Reviewerly/exHarmony huggingface dataset:
Two-Dimensional Broad Learning System for Data Analytic

Recently, a novel learning algorithm called the broad learning system (BLS) has been established by using the theory of pseudoinverse and compressed sensing technology. As an alternative to deep learning, BLS has gained extensive attention in data analytics. Unlike the existing deep structures based learning tools (such as deep neural networks), it is able to rapidly achieve incremental learning, and does not require tedious retraining to remodel the system. However, given image data modeling tasks, 1-D BLS requires a conventional vectorization operation, the dimension of input will be quite large for high resolution image. This operation may result in huge computational complexity, thereby increasing the time of model construction. To resolve this issue, a 2-D version of BLS, namely 2-D-BLS, is first proposed to fast construct randomized learner models by using matrix inputs. Specifically, the proposed method employs left and right projecting vectors to replace the usual high dimensional input weight. Moreover, the theoretical analyses on the superiority of 2-D-BLS against BLS are presented in terms of the algorithm complexity and the difference of random parameter space. Empirical results on MNIST dataset, NORB 3-D object image recognition dataset, ORL dataset, YaleB dataset, and handwritten digit dataset demonstrate that the proposed 2-D-BLS outperforms the existing extensions of BLS and state-of-the-art 2-D machine learning methods in terms of the learning efficiency, showing good potential of 2-D-BLS for image data analytics. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—as a novel learning tool, BLS has achieved great success in many fields. Image recognition is one of the most widely used fields of artificial intelligence. But, BLS is a 1-D model, the vectorization operation is necessary to convert matrix input data into a row or a column vector. This operation will lead to “explosion of dimensionality” and increase the time of model construction when it is coming to image data analytic with 2-D input. This article first proposes a 2-D extension of BLS for high resolution image data analytic. The proposed method can not only avoid the “explosion of dimensionality,” but also construct the whole structure with matrix-inputs rapidly and dynamically. The improvement of this method is expected to provide a novel and great choice for the field of image recognition, such as face recognition, industrial inspection, behavior recognition, etc., in the future.
Super-Resolution Image Reconstruction Algorithm Based on Improved Information Distillation Network

Recently, convolution neural networks have been demonstrated remarkable process on single image super-resolution. However, as the depth of the networks increase, the computing time also increase. At the same time, there are higher requirements for the hardware computing power and memory. Information distillation network is a lightweight super-resolution reconstruction network, which has the advantages of fast running speed and few network parameters. However, there are still some deficiencies in the utilization rate of shallow features and the high-frequency details extracted from features. In this paper, an improved information distillation network is proposed. On the one hand, the convolution kernels of different scales are used to extract the high-frequency information of images under different scales, and the feature maps of different scales are fused and put into the enhancement unit to increase the feature channels; on the other hand, the idea of dense connection is used to build a network to improve the utilization rate of shallow features. Experimental results on the Set5, Set14 and BSD100 datasets show that the proposed algorithm improves the Peak Signal-to-Noise Ratio and Structural Similarity compared with the five mainstream algorithms such as Bicubic, SRCNN, VDSR, LapSRN and IDN.
Object Oriented Programming Untuk Sistem Informasi Pencatatan Hutang Dalam Pengadaan Obat Pada Rsud X

In the drug procurement process, there is no specific record of debt by the Pharmacy section or by the Finance Sector. The records that are trying to be done are like payment receipts from the Finance Sector paying invoices from the drug industry that have provided drugs to the Pharmacy Section. So that the financial sector in managing debt as well as in recognizing debt and the maturity of debt payments of each supplier. With these obstacles, the RSUD requires an Information System for Recording Debts in Procurement of Auxiliary Drugs for the Pharmaceutical Sector and the Financial Sector in recording Debts, especially in the procurement of Drugs. This data system uses object-oriented programming with a SQL Server database to create an information system that can speed up the forest recording process in drug procurement&#x0D; &#x0D; Keywords: Object Oriented Programing, hutang, RSUD
Deformation-Graph-Driven and Deformation Aware Spectral Pose Transfer

<p indent=0mm>A deformation graph driven and deformation aware spectral pose transfer method is proposed in order to improve the ability of the spectral pose transfer method in preserving shape details and reducing user interactions. First, a deformation graph is introduced to drive the source model deforming under the guidance of low-frequency spectral coefficients of the reference model to obtain a deformed source model. Second, the deformed source model is automatically segmented to parts according to the deformation. Third, repeating the spectral pose transfer on two source parts separately yields two deformed source parts. This process can be repeated again if necessary. Finally assembling all deformed parts in a reversed order results in the completely deformed source model. Comparison with the method of Yin et al. is conducted via a couple of examples. Experimental results show that the secondary pose migrations are reduced by 38%, the average distance error is reduced by 54%, and both surface area and volume variations are reduced by about 12.5%. It improves the quality of transfer results to a certain extent, reduces the number of middle-frequency pose transfer, and also reduces manual interaction.
Classification of Hepatocellular Carcinoma Using Machine Learning

Hepatocellular Carcinoma (HCC) proves to be challenging for detection and classification of its stages mainly due to the lack of disparity between cancerous and non cancerous cells. This work focuses on detecting hepatic cancer stages from histopathology data using machine learning techniques. It aims to develop a prototype which helps the pathologists to deliver a report in a quick manner and detect the stage of the cancer cell. Hence we propose a system to identify and classify HCC based on the features obtained by deep learning using pre-trained models such as VGG-16, ResNet-50, DenseNet-121, InceptionV3, InceptionResNet50 and Xception followed by machine learning using support vector machine (SVM) to learn from these features. The accuracy obtained using the system comprised of DenseNet-121 for feature extraction and SVM for classification gives 82% accuracy.
A Novel Analysis of Efficient Energy Architecture in Cryptography

The Cryptography is the method of allocation and hiring the information in the image. In this unknown image is divided into no of shares and distributed into no of participates. This process contains two cases. First one is the sender here image is based on GAS calculation, Second case is receiver side the unknown image is extracting and overlapping. The password is provided to both sender and receiver side. Finally we observe that unknown image at receiver side.
Fuzzy Image Adaptive Recognition Algorithm Based on PCA-SVM Model

Face recognition is an important technology in the field of fuzzy image recognition. In this paper, combined with the current two algorithms, PCA and SVM, the low-dimensional face image feature vector is extracted by PCA, and then SVM is used for classification and recognition. Using MATLAB to simulate the ORL face database, the accuracy of the test results is over 90%, which indicates that the method is effective.
Next lesson – Let's Go to the Theater!

Theatrical pedagogics allows for the considerable change of the ordinary school lessons and transforms its educational objectives. The students become stronger in their confidence of their skills to use Russian language as a means of communication and willingness to improve their knowledge. Dramatization in Russian language brings positive emotional experience, cultivates creative abilities in children, adds to their personality, trains interest towards the Russian language and Russian culture. Theatrical lessons create conditions for increasing motivation in studying Russian language with lessons in Russian as a foreign language.
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation

Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g.their gender or ethnicity.Style transfer is an effective way of transforming texts in order to remove any information that enables author profiling.However, for a number of current state-of-theart approaches the improved privacy is accompanied by an undesirable drop in the downstream utility of the transformed data.In this paper, we propose a simple, zero-shot way to effectively lower the risk of author profiling through multilingual back-translation using off-the-shelf translation models.We compare our models with five representative text style transfer models on three datasets across different domains.Results from both an automatic and a human evaluation show that our approach achieves the best overall performance while requiring no training data.We are able to lower the adversarial prediction of gender and race by up to 22% while retaining 95% of the original utility on downstream tasks.
Kreativitas Guru dalam Mengembangkan Media Pembelajaran Digital pada Pembelajaran Sejarah Daring (Studi Deskriptif Terhadap Guru Sejarah SMA Di Kota Bandung)

This research is motivated by the findings of researcher at the research location which show that in the online history learning process, teacher creativity is needed, one of which is in terms of developing digital learning media. So the study in this research is the creativity of teachers in developing digital learning media in online history learning, with the main problem formulation "how is the creativity of teachers in developing online history learning media?" This study uses a descriptive study that aims at obtaining a description of the creativity of high school history teachers in Bandung City in developing digital learning media based on indicators of flexibility, fluency, originality, and presentation. This includes studying the constraints and efforts as well as student’s responses to the developed media. The results of this study indicate that teachers have developed digital learning media which are realized in several forms, such as slides, videos, and podcasts. However, from the media products that have been developed, podcasts are the media with the most prominent elements of originality compared to others. This shows that there are still several aspects that have not been met based on the findings of each indicator. One of them is caused by the obstacles felt by the teacher both from internal and external factors. However, students still feel helped by the media developed by the teacher to understand the material presented, but it does not mean that all students feel that the media is interesting. This is largely determined by what media the teacher uses during the online learning process.
Ecuadorian Kichwa to Spanish automatic translation: an initial approximation based on LSTM Neural Networks for educational applications

According to the latest estimates of UNESCO, currently, the Kichwa is experiencing a rapid loss among young generations. Some studies predict that approximately half of 7000 World languages will be extinct by the end of the XXI century. For these reasons, it is fundamental to develop efforts to rescue and revitalize the indigenous languages as a fundamental part of the cultural heritage of ancient nations. In this paper, we describe an initial approximation to develop an automatic translation tool from the Ecuadorian Kichwa variant to Spanish. However, this task is challenging, because currently there are not enough digital bilingual texts (Ecuadorian Kiwcha - Spanish) to design automatic translation tools. Our proposal compared Long Short-Term Memory Neural Network (LSTM-NN) and Transformer NN as is described in state of the art. Although the initial results do not have the precision required to have a suitable translator (due to the lack of enough bilingual texts), this first approximation shows that it is feasible to use LSTM NN to continue the research and development process in this area.
THE EFFECT OF WEIGHT OPTIMIZATION USING GENETIC ALGORITHM ON THE CLASSIFICATION OF DHF VULNERABILITY LEVEL

Dengue Hemorrhagic Fever (DHF) is a disease transmitted by the Aedes Ageypti mosquito. In South Kalimantan, especially in the city of Banjarbaru, the number of cases tends to increase every year. Existing research has identified the level of dengue susceptibility by using computational methods, one of which is classification. The method used in this research is Neural Network Backpropagation with weight optimization using Genetic Algorithms for data classification of dengue disease in Banjarbaru City. The purpose of this study was to determine the performance of the classification of dengue susceptibility levels using Neural Network Backpropagation and weighting using Genetic Algorithms. The results showed that the performance obtained for the classification of the level of dengue susceptibility using the Neural Network Backpropagation Algorithm was 83.33% in the accuracy, 96.51% precision, and 84.69% recall, whereas when using the Neural Network Backpropagation Algorithm based on Genetic Algorithm for weight optimization, obtained an accuracy value of 96.29%, a precision of 98.97%, and a recall of 97%.
Applicable technologies to improve teaching processes at the higher education level

The general objective of the study is to analyze the role of the technologies applied in the improvement of higher-level teaching processes at the Toribio Rodriguez de Mendoza National University. The approach is mixed since the documentary analysis and the survey are used to obtain the data; the population consists of the main theoretical contents, Background and theories on the technologies to be used to improve higher-level teaching procedures and by 10 teachers from the Toribio Rodriguez de Mendoza National University,to whom a small survey was applied in order to identify the technological resources applied by teachers to support the teaching process learning, which promote the development of content and its implementation at the Toribio Rodriguez de Mendoza National University. The methods used were: Systemic, hermeneutic, hypothetical-deductive, statistical and analytical method. The incorporation of ICT into regular teaching practice is becoming closer to reality every day, thanks to the commitment and interest of teachers in the subject and the spread of educational portals on the Internet, where teaching resources for the classroom can be found.
Unearthing principal parameters to ameliorate score in National Institutional Ranking Framework deploying Deep Learning approach

The National Institutional Ranking Framework (NIRF) is a ranking framework followed nationwide and adopted by the Educational Ministry, Government of India, for ranking Higher Education Institutions (HEIs) and envision students to better choose their higher educational arena.This nationalized Framework was ratified and launched by the Minister of Human Resource Development.Since 2016, NIRF publishes 100 HEIs in ranking order based on multiple parameters like teaching, learning, research and many more.In this paper, we identify crucial parameters which influence the overall score of an institution.Our proposed deep learning architecture which employs Neural Network predicts with the improvement of what parametric value, an HEI could rise up the ladder in the ranking framework.Furthermore, we implement our work using Orange data science toolkit followed by python framework and elaborately discuss the results.
Are Collateral-Constraint Models Ready for Macroprudential Policy Design?

We study the design of macroprudential policies based on quantitative collateral-constraint models. We show that the desirability of macroprudential policies critically depends on the specific form of collateral used in debt contracts: While inefficiencies arise when current prices affect collateral---a frequent benchmark used to guide policies---they do not when only future prices affect collateral. Since the microfoundations and quantitative predictions of models with future-price collateral constraints do not appear less plausible than those using current prices, we conclude that additional empirical work is essential for the use of these models in macroprudential policy design.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Labels Propagation for Profiles Categorization in Social Networks

Categorization of profiles in social networks represents one of the most interesting current challenges. Indeed, nowadays social networks have an inevitable role in the decision-making of Internet users. Detecting categories of profiles, which can play the role of influencers, can make a difference in understanding the structures and the functions of networks. With the exponential growth of data, and since we are talking about machine learning methods, classical techniques have become of poor efficiency. In this article, we introduce Weighted Categories Propagation (WCP), an improvement of existing methods which benefit from the labels propagation to consider the influence of social relationships on profile categorization. The efficiency of WCP is experimentally validated. Experiments carried out on the data set of RepLab 2014 indicate that WCP outperforms similar methods.
Special Section: Anarres Project for Alternative Futures Collection

This article contains four essays from the Anarres Project, a forum for conversations, ideas, and initiatives that promote a future free of domination, exploitation, oppression, war, and empire. In the spirit of philosophy in the contemporary world, the selection includes recent work on the pandemic and related struggles for justice in the past year. An introduction to the project is included.
A Bayesian network approach to handle uncertainty in Web Ontology Language

In information management systems ontology play a vital role. An ontological application should include a mechanism for handling uncertainty. Ontological innovations are to become the web's future in the coming period, but there are still some features such as exceptions, vulnerability and default values missing. Ontological languages such as OWL and RDF are by necessity distinct in nature, so ambiguous details cannot be treated. In this research, article uncertainty is handled in the ontology by Bayesian network. A probabilistic model of uncertainty available in the knowledge base is the Bayesian network. The Bayesian network is a conceptual model that is ideal for the representation and analysis of ambiguity and information found in data. The probability of uncertainty can be used for many real-life scenarios in the knowledge-base. We also introduced defaults and exceptions along with uncertainties to enhance performance and improve the OWL features. The source code is then translated to a jar-file with maven and it can be used in Protégé itself.
Ward_p_HMM: A Shilling Attack Detection Technique Using Ward_p Method and Hidden Markov Model

Collaborative Filtering Recommender Systems (CFRSs) are widely employed in several applications because of its satisfying performance in the customized recommendation. Recent studies show that CFRSs are at risk of shilling attacks where attackers inject shilling profiles into the system. Malicious user injected ratings not only severely impact genuineness of recommendations but also user's trustworthiness within recommendation systems. Existing unsupervised clustering technique uses Ward method which is an iterative method of low scalability. For addressing this issue, in this work an unsupervised SA detection technique named WardpHMM has been proposed. It uses Wardp and Hidden Markov Model (HMM). In this proposed method HMM is used to measure difference of user’s rating behavior. It generates User Suspicious Degree (USD) of each user by analyzing user’s Suspicious Degree Range of Items (SDRI) and User’s Matching Degree (UMD). Then Wardp method is applied to merge users based on USD and to acquire group of Attack Users (AUs). For performance analysis of the proposed technique Amazon-ratings sample dataset has been used. The performance comparison shows that proposed WardpHMM technique outperforms baseline technique with respect to precision, recall and F1-score.
Progressive Sampling-Based Joint Automatic Model Selection of Machine Learning and Feature Selection

In most machine learning applications, selecting an appropriate machine learning model requires advanced knowledge and many labor-intensive manual iterations. As a result, automatic machine learning is particularly important in order to lower the threshold for machine learning. In addition, feature selection is a very important data preprocessing process. Selecting important features can alleviate the dimension disaster problem, and removing irrelevant features can reduce the difficulty of learning tasks. The existing automatic selection methods cannot perform the automatic selection of machine learning model and feature selection model simultaneously on large-scale data. Therefore, in order to adapt to the rapid development of the era of big data, this paper proposes to establish a unified hyperparameter space for machine learning and feature selection, and adopt Bayesian optimization model based on progressive sampling for automatic model selection. By extensive experiments, we show that our approach can significantly reduce search time and classification error rates compared to the most advanced automated model selection methods.
VIRTUAL LEARNING ENVIRONMENTS FOR ESL COURSES

Abstract: As the time in which online teaching and learning was still an element of novelty has long been gone, virtual learning environments have to be studied thoroughly so that they will provide students not only with the necessary knowledge, but also with the proper tools to meet their learning objectives. The advancement in information technology and the access to an almost inordinate number of learning and teaching tools should have already been fructified and, as a result, not only teachers, but also learners should have already picked up the fruit of knowledge grown in the vast virtual environment of the Internet. However, as education has recently moved almost entirely online, some questions have arisen. Are the Virtual Learning Environments (VLEs) enough to offer ESL students both motivation and knowledge? Will foreign languages benefit from this growing trend or will traditional, face-to-face interaction, prove to have been more efficient? The present article will look into some of these questions and into the benefits of VLEs in the context of the Covid-19 pandemic.