Reviewerly/exHarmony huggingface dataset:
Implementing Term Frequency-Inverse Term Frequency at Tweets in Indonesian Fraud Crime Cases

Crime analysis is a methodical approach to identifying and analyzing patterns and trends in crime. Using crime analysis and text mining, we can analyze the modus operandi of crime to reduce the offenders. However, many fraud victims rarely report their problems to the police and tend to share their fraud case stories with their social media. Therefore, it needs a capable dataset to further analyze the fraud case. TFIDF as a weighting approach is used to find out the importance of a word. Thus, this study makes data derived from original data into data that can be processed for analysis. The data used are data from social media Twitter with 39,964 data that have keyword “penipuan” in Indonesian. This study uses text preprocessing techniques to clean the data from information which is not useful for the analysis process. The phases are data crawling, data cleansing, stemming, filtering, tokenizing, and visualizing data. After preprocessing data, the data will be processed into the terms frequency that appears and visualizes it. As a result, in the TF-IDF approach, the word “nomer” is the first for the word that often appear. It can be hypothesized that victims usually share their experiences of fraud that had related to the victim's personal number.
Sistem Keamanan Rumah Yang Dapat Dikendalikan Melalui Android

The number of theft and increased crime makes many people afraid to leave the house because of the many crimes, therefore I aim to solve the problem in this case by creating a home control tool that can be controlled via Android. So people do not need to be afraid to leave the house because it can be controlled through Even great distances, of course this is very helpful in the problem of crime and can reduce crime rates
PENTINGNYA PERSONAL DIGITAL ARCHIVE DALAM KEHIDUPAN SEHARI – HARI (THE IMPORTANT OF PERSONAL DIGITAL ARCHIVE IN DAILY ACTIVITY)

Personal Digital Archive is one of the important things in daily life, as the purpose of preserving private archives that are mostly produced in digital form. However, some people tend not to consider it so important, sometimes they end up with missing, corrupted, and inaccessible personal files in that digital form. There will be so many benefits of the private archive preservation in the long term. Good personal digital archive management has a very significant impact on a person, even after the death of that person. This may be useful for future research or inheritance for families after the death. This paper discusses views on how important to manage personal digital archiving in daily life, as well as guidance on how to do personal digital archiving as a good effort of preservation of personal digital archive for long period of time. It is an easy thing to collect personal digital content, but it will take a lot of time to manage it. The average person can collect hundreds of thousands of emails, digital photos, text messages, audio visual recordings, and other digital files in a lifetime. If the file is not properly managed, it will be difficult to find, identify, and access the content in the future.
Redefining language and literature learning in the transformation era

The study was aimed at redefining the learning of language and literature from the perspective of media use in the lesson planning and implementation and students’ responses towards language and literature learning. The subjects of the study were teachers and students of state junior high schools in a regency in Yogyakarta Province. The subjects were taken in three stages, first, with the cluster technique: the eastern, western, central, and northern zones, and second, with the school strata technique: as excellent, moderate, and low. The choice of strata technique was determined based on the results of the 2017 National Exam score ranking. Third, the selection of schools and classes was made randomly. Primary data were obtained from observation, interview, and document analysis; secondary data were obtained from a questionnaire. Results can be described as follows. First, media use designed in the lesson plan included conventional, new, and audio-visual media. Second, media use in the carrying out of the lesson was realized by the teachers in the forms of conventional visual and audio-visual media. Third, students gave fair responses to the use of instructional media in language and literature learning. In the low-coded schools, the use of media designed in the lesson plans was more varied than that of the moderate- and excellent-coded schools; the most preferred new visual media were PowerPoint. In moderate- and low-coded schools, instructional media were shown to have positive impacts on student activity in learning; namely, students were more active because learning was more fun. Contradictorily, instructional media did not have positive impacts in excellent-coded schools on student activity in learning; namely, students were less active because learning was considered unpleasant. As seen from the perspective of the transformational era, no teacher had used interactive digital media.
Double moving average method for predicting the number of patients with dengue fever in Gorontalo City

Dengue Fever (DF) is an infectious disease caused by the dengue virus which is transmitted by the Aedes Aegypti mosquito. This disease is one of the health problems in Indonesia, especially in Gorontalo City. According to data from the Departement of Health of Gorontalo City, the number of dengue fever patients reached 752 people within 2011 – 2017. Therefore, the related departement keeps predicting the number of patients for future incidence to find a way to prevent increase. The research aimed to create a system that can predict the number of dengue fever patients using the Double Moving Average method. Prediction runs by processing the number of cases of dengue fever patients in 2011-2017 from all sub-districts. Then, The result of prediction found that there would be 116 patients of Dengue Fever in 2018, in which it increased from 2017
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation

Controllable semantic image editing enables a user to change entire image attributes with a few clicks, e.g., gradually making a summer scene look like it was taken in winter. Classic approaches for this task use a Generative Adversarial Net (GAN) to learn a latent space and suitable latent-space transformations. However, current approaches often suffer from attribute edits that are entangled, global image identity changes, and diminished photo-realism. To address these concerns, we learn multiple attribute transformations simultaneously, integrate attribute regression into the training of transformation functions, and apply a content loss and an adversarial loss that encourages the maintenance of image identity and photo-realism. We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work, which primarily focuses on qualitative evaluation. Our model permits better control for both single- and multiple-attribute editing while preserving image identity and realism during transformation. We provide empirical results for both natural and synthetic images, highlighting that our model achieves state-of-the-art performance for targeted image manipulation.
An Educational News Dataset for Recommender Systems

Abstract Datasets are an integral part of contemporary research on recommender systems. However, few datasets are available for conventional recommender systems and even very limited datasets are available when it comes to contextualized (time and location-dependent) News Recommender Systems. In this paper, we introduce an educational news dataset for recommender systems. This dataset is the refined version of the earlier published Adressa dataset and intends to support the university students in the educational purpose. We discuss the structure and purpose of the refined dataset in this paper.
Ensemble approach to code smell identification : Evaluating ensemble machine learning techniques to identify code smells within a software system

The need for automated methods for identifying refactoring items is prelevent in many software projects today. Symptoms of refactoring needs is the concept of code smells within a software system. ...
Integration of Google Tools with Koha LMS for EnhancingLibrary Services: Case Study at St. Xavier’s College Central Library

Google products can play a vital role in improving the library services. Now most of the libraries are using online applications to fulfill user’s demands. Using Google apps can attract more users to reference services. The present paper explains various types of Google products / tools which can be used for the improvement of library services. Explains how these tools can be integrated with KOHA LMS for enhancing library services. Aauthor opines that Google products can support efficient and time-saving library services in a cost effective way.
CLUSTERING FRAGMEN METAGENOM MENGGUNAKAN METODE GROWING SELF ORGANIZING MAP (GSOM) ( Studi Kasus Dinas Lingkungan Hidup Kota Jayapura)

Metagenome is a microorganism that is taken directly from nature. The sequencen process of metagenome results in the mixing of various organisms. The data collection method used was Observation, using a direct plunge technique to the Jayapura City Environmental Department. This study uses data from metagenome fragments of 300 microbes. The technique of collecting metagenome fragment data used is cluster sampling. The research location is at the Jayapura City Environmental Agency. The purpose of this study is to analyze the effectiveness and efficiency of the Growing Self Organizing Map method in large-scale microbial grouping with short fragment lengths based on oligonucleotide frequencies. For feature extraction, k-mer frequency and spaced are used. Short fragments are used because in previous studies, the length of the fragment used was a long fragment (? 8 kbp), so in this study we want to overcome the drawbacks of using short fragments in the grouping of metagenome fragments. The results of the grouping of metagenome fragments will be tested for effectiveness and efficiency.
DEVELOPMENT OF THEORY AND PRACTICE OF LABORATOR LESSONS IN HIGHER SCHOOL BY A. D. BONDAR

The article analyzes the contribution of the professor of the Department of Pedagogy of Taras Shevchenko National University of Kyiv A.D. Bondar in the development of theoretical principles and methods of conducting laboratory and practical classes in higher education, identifies the possibility of using this pedagogical experience in modern conditions. It is established that today the work and pedagogical activity of A.D. Bondar is insufficiently studied, although his work reflects the results of research on important didactic problems that are relevant today. It is analyzed that among the various types of classes in higher education an important place belongs to laboratory and practical work, because they follow one of the leading principles of didactics – the principle of connection of theory with practice. According to A.D. Bondar, laboratory and practical works are closely connected with lectures and independent work of students, they, with their proper organization, help students to creatively use the theoretical knowledge that they acquired during lectures and in the process of studying literary sources. This type of training activates the cognitive activity of students, gives them the opportunity to show initiative and ingenuity, better master the program material, get acquainted with the latest installations and devices, equipment and materials, experiment in laboratory or production conditions, helps teachers to involve students in research work, promotes the development of their thinking, ingenuity, innovation. According to A.D. Bondar, laboratory classes are an effective way to study the material, give students the opportunity to apply their knowledge in practice and see the results of their work. At the same time, many factors significantly reduce the effectiveness of this important type of training. It is proved that the improvement of the quality of laboratory classes can help, in particular, the implementation of guidelines developed by A.D. Bondar almost half a century ago.
Filled Pause of Spontaneous Speech Detecting based on Multi-Features

The filled pauses in spontaneous speech can not only be used as the basis to evaluate the fluency of speech and predict the mental state of speaker, but also affect the accuracy of automatic speech recognition system (ASR) in speech recognition.In the field of speech recognition, the detection of hesitant words in spontaneous speech is a very significant problem.The paper presents a method that an improved support vector machine (SVM) classifier and the spectral, formant and intensity of speech were used to distinguish between filled pauses and normal words in spontaneous speech of mandarin, the classification accuracy is 91.76% on the closed data set. The filled pauses and normal words in the spontaneous speech data set have been marked manually.
THE IMPLEMENTATION OF TBLT PROCEDURE AT THE FIRST GRADE OF TOURISM DEPARTMENT

The purpose of the research is to find out the Implementation of Task Based Language Teaching Procedure at the First Grade of Tourism Department in SMKN 2 Bukittinggi. This research was descriptive qualitative. The data were obtained through observation and interview. The observation was conducted in four meetings and informants of this research were the teacher who taught at the First Grade of Tourism Departments and some student. The data were analyzed by using flow model of Miles and Huberman in Sugiono. The finding reveals that Task Based Language Teaching Procedure in generally had implements by the teacher with process pre task, task, planning, report, analysis, and practice but the teacher did not apply yet all of Process. The teacher did not implement one of activities from task process that was walk around to monitor and to encourage students to communicate in the target language because in the class the teacher prefer walked around to monitor whether the students did the task or not. For the other process had implements by the teacher but not always implemented in every meeting depend on the topic of the learning.
Least Probable Disagreement Region for Active Learning

Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly evaluated; however, in numerous cases in machine learning, especially when it involves deep learning, conventional distance such as the lp from sample to decision boundary is not readily measurable. This paper defines a theoretical distance of unlabeled sample to the decision boundary as the least probable disagreement region (LPDR) containing the unlabeled sample, and it discusses how this theoretical distance can be empirically evaluated with a lower order of time complexity. Monte Carlo sampling of the hypothesis is performed in approximating the theoretically defined distance. Experimental results on various datasets show that the proposed algorithm consistently outperforms all other high performing uncertainty based active learning algorithms and leads to state-of-the-art active learning performance on CIFAR10, CIFAR100, Tiny ImageNet and Food101 datasets. Only the proposed algorithm outperforms random sampling on CIFAR100 dataset using K-CNN while all other algorithms fail to do so.
RGB-D Image Saliency Detection Based on Multi-modal Feature-fused Supervision

RGB-D saliency detection identifies the most visually attentive target areas in a pair of RGB and Depth images. Existing two-stream networks, which treat RGB and Depth data equally, are almost identical in feature extraction. As the lower layers Depth features with a lot of noise, it causes image features not be well characterized. Therefore, a multi-modal feature-fused supervision of RGB-D saliency detection network is proposed, RGB and Depth data are studied independently through two-stream , double-side supervision module is used respectively to obtain saliency maps of each layer, and then the multi-modal feature-fused module is used to later three layers of the fused RGB and Depth of higher dimensional information to generate saliency predicted results. Finally, the information of lower layers is fused to generate the ultimate saliency maps. Experiments on three open data sets show that the proposed network has better performance and stronger robustness than the current RGB-D saliency detection models.
LibGuides: Student Services: Getting Started

Guide to student assistance in the areas of food, housing, healthcare, childcare, books. Resources for students needing food, housing, childcare, and health assistance.
DESIGN AND BUILT AN EXPERT SYSTEM APPLICATION FIR DIAGNOSING HUMAN EYE DISEASES BY USING FORWARD CAHINING METODH WEB-BASED

The eye is one of the five senses that has a very important role in human life, namely as the organ of vision. If there is interference with the eye or there is a disease, it is very influential on human vision. Its mean that we can conclude that eye is very important in our lie and must be keep for our body. The purpose of this research to planning and develop the diagnose of expert system about diagnosing of diseases in the eye which include disease information about symptoms or the solution and have role to replace and imitate the process of the reason from an expert in solve the specification problem. the method used for reasoning is forward chaining method. The result of this research is expert system of diagnosing eyes diseases in humans which computerized who can be used for giving information and have benefit in diagnosing disease.
SMART Integrated Leadership (SMILE) for Smart City

The concept of a smart city which is becoming a big issue in big cities around the world encourages the active role and participation of the community in city management using a citizen centric approach so that there is a more dynamic and close interaction between citizens and service providers. The aim of implementing a Smart City is to be able to form and implement a city that is safe, comfortable, controlled and facilitates access for its citizens and strengthens the competitiveness of the city in terms of economy, social and technology. Smart cities must have a strong governance-oriented approach that emphasizes the role of social capital and relationships in urban development. The Smart City concept is no longer limited to ICT diffusion, but looks at people and society's needs. To meet and exceed citizen expectations, city leaders must innovate in core service areas in the application of the Smart City concept. In order to create innovative, creative and resilient leaders, it is necessary to design a Smart Integrated Leadership (SMILE) to support the success of a Smart City.&#x0D; Keywords: Smart City, Smart Integrated Leadership &#x0D; Abstrak&#x0D; Konsep kota cerdas (Smart City) yang menjadi isubesar di kota-kota besar di seluruh dunia mendorong peran aktif dan partisipasi masyarakat dalam pengelolaan kota menggunakan endekatan citizen centric sehingga terjadi interaksi yang lebih dinamis dan erat antara warga dengan penyedia layanan. Tujuan implementasi Smart City adalah untuk dapat membentuk dan menerapkan suatu kota yang aman, nyaman, terkendali dan mempermudah akses bagi warganya serta memperkuat daya saing kota dalam hal perekonomian, sosial dan teknologi. Smart city harus memiliki pendekatan pemerintahan yang berorientasi kuat yang menekankan peran modal sosial dan hubungan dalam pembangunan perkotaan. Konsep Smart City tidak lagi terbatas pada difusi TIK, tetapi melihat orang dan kebutuhan masyarakat. Untuk memenuhi dan melampaui harapan warga, pemimpin kota harus berinovasi di bidang layanan inti dalam penerapan konsep Smart City. Dalam rangka menciptakan para pemimpin inovatif, kreatif dan tangguh maka perlu dirancang Smart Integrated Leadership (SMILE) untuk mendukung suksesnya Smart City.&#x0D; Kata kunci: Smart City, Smart Integrated Leadership
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews

In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews. Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as `positive', `negative' and `neural', BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., `shaver' or `cream') while its associated sentiment gradually varies from negative to positive. BTM is built on the Poisson factorisation model with the incorporation of adversarial learning. It has been evaluated on a dataset constructed from Amazon reviews. Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and uniqueness, and extracting better-separated polarity-bearing topics.
IMPROVING STUDENTS’ VOCABULARY MASTERY BY USING SONG LYRIC AT MTS NURUL YAQIN TONDANO

This research aims at describing students’ achievement of vocabulary mastery, and is conduct at MTsS Nurul Yaqin Tondano in the academic year 2019-2020. One class in the first grade is selected as the subject of the experiment. The class consists of 25 students. These students are the participants in this study. The writers use the pre-experimental design with one class pre-test and post-test. It is quantitative research. The test or data collecting instrument to use in this research is a self-made vocabulary test in fill-in format. The result of computing the data analysis of mean score in pre-test and post-test there is a significant difference. The means of pretest and posttest data are 3.1 and 4.9 respectively, and the standard deviations of pretest and posttest data are 1.3 and 1.2 respectively. It could conclude that the use of song lyrics as a teaching technique improve student’s vocabulary mastery is considering effective because it would create conducive and active learning condition which then motivate students to improve their vocabularies. Keywords: Vocabulary Mastery, Song lyric.
AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

With the rapid adoption of machine learning (ML), a number of domains now use the approach of fine tuning models which were pre-trained on a large corpus of data. However, our experiments show that even fine-tuning on models like BERT can take many hours even when using modern accelerators like GPUs. While prior work proposes limiting the number of layers that are fine-tuned, e.g., freezing all layers but the last layer, we find that such static approaches lead to reduced accuracy. We propose, AutoFreeze, a system that uses an adaptive approach to choose which layers are trained and show how this can accelerate model fine-tuning while preserving accuracy. We also develop mechanisms to enable efficient caching of intermediate activations which can reduce the forward computation time when performing fine-tuning. We extend AutoFreeze to perform distributed fine-tuning and design two execution modes that minimize cost and running time respectively. Our evaluation on ten NLP tasks shows that AutoFreeze, with caching enabled, can improve fine-tuning on a single GPU by up to 2.55x. On a 64 GPU cluster, for fine-tuning on the AG's news dataset, AutoFreeze is able to achieve up to 4.38x speedup when optimizing for end-to-end training time and 5.03x reduction in total cost when optimizing for efficiency, without affecting model accuracy.