Reviewerly/exHarmony huggingface dataset:
LibGuides: Library Skills: Getting started

If you are new to USW Library, this guide will help and support you to find the information you need with tutorials, video guides and more. This guide is also available in Welsh
Enhancement Of Agriculture Based Crop Yield Prediction Using R Tool And Machine Learning

The Crop yield-based prediction technique is a significant field of research that helps to ensure the food security and safe guard the food all over the nation. The projected system assists and support to predict enhanced and improved yield of crop and as well suggest the preeminent crop in dissimilar types of climatic conditions based on various parameter associated to the climates. This calculation will assist the farmer in the direction of choosing, whether the selected and particular crop is appropriate for that region and during the demanding season. In accumulation this website-based application gives information about details of productivity rate, irrigation, and other crop related details. Therefore, with the help of this website along with the different features help out the farmers to increase the crop production rate and crop quality improvement.
П. М. ЭРДНИЕВ - ОСНОВОПОЛОЖНИК МЕТОДИЧЕСКОЙ СИСТЕМЫ УКРУПНЕННЫХ ДИДАКТИЧЕСКИХ ЕДИНИЦ (УДЕ) КАК СОСТАВНОЙ ЧАСТИ ПЕДАГОГИКИ СОТРУДНИЧЕСТВА

The article highlights the contribution of the well-known scientist and teacher Erdniev Purvi Muchkaevich, graduate of the Faculty of Physics and Mathematics of the Barnaul State Pedagogical Institute, to the formation and development of one of the components of the pedagogy of cooperation - a methodological system of consolidation of didactic units based on the advance of existing programs.
Some Important Features of Two Computer-assisted Translation Tools: Wordfast and SDL Trados Studio

In this paper, I will attempt to examine the features and functions of two tools in an analytical and comparative way. My focus will be on comparing and commenting on various features of their translation memory tools, such as matching functions, operational functions, alignment, etc. Through my experience with Wordfast and SDL Trados Studio, I found it very easy to use and to become familiar with SDL Trados. There are benefits and drawbacks to working with the TMs of both tools. In contrast to SDL Studio TM, Wordfast TM is very complicated to handle. In conjunction with my language pair, I found translating an HTML file with Wordfast complicated and time-consuming.
Vision based Hand gesture pattern recognition enabled home automation system using Internet of Things

Controlling electric home appliances and gadgets with the help of switches is difficult for old and disabled people. The main motive of this paper is to help them by automating the control of electrical home appliances using hand gestures and applaud patterns. This paper presents a Support Vector Machine (SVM) and Convolution Neural Network (CNN) based hand gesture recognition system to automate various home devices like lights, fans, etc. This system uses real time image processing for hand gestures recognition by using a simple android-based application and Arduino UNO microcontroller. The captured real-time image by the android application will be processed  using the computer application. The proposed system perform image processing algorithm based on SVM and CNN is used to recognize the input hand gesture. A pre-trained CNN network Resnet50 is used for feature extraction. The CNN feature extraction technique is very powerful, and it outperforms existing hand-crafted feature extraction techniques like HOG,LBP and SURF. The CNN and SVM used together results in more accurate gesture recognition which improves the performance of the system. The system offers an additional applaud pattern recognition module which also can be used by the user for controlling the home appliances.
Practical evaluation of the viability of sensorsand algorithms to detect falling and burial by avalanche

Avalanches kill on average 250 people per year. Fall detection algorithms have been used to detect many different types of falls. In this work, a robust humanoid test rigwas designed and built to i ...
Learning Robust Representations for Low-resource Information Extraction

Author(s): Zhou, Yichao | Advisor(s): Wang, Wei | Abstract: Information extraction (IE) plays a significant role in automating the knowledge acquisition process from unstructured or semi-structured textual sources. Named entity recognition and relation extraction are the major tasks of IE discussed in this thesis. Traditional IE systems rely on high-quality datasets of large scale to learn the semantic and structural relationship between the observations and labels while such datasets are rare especially in the area of low-resource language processing (e.g. figurative language processing and clinical narrative curation). This leads to the problems of inadequate supervision and model over-fitting. In this thesis, we work on the low-resource IE algorithms and applications. We believe incorporating the supervision from domain-specific auxiliary knowledge and learning transferable representations can mitigate the deficiency of low-resource IE. Specifically, we explore pre-training domain-specific deep language models to acquire informative word/sentence embeddings to curate clinical narratives. We experiment with multi-modal learning techniques to recognize humor and to recommend keywords for advertisement designers. We also extract attributes of interest from the semi-structured web data by building transferable knowledge representations across different websites. For more applications of the low-resource IE, we build a COVID-19 surveillance system by inspecting users' daily social media data. Extensive experiments prove that our algorithms and systems outperform the state-of-the-art approaches and are of impressive interpretability as well.
A Large-Scale Database for Graph Representation Learning

With the rapid emergence of graph representation learning, the construction of new large-scale datasets are necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique. By carefully analyzing existing graph databases, we identify 3 critical components important for advancing the field of graph representation learning: (1) large graphs, (2) many graphs, and (3) class diversity. To date, no single graph database offers all of these desired properties. We introduce MalNet, the largest public graph database ever constructed, representing a large-scale ontology of software function call graphs. MalNet contains over 1.2 million graphs, averaging over 17k nodes and 39k edges per graph, across a hierarchy of 47 types and 696 families. Compared to the popular REDDIT-12K database, MalNet offers 105x more graphs, 44x larger graphs on average, and 63x the classes. We provide a detailed analysis of MalNet, discussing its properties and provenance. The unprecedented scale and diversity of MalNet offers exciting opportunities to advance the frontiers of graph representation learning---enabling new discoveries and research into imbalanced classification, explainability and the impact of class hardness. The database is publically available at this http URL.
LibGuides: ICT LibGuide: Training videos

The aim of this ICT subject guide is to make your studies pleasant and enrich your access to information resources. Through it, you will have access to general resources and other Open Access resources you may use for your studies.
Inductive Plausibility and Certainty: A Multimodal Paraconsisent and Nonmonotonic Logic

AbstractIs it possible to combine different logics into a coherent system with the goal of applying it to specific problems so that it sheds some light on foundational aspects of those logics? These are two of the most basic issues of combining logics. Paranormal modal logic is a combination of paraconsistent logic and modal logic. In this paper, I propose two further combinatory developments, focusing on each one of these two issues. On the foundational side, I combine paranormal modal logic with normal modal logic, resulting into a paraconsistent and paracomplete multimodal logic dealing with the notions of plausibility and certainty. On the application side, I combine this logic with Reiter’s default logic, resulting into an inductive and consequently nonmonotonic paraconsistent and paracomplete logic able to represent some key inductive principles.
Manipulating Single Surface Plasmon Polariton via Tailored Atom-Photon Interaction

We study the interaction of a single surface plasmon polariton with a finite number of atoms and with an infinite atomic lattice. Using only two-level atoms, we achieve subscattering, superscattering, and electromagnetically induced transparency (EIT).
PERCEPTIONS, ATTITUDES AND BENEFITS OF USING SMARTPHONE APPLICATIONS TOWARD THE LEARNERS’ VOCABULARY IMPROVEMENT

Using quasi- experimental approach, comparative and correlation research design, this study primarily focused on the perceptions, attitudes, and benefits of smartphone application toward the learners’ vocabulary improvement of grade 9 students of Paaralang Sekundarya ng Lucban Integrated School, Lucban, Quezon for the school year 2020- 2021. The respondents of the study were forty (40) students from Grade 9 level of Paaralang Sekundarya ng Lucban Integrated School.                This is a descriptive study using the questionnaire as the main tool in gathering the data. The data gathered were treated using frequency counts and percentage statistics, weighted mean, standard deviation and inferential statistics of Spearman’s Rank Correlation.                The mean level of learner’ perceptions on vocabulary learning experiences using smartphone applications were 3.62 interpreted as “Very High Positive” indicates that the learners perceived that the use of smartphone applications provided them an upmost vocabulary experience. On the other hand, the mean level of learner’ attitudes on vocabulary learning experiences using smartphone applications were 3.30 interpreted as “High Positive Attitude” indicates that the learners’ attitudes noted that the use of smartphone applications provided them a great expectation in vocabulary learning experience. This concludes that there is a significant relationship between students’ perceptions, attitudes and benefits of smartphones applications to the students’ apps evaluation and vocabulary performance.
Research Guides: Music: eBook databases

Learn about resources selected for students in Music classes including peer-reviewed articles, eBooks, citation help and more! Find print books and eBooks related to nursing.
Offline Handwritten Character Recognition using Convolutional Neural Network

Abstract: This paper focuses on the task of recognizing handwritten Hindi characters using a Convolutional Neural Network (CNN) based. The recognized characters can then be stored digitally in the computer or used for other purposes. The dataset used is obtained from the UC Irvine Machine Learning Repository which contains 92,000 images divided into training (80%) and test set (20%). It contains different forms of handwritten Devanagari characters written by different individuals which can be used to train and test handwritten text recognizers. It contains four CNN layers followed by three fully connected layers for recognition. Grayscale handwritten character images are used as input. Filters are applied on the images to extract different features at each layer. This is done by the Convolution operation. The two other main operations involved are Pooling and Flattening. The output of the CNN layers is fed to the fully connected layers. Finally, the chance or probability score of each character is determined and the character with the highest probability score is shown as the output. A recognition accuracy of 98.94% is obtained. Similar models exist for the purpose, but the proposed model achieved a better performance and accuracy than some of the earlier models. Keywords: Devanagari characters, Convolutional Neural Networks, Image Processing
Návrh metodiky zaznamenávání činnosti na prvcích kritické informační infrastruktury v prostředí energetických systémů

This thesis deals with the design of a new methodology for recording activity on critical information infrastructure elements in the environment of energy systems. In order to introduce the issue, energy systems and their sub-parts are described in general first. Next, the current legislative framework and the current state of the issue of the safety of energy systems in connection with cyberattacks that have lately been targeted specifically at the energy sector are introduced. For the purpose of designing the new methodology, different perspectives of the efficiency of ensuring monitoring activities and events with emphasis on ensuring an effective approach to building a security monitoring system architecture that is in line with the needs of companies or organizations are then analysed. Based on the analysis of possible approaches to building architecture, relevant internationally recognized frameworks that deal with said issue are also introduced. Based on established criteria reflecting specific properties of energy systems, a comparative analysis is performed with the aim to choose a relevant framework of enterprise architecture, which forms the basis of the new methodology. Using the outputs of the preformed comparative analysis, a new methodology supporting thedesign of a comprehensive security infrastructure, or rather security monitoring, which fully reflects the requirements for recording activities on critical information infrastructure elements in environment of energy systems, is proposed. The newly proposed methodology is the main goal and output of this work.
Scalable communication infrastructure through resource provisioning approach for reliable social networking system

Nowadays social networking system serves as an important information technology (IT) infrastructure of growth for community. Demands for scalable resource sharing in the system have become extremely important due to strong requirements imposed by the dynamic behaviour of users and resource communities. With the increasing number of entities in the system, inadequate information and unsuccessful accessibility of resources have becoming critical factors that degraded system performance. We present a hybrid resource provision scheme for improving resource communication in terms of availability and reliability while aiming for cost-effective processing. Specifically, our resource provision scheme incorporates an automated analytic for achieving accurate resource information while minimizing processing overheads and inter-communication latency. The provision scheme then incorporates trust-based scheduling policy to deal with diverse processing requirements and heterogeneous resources. Simulation experiments proved the efficacy of our scheme in achieving better trade-off between system scalability and performance; and helps sustain cost-effective computing.
An Overview of Russian Methods and Best Practices for Organization and Development of Monitoring Activities of Universities

This article provides an overview of the Russian methods for organizing and developing control over the activities of universities and develops a model for standardizing quality in education. The best internal control practices were analyzed in the leading Russian universities (HSE, Financial University, Plekhanov Russian University of Economics, MEPhI, I. M. Sechenov First Moscow State Medical University, Saint Petersburg State University of Economics). In addition, the article describes the typical structure of the questionnaire “Assessing the level of employer satisfaction with the quality of graduate training” used by the universities in Russia.
Hybrid Printed Text Document Restoration Technique using YOLO and Connected Components and Binarization method

In this research paper we propose a hybrid method to restore or binarize the printed English text document images with goal of enhancing text readability and document enhancement. In this digital world most of the documents are now converted to digital formats, and this conversion is done using electronic scanning. The source of this hard text documents are government documents, patient reports stored in hospitals, old legal records, documents stored in libraries etc. In particular, the readability of the text documents are very poor due to the degradation in terms of lower contrast and presence of degraded artefacts, presence of background dark spots, bleed through, shades, paper deterioration, show through’s ,underwriting etc. So far a lot of research has been done on restoration of text documents and some methods are really doing well in certain conditions. Our approach is based on extracting text region from the document and identifying the components in that text region and placing those text components in another empty document. This method improves the readability of the document and successfully restores the document.
Basic Hyperparameters Tuning Methods for Classification Algorithms

Considering the dynamics of the economic environment and the amount of data generated every second, the decision-making process is changing and becomes data driven, highly influencing the business strategies setup in order to keep the competitive advantage. However, without technology, data analysis would not be feasible, reason why machine learning is seen as a disruptive innovation for businesses, especially due to its capacity to convert data into actionable outcomes. Though, for a high-quality machine learning model result, algorithm selection and hyperparameters optimization play vital roles, hence became high-interest topics in the field. To achieve this, various automatic selection methods have been proposed and the aim of this paper is to compare two of them â€“ GridSearch and RandomizedSearch - and assess their impact on the model accuracy by comparing with the results obtained when default hyperparameters were applied.
PENGGUNAAN ALIH KODE DAN CAMPUR KODE DALAM PEMBELAJARAN BAHASA INDONESIA SISWA KELAS X SMA NEGERI 6 KOTA TERNATE

This research was conducted with the aim to determine the use of code switching and code mixing, also to find out the factors causing code switching and code mixing in Indonesian language learning in class X SMA Negeri 6 Ternate City. The method used in this study is a Qualitative Descriptive Method. The data source was Indonesian language teacher and class X students of SMA Negeri 6 Ternate City, which only consisted of a portion of 20 students. The research data is the use of code switching and code mixing of class X students of the 6th eerily of Ternate City. Based on data analysis, it was concluded that the data collection technique used was direct observation of a problem located in SMA Negeri 6 Ternate City. This study aimed to obtain additional data. This interview was conducted to reveal the effectiveness of the use of code switching and code mixing in Indonesian language learning. Records conducted by researchers to obtain data from the research objectives. Occurrence of the use of code switching and code mixing in Indonesian language learning for class X students of SMA Negeri 6 Ternate City, namely the use of code switching and code mixing is the switching of formal language codes, informal language code switching, and the occurrence of code switching between languages while the code mix is mixed code of words, phrases, clauses. Factors causing code switching and code mixing in Indonesian language learning in class X SMA Negeri 6 Ternate City are the occurrence of code switching due to speakers or speakers, listeners or interlocutors, Changes in the situation due to the presence of a third person, Changes in the situation from formal to informal or conversely, Change of Speaker Topic while mixed code because of the background of the attitudes of speakers and linguists.
Modern requirements for improving information and library education in the context of digitalization of society

This article analyzes world trends and foreign experience in creating a new paradigm in the development of information and library education in the electronic era. The necessity of using innovative technologies in bachelor’s degree in teaching information and library disciplines, which today are at the intersection of such sciences as information science and library science, is discussed. What, in turn, needs to be done and what will help to train highly qualified specialists for information and library specialists of the republic.
On An Entropy Estimator Based On a Non-parametric Density Estimator For Non-negative Data

In the recent decades, entropy has become more and more essential in statistics and machine learning. It features in many applications involving data transmission, cryptography, signal processing, network theory, bio-informatics, and so on. A large number of estimators for entropy have been proposed in the past ten years. Here we focus on entropy estimation for non-negative random variables. Specifically, the use of entropy estimator based on Poisson-weights density estimator is found to be of interest. We establish some asymptotic properties of the resulting estimators and present a simulation study comparing these with well known estimators in literature.
Differential Privacy Preservation Mechanism Using Bernstein Polynomial Function For Heart Disease Dataset

Information is put away in various frameworks as emerging technologies allowsinfluential data gathering and processing. Protection and security have turn into alongstanding challenging issue with advances in data and communication innovation.Privacy preserving data mining makes the customer information more secure by means ofdata perturbation and also it makes it harder to identify a person in an occurrence of datais spilled. Machine learning has got attention recently due to an energetic advancement ofdifferential privacy (DP). DP is golden response scheme to address the privacy protectionin analysis of data but it is quite hard to implement on real world data. The proposedsystem uses Bernstein polynomial function under differential privacy for perturbation.Heart disease dataset is used in this work to analyze the performance between the originaland the modified dataset using DP with the classifier models decision tree, linear model,random forest, SVM, linear model and neural network. The experiment results show theminor variations in the accuracy, sensitivity and specificity measures.
Study of Linke's Turbidity Values at Some Iraqi Sites Based on Solar Radiation

Turbidity was calculated by solar irradiance (Linke's Turbidity) for sixteen Iraqi sites. These locations were distributed among middle, north and south. These results have been updated depending on direct solar radiation, diffused solar radiation, total solar radiation, and solar constant as inputs for mathematical models in computer programs. Taking into account the hours of actual and theoretical brightness of the sun, the angle of the sun's rays, and the altitude angle of the sun during the months of the year. The results showed that the turbidity in the Iraqi sites considered in this research depends mainly on the months of the year regardless of the site location at the north, middle or south of Iraq. The turbidity has the greatest values during the winter season, specifically in December, where the average turbidity ranged for those sites. In this month (4.85 - 5.73), then January (4.75 - 5.72), it begins to decrease until the value of turbidity in most sites reaches its minimum level in the month of September, where the average turbidity of the studied sites is ranged (except for the site of Najaf) in this month (2.82 - 3.10). While the Najaf site was unique in registering the minimum average value of turbidity in the month of June by (3.25). The results showed that the turbidity in all the sites included in this research ranges between (2.82 - 5.73) during the year.