Reviewerly/exHarmony huggingface dataset:
Deteksi Aktivitas Mencurigakan Peserta Computer Based Test Menggunakan IP Camera

Common surveillance device that used to monitor an area is known as CCTV. The CCTV will provide results in the form of video recordings, which can then be accessed by wireless communication. In its use, CCTV needs humans to monitor the real condition of the area/place. Then the use of CCTV becomes less efficient when used to oversee a place where the room rarely has movement. Because CCTV cannot detect or identify suspicious actions automatically. This research aim to develop a method that can be used to identify the activity (irregular movements) automatically. In this case, the change to be determined was the activities towards the Politeknik Caltex Riau Computer Based Test (CBT) participants. The CBT room has been employed by the IP Camera to identify participant activities. The IP camera captures the image and the image is then processed by the feature extraction method. Proposed feature exctraction method are background subtraction and pixel mapping. Pixel mapping is a method that maps objects based on specified ratio data. There are 18 ratio data generated by this feature extraction process. The determination of the illegal activities done by using the k-Nearest Neighbor. The Algorithm detects the illegal movement by using 502 datasets, and the accuracy obtained was between 98% - 98.4% with an average accuracy of 98.2% for the value of neighborliness = 3. The result can conclude that the method can identify the illegal activities of a CBT participant in the CBT room
Preparation of Masters in the Russian Educational System in the Context of Modern Social Challenges

The relevance of the issue under study lies in meeting global, national and regional challenges of modern time in the course of vocational preparation of Master’s degree students. The purpose of article is to define psychological and pedagogical conditions that have an impact on the process of teaching Masters of Education in the context of social challenges and the created portrait of a student of Southern Federal University as a consumer of educational services. As for these psychological and pedagogical conditions, we would like to refer to the concept of pedagogical education development approved at Southern Federal University; the strategy of preparing Masters in the direction "Pedagogical Education"; the stakeholder-focused model of Masters’ preparation. The devised psychological and pedagogical conditions were step by step discussed by the professional community of Southern Federal University and invited employers. Research findings define its practical importance: in the course of the conducted research, the authors have worked out, systemically described, put into practice and presented the results to the professional community for independent assessment at a conference, a round table and a pedagogical forum.
A Cost-Sensitive Shared Hidden Layer Autoencoder for Cross-Project Defect Prediction

Cross-project defect prediction means training a classifier model using the historical data of the other source project, and then testing whether the target project instance is defective or not. Since source and target projects have different data distributions, and data distribution difference will degrade the performance of classifier. Furthermore, the class imbalance of datasets increases the difficulty of classification. Therefore, a cost-sensitive shared hidden layer autoencoder (CSSHLA) method is proposed. CSSHLA learns a common feature representation between source and target projects by shared hidden layer autoencoder, and makes the different data distributions more similar. To solve the class imbalance problem, CSSHLA introduces a cost-sensitive factor to assign different importance weights to different instances. Experiments on 10 projects of PROMISE dataset show that CSSHLA improves the performance of cross-project defect prediction compared with baselines.
A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories

Deep neural networks are vulnerable to adversarial attacks.
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

Machine learning techniques have been successfully applied tosuper-resolution tasks on natural images where visually pleasing results aresufficient. However in many scientific domains this is not adequate andestimations of errors and uncertainties are crucial. To address this issue wepropose a Bayesian framework that decomposes uncertainties into epistemic andaleatoric uncertainties. We test the validity of our approach bysuper-resolving images of the Sun's magnetic field and by generating mapsmeasuring the range of possible high resolution explanations compatible with agiven low resolution magnetogram.
Decision Support System for the Selection ofOutstanding Students Using the AHP-TOPSISCombination Method

This research develops a decision support system for the selection of outstanding students bycombining AHP and TOPSIS methods. AHP method was used because it could be implementedto this data and do the priority ranking process for each criterion based on pairwise comparisonmatrix. The TOPSIS method was used because the concept of the chosen alternative does notonly have the shortest distance from the positive ideal solution, but also has the longestdistance from the negative ideal solution. The purpose of this study was finding out the workingsof the TOPSIS method and the AHP-TOPSIS combination method, as well as to find out thecomparison of the best methods between TOPSIS and the combination method of AHPTOPSISin the selection of outstanding students. The concept of TOPSIS is simple and easy tounderstand and has the ability to measure decision alternatives while AHP is not chosenbecause the AHP method is widely used in the case of criteria weighting and prioritydetermination of each criterion. However, if the two methods were combined the results will bebetter because in AHP there is an eigenvector concept which is used to do the priority rankingprocess for each criterion based on pairwise comparison matrix, then the results of theweighting criteria are processed by the TOPSIS method for ranking process. The application ofthe TOPSIS method on the selection of outstanding students can be analyzed with the results ofthe presentation using Hamming Distance incompatibility is 93%. Meanwhile, the application ofthe AHP-TOPSIS combination method gets the presentation results using Hamming Distanceincompatibility is 91%. Based on these results in this study it can be concluded that the AHPTOPSIScombination method is better than the TOPSIS method.
The Word Collector by P.H. Reynolds

Reynolds, Peter H. The Word Collector. Scholastic, 2018.&#x0D; The story, written and illustrated by Peter Reynolds, is about a boy named Jerome and his discovery of words. While other children collect stamps, coins, rocks, and art, Jerome collects words. Words "catch his attention" and "jump at him." He collects "short and sweet words" and "two-syllable treats." He fills his scrapbook with all the "marvelous" words he hears, reads, and sees. But once while he’s transporting his collection, he slips and his words all get mixed up. In this jumbled up state, he starts noticing how they can be strung together to make poems and songs, and how he can use them in simple and powerful phrases. In the end, he shares his collection with children in the valley and he’s left with no words to describe how happy that makes him.&#x0D; The accompanying illustrations are bright, colourful, and detailed in terms of visualizing the text. With illustrations showing diversity in characters, dialogues are written in speech bubbles while Jerome’s "words" are shown on the pieces of rectangular paper he writes them down on. The jacket illustrations show Jerome standing with a bright blue background behind him and his numerous words flying around him. The inside hardcover is yellow with only the word slips jumbled around. The last page in the book is Peter Reynold’s own words strung together to give an inspiring message: “Reach for your own words. Tell the world who you are. And how you will make it better.”&#x0D; Inspiring and easy to read, this is a delightful book to help children discover the magic and power of words. Teachers and parents can help children in activities inspired by the book to play around with words and build their vocabulary. &#x0D; Highly recommended: 4 out of 4 starsReviewer: Komel Ahmed&#x0D; Komel Ahmed is currently working towards her B.Ed. in Elementary Education at the University of Alberta. She loves reading with her two amazing children.
Research Guides: Circulation Student Supervisor Support: Working in Alma

Standards, procedures and Alma instructions for Drake Library circulation desk student supervisors
Procentage of E-commerce Utilization among UIN Maulana Malik Ibrahim Malang Students

This study aims to discover the influence of e-commerce on student life, both in terms of understanding and its use. The variables analyzed include the level of understanding, the experience of a transaction through e-commerce, reasons for choosing e-commerce, e-commerce knowledge sources, and a list of e-commerce sites they have used. Respondents were students of the State Islamic University of Maulana Malik Ibrahim Malang. A simple random sampling technique used in this study. As for the data collection, using a questionnaire technique. The obtained data will be processed and analyzed. Generally, the results of the study explain that most students know ecommerce, but the use is still not optimal, even very less.
Incremental extraction of a NoSQL database model using an MDA-based process

In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property "schema less" which means absence of the data model when creating a database. This property brings an undeniable flexibility by allowing the evolution of the model during the exploitation of the base. However, the expression of queries requires a precise knowledge of this model. In this paper, we propose an incremental process to extract the model while operating the document-oriented NoSQL database. To do this, we use the Model Driven Architecture (MDA) that provides a formal framework for automatic model transformation. From the insert, delete and update queries executed on the database, we propose formal transformation rules with QVT to generate the physical model of the NoSQL database. An experimentation of the extraction process was performed on a medical application.
Development of Rubric of Attitude Assessment of Writing Explanation Text Based on Information Literacy on 11th Grade Students of Vocational High School Pencawan Medan

This study aims to produce an attitude assessment rubric product in the form of an attitude assessment rubric handbook on writing explanation text material and knowing the process of developing and the feasibility of the product. This research method is a method of research and development Research and Development (R &amp; D). Data is collected through validation of material experts and evaluation experts, the response of Indonesian teachers in Medan Pencawan SMKS and 35 class XI SMK students as research subjects. Data collection in the form of questionnaire assessment. The analysis is done by changing quantitative data into qualitative data using a Likert scale. The product evaluation in the form of a guide book on attitude assessment rubrics by material experts shows that the presentation of the suitability of the task material with KI and KD is 88% in the excellent category, the language and writing rubric attitude assessment is 97% in the excellent category, the learning activities of students have a percentage of 94% with a very good category, and supporting material has a presentation of 88% with a very good category. The overall average is 92% in the excellent category. The attitude rubrics assessment book by attitude evaluation experts with an overall average of 96% falls into excellent category. The attitude assessment handbook rubric assessment by Indonesian language teachers has an average total percentage of 89% with the criteria of "Excellent". In the test results the use of pretest with an average value of 73.17 and posttest with an average value of 80.48.
Faster Update Time for Turnstile Streaming Algorithms

In this paper, we present a new algorithm for maintaining linear sketches in turnstile streams with faster update time. As an application, we show that $\log n$ \texttt{Count} sketches or \texttt{CountMin} sketches with a constant number of columns (i.e., buckets) can be implicitly maintained in \emph{worst-case} $O(\log^{0.582} n)$ update time using $O(\log n)$ words of space, on a standard word RAM with word-size $w=\Theta(\log n)$. The exponent $0.582\approx 2\omega/3-1$, where $\omega$ is the current matrix multiplication exponent. Due to the numerous applications of linear sketches, our algorithm improves the update time for many streaming problems in turnstile streams, in the high success probability setting, without using more space, including $\ell_2$ norm estimation, $\ell_2$ heavy hitters, point query with $\ell_1$ or $\ell_2$ error, etc. Our algorithm generalizes, with the same update time and space, to maintaining $\log n$ linear sketches, where each sketch partitions the coordinates into $k<\log^{o(1)} n$ buckets using a $c$-wise independent hash function for constant $c$, and maintains the sum of coordinates for each bucket. Moreover, if arbitrary word operations are allowed, the update time can be further improved to $O(\log^{0.187} n)$, where $0.187\approx \omega/2-1$. Our update algorithm is adaptive, and it circumvents the non-adaptive cell-probe lower bounds for turnstile streaming algorithms by Larsen, Nelson and Nguy{\^{e}}n (STOC'15). On the other hand, our result also shows that proving unconditional cell-probe lower bound for the update time seems very difficult, even if the space is restricted to be (nearly) the optimum. If $\omega=2$, the cell-probe update time of our algorithm would be $\log^{o(1)} n$. Hence, proving any higher lower bound would imply $\omega>2$.
Multi-attention Networks for Temporal Localization of Video-level Labels

Temporal localization remains an important challenge in video understanding. In this work, we present our solution to the 3rd YouTube-8M Video Understanding Challenge organized by Google Research. Participants were required to build a segment-level classifier using a large-scale training data set with noisy video-level labels and a relatively small-scale validation data set with accurate segment-level labels. We formulated the problem as a multiple instance multi-label learning and developed an attention-based mechanism to selectively emphasize the important frames by attention weights. The model performance is further improved by constructing multiple sets of attention networks. We further fine-tuned the model using the segment-level data set. Our final model consists of an ensemble of attention/multi-attention networks, deep bag of frames models, recurrent neural networks and convolutional neural networks. It ranked 13th on the private leader board and stands out for its efficient usage of resources.
Perengkingan Pengetahuan Dalam Time Series Data Mining dengan J-Measure

Computer system development is increasing very rapidly in generating and collecting data that can be seen in terms of the application of computerized systems that continuously improve transaction data in the business world and in government systems, as well as the ability of hardware to store data with large capacity, increasing interest in mining data in accordance with technological developments, including problems related to computer science and data representation which are considered effective and efficient solutions. In this study the technique used is processing time series data, some of the knowledge produced is then ranked so that priority knowledge is obtained with a high level of confidence. Proximity distance using j-measure euclidean and cracking. Based on the results of cracking found rules based on the level of confidence that can be used as a decision support
Incremental Sense Weight Training for the Interpretation of Contextualized Word Embeddings

We present a novel online algorithm that learns the essence of each dimension in word embeddings by minimizing the within-group distance of contextualized embedding groups. Three state-of-the-art neural-based language models are used, Flair, ELMo, and BERT, to generate contextualized word embeddings such that different embeddings are generated for the same word type, which are grouped by their senses manually annotated in the SemCor dataset. We hypothesize that not all dimensions are equally important for downstream tasks so that our algorithm can detect unessential dimensions and discard them without hurting the performance. To verify this hypothesis, we first mask dimensions determined unessential by our algorithm, apply the masked word embeddings to a word sense disambiguation task (WSD), and compare its performance against the one achieved by the original embeddings. Several KNN approaches are experimented to establish strong baselines for WSD. Our results show that the masked word embeddings do not hurt the performance and can improve it by 3%. Our work can be used to conduct future research on the interpretability of contextualized embeddings.
All It Takes is 20 Questions!: A Knowledge Graph Based Approach

20 Questions (20Q) is a two-player game. One player is the answerer, and the other is a questioner. The answerer chooses an entity from a specified domain and does not reveal this to the other player. The questioner can ask at most 20 questions to the answerer to guess the entity. The answerer can reply to the questions asked by saying yes/no/maybe. In this paper, we propose a novel approach based on the knowledge graph for designing the 20Q game on Bollywood movies. The system assumes the role of the questioner and asks questions to predict the movie thought by the answerer. It uses a probabilistic learning model for template-based question generation and answers prediction. A dataset of interrelated entities is represented as a weighted knowledge graph, which updates as the game progresses by asking questions. An evolutionary approach helps the model to gain a better understanding of user choices and predicts the answer in fewer questions over time. Experimental results show that our model was able to predict the correct movie in less than 10 questions for more than half of the times the game was played. This kind of model can be used to design applications that can detect diseases by asking questions based on symptoms, improving recommendation systems, etc.
Global Mapping of Mineralo-Lithological Indices Derived With Aster Multispectral Thermal Infrared Data

ASTER measures the spectral radiation from the Earth in thermal infrared (TIR; 7–14 μm) region at five bands where the major terrestrial minerals exhibit distinct spectral properties. The mineralo-lithological indices for ASTER-TIR proposed by the author have been utilized for regional mapping in detecting silica, carbonate, sulfate and mafic-ultramafic minerals as well as delineating silicate rocks. On the other hand, the global emissivity dataset (GED) are developed and recently supplied to the public. In this study, the global map generated with the indices for the GED is shown. It is compared with the regional map covering a part of Tibetan Plateau produced with the procedures developed by the author. The result indicates the global map revealing the rough trend of lithology and mineralogy on Earth, however, the quality is not sufficient. The combined use of the regional and global maps are suggested for efficient analysis of global mineralo-lithology.
ANALISIS PENGARUH PENGGUNAAN TEKNOLOGI INFORMASI TERHADAP KINERJA KARYAWAN (STUDI KASUS PADA KARYAWAN PT INDUSTRI KERETA API)

PT Industri Kereta Api or PT INKA (Persero) is the first state-owned enterprise (BUMN) integrated railway manufacturing in Southeast Asia. The problems that occur in PT Kereta Api (Persero) are indications that the performance of employees has greatly increased. Increased Employee Performance is caused by developments in the use of information technology. This study aims to analyze the effect of the use of information technology on employee performance (case studies on employees of PT Industri Kereta Api). The population in this study was 100 employees and used saturated samples. The data used are primary data with the method of collecting data is a questionnaire, research conducted on all population units and all respondents and then taken as a sample. The analysis technique used is the validity, reliability, simple regression analysis, hypothesis testing and coefficient of determination. Based on the results of research, information technology has a positive and significant effect on employee performance. Based on the results of the test, it shows that from the T Test, the regression model meets the conditions used to predict employee performance, or it can be said that the independent variables jointly influence employee performance. While based on the coefficient of determination analysis, employee performance can be explained by an information technology variable of 99.5%. Keywords: Information Technology, Employee Performance
The Impacts Of Students’ Attitudes And Motivations In Learning English At Lembaga Kursus Kampung Inggris Transmigrasi (LKKIT) Marabahan South Kalimantan

Today in the global world, English is one of the most important languages in the world, because it is one of the languages that really connects the whole world together. English has played a large role in various sectors including technology, medicine, education, and others. Therefore, in this aspect, it is not surprising that there are so many English language courses nowadays. One of them that the researchers will discuss in this study is one of the institutions of English language courses made for transmigration children in rural areas.The researcher raises the title The Impacts of Students’ Attitudes and Motivation in learning English at Lembaga Kursus Kampung Inggris Transmigrasi (LKKIT) Marabahan South Kalimantan. Researchers want to know what students' attitudes and motivations in learning English and the Impacts of attitudes and motivations in Learning English. Researchers conducted research at Lembaga Kursus Kampung Inggris Transmigrasi (LKKIT) Marabahan, South Kalimantan, which is precisely located in Karang Indah Village, Mandastana District, Barito Kuala Regency, South Kalimantan. By collecting data through the results of questionnaires and interviews with students and tutors of LKKIT.The results of this study indicate that students of LKKIT are students who have less positive attitudes towards learning English. Students’s LKKIT consider English lessons to be difficult, bored and not exciting lessons. Whereas the intrinsic motivation of students’ LKKIT showed positive results that they were intrinsically motivated and had the motivation and desire to learn English that already existed within themselves. But not with attitude and extrinsic motivation of students who show negative results which means students are not extrinsically motivated. Because the purpose of students in learning English there is no desire to get high grades or get prizes and praise. From these results the attitude and motivation of LKKIT students has an Impacts on the reduction in the number of students in LKKIT from year to year due to several external factors, such as lack of support, praise and inadequate facilities and lack of attention and support from the government around.