Reviewerly/exHarmony huggingface dataset:
Recommendation for Classification of News Categories Using Support Vector Machine Algorithm with SVD

Online news is a digital information media currently has a very easy and flexible updating process. The News Document grouping process is implemented in several stages, including Text Mining which includes Text Pre-processing which includes Tokenizing, Stopword removal, Stemming, Word Merging, TF-IDF and Confusion Matrix. Of the several techniques in Text Mining, the most frequently used for News Document classification is the Support Vector Machine (SVM). SVM has the advantage of being able to identify separate hyperplane that maximizes the margin between two or more different classes. The selection of features in SVM significantly affects the classification accuracy results. Therefore, in this study a combination of feature selection methods is used, namely Singular Value Decomposition in order to increase accuracy and reduce the Classifier Time Support Vector Machine. This research resulted in text classification in the form of categories Entertainment, Health, Politics and Technology. Based on the Support Vector Machines Algorithm, an accuracy rate of 81% was obtained with 360 Data Training and 120 Data Testing, after adding the Singular Value Decomposition feature with a K- Rank value of 50%, a significant increase in accuracy was obtained with an accuracy value of 94% and The time of Algorithm process is faster.
Penggunaan Media PEMA (Papan Edukasi Main Anak) Dalam Meningkatkan Pemerolehan Kosakata Anak Usia 3-4 Tahun

&lt;p&gt;&lt;strong&gt;Early childhood needs to optimize various aspects of their development. One of them is the language aspect, the language skills of children can be seen from the way they communicate with other people. This needs to be supported by its vocabulary. At the age of 3-4 years, the average child knows 800 words, but still speaks in short sentences. If there is something complex to talk about, he will reveal it in two or three sentences. Currently, there are various kinds of game tools that are obtained by children, through educational play tools PEMA (children's play educational board) it is hoped that children can help children in acquiring vocabulary. The skill developed is the concept of printed letters. Children have the opportunity to interact with printed letters. Learn to recognize letters to achieve early reading skills for children. This study uses action research conducted on children aged 3-4 years. This study began by conducting a pre-test for the 5 children. Furthermore, parents document the use of PEMA media. The third is reflection of observation, reflection of documentation is interpreted in order to plan improvements that will be carried out in the next cycle. Activities are carried out repeatedly until the most appropriate one is found for each child. The results of the study are expected to increase children's vocabulary acquisition through PEMA media.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; - Pemerolehan, Kosakata, Media, Anak Usia Dini, PEMA&lt;strong&gt;&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;
Applications of Artificial Intelligence in Cyber-Physical Systems

The cyber-physical system is becoming data-rich, allowing new and higher degrees of autonomy and automation. Although significant progress has been made in cyber-physical system technology, the need for innovation across application fields is growing, and fundamental science is becoming more important to stay up to date. Traditional models of cyber-physical systems are being questioned by new models that emerge from machine learning and deep learning. In cyber-physical systems, some of the innovations are self-driving cars, robots, and quadcopters. Such autonomous drones and vehicles utilize machine learning and artificial intelligence techniques that operate effectively in the real world. Artificial intelligence has been used to analyze the data provided by actuators and sensors to provide optimized services and security to the system. This chapter presents current work in different domains that use cyber-physical systems and artificial intelligence.
Ensure the Proper Wearing of Face Masks Using Machine Learning to Fight Covid-19 Virus

In this pandemic time, wearing face masks is mandatory to all because of the possibility that a person can get COVID-19 virus through their mouth, nose or eyes, which could possibly happen when a person has a direct or close contact to a person with that virus. But, despite the strict implementation, some people disregard the proper wearing of face masks and unaware the risks of possible virus transmission for such negligence. In this paper, it will demonstrate how a Convolutional Neural Network (CNN) can detect if a person is wearing a face mask or not and the additional parameter to support to detect if the face mask is properly worn by a person by considering the facial landmarks thru face recognition using Histogram of Oriented Gradients (HOG) feature descriptor with a linear SVM machine learning algorithm. Two (2) processes are involved in proper wearing of face masks detection. It needs to pass in Face Mask Detection to proceed to the next process which is the Face detection wherein the result of checking should return false to confirm the proper wearing of the face mask of a person.
Analysis Model of Spoken English Evaluation Algorithm Based on Intelligent Algorithm of Internet of Things

With the in-depth promotion of the national strategy for the integration of artificial intelligence technology and entity development, speech recognition processing technology, as an important medium of human-computer interaction, has received extensive attention and motivated research in industry and academia. However, the existing accurate speech recognition products are based on massive data platform, which has the problems of slow response and security risk, which makes it difficult for the existing speech recognition products to meet the application requirements for timely translation of speech with high response time and network security requirements under the condition of network instability and insecurity. Based on this, this paper studies the analysis model of oral English evaluation algorithm based on Internet of things intelligent algorithm in speech recognition technology. Firstly, based on the automatic machine learning and lightweight learning strategy, a lightweight technology of automatic speech recognition depth neural network adapted to the edge computing power is proposed. Secondly, the quantitative evaluation of Internet of things intelligent classification algorithm and big data analysis in this system is described. In the evaluation, the evaluation method of oral English characteristics is adopted. At the same time, the Internet of things intelligent classification algorithm and big data analysis strategy are used to evaluate the accuracy of oral English. Finally, the experimental results show that the oral English feature recognition system based on Internet of things intelligent classification algorithm and big data analysis has the advantages of good reliability, high intelligence, and strong ability to resist subjective factors, which proves the advantages of Internet of things intelligent classification algorithm and big data analysis in English feature recognition.
Cryptocurrency Risk Analysis During the Covid-19 Pandemic: Value at Risk (VaR) Approach

The COVID-19 outbreak had a severe impact on almost all countries in the world. One aspect of concern during the pandemic is an investment. In this study, the GARCH model used to estimate Value at Risk (VaR) on cryptocurrency investments as a tolerable loss during the COVID-19 pandemic. Data in this study are ten cryptocurrencies with the largest capitalization. The observation period when WHO declared COVID-19 as a pandemic, namely on March 11, 2020 - June 11, 2020. From this study, the highest cryptocurrency VaR value is Crypto.com with an amount of 0,18214. It means that the maximum loss that an investor can tolerate with an investment of $500.000.000 for a 95 percent confidence level is $ 91.070.392.
Compensating the absent or incomplete data required in vulnerability analyses via GIS. A case study on the surface geology and building stock of Ia&amp;#537;i City, Romania

&amp;lt;p&amp;gt;Access to comprehensive and updated statistical and spatial databases represents a prerequisite of sound risk, hazard, vulnerability and resilience analyses, which have been advancing in terms of complexity and efficiency for the last 50 years, alongside the development of GIS techniques. Without adequate quantitative and qualitative datasets, research is sensitive to inaccurate and imprecise results, failing to meet the requirements for which it was designed and having zero input to the scientific progress.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Most developing countries - including Romania, face the problem of incomplete, inaccurate or outdated data in many fields of research, including geology-related fields and statistics about the building stock and transport infrastructure.&amp;lt;strong&amp;gt; &amp;lt;/strong&amp;gt;These types of data are fundamental for vulnerability assessments of urban spaces to seismic or landslide hazards. This paper aims to provide a GIS-based methodology for acquiring datasets of the geological surface deposits and of the building stock at the scale of urban settlements, focusing on Ia&amp;amp;#537;i City in the NE of Romania, respectively on the &amp;amp;#538;ic&amp;amp;#259;u area of this city.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The mapping of geological surface deposits relies on automatic image analysis and landforms extraction algorithms that identify and delineate geological deposits based on slope and curvature, using High-resolution DEMs, as well as on cluster analysis. Slope deposits are delineated via watershed segmentation performed by Vision with Generic Algorithms (ViGRA), whereas the Schmidt-Hewitt classification is used to delineate floodplain and ridge deposits. The building stock is extracted from LiDAR point clouds with densities of 4 to 6 points per square meter using various approaches: neural network and deep learning for classification, and bounding rectangles for building boundary extraction. While LiDAR data is not available, high resolution imagery provided by the Copernicus programme can be used in conjunction with classification and edge detection algorithms to delineate building objects. The results are promising and show how the already available tools can be used to fill in the gaps of the &amp;amp;#8220;no-data problem&amp;amp;#8221; and overcome such a challenging issue.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The obtained spatial data, namely surface deposits and building stock, become a major asset for the further vulnerability assessments that integrate geotechnical and physical aspects. This may help identify what local scale elements contribute to disaster resilience or, on the contrary, what fuels vulnerability.&amp;lt;/p&amp;gt;
Stereotactic Localization: From Single-Slice to Multi-Slice Registration Including a Novel Solution for Parallel Bipanels

Frame-based stereotactic localization generally assumes that all required fiducials are present in a single-slice image which can then be used to form targeting coordinates. Previously, we have published the use of novel localizers and mathematics that can improve stereotactic localization. As stereotactic procedures include numerous imaging slices, we sought to investigate, develop, and test techniques that utilize multiple slices for stereotactic localization and provide a solution for a parallel bipanel N-localizer. Several multi-slice equations were tested. Specifically, multi-slice stereotactic matrices (ms-SM) and multi-slice normal to parallel planes (ms-nPP) were of particular interest. Bipanel (2N) and tripanel (3N) localizer images were explored to test approaches for stereotactic localization. In addition, combination approaches using single-slice stereotactic matrices (ss-SM) and multi-slice methods were tested. Modification of ss-SM to form ms-SM was feasible. Likewise, a method to determine ms-nPP was developed. For the special case of the parallel bipanel N-localizer, single-slice and multi-slice methods fail, but a novel non-linear solution is a robust solution for ms-nPP. Several methods for single-slice and multi-slice stereotactic localization are described and can be adapted for nearly any stereotactic system. It is feasible to determine ms-SM and ms-nPP. In particular, these methods provide an overdetermined means to calculate the vertical z, which is determined for a tripanel system using single-slice methods. In addition, the multi-slice methods can be used for extrapolation outside of the localizer space. Importantly, a novel non-linear solution can be used for parallel bipanel N-localizer systems, where other methods fail. Finally, multi-slice stereotactic localization assumes strict patient and imaging system stability, which should be carefully assessed for each case.
The Effect of Preprocessing with Gabor Filters on Image Classification Using CNNs

In image classification tasks, preprocessing of input images is one of the promising approaches for improving the performance.In this study, we investigated the effect of neuro-inspired preprocessing, such as Gabor filtering.We compared the averaged classification accuracy of multiple CNNs with the following three types of preprocessing: no preprocessing, Gabor filtering, and calculation of the difference between two Gabor filtered signals in the opposite color channels.The results showed that Gabor filtering increased the classification accuracy.
Preprocessy: A Customisable Data Preprocessing Framework with High-Level APIs

Data preprocessing is an important prerequisite for data mining and machine learning. In this paper, we introduce Preprocessy, a Python framework that provides customisable data preprocessing pipelines for processing structured data. Preprocessy pipelines come with sane defaults and the framework also provides low-level functions to build custom pipelines. The paper gives a brief overview of the features and the high-level APIs of Preprocessy along with a performance comparison against Scikit-learn and Pandas on two datasets. Preprocessy provides functions for handling missing data and outliers, data normalisation, feature selection and data sampling. The goal of Preprocessy is to be easy to use, flexible and performant. Preprocessy helps beginners and experts alike by making data preprocessing an easier and faster task.
Curriculum changes in Brazilian Librarianship and its relations with the gendering of the librarian profession

Introduction:It highlights how gender relations are inserted in the logic of power and are supported by the production of diffusion of discourses to penetrate people's daily lives and produce docile individuals. Objective: To present a theoretical discussion about howthe curricular changes of Librarianship courses in Brazil –sometimes more humanistic, sometimes more technical –influenced the process of feminization of the profession and the insertion of the profession in the list of professions notably marked by thesexual division of labor. Method:bibliographic review about the role of curricula as disseminators of discourses and power relations and in the gendering of professions; historical course of Librarianship curricula in Brazil and its insertion of gendered discourses in the librarian profession. Results:This work sought to show how the curriculum was used as a device for the dissemination of certain discourses in relation to the librarian profession and how it contributed to its feminization. Conclusion:It is proposed that the librarian profession has become feminized over time and changes in curricular focus have contributed to this movement, where the adoption of more technical curricula has contributed to the insertion of the profession in the list of professions notably marked by the sexual division of labor. Finally, it is pointed out that, from the 1980s, a timid approach of men to the area also led to a change in the curricular focus and in the discourse around the profession.
EFL Students’ Independent Learning With Pear Deck Interactive Slides

This paper introduces the implementation of Pear Deck to provide a self-learning platform for students. As online classes have become common due to the COVID 19 pandemic, teachers have been using additional tools to support students’ independent language learning. Among others, Pear Deck is superior as it allows students to access interactive slides at their own pace. This paper gives details about Pear Deck, including how to access it as well as what features this tool provides. In this review, the authors argue that despite a few limitations, Pear Deck offers a wide array of benefits and can motivate students to study independently and enable teachers to supervise them.
Morphometric analysis of volcanic structures using digital elevation models and models developed from radar images in the Apan volcanic field, M&amp;#233;xico.

&amp;lt;p&amp;gt;The present project aims to make a preliminary assessment of the volcanic risk represented by the Apan Volcanic Field (CVA). The methodology was divided into two parts. In the first, Digital Elevation Models (DEM) published by official sources were used to identify unreported structures and perform morphometric analysis of previously dated structures. In the second stage, a new DEM was developed from interferometric methodologies to compare the results with those obtained from official sources. Two SAR satellite images from the SENTINEL-1 satellite of ESA's Copernicus program were used. Being the first of October 14, 2021, leader image, and the second of October 26, 2021, slave image. These images were processed in ESA's SNAP software. For the morphometric analysis, volcanic structures have been classified into three major categories: Young cones (0.18 Ma - 0.5 Ma), Intermediate cones (0.5 Ma-1 Ma), and Old cones (1 Ma-3 Ma). From the official DEM analysis, 243 volcanic structures were reported within the study area with a preliminary predominance of structures that fall in the range of old cones, 4 areas with a higher concentration of volcanic structures were detected in which some highly populated localities are found. In addition, demographic parameters were used for a better preliminary risk assessment in the study area. Official and Radar images DEMs were used for the morphometric analysis and the results were compared with the previously published models. Finally, it was concluded the importance of the CVA by comparison with other two Mexican volcanic fields CVA represents a moderate volcanic risk, for which a greater number of studies and monitoring in the area is recommended. &amp;amp;#160;This project provides a new understanding of the volcanic hazard and risk associated with the CVA and the development of the surrounding social environment.&amp;lt;/p&amp;gt;
Hybrid Model for Software Fault Prediction

Development programs have a crucial and expensive evaluation process to assess the viability of the end product of software development projects. Manually detecting software defects becomes a time consuming and costly task as project scale and complexity expand. Using automatic predictors to work on faulty parts as an alternative to manual anomaly detection allows the software developer to analyze the broken element further. In this case, better fault determinants may often find a program to be added to a software constancy application. As a result, multiple validated and constructed base predictors exist. Simple predictors, especially fault-detection abilities, can be combined with an ensemble approach to increase efficiency even further. The primary aim of the research is to evaluate the fault diagnostic utility of baseline and ensemble predictor models. The proposed study, which is being applied to the PROMISE directories, focuses on diagnosing application faults using machine learning and deep learning–based classification methods, and then analyzing empirical findings to decide the best match model. The study's goal would be to demonstrate that ensemble predictors can improve fault detection accuracy to a degree.
On a New Operation over Intuitionistic Fuzzy Sets

A new operation over intuitionistic fuzzy sets and intuitionistic fuzzy pairs is introduced. Some of its properties are presented. The numerical example is given.
IMPROVING THE EFFICIENCY OF UNIVERSITY LIBRARIES IN MALAYSIA USING ZERO-SUM GAINS DEA MODEL

Measuring the efficiency and optimizing available resources are vital for library management. This research empirically examines the measurement of efficiency of 10 public university libraries in Malaysia and provides an opportunity for inefficient libraries to improve their efficiency by proposing the appropriate number of resources. Data encompassing the number of staff, materials availability, and material circulation were gathered between 2016 and 2019. This study implements the zero-sum gains data envelopment analysis (ZSG-DEA) model to improve the efficiency. The findings show that in the early stage, five libraries were efficient in 2016 and 2018 while six libraries were efficient in 2017 and 2019. Comparable efficiency scores between conventional DEA and ZSG-DEA can be seen as all the inefficient libraries manage to attain better efficiency scores. Overall, all inefficient libraries can increase their efficiency rates. Despite the scores still not achieving the highest rate of efficiency, this study may assist librarians on managing library operations efficiently.
Quantization of Product using Collaborative Filtering Based on Cluster

Abstract: Because of strict response-time constraints, efficiency of top-k recommendation is crucial for real-world recommender systems. Locality sensitive hashing and index-based methods usually store both index data and item feature vectors in main memory, so they handle a limited number of items. Hashing-based recommendation methods enjoy low memory cost and fast retrieval of items, but suffer from large accuracy degradation. In this paper, we propose product Quantized Collaborative Filtering (pQCF) for better trade-off between efficiency and accuracy. pQCF decomposes a joint latent space of users and items into a Cartesian product of low-dimensional subspaces, and learns clustered representation within each subspace. A latent factor is then represented by a short code, which is composed of subspace cluster indexes. A user’s preference for an item can be efficiently calculated via table lookup. We then develop block coordinate descent for efficient optimization and reveal the learning of latent factors is seamlessly integrated with quantization. In this paper we also propose similarity method that has the ability to exploit multiple correlation structures between users who express their preferences for objects that are likely to have similar properties. For this we use a clustering method to find groups of similar objects. Index Terms: Product Quantization, Clustering, Product Search, Collaborative Filtering