Reviewerly/exHarmony huggingface dataset:
PENGARUH PELAYANAN REFERENSI PERPUSTAKAAN PERGURUAN TINGGI BERBASIS DIGITAL TERHADAP MAHASISWA DAN FAKTOR-FAKTOR YANG MEMENGARUHI KEEFEKTIFAN PROSES PELAYANAN REFERENSI DI MASA PANDEMI COVID-19 (STUDI KASUS PERPUSTAKAAN INSTITUT PERTANIAN BOGOR)

College libraries certainly have an important role in the course of the educational process in the tertiary institutions that oversee it. The services available in the library are used by students to fulfill their needs for information. These services are available directly so as to provide students who need library services as possible. However, during this pandemic, library services that are provided directly, of course, must comply in order to comply with the Covid-19 health protocol. Therefore, the library transfers all services that are directly related to library services, including digital, including reference services. This study uses a descriptive qualitative approach with the literature study method, the observation method to analyze and analyze how the influence of the digital-based Bogor Agricultural Institute library reference on students and to find and explain in detail what factors can influence the reference service process. so that it can form an effective reference. It was found that this digital-based reference service had an effect in particular on the effect of service and information control on student satisfaction if the results of the references received were in accordance with their needs and found that library material collections, library users, librarians and library staff, library facilities, library funds or budgets, management library as well as connection speed and stability of the internet network have a big influence on the realization of an effective digital-based reference service.
LibGuides Home: Remote Online Library Support: Students Suceeding

This guide will support you while you are working remotely and will direct you to Library resources and support.
Fully Automated Ontology Increment`S user Guide Generation

This research focuses on a domain and schema independent user-guide generation for ontology increments.Having a user guide or a catalogue/manual is vital for quick and effective knowledge dissemination.If a user guide can be generated for an ontology as well, there could be ample advantages.Stakeholders can scan across the user guide of the ontology and verify the eligibility of it, against the intended purposes.Additionally, this could be useful in ontology`s version management requisites and knowledge verification requirements as well.Even though, ontology construction being iterative and incremental operational, there will be several intermediate versions before it reaches to the fine-tuned final version.Therefore, manual user guide creation will be a tedious and impossible operation.Consequently, this research focuses on a novel algorithmic approach to domain and schema independent ontology verbalization.A special algorithm is created to alter the functionality of Google's AliceBot to work as a verbalizer, instead of a chatterbot.Artificial Intelligent Modelling Language (AIML) technology is utilized to create the templates for the ontology specific knowledge embeddings.This entire process is fully automated via the proposed novel algorithm, which is a key contribution of this research.Eventually, the generated user guide generation tool is evaluated against three different domains with the involvement of fifteen stakeholders and 82% of averaged acceptance has been yielded.
A generic type system for featherweight Java

We introduce a generic type system for Featherweight Java (FJ) that is parametrized with a monad-like structure, and prove a uniform soundness theorem. Its instances include some region type systems studied by Martin Hofmann et al. as well as a new one that performs more precise analysis of trace-based properties. Their soundness is guaranteed by the uniform theorem. We only need to verify some natural conditions. Instead of refining the FJ type system as in the previous work, our region type system is separate from the FJ type system, making it simpler and also easier to move to larger fragments of Java. Moreover, the uniform framework helps to avoid redundant work on the meta-theory when extending the system to cover other language features such as exception handling.
EpiCollect5 Software Ease of Use among Medical students: Evaluation Survey

Context: Mobile- and web-based application like EpiCollect5 makes work easier by paving a way of paperless and easy way of data collection and exporting. Aim: The aim of the study was to evaluate the ease of use of EpiCollect5 software among the medical undergraduate’s students who had used this application. Settings and Design: A cross-sectional study was conducted among undergraduate students in a tertiary care institute. Materials and Methods: The present study was conducted using a pre-tested semi-structured and self-reported questionnaire to obtain information on ease of use of EpiCollect5 software from the medical undergraduates. The data entry was done in Epidata 3.1 while analysis was done using Epidata 2.2.2.182. Results: It was easy and simple to use the EpiCollect5 software reported by majority (94%) of the students and almost 90.6% of the participants felt that the software is user friendly. Conclusion: Majority of the students agreed with the user-friendly nature of the software and its applicability in collecting data from the community. Key words: Data collection, EpiCollect5, Mobile application, Software
Case Study : Laksa Laksam Berkuah Beris Pintu Gerbang / Syafiqah Quraatulaini Ibrahim

the objective of the study is to work on the background,organizational structure, products and services that Laksa Laksam Berkuah Beris Pintu Gerbang provides and produces. The technology that used for Laksa Laksam Berkuah Beris Pintu Gerbang is steamer machine. Usually, steamer machine is needed by the company to easethe process of steam the laksam’s noodle. The demand of laksam is high among East Cost people. Besides that, the company also using social media such as Facebook as initiative to market the product of laksa and laksam. Most of the production in this company is depends onhuman source. They have only one machine which is steamer machine for steam the laksam’s noodle. Lastly, the study focus on financial management of the company as well as how the company manage to gain the achievement from the financial management. Every business mustdesign a strategy for achieving the company vision. Strategy is an action plan to achieve the goals and vision
Expressing criticism of public figures on Chinese social media:A case study of Wuhan Diary

On 13 May, a Chinese writer Fang Fang asked Zhang Boli, the academician of the Academy of Engineering, to apologise to her for he reprimands Fang’s motive in disseminating false information in her Wuhan Diary about the national campaign against the Covid-19 pandemic during the Wuhan lockdown. The hashtag #Fangfang asked Zhang Boli to apologise went viral on Weibo (Chinese twitter) with netizens taking sides distinctively. The current project takes interest in how netizens address the two characters whilst expressing their stance (i.e. anti- Fang, pro-Fang, neutral). It explores three aspects of address terms: 1) types address terms e.g., honorifics, derogatives, parody; 2) position of address terms, viz., sentence-initial, sentence- middle and sentence-final; 3) collocations of address terms, e.g., address terms in a noun phrase. It also explores the statistical relationship between three aspects of address terms/independent variables and the netizens’ stance/dependent variable by using R functions such as decision tree and multinomial logistic regression. This study aims to enrich the research of stance-taking in online forums by introducing address terms as well as statistical analysis.
Case Study : Dobi Desa / Nabihah Ismadi

Dobi Desa offers a concept of cleaning services to individuals and families, including dry cleaning and laundry services. The company also offered rapid cleaning services to meet the needs and requirements of its customers. The company staff are always ready and focus on quality services to ensure that all items are delivered to the customer in a clean and very neat condition, which means ready to wear. Removal of spots and high-quality finishing are some of the features that make Dobi Desa different from others. In order to maintain these features, the company has trained and equipped the staff with all the necessary knowledge and skills in the handling of different fabrics to ensure that all the goods delivered are of good quality and satisfy the customers.The company also offer the Syariah Compliant Wet Cleaning Methods for Muslims. Unlike traditional dry cleaning which uses no water during the cleaning process, the wet cleaning system is safe for the cleaning of sajadah (prayer mat), hijab, and prayer clothing. This is particularly when the wet cleaning method uses water based bio-degradable high grade smart detergents that can provide the comfort feeling for those who use the garments during pray
Segmentation of Multivariate Time Series with Convolutional Neural Networks

This paper addresses an important problem of time series analysis at test benches. A new method is presented that allows automated segmentation of measurement time series using a Convolutional Neural Network (CNN). The CNN is trained for this purpose with specifically generated data. The results show a high quality and efficiency. The field of application of the algorithm is not limited to the automotive industry focused on here, but can be easily transferred to other areas that allow a visual segmentation of data.
Prediction of daily reference evapotranspiration using machine learning algorithms across the central United States

<b><sc>Abstract.</sc></b> Accurate prediction of reference evapotranspiration (ET<sub>0</sub>) plays a significant role in irrigation water management and helps to increase the agricultural production. Our study evaluates the power of random forest (RF) and extreme learning machine (ELM) for predicting daily ET<sub>0</sub>. Four different input combinations were acquired using minimum and maximum temperature (T<sub>min</sub> and T<sub>max</sub>), relative humidity (H<sub>r</sub>), net solar radiation (R<sub>n</sub>), and wind speed (U<sub>2</sub>) from 39 weather stations from 1979-2014 for warm season (May through September) across the central United States. Results revealed that the RF model showed better performance when used with all or limited input variables of T<sub>max</sub>, T<sub>min</sub>, R<sub>n</sub>, U<sub>2</sub>, and H<sub>r</sub>. Coefficient of determination (R<sup>2</sup>) ranged between 0.769 and 0.997 and when limited or all parameters were used, respectively. Considering the prediction accuracy, the RF model can be recommended over ELM. These models can be utilized to predict ET<sub>0</sub> in other parts of the world with limited temporal or spatial climate parameters.
Explainable Deep Learning: Paradigms for Earth Observation

The volume and variety of valuable Earth Observation (EO) images as well as non-EO related data is rapidly growing. The open free data access becomes widespread and has an enormous scientific and socio-economic relevance. EO images are acquired by sensors on satellite, suborbital or airborne platforms. They extend the observation beyond the visual information, gathering physical parameters of the observed scenes in a broad electromagnetic spectrum. The sensed information depends largely on the imaging geometry, orbit, illumination and other specific parameters of the space instruments. Typical EO systems can be classified into optical or radar instruments. During the last years, both types of sensors deliver widely different images, and both have made considerable progress in spatial and radiometric resolution, image acquisition strategies, and data rates.Generally imaging sensors generate an isomorphic representation of the observed scene. This is not the case for EO, the observations are a doppelganger of the scattered field, an indirect signature of the imaged object. This positions the load of EO image understanding, and the utmost challenge of Big EO Data Science, as new and particular challenge of Machine and Deep Learning and Artificial Intelligence (AI). The presentation reviews and analyses the new approaches of EO imaging leveraging the recent advances in physical process based AI methods and signal processing, and leading to explainable paradigms where intelligence is the analytical component of the end-to-end sensor and Data Science chain design. A particular focus is on the semantic aspects as a key component in the explainable learning paradigms.
Attention Mechanism-Based News Sentiment Analyzer

Sentiment Analysis is the task of determining the feeling or opinion of a chunk of text. One of the crucial applications of sentiment analysis is to classify the various news articles into three fundamental categories of sentiments—negative, positive, and neutral. By considering the sentiments of news articles, we can figure out whether the writer’s sentiment is negatively or positively oriented. Multiple models have been constructed to analyze the news articles but none of them compassed at both the levels of a sentence as well as the whole document. In this paper, I have proposed a method which can perform this task at both the levels with accurate results, by using LSTM network and a deep learning framework, i.e., attention network.
Steps towards Autonomous Driving: Deep Semantic Segmentation among vehicle viewpoints

Giving the machines the ability to see and understand the overall context of what they are looking is the ultimate goal of Computer Vision. Simultaneously, Deep Learning aims at schematize the human brain structure building models that will learn from huge amount of data. From the combination of this two Machine Learning fields comes out lots of algorithms that reaches state-of-the-art performances on many tasks, including the one of the autonomous driving. It is important, in fact, that self-driving cars understand the environment where they are operating and that’s why have been developed the so-called semantic segmentation and domain adaptation networks. Both of this mentioned methods required huge amount of data to be trained and here is inserted the work done on this master thesis; indeed, it deals with the development of a completely new dataset for semantic segmentation, IDDA, characterized by its greatness and variety, allowing multi-source and domain-shift problem training and testing. The thesis also studies and tries to solve this problem, discovering that, till now, the available domain adaptation techniques, fails to completely close this gap and behave in an unexpected way when dealing with different viewpoints.
LibGuides: TTC Digital Archives: TTC Library Research Guides

The digital collections support the mission of the Library by providing access to items of historical significance to the TTC community by promoting intellectual growth and cultural development of TTC students, faculty and staff.
The integrative model for the semantic space of music and a contemporary musical educational process: the scientific and creative heritage of Mikhail Borisovich Ignatyev

The advent of computer technology has led to the creation and development of new forms of teaching musical art. The emergence of music computer technologies (MCT) was the basis for creating both new forms in the educational process and new subjects, the appearance of new disciplines and new educational trends in the system of a contemporary musical education. These new subjects, such as “Musical Informatics,” “Computer Music Creative Work,” “Computer Arrangement and Composition, Sound and Timbre Programming,” “Electronic Musical Instruments” and many others, fully reflect the essence of the changes that have occurred in approaches to the study of music art. This article analyzes the possibility of including an integrative model for the semantic space of music, developed with the participation of the prominent cybernetics, scientist Mikhail Borisovich Ignatyev, in a contemporary musical educational process.
LibGuides: Teaching Elementary- For Beginners: Apps and Games

In this lib guide, you will discover helpful tips, tricks, and information for beginning teachers. There is no need to reinvent the wheel. I have provided resources for classroom management, movement, Math websites, ELA websites, articles, Ted Talks, and, This sections has lots of fun and engaging games for all subjects.
Collaborative Learning and Communication Skills in Virtual Environments in Times of Pandemic

The coronavirus continues to ravage the world; humanity has had a preview of many changes in all areas; one of them was in education where most countries, under pressure from the pandemic, were forced to move to a distance education model where direct interaction is not possible and didactic methodologies must be innovated to achieve the development of university student competencies. In this study, the concepts are presented and collaborative learning is analyzed in relation to communicative skills as important aspects for the achievement of competencies, the development of the student in the virtual communication process and the social relationships that generate new ways of learning.
Integrated Payroll and Personnel Information System (IPPIS) for Sustainability of University Education in Nigeria after COVID-19 Pandemic

The study examined the influence of Integrated Payroll and Personnel Information System (IPPIS) on Sustainability of University Education in Nigeria after Covid-19 Pandemic.  Ex-post-facto design was used for this study. Two research hypotheses were formulated and tested. Population of the study was 137,698 lecturers in 43 public universities in Nigeria. Yamane (1967) formula and simple random sampling technique were used to generously draw a sample of 420 respondents from the population. The validated instrument for the study was Integrated Payroll and Personnel Information System Rating Scale (IPPISRS).The internal consistency of the instrument was determined through a pilot test which yielded a Cronbach Alpha reliability coefficient of .74 to .79 for the sub-scales. The data obtained from the field were analyzed using Simple linear regression at .05 level of significance. Results of the study indicated that Sustainability of University Education in Nigeria after Covid-19 Pandemic is significantly influence by accurate budget estimate (P = .000, F = 4.231) and elimination of payroll fraud (P = .001, F = 3.971). Based on the results, it was recommended that university budget estimation should be made accurate through computerization in order to ensure the sustainability of university education after COVID-19 pandemic and university staff payroll should be automated in order to check payroll fraud for sustainability of university education after COVID-19 pandemic.
Comprehensive review on Clustering Techniques and its application on High Dimensional Data

Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy
A Feature Selection Method for Classifying Highly Similar Text Documents

In the era of big data, the importance of data classification is increasing. However, when it comes to classifying text documents, several obstacles degrade classification performance. These include multi-class documents, high levels of similarity between classes, class size imbalance, high dimensional representation space, and a low frequency of unique and discriminative features. To overcome these obstacles and improve classification performance, this paper proposes a novel feature selection method that effectively utilizes both unique and overlapping features. In general, feature selection methods have ignored unique features that occur only one class because of low frequency while it provides better discriminative-power. On the contrary, overlapping features, which are found in several classes with high frequency, have been also less preferred because of low discriminative-power. The proposed feature selection method attempts to use these two types of features as complementary with aims to improve overall classification performance for highly similar text documents. Extensive numerical analysis have been conducted for three benchmarking datasets with a support vector machine (SVM) classifier. The proposed method showed that not only the class with high similarity but also the general classification performance is superior to the conventional feature selection methods, such as the global feature set, local feature set, discriminative feature set, and information gain.
An Adaptive Data Hiding Method for Compressed Videos in HEVC Standard

High efficiency video coding (HEVC) standard is highly contributive in data hiding. Combining the HEVC standard with data hiding methods is a complex task, and because the highly efficient coding process is a powerful attack, eliminating some of the original video data would not have a significant effect on obtaining the best compression rates and transmission efficiency. The data embedded in the pixels is lost when compressed with the HEVC standard. In order to solve this problem, we need to use features other than the pixel value .In this paper, an efficient data hiding method is proposed through intra prediction modes in HEVC, where the intra prediction modes is applied as the secret data carrier obtained from the N smallest prediction units. The experimental results show that the average PSNR decreased by 0.11 db, while the bit rate increased by on average 0.25%.
Genetic Algorithm Reflects the Process of Natural Selection

Genetic Algorithm (GA) may be a search-based optimizationtechnique supported the principles of Genetics and survival. It’sfrequently wont to find optimal or near-optimal solutions todifficult problems which otherwise would take a lifetime tounravel. It’s frequently wont to solve optimization problems, inresearch, and in machine learning. Nature has always been anexcellent source of inspiration to all or any mankind. GeneticAlgorithms (GA’s) are search based algorithms supported theconcepts of survival and genetics. GA’s are a subset of a waylarger branch of computation referred to as EvolutionaryComputation. GA’s were developed by John Holland and hisstudents and colleagues at the University of Michigan, mostnotably David E. Goldberg and has since been tried on variousoptimization problems with a high degree of success.