets, using the evaluation criteria which have been proposed by the corresponding benchmarks. In order to fully understand the role of each branch and the effect of fusion strategy, our approach is compared with corresponding individual branches meanwhile the combination of our branches is compared with different fusion strategies. Finally, our approach is compared with state-of-the-art trackers, and the results show that our method has good accuracy and robustness when compared with other methods.
PENGEMBANGAN LEMBAR KEGIATAN PESERTA DIDIK BERBASIS HIGHER ORDER THINKING SKILLS PADA MATERI SKALA KELAS V SEKOLAH DASAR

This research is purposed to develop Student’s Activity Sheet based Higher Order Thinking Skills in Skala Material for Elementary School Grade V, to investigate the feasibility product LKPD based on Professional Validator assessment, to investigate Student’s response towards LKPD product, and to investigate the study result after learning process with developmental product LKPD. This research conducted in SD Negeri Karangmulyo with the subject of research is 18 students. The research method used is Research and Development (R&amp;D). The results show that the LKDP product was successfully developed with a very good result based on validator matter experts, a good assessment based on validator media experts, a good assessment based on validator language experts, and good assessment based on class teacher. The Limited trial indicates a positive response from the students with a percentage of 100%. Then, the first trial in this research indicates a positive response from students with a percentage of 90,74%. The results of the student's score test after used LKDP product obtained an average of 84,94% with the KKM indicate the percentage of 100%. Therefore, developing product LKDP based on Higher Order Thinking Skills has very good quality and helped a student in learning Skala Materials for elementary school grade V.
Development of Mathematical Learning Test For Student Groups With Linguistic Intelligence

This research is motivated by the availability of mathematical learning outcomes test instruments that are in accordance with the intelligence of students are inadequate. Therefore this study aims to develop the necessary tests. The test development follows the model proposed by Djemari Mardapi, which starts from the preparation of test specifications, writing test questions, testing the test, refining the test and assembling the test in full. Learning outcomes indicators refer to the syllabus in the 2013 curriculum and combine it with indicators of linguistic intelligence which then produce specific questions for groups of students with linguistic intelligence. The results of the field trials show that the test items developed have the ideal differentiation and difficulty level. The results of the application of the test on 27 students with linguistic intelligence showed that the mathematics learning outcomes of students of class VIII linguistic intelligence in SMP Negeri 7 Cirebon had reached KKM of 70.37%. Of the 27 students there were 3 students (11.11%) included in the less category, 5 students (18.52%) which included enough categories, 15 students (55.56%) which included good categories, and 4 students (14.81%) which included very good categories. From the results of the research, it was found that dominant students with linguistic intelligence who were given tests according to linguistic intelligence tended to obtain better mathematics learning outcomes than students with other intelligences who were given similar tests
Synchronization of Machine Learning into Electronic Health Records

The introduction of EHR (Electronic Health Record), in the medical field has been under discussion for a while but due to a very low acceptance rate of this technology by physicians, it has proven to be a risky gamble in the successful implementation of EHR.EHR uses data accumulated on the subject's health to determine tests required, health analysis and real-time records to help the physician provide more accurate and detailed analysis on the subject.Due to Health Information Technology for Economic and Clinical Health (HITECH) there has been an increase in the amount of data accumulation by EHR.The data has great potential because of the large archive of information across the globe, but due to the random collection of data, it has resulted in the development of an unstructured record which has resulted to difficulty in transactions [1].Even though there has been a large collection of data around the globe, the major issue has been making use of this data in a logical manner for purposeful implementation.The intention behind this paper is how to proceed with the implementation of machine learning in EHR along with its steps in order to analyze the data [2, 3] so that one can understand the pattern generated by the data provided.There are several machine learning algorithms for the interpretation of this data, but not all data are compactable with all the algorithms, thus in this paper the method of data gathering to applying machine learning algorithms on the data is explained and various ways to perform different steps are also discussed in detail.
OAuth 2.0 Token Binding

Most of the OAuth 2.0 deployments do rely upon bearer tokens. A bearer token is like “cash.” If I steal 10 bucks from you, I can use it at a Starbucks to buy a cup of coffee—no questions asked. I do not need to prove that I own the ten-dollar note. Unlike cash, if I use my credit card, I need to prove the possession. I need to prove I own it. I need to sign to authorize the transaction, and it’s validated against the signature on the card. The bearer tokens are like cash—once stolen, an attacker can use it to impersonate the original owner. Credit cards are like proof of possession (PoP) tokens.
Determination the Different Categories of Buyers Based on the Jaynes’ Information Principle

Purpose:The article aims to reduce the volume of statistical data, necessary for determination the buyer's structure.The correct clustering of clients is important for successful activity for both commercial and non-profit organizations.This issue is devoted to a large number of studies.Their main mathematical apparatus is statistical methods.Input data are results of buyer polls.Polls are labor-consuming and quite often annoying buyers.The problem of determination of structure (various categories) of buyers by the mathematical methods demanding a small amount of these polls is relevant.Design/Methodology/Approach: The approach offered in this report based on the Jaynes' information principle (principle of maximum entropy).Jaynes idea is as follows.Let us consider a system in which the conditions cannot be calculated or measured by an experiment.However, each state of the system has a certain measured implication, the average value of which is known (or can be defined), and the average result of these implications is known from the statistical data.Then the most objective are probabilities of states maximizing Shannon's entropy under restrictions imposed by information about average implications of states.Findings: In this work the task of determination of percentage of buyers for computer shop by the average check is set and solved provided that average checks for each concrete category of buyers are known.Input data for calculation are their average checks.Determination of these values requires much less statistical data, than to directly determine relative number of buyers of various categories.Practical Implications: The results are of particular interest to marketing experts.Originality/Value: The article deals with practical situation when initially there are only three different groups of customers.For this case, the problem of maximizing entropy under given constraints reduced to the problem of finding a solution to a system of three equations, of which only one is nonlinear.This is a completely new result.
RECOMMENDER SYSTEM – AN OVERVIEW

The purpose of this paper is give an overview of the concept of recommendation systems, to highlight the existence of recommender systems in various aspects of human life and how recommendation is becoming an integral part of human life. The chanllenges that the recommendation systems are facing
Density of State Method for Complex Action Systems

This is the 11 th edition in a series of workshops that had been previously organised in Poland
Research on terahertz image denoising using quadtree-based non-local means

In this paper, a quadtree-based non-local means image denoising method for terahertz images is used. Firstly, the noise image is decomposed by quadtree to obtain different size sub-blocks, which makes better use of the non-local selfsimilarity of the image. Then, the non-local means filter based on bisquare weighting function is used to denoise each sub-block to improve the quality of the reconstructed image. Finally, the sub-blocks are aggregated to get a complete denoised image. The experimental results of terahertz image denoising show that the method can preserve the details of the image, effectively remove the background noise caused by the imaging system, and has a good denoising effect on terahertz image.
Аctual Issues Аnalysis Regarding Perspective Public-Key Cryptography

An analysis of current research on cryptography on lattices is given. The analysis takes place in accordance with the most relevant algorithms that have gone through the second stage of the US NIST competition. Some of them are combined — include several similar algorithms from the past stage. For a detailed consideration of them, a number of relevant topics for post-quantum algorithms are presented, which allows them to be described and categorized more substantially
WHY AND HOW TO CAPTURE THE SEMANTICS OF WEB USER INTERFACES

Structured data on the Web has become very important over the last few years.Schema.org has proven to be the most recognized vocabulary for structuring data on the Web.However, most annotations using Schema.org'svocabulary describe exclusively content on websites.Today, these structured content data are mainly consumed by search engines to automatically browse websites with high speed and accuracy and to take over search efforts for humans.Annotated web user interfaces can hardly be found on websites or applications.Schema.org's offered vocabulary to describe actions in a structured way plays a minor role on websites or applications and is mainly applied on rich emails.Also most structured data tools and platforms do not support the annotation of actions triggered via the user interface of a website or application.In this paper we motivate the importance of a semantic description of web user interfaces and present possible application areas to consume these structured data.Thereon, we investigate the suitability of Schema.org for the semantic enrichment of web user interfaces.Since there are hundreds of classes and properties in Schema.org,we present a conceptual model of Schema.orgclasses and properties suitable to annotate user interfaces of websites or applications.We also show to what extent interactive elements on HTML5 websites or applications can be automatically mapped to these model elements.As a proof of concept we introduce Schemator, our WYSIWYG tool to support the semantic enrichment process of web user interfaces.Schemator is not only a comfortable structured data tool but acts also as a SAAS platform to store and manage structured web interface data separated from the underlying websites and applications.
Pengaruh Penggunaan Media Manipulatif terhadap Hasil Belajar Matematika Siswa Kelas V SDN 52 Kota Bengkulu

This study aims to determine the effect of manipulative media on student learning outcomes in learning mathematics class V SDN 52 Kota Bengkulu. This research is a quantitative type. The research method used is Quasi Experimental with design type The Matching Only Pretest-Posttest Control Group Design. The population in this study is all students of grade V SDN Kota Bengkulu using KTSP. While the sample of class V students in SDN 52 Kota Bengkulu consists of VA, VB, VC and VD. The sampling technique uses cluster random sampling. From the scramble obtained VC class of 28 students as experiment class and VA class amounted to 29 students as control class. The research instrument used in the form of cognitive learning result test is in the form of multiple choice questions given through pretest and posttest in the experimental class and control class. Data of this research are analyzed by using descriptive analysis and inferential analysis that is t-test. Data of research result which is processed by using t-test is got tcount value 2,84 and t table value equal to 1,625. Systematically seen t count (2.84)&gt; t table (1,625) at 5% significant level, it can be concluded that there is influence of the use of manipulative media on student learning outcomes on learning matemtika class V SDN 52 Kota Bengkulu.