Reviewerly/exHarmony huggingface dataset:
Penerapan Data Mining Dalam Mengelompokkan Data Impor Tembaga Menurut Negara Asal Menggunakan Algoritma K-Means

Import is an activity of transportation of goods or commodities from country to country. The import process is generally the activity of entering goods or commodities from other countries into the country. Copper is one of the most important metals and plays a major role in human history and is among the first mined metals. Copper is a good conductor of heat and electricity. In addition this element has a very fast corrosion. Pure copper is smooth and soft, with a reddish orange surface. Copper import activity from year to year continues to increase. There needs to be a deep presentation regarding copper imports. In this study, the authors use the data mining technique k-means clustering method to classify copper import data according to the original destination. The results of this study are a copper import data cluster. Copper import clusters consist of two clusters namely high and low clusters. High clusters are from Japan and China, while low clusters consist of South Korea, Thailand, the Philippines, Australia, Malaysia, Singapore, Myanmar and Chile.
JPEG-deblocking of Blood Cell Images using Deep Learning

This thesis investigates the use of convolutional neural networks as a reconstruction or JPEG- deblocking model for JPEG-compressed blood cell images, needed due to the well known block artifacts caused by JPEG-compression. CellaVision develops automated microscopy for blood analysis that detects and classifies blood cells from images. The automated analysis is carried out on high resolu- tion microscope images, before the images are compressed to JPEG-75 format, where 75 is the quality factor. We investigate how hard the blood cell images can be compressed to still enable acceptable reconstruction quality for display to the user.We propose a CNN-model that reconstructs blood cell JPEG-images of quality factor 50 and higher, to PSNR and SSIM values on average higher than JPEG-75 blood cell images. Using our method, 99.9% of the blood cell images are improved in terms of PSNR and SSIM. However, these metrics do not take into account opinions of professional laboratory technicians who are the main users of CellaVision’s application. A comparison is made between a model predicting in the RGB colorspace and the YCbCr colorspace, the later being exploited by the JPEG-compression algorithm.Results show that models trained on input images of higher or random quality outperform models trained on lower quality, even in the reconstruction of low quality images. Predicting from a higher quality factor is safer when considering quality criteria for medical images and their use in diagnosing, where image quality is critical. With our thesis we propose that CellaVision could store the images with JPEG-50 and still achieve a reconstructed image with a quality as good as JPEG-75, based on the SSIM and PSNR metrics, and thereby save 31% of storage space compared to today.
A Proposed Adoption Model for Blockchain Technology Using the Unified Theory of Acceptance and use of Technology (UTAUT)

Blockchain technology is a decentralized transaction and data management technology. It is described as a distributed database of records known as public ledger, which contains all the transactions or digital events executed and shared within the participating parties and visible to everyone. Each transaction is verified by consensus of the majority of the participants and once entered, it cannot be erased. The innovation of blockchain technology has proved to be a driving power that gave the industry its next giant leap forward, yet it is still going through slow adoption. Therefore, the purpose of this research is to examine the factors influencing the adoption of blockchain technology using the unified theory of acceptance and use of technology (UTAUT). A conceptual model is developed by extending UTAUT model to include two more constructs which are perceived risk and trust. Further empirical research will be conducted to test and validate the model.
Comparison of Second Order Optimization Algorithms in Neural Networks Applied on Large-Scale Problems

This bachelor thesis compares the second order optimization algorithms K-FAC and L-BFGS to common ones of firstorder, Gradient Descent, Stochastic Gradient Descent, and Adam, applied on neural netw ...
LibGuides: HIST2 - Primary Source Research Guide: Home

This guide provides resources and suggestions for locating both primary source materials and secondary sources that provide context for understanding those primary sources.
CODE-MIXING IN SPEAKING OF TENTH GRADE STUDENTS AT SMA N 1 METRO ACCADEMIC YEAR 2019/2020

Code-mixing is the use of two different languages in a sentence without changing the topic. The objectives of this research are to find out the types of code-mixing utilized in students speaking and to know the factors of code-mixing mostly occurred in students speaking. Due to covid-19, the researcher collects the data by using Triangulation: Participant Observation, In-depth Interview and Documentation through Zoom and WhatsApp Application. Analytical research is applied to analyze code-mixing in students speaking of tenth grade students at SMA N 1 Metro accademic year 2019/2020. The result shows that there are 23 out of 33 students use code-mixing in their speaking. It is found 45 code-mixing devided into three types including 21 Insertion which characterized by  inserting one or more Indonesian vocabularies into an English sentence, 19 Alternation which characterized by the existence of clause or sentence alternation between Indonesian and English, and 5 Congruent Lexicalization which characterized by the existence of dialect  from Indonesian inserted in students English Speaking. It is found that there are 4 factors of code-mixing namely interlocutor or the person whom they are talking to, culture or habit, confidence and lack of vocabulary.
Pengaruh Self-Efficacy Terhadap Motivasi Berprestasi Mahasiswa Jurusan Manajemen Berbasis KKNI

This study aims to find out how much self-efficacy can affect student achievement motivation. Achievement motivation is an impulse that is in a person to achieve success and avoid failure that leads to behavioral tendencies to maintain and enhance a success that has been achieved by referring to the benchmark of the best achievements ever achieved by both himself and others. So that achievement motivation also affects the choice of activities undertaken by someone. In the learning process, self-efficacy (self-efficacy) which is owned by someone can influence achievement motivation. Self-efficacy refers to belief in one's ability to organize and carry out the actions needed to manage the situation.
EMPIRICAL UPPER BOUND IN OBJECT DETECTION

Object detection remains one of the most notorious open problems in computer vision. Despite large strides in accuracy and speed in recent years, modern object detectors have started to saturate on popular benchmarks. How far can we push the detection accuracy with the current deep learning tools and tricks? In this work, by employing two popular state-of-the-art object detection benchmarks, MMDetection and Detectron2, and analyzing more than 15 models over 4 large-scale datasets, we systematically determine the upper bound in AP, which is 91.6% on PASCAL VOC (test2007), 78.2% on MS COCO (val2017), and 58.9% on OpenImages (V4 validation set), regardless of the IOU. These numbers are much higher than the mAP of the best model (e.g., 58% on MS COCO according to the most recent results). Interestingly, the gap seems to be almost closed at IOU=0.5. We also analyze the role of context in object recognition and detection and find that the canonical object size leads to the best recognition accuracy. Finally, we carefully characterize the sources of errors in deep object detectors and find that classification error (confusion with other classes and misses) explains the largest fraction of errors and weighs more than localization error. Further, models frequently miss small objects, more often than medium and large ones. Our work taps into the tight relationship between object recognition and detection and offers insights to build better object detectors. Similar analyses can also be conducted for other tasks in computer vision such as for instance segmentation and object tracking. The code is available at [TBA].
Detailed Analysis of Various Data Compression Techniques

Data compression refers to reducing the volume of space needed to keep data or reducing the amount of time required to transmit the data. The size of data is diminished by removing the unnecessary information. In this study many different data compression techniques have been studied, such as Shannon Fano, Shannon Fano Elias, Huffman binary and ternary coding. It is found that Huffman coding is the most suitable among these techniques as it provides smallest codeword length. Keywords: Data compression, Shannon Fano, Shannon Fano Elias, Huffman binary coding, Huffman ternary coding Cite this Article Shashank Gautam. Detailed Analysis of Various Data Compression Techniques. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(1): 1–4p.
Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification

&lt;span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"&gt;&lt;span&gt;This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.&lt;/span&gt;&lt;/span&gt;
MANAJEMEN SEKOLAH DI MASA PANDEMI (BELAJAR DARI RUMAH BERBASIS SISTEM INFORMASI MANAJEMEN YANG TEPAT) DALAM RANGKA IMPLEMENTASI KEBIJAKAN BELAJAR DARI RUMAH

In realizing the quality of early Online class education and community education, 2020 program was prepared by involving the Education and Culture Office in Rengat, Indragiri Hulu- Riau. The program is a devotion of STIE Indragiri lecturer. It is to train teachers to gain success of School Management during the Pandemic (Learning From Home Based on the Right Management System of Information) in the Context of Implementing the Learning From Home Policy.This devotion is one solution to spread knowledge and skill for the teachers in giving online school subject explanation. A lot of teachers difficult to operate the media to teach and the parents are hard to understand. The devotion was held for 3 days and it ran well. The school should facilitate the teachers during the class in order to achieve the goal. pandemic is not the end of everything. Pandemic is new normal ways to study and school and teachers should be ready with the system of information.
Homo Colossus Energy Calculator

This thesis covers the creation of a series of questions that have the purpose of estimating the amount of energy that an individual uses over the course of a year. These questions are designed for ...
It’s absolutely divine! Can fine-grained sentiment analysis benefit from coreference resolution?

While it has been claimed that anaphora or coreference resolution plays an important role in opinion mining, it is not clear to what extent coreference resolution actually boosts performance, if at all. In this paper, we investigate the potential added value of coreference resolution for the aspect-based sentiment analysis of restaurant reviews in two languages, English and Dutch. We focus on the task of aspect category classification and investigate whether including coreference information prior to classification to resolve implicit aspect mentions is beneficial. Because coreference resolution is not a solved task in NLP, we rely on both automatically-derived and gold-standard coreference relations, allowing us to investigate the true upper bound. By training a classifier on a combination of lexical and semantic features, we show that resolving the coreferential relations prior to classification is beneficial in a joint optimization setup. However, this is only the case when relying on gold-standard relations and the result is more outspoken for English than for Dutch. When validating the optimal models, however, we found that only the Dutch pipeline is able to achieve a satisfying performance on a held-out test set and does so regardless of whether coreference information was included.
Konstribusi Pelaksanaan Microteaching dan Pengalaman Lapangan Persekolahan 2 (PLP II) terhadap Minat Menjadi Guru pada Mahasiswa Pendidikan Akuntansi FKIP UMS Angkatan 2016/2017

The objectives of this study were: 1) To determine the contribution of the implementation of microteaching to the interest in becoming a teacher in accounting education students of the FKIP Muhammadiyah University of Surakarta. 2) To determine the contribution of school field experience 2 (PLP II) to the interest in becoming a teacher in accounting education students of the FKIP Muhammadiyah University of Surakarta. 3) To determine the implementation of microteaching and school field experience 2 (PLP II) together contribute to the interest in becoming a teacher in accounting education students of the Muhammadiyah University of Surakarta, this research is a type of quantitativeresearch with a survey design. The population in this study were all students of Accounting Education class 2016 Muhammadiyah University of Surakarta with a total of 315 students, while the sample was taken 161 students. The required data is obtained through questionnaires and documentation. The previous questionnaire was tested with validity and reliability tests. The data analysis technique used is multiple linear regression analysis, t test, F test, and relative and effective contribution. The regression analysis results obtained the regression line equation:Y = 7.536 + 0.343 X1 + 0.509 X2. The equation shows that interest in becoming a teacher is influenced by the implementation of microteaching and school field experience 2 (PLP II). Conclusion: 1) The implementation of microteaching on the interest in becoming a teacher is acceptable. This is based on multiple linear regression analysis (t test), it is known that tcount> ttable, 3.787> 1.960 and a significance value <0.05, namely 0.000 with a relative contribution of 40% and an effective contribution of 25.2%. 2) School field experience 2 (PLP II) on theinterest in becoming a teacher is acceptable. This is based on multiple linear regression analysis (t test), it is known that tcount> ttable, namely 5,580> 1,960 and a significance value Ftable, namely 134.705> 3,000 and a significancevalue <0.05, namely 0.000 with Determination coefficient (R2) 0.630
Aspect-Similarity-Aware Historical Influence Modeling for Rating Prediction

Many e-commerce services provide customer review systems. Previous laboratory studies have indicated that the ratings recorded by these systems differ from the actual evaluations of the users, owing to the influence of historical ratings in the system. Some studies have proposed using real-world datasets to model rating prediction. Herein, we propose an aspect-similarity-aware historical influence model for rating prediction using natural language processing techniques. In general, each user provides a rating considering different aspects. Thus, it can be assumed that historical ratings provided considering similar aspects to those of later ones will influence evaluations of users more. By focusing on the review-topic similarities, we show that our method predicts ratings more accurately than the previous historical-inference-aware model. In addition, we examine whether our model can predict “intrinsic rating,” which is given if users were not influenced by historical ratings. We performed an intrinsic rating prediction task, and showed that our model achieved improved performance. Our method can be useful to debias user ratings collected by customer review systems. The debiased ratings help users to make decision properly and systems to provide helpful recommendations. This might improve the user experience of e-commerce services.
Beam and Diffuse to Global Solar Irradiation Correlation Coefficients for Daejeon

The total solar irradiation on horizontal surfaces is separated into the beam and diffuses components. Although horizontal global irradiance is a commonly measured parameter for many sites, horizontal diffuse irradiance is not so readily obtainable. For such sites that measure global irradiation alone, a simple but reasonably accurate method is required to estimate diffuse irradiance from its global counterpart. This study investigates the applicability of correlation coefficients models correlating hourly diffuse and beam fraction and hourly clearness index in Daejeon. The three diffuse to global correlation coefficients models (Orgill and Holland model, CIBSE Guide J model, and Erbs et al. model) are selected and the three modified beam to global correlation coefficients models are generated. MBE, RMSE, r-squared of Daejeon and Daejeon boundary site-fitted models are compared with the case of original coefficients. The comparison result shows that the beam and diffuse to global solar irradiation correlation coefficients models with boundary site-fitted coefficients are best suitable for Daejeon. Further researches will be conducted to find the boundary site-fitting method using measured data of other cities and correlation coefficients models using solar altitude, cloud cover, and sunshine duration.
INNOVATIVE POTENTIAL OF RUSSIAN UNIVERSITIES: IMPLEMENTATION STRATEGY, RESEARCH METHODS, DEVELOPMENT FACTORS

The article discusses the various aspects of the formation, functioning and development of Russian universities as innovation centers and suggests ways for their further development. The purpose of the article is to identify and analyse the innovative potential of Russian universities, to uncover socio-economic, institutional, organizational and managerial issues of functioning and justify the ways, methods and tools for their development.The research methodology is based on a systematic approach and includes the combined use of the following research methods: justification of ways, methods and tools for their development, the identification and analysis of factors that influence the external environment on the development strategy of universities, a method for assessing and analyzing factors, processes and phenomena that influence the development of universities as innovation centers, methodological methods for determining and analyzing the strengths, neutral and weaknesses of university innovation development. The main results of the research.
PERANCANGAN SISTEM INFORMASI SIRKULASI BERKAS NOTARIS (STUDI KASUS : KANTOR NOTARIS DAN PEJABAT PEMBUAT AKTA TANAHSYLVIA FRANSISKA TAN, SH., MH. PONTIANAK)

The development of information technology at this time, especially computers are increasingly sophisticated and popular around the world. With the increasingly sophisticated computer technology, the more important the role of computers in aspects of human life today. The file processing information system and the numbering of deeds at the notary office and Officers of Land DeedSylvia Fransiska Tan, S.H., M.H. are still manual. Vocational methods used are methods of observation, interview, documentation and literature study. The technique of system design analysis used in this research is Unified Modeling Language (UML). Application design techniques used are using database design with Microsoft Office Access 2013 and application design with Microsoft Visual Basic .Net 2010 as well as report design with Crystal Report 13.0. The results of the research on the notary office and Land Acquisition Officer Sylvia Fransiska Tan, S.H., M.H. namely an information system application of file circulation and numbering of notarial deeds starting from the receipt of order, circulation and monitoring of file status and numbering deed, resulting in the output of a quality report and a fast information system and can facilitate employees in search and tracking files to create efficiency time and the effectiveness of employment of employees at notary offices and Officers of Land Deed Sylvia Fransiska Tan, S.H, M.H. The conclusion of this research is that with the proposal system it can help notary office and Land Acquisition Officer Sylvia Fransiska Tan, S.H., M.H. to produce more precise and accurate information. Suggestions from authors with respect to the proposed system are always doing database backup and Maintenance of software and hardware must be done periodically to ensure the system used to run smoothly. Keywords ___ Information, Systems, Circulation, File
THE STUDENTS’ PERCEPTION OF USING  EXTENSIVE READING MATERIAL BASED ON CEFR FRAMEWORK

Extensive Reading is an activity to read a text-based on someone's interest, and it is an effective method to improve reading skills. Nowadays, the university promotes Extensive Reading to boost students’ interest in reading along with improving reading skills. The purpose of this research is to investigate the students’ perception of using material Extensive Reading based on the CEFR Framework. This research using the qualitative method and collecting the data through SHOWeD. The participants in this research were 14 EFL college students of University in Indonesia who are following the Extensive Reading class for 16 meetings in 4 months. The result finds out that the students have positive perceptions toward Extensive Reading based on CEFR as material in reading.
Classification of Dementia MRI Images Using Hybrid Meta-Heuristic Optimization Techniques Based on Harmony Search Algorithm

Magnetic Resonance Imaging is a commonly used modality to diagnose dementia and there is a massive requisite for an automated MRI image classification algorithm to assist the clinician during diagnosis. The main objective of this research work is to categorize the brain MRI images of patients as demented and non-demented using harmony search based hybrid meta-heuristic optimization algorithms. For this analysis, 65 non-demented and 52 demented subjects collected from Open Access Series of Imaging Studies are used. With appropriate modifications on original algorithms, the classification performance of four meta-heuristic techniques namely Particle Swarm Optimization, Artificial Bee Colony, Ant Colony Optimization, Harmony Search are tested individually as transformation technique based classifier. Then Harmony Search will be hybridized with above mentioned other three meta-heuristic techniques and the classification performance improvement is analyzed. Harmony search based hybrid optimization techniques are widely reported for solving numerical optimization problems, feature extraction, clustering and training neural networks. To the best of our knowledge, there are no reports in the literature regarding the usage of harmony search based hybrid optimization techniques as transformation technique to classify medical images. Particle Swarm Optimization hybridized with Harmony Search algorithm provides the highest accuracy of 84% in dementia MRI image classification.