Reviewerly/exHarmony huggingface dataset:
PRODUKSI MAGGOT BSF (BLACK SOLDIER FLY) SEBAGAI PAKAN YANG DIBUDIDAYA DENGAN MEDIA YANG BERBEDA

This study aimed to determine the production of BSF maggots as feed cultivated with different media. The research material was 6 grams of BSF larvae eggs. The method used in this study was an experiment with a completely randomized design (CRD) where there were four treatments three times repetition so that 12 experimental units were obtained. The experiment consisted of P0 = rice bran, P1 = market waste, P2 = household waste, and P3 = Restaurant Waste. The results showed that BSF maggot production used different media against rice bran, market waste, household waste, and restaurant waste had a significant effect (P&gt; 0.05) on the maggot production. The best treatment is in the P3 treatment, and the lowest treatment is in the P0 treatment, where the P3 treatment uses a comparison of the restaurant's organic waste media with the same amount, namely 7.3 kg to 21 days of age from 0.50 grams of BSF maggot eggs and is able to produce 2816.66 adult maggot gram
How to marry a star: Probabilistic constraints for meaning in context

In this paper, we derive a notion of word meaning in context from Fillmore's 'semantics of understanding', in which a listener draws on their knowledge of both language and the world to 'envision' the situation described in an utterance. We characterize utterance understanding as a combination of cognitive semantics and Discourse Representation Theory, formalized as a situation description system:a probabilistic model which takes utterance understanding to be the mental process of describing one or more situations that would account for an observed utterance. Our model captures the interplay of local and global contexts and their joint influence upon the lexical representation of sentence constituents. We implement the system using a directed graphical model, and apply it to examples containing various contextualization phenomena.
RETRACTED: The Strategy of Statistical Analysis of Audit Data in the Era of Big Data is Explored

Abstract In the current era, audit data statistical analysis strategies have a variety of forms, we can also use a variety of forms to explore. But because the large amount of information has made things complex and changeable, it presents us with a lot of challenges. Since audit data is one of the necessary areas for socialist construction and development, we should value them. Therefore, the purpose of this paper is to use advanced algorithms to analyze the statistical analysis strategy of audit data in the era of big data. Based on the privacy and security provisions of the data age, this paper uses the data flow analysis algorithm to carry out comprehensive modeling analysis to detect the experimental results in order to improve the accuracy of the analysis. The experimental results show that using this algorithm to perform appropriate modeling operations can improve the speed and correctness of statistical analysis of audit data.
A Probabilistic Logic Program that Predicts COVID-19 Infection and the Possibility a Patient to Recover

COVID-19 is a disease that has infected almost the whole world and has been pronounced as a global pandemic. The digital health domain has already tried to respond to the pandemic challenges by developing algorithms and applications that make predictions on the infection and the corresponding outcome in case of infection. In this direction, in this paper, we present "COVID-19 Detect & Predict", an application that can detect and predict COVID-19 infection through probabilistic logic reasoning, and in case of infection, it can also predict the possibility a patient to recover. We demonstrate the effectiveness of our solution on realistic datasets, showing its potential benefits. Our approach is the first to detect and predict COVID-19 infection through probabilistic logic reasoning to the best of our knowledge.
Z-WASTE: APLIKASI RAMAH LINGKUNGAN BERBASIS MOBILE

Plastic waste has reached pandemic levels worldwide. It adversely affects wildlife, wildlife habitat, and humans. Meanwhile, adopting information technology may reduce plastic waste problem. In this study, a Zerowaster application was developed that can provide green spot information. The idea of the developed application is unique from existing applications. Zerowaster application does not make plastic waste as an object that is informed, but rather shows information on clean places or where there is no trash. Prototype method is used in this study. And Blackblox is chosen as a method to test the application. Application development is started by desgining interface, then developinng a webpage admin, implementing Application Programming Interface (API) and Geographic Information System (GIS). The result shows that Zerowaster application running well on mobile application as well as showing green spot for the users.
Specification of the Schema of Spreadsheets for the Materialization of Ontologies from Integrated Data Sources

In Ontology-Based Data Access (OBDA), a knowledge base known as an ontology models both the problem domain and the underlying data sources. We are concerned with providing with tools for performing OBDA with relational and non-relational data sources. We developed an OBDA tool that is able to access H2 databases, CSV files and Excel spreadsheets allowing the user to explicitly formulate mappings, and populating an ontology that can be saved for later querying. In this paper, we present a language for specifying the schema of the data in a spreadsheet data application, which then can be used to access the contents of a set of Excel books with the ultimate goal of materializing its data as an OWL/RDF ontology. We characterize the syntax and semantics of the language, present a prototypical implementation and report on the performance tests showing that our implementation can handle a workload of Excel tables of the order of ten thousand records. We also show a case study in which the ontology of an idealized university library can be defined using the our tool integrating both relational and spreadsheet data.
RETRACTED: The new trend of wearable technology in college foreign language classroom and its impact on college English teaching

With the progress of new sensor technology and Internet technology, wearable devices have gradually become the new favorite of the science and technology industry. They have been applied to various fields because of their wearability, mobility, user-centered, interactive, integrated and augmented reality characteristics. Given the shortcomings of traditional foreign language education at university, wearable technology is introduced into foreign language classroom as an auxiliary form of traditional teaching. This article chooses two courses (171 and 172 courses) to test whether wearable devices have an impact on college English teaching. The English level of these two classes is similar. With the assistance of wearable technology, level 171 is selected as the experimental course; 172 is a regular course, using only traditional teaching methods, and the experiment lasted for a semester. Practical teaching is also carried out in the class. Practical teaching results show that wearable technology assisted English teaching can not only improve students’ ability to master knowledge, but also greatly enhance students' interest in learning English.
A Robust Watermarking Scheme based on Delaunay Image Reconstruction Robust to Affine Transformation

Abstract This paper proposes a sub-channel image reconstruction algorithm based on Delaunay triangle. First, according to the different sensitivity of the human eye to the three color channels, different channels are used to avoid the influence of watermark embedding on pixels around the feature points. Then, feature points should be selected on the selected channel to make them have sufficient robustness. At this time, the location of the feature points extracted on the corresponding channel can be used as the watermark embedding position. Finally, considering the influence of geometric attacks such as affine transformation, this paper proposes a reconstruction algorithm based on Delaunay triangle, which uses feature point information to reconstruct the image before the watermark extraction, and extract the watermark on the reconstructed image. The effect of watermark extraction has been improved.
A Method of Finding Hidden Key Users Based on Transfer Entropy in Microblog Network

Finding key users in microblog has been a research hotspot in recent years.There are two kinds of key users: obvious and hidden ones.Influence of the former is direct while that of the latter is indirect.Most of existing methods evaluate user's direct influence, so key users they can find usually obvious ones, and their ability to identify hidden key users is very low as hidden ones exert influence in a very covert way.Consequently, the algorithm of finding hidden key users based on topic transfer entropy, called TTE, is proposed.TTE algorithm believes that hidden key users are those normal users possessing a high covert influence on obvious ones.Firstly, obvious key users are discovered based on microblog propagation scale.Then, based on microblogs' topic similarity and time correlation, the transfer entropy from ordinary users' blogs to obvious key users is calculated and used to measure the covert influence.Finally, hidden influence degrees of ordinary users are comprehensively evaluated by combining above indicators with the influence of both ordinary users and obvious ones.We conducted experiments on Sina Weibo, and the results showed that TTE algorithm had a good ability to identify hidden key users.
Trend of Library and Information Science Research on Library and Information Science Journals in Indonesia (2013-2019)

This research aims to identify trend in the development of Library and Information Science in Indonesia through scientific journals of Library and Information Science having been indexed by SINTA (Indeks Sains dan Teknologi) during 2013-2019. This research is a quantitative research using bibliometrics method. Population in this research is 741 articles published on 8 (eight) journals, Berkala Ilmu Perpustakaan dan Informasi published by Universitas Gadjah Mada, Jurnal Kajian Informasi dan Perpustakaan published by Universitas Padjadjaran, Jurnal Ilmu Informasi, Perpustakaan dan Kearsipan published by Universitas Indonesia, Edulib Journal Perpustakaan dan Ilmu Informasi published by Universitas Pendidikan Indonesia, Jurnal BACA: Jurnal Dokumentasi dan Informasi published by Lembaga Ilmu Pengetahuan Indonesia, Khizanah Al-Hikmah: Jurnal Ilmu Perpustakaan, Informasi, dan Kearsipan published by Universitas Islam Negeri Alauddin Makassar, Lentera Pustaka: Jurnal Kajian Ilmu Perpustakaan, Informasi, dan Kearsipan published by Universitas Diponegoro, and Record and Library Journal published by Universitas Airlangga during the period of 2013-2019. Research trend of Library and Information Science from 2013 to 2019 shows most topics discussed are Library and Information Science (LIS) 27.68%, Information Science Research (17.21%), and Subject Specific Sources and Applications (13.40%). The interesting finding is percentage of collaborative research is higher than individual research, 54.93%.
RETRACTED: Construction of maintenance staffing model for managing sports venues in comprehensive sports games: A research based on the practice of the 7th military world games

The operation and maintenance of sports venues is the basis of ensuring the orderly organization of various events in large-scale comprehensive sports games. Due to the lack of evaluation methods and an index system, it was difficult to predict the input of operating and managing sports venues in large-scale comprehensive sports games. Excessive manpower is often put to guarantee the smooth operation work, causing the problem of waste on all resources. Based on the practice of the operational work for specified sports fields during the 7th World Army Games, this paper establishes a staffing allocation model for the maintenance and operation of sports venues. On the premise of building up a clear maintenance scope, a clarified organization structure and working procedure, the maintenance index of 15 specified venues is evaluated with analytic hierarchy process (AHP), and a set of allocation criteria of the operation team is put forward. The maintenance coefficients of studied venues range from 0.08 to 0.91, and the venues with highest coefficients are the main stadium, the naval pentathlon venues and the soccer field, etc.
A Machine Learning Approach for the Tune Estimation in the LHC

The betatron tune in the Large Hadron Collider (LHC) is measured using a Base-Band Tune (BBQ) system. The processing of these BBQ signals is often perturbed by 50 Hz noise harmonics present in the beam. This causes the tune measurement algorithm, currently based on peak detection, to provide incorrect tune estimates during the acceleration cycle with values that oscillate between neighbouring harmonics. The LHC tune feedback (QFB) cannot be used to its full extent in these conditions as it relies on stable and reliable tune estimates. In this work, we propose new tune estimation algorithms, designed to mitigate this problem through different techniques. As ground-truth of the real tune measurement does not exist, we developed a surrogate model, which allowed us to perform a comparative analysis of a simple weighted moving average, Gaussian Processes and different deep learning techniques. The simulated dataset used to train the deep models was also improved using a variant of Generative Adversarial Networks (GANs) called SimGAN. In addition, we demonstrate how these methods perform with respect to the present tune estimation algorithm.
Analysis of Difficulty Level and Discriminating Power Between Multiple Choices and Essay Items on Math Test

This analysis was intended to compare the level of difficulty and discriminating power on multiple choices and essay items on math test.The research was done by design treatments by subject's method.The research object was 22 items of multiple choices items and 22 essay items.The essay items were generated from multiple choices item that modified into essay items by eliminating the options.The essay items were divided into two clusters and each of them consist of 11 items.The population in this research was public and private Junior High Schools in DKI Jakarta Province, while the sample was defined by two-staged random sampling and stratified random sampling technique.There were 452 students act as the sample, taken from 6 private and public schools.Multiple choices item was scored by following rule; score 1 for correct respond and score 0 for incorrect respond, in meantime essay items were scored by using 3 different scoring rubrics, namely; Rubric Model 1 (0, 1), Rubric Model 2 (0, 1, 2) and Rubric Model 3 (key words).The difficulty level and the different power of students' response data that has been scored are calculated from the multiple choice and essay items, and then analysed by using descriptive statistical methods and inferential statistics and Rasch models.The results of the analysis with the Classic method obtained an average level of difficulty multiple choice questions greater than the essay items either on Model-1, Model-2, or Model-3.While the analysis using the Rash Model, the average level of difficulty multiple choice items is relatively the same as the essay items.For the average discriminating power, both using the Classic method and the Rasch Model, the average discriminating power of multiple choice items is greater than the essay items.In other words, based on research the form of multiple choice items is easier than the essay items, whereas the discrimating power of multiple choice items is greater than the essay items.
HUMAN IMAGE MATTING BASED ON CONVOLUTIONAL NEURAL NETWORK AND PRINCIPAL CURVATURES

Abstract. Image matting often requires advanced image processing, especially in conditions, when small details such as hair are present in the image. In this article the hybrid method for human image matting based on convolutional neural network and principal curvatures is proposed. The U-Net based neural network is used to predict a rough foreground segmentation mask. Then the obtained foreground mask is refined by principal curvatures method to process the elongated hair-like structures. Test results show that the proposed method can improve the coarse human segmentation.
A morphological analyser for K’iche’

espanolEste articulo describe el desarrollo de un modelo computacional de la morfologia quiche. La lengua quiche es una lengua maya que se habla en Guatemala. Es un idioma del tipo aglutinante con morfologia de prefijos y sufijos. Tanto la morfologia verbal como la morfologia nominal son complejos a un nivel moderado. El quiche es una lengua de pocos recursos computacionales y esta publicacion es la primera que describe una herramienta computacional para el idioma, y alguna de las primeras para cualquier lengua del grupo maya. La herramienta esta desarrollada con HFST, una caja instrumentos para implantar transductores de estados finitos. Una evaluacion indica que la cobertura de vocabulario esta adecuada, entre 86% y 96% calculado sobre diversos corpus libres. Una evaluacion manual indica una sensibilidad por 90% sobre un conjunto de pruebas anotadas a mano. Tanto el analizador como el conjunto de pruebas estan disponibles bajo una licencia de software libre. EnglishThis paper describes the development of a free/open-source computational morphological description for K’iche’, a Mayan language spoken in Guatemala. The language is of the agglutinative morphological type, with both prefixing and suffixing morphology. Both the nominal and verbal morphology are moderately complex. K’iche’ is under-resourced and this is the first publication describing a computational tool for the language, and one of the first publications describing a computational tool for any language of the Mayan group. We use the Helsinki Finite-State Toolkit (HFST) for implementing the finite-state transducer. An automatic evaluation of the coverage of our implementation shows that the coverage is adequate, between 86% and 96% on range of freely available corpora. A manual evaluation gives a recall of over 90% over a hand-annotated test set. Both the analyser and the hand-annotated test set are available under a free/open-source licence.
RETRACTED: A Practical Research on the Online Learning Mode of Business English Majors under the Background of “Internet+”

Abstract In the context of “Internet +”, we need to strengthen research on the application of the online learning model for business English majors. This article analyzes the various effects of online learning models on business English majors. This article will discuss the shortcomings in the application of the current online learning model for business English majors. Only by mastering relevant materials can we propose effective practical strategies, improve the application level of online learning models and students’ learning effects, and give full play to the positive role of Internet technology in the teaching of business English.
The Performance Influence of Distribution Government Assistance toward School Dropout Communities Based on the E-Proposal Banper Management Information System, Organizational Transformation and HR Capabilities in Course Institutions

The purpose of this study was to evaluate the performance of the distribution of government assistance to dropout communities based on the e-proposal management information system, organizational transformation, and human resource capabilities of course institutions. The sampling technique was purposive sampling—data collection using questionnaires and FGD. The data analysis technique used Structural Equation Modeling (SEM) analysis processed with Smart PLS software. The research results prove that organizational transformation does not directly affect organizational performance in the distribution of government assistance. Meanwhile, HR capabilities and management information systems of e-proposal Banper directly affect organizational performance in the distribution of government assistance. Then organizational transformation indirectly has a significant effect on organizational performance in distributing government assistance by mediating the management information system e-proposal Banper. Likewise, human resource capability indirectly has a significant effect on organizational performance in distributing government assistance by mediating the management information system e-proposal Banper. The results of the strategy analysis through the input strategy stage with CPM, matching stage with TOWS, and decision stage with QSPM, concluded that the strategy used from the QSPM results is a strategy for developing SIM e-proposal assistance.
Machine Learning based Activity learning for Behavioral Contexts in Internet of Things

Ontology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform “Activities of Daily Life (ADL)” not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant’s routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant’s newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution.
Single-Image Super-Resolution Using Panchromatic Gradient Prior and Variational Model

Single-image super-resolution (SISR) is a resolution enhancement technique and is known as an ill-posed problem. Motivated by the idea of pan-sharping, we propose a novel variational model for SISR. The structure tensor of the input low-resolution image is exploited to obtain the gradient of an imaginary panchromatic image. Then, by constraining the gradient consistency, the image edges and details can be better recovered during the procedure of restoration of high-resolution images. Besides, we resort to the nonlocal sparse and low-rank regularization of image patches to further improve the super-resolution performance. The proposed variational model is efficiently solved by ADMM-based algorithm. We do extensive experiments in natural images and remote sensing images with different magnifying factors and compare our method with three classical super-resolution methods. The subjective visual impression and quantitative evaluation indexes both show that our method can obtain higher-quality results.
Desafios da Pandemia para o Futuro da Educação: O Caso Coppead

ABSTRACT This teaching case describes the process experienced by Coppead, one of the main graduate schools in business in Brazil, to adapt to e-learning. The change is driven by the social isolation adopted in the country due to the COVID-19 pandemic. The case reports on the decisions that the faculty collegiate, led by the director Elaine Tavares, has needed to take to circumvent the quarantine and ensure teaching continuity at Master, Doctoral, and Executive MBA levels. The case discusses the main challenges during the transition, but most importantly highlights the strategic opportunities that this shift has offered for innovation in the coming years.