Reviewerly/exHarmony huggingface dataset:
STUDY AND POPULATION DENSITY OF ANURANS IN GANDIPHALI AND GODLYAHEDI, TEHSIL LADPURA, (DISTT) KOTA, RAJASTHAN, INDIA

Abstract- Gandiphali located (25 5 11 and 25 4 22 north latitude and 76 0 24 and 76 0 19 east longitude area is and godlyahedi (25 7 50 and 25 8 30 north latitude and76 1 10 and 76 1 30 in east longitude ) both villages are the medium size villageslocated in Ladpura Tehsil of Kota district, Rajasthan, both for 20km from Kota to eastGandiphali and north-east Godlyahedi.Both villages are rich in biodiversity of flora and fauna and also the rich biodiversityof Anurans.The study is carried out from September 2018 to February 2019, and by theobservation is the knowledge gained about the total population and density of anuransand to identify the species of anurans. Direct sighting method is used for getting evidence ofthe species of frogs and toads in the area, the field observations and data collected weekly,with the help of high quality Camera and other supporting Equipment‟s and we capture thephotos and videos of anurans for identify the species, by using key of anurans and BNHSBook. Grid method used for population estimation of anurans in the area.Identify Hoplobatrachus tigerinus, Rana tigerina also known as Indian bull frog,Euphlyctis cyanophlyctis also known as skittering frog, Bufo melanostictus, Common Indiantoad. In the study six wells, 4 sights point near the wells and 6 sights point in pond toIdentify species as well as density and diversity. Study reveals population and density ofanurans that 15-25 anurans found in per 20 square meter area. So per whole areacalculation the population and density is 292/4218.2566 ⇨ 0.07 anurans per squaremeter. Our study is based on conservation strategies for near future to protect the anurans,we observe the rich diversity of anurans in the both area, our study suggests to protect thearea because of rich diversity of anurans.Key word – Anurans, Identification, population
Myten om NOCCO -En multimodal kritisk diskursanalys av NOCCOs Instagramkonto

The aim of this study is to analyze the functional beverage NOCCO’s Instagram account. The purpose is to expose how they use discourses of health and fitness in their marketing. In particular we an ...
Exploring the Plot Analysis 'Kami (Bukan) Sarjana Kertas Novel': Seeking for Characters' Problems

This research purposes to analyze the plot of novel written by Indonesian writer named J.S Khairen entitled “Kami (Bukan) Sarjana Kertas”. Chooses this novel because the idea or main story of this novel related to how young people these days recognizing their own passions, how they struggling from many problems that come on their journey to reach their dreams. This analysis using qualitative research as its methodology to identify the plot of this novel. Novel as a part of prose in literature has two elements, such as intrinsic and extrinsic elements. However this study only focuses its analysis on the intrinsic elements. This study revealed the plot as a structure of events, which is as shown in the arrangement and presentation of the events to accomplish a certain artistic value. The plot is manifested through acts, manner, and attitude of story main characters based on Teori Pengkajian Fiksi proposed by Nurgiyanto (2012).
Prominent Feature Selection for Sequential Input by Using High Dimensional Biomedical Data set

The main aim of feature selection algorithms is to select prominent (optimal) features that are not irrelevant and redundant. Reducing the number of features by keeping classification accuracy the same is one of the critical challenges in Machine Learning. High dimensional data contains thousand of features with the existence of the redundant and irrelevant features which negatively affect the generalization capability of the system. This paper designs the improved genetic-based feature selection (IGA) for Online Sequential—Extreme Learning Machine (IGA-OSELM) algorithm with additive or radial basis function (RBF). Experimental results are calculated for the Extreme Learning Machine (ELM), OSELM, IGA-ELM, and IGA-OSELM. With the result, it is inferred that IGA-OSELM maintains the classification accuracy by minimizing 58.50% features. The proposed algorithm is compared with the other popular existing sequential learning algorithms as the benchmark problem.
Current education, an innovative change to improve university education through innovation and collaborative work

Education must migrate to issues that today's society demands, attending to present and future scenarios in order to provide efficient and effective solutions. The study aims to demonstrate that collaborative work impacts the personal, educational and work relationships of students. The work is supported by quantitative data applied to an enrollment of 70 university students. As an innovation, the strategies that are implemented to enhance the abilities and skills of the students are elucidated. In Amazcala, 45% of respondents say that teachers are not up-to-date, 52% of the sample alludes that educational innovation does not help promote collaborative work, and 83% mentioned that they have never managed innovation in class. In this sense, innovation, collaborative work, communication and the use of technology are factors that have a direct impact in the educational field because through them the student can acquire a full development in his school career and in the workplace, what permeates the path that current education must follow to develop favorable knowledge in students who are about to enter the professional field.
Probabilistic vs deep learning based approaches for narrow domain NER in Spanish

This work presents an experimental study on the task of Named Entity Recognition (NER) for a narrow domain in Spanish language. This study considers two approaches commonly used in this kind of problem, namely, a Conditional Random Fields (CRF) model and Recurrent Neural Network (RNN). For the latter, we employed a bidirectional Long Short-Term Memory with ELMO’s pre-trained word embeddings for Spanish. The comparison between the probabilistic model and the deep learning model was carried out in two collections, the Spanish dataset from CoNLL-2002 considering four classes under the IOB tagging schema, and a Mexican Spanish news dataset with seventeen classes under IOBES schema. The paper presents an analysis about the scalability, robustness, and common errors of both models. This analysis indicates in general that the BiLSTM-ELMo model is more suitable than the CRF model when there is “enough” training data, and also that it is more scalable, as its performance was not significantly affected in the incremental experiments (by adding one class at a time). On the other hand, results indicate that the CRF model is more adequate for scenarios having small training datasets and many classes.
MUSIC WITH AND WITHOUT IMAGES

Ji Kang's 嵇康 argument against the presence of emotions in music in “Sound Has Neither Sorrow nor Joy” (sheng wu ai le lun 聲無哀樂論) relies centrally on his claim that harmonious sounds are “without image” (wu xiang 無象). In contrast, the Ru 儒 (Confucian) view of music is that it transmits images and emotions between musicians and listeners, which provides the basis for the musical cultivation of virtue (de 德). In this paper, I provide a reading of Ji Kang's argument that elucidates key differences between music with and without images, and offer an analysis of these differences in terms of intransitive musical meaning.
Research Guides: Library and Canvas Integration: Home

This guide is to help navigate and utilize all the library resources available to faculty and instructors in Canvas.
Investigating Factors for Assessing the Quality of Academic User-Generated Content on Social Media

As scholars increasingly use academic user-generated content from various social media to support their research, evaluating the quality of this content becomes challenging. This study recruited researchers to conduct retrieval experiments and participate in post-experiment interviews, aiming to identify the factors that affect their evaluation of various types of academic user-generated content. By analyzing the interview data, 23 factors affecting the evaluation were obtained. This pilot study provides foundations for building a quality evaluation model for academic user-generated content.
A Self-Sensing Technique Using Inductively-Coupled Coils for Deformable User Interfaces

We present a device shape sensing method using coils that are inductively coupled in the horizontal direction. By observing the change in the coupling characteristics between the coils arranged side by side, the positional relationship between the coils can be detected. By using the proposed technique, the shape of an object having a coil can be sensed without an external sensing mechanism such as a camera. In this paper, we show two types of prototypes, a rigid device combining multiple small tiles and a flexible device, and verify the basic characteristics. By utilizing the proposed technique, a deformable user interface with various inputs can be constructed simply by forming simple metal wiring on the device.
Limits of preprocessing

It is a classical result that the inner product function cannot be computed by an AC0 circuit [17, 1, 22]. It is conjectured that this holds even if we allow arbitrary preprocessing of each of the two inputs separately. We prove this conjecture when the preprocessing of one of the inputs is limited to output n + n/(logω(1)n) bits. Our methods extend to many other functions, including pseudorandom functions, and imply a (weak but nontrivial) limitation on the power of encoding inputs in low-complexity cryptography. Finally, under cryptographic assumptions, we relate the question of proving variants of the main conjecture with the question of learning AC0 under simple input distributions.
LibGuides: FHS-PK | COVID-19 - Accessing Library Resources and Services: E-Journals

This how-to guide will educate you about the library resources and services during the lockdown situation in Pakistan.
An evaluation of image preprocessing for classification of Malaria parasitization using convolutional neural networks

In this study, the impact of multiple image preprocessing methods on Convolutional Neural Networks (CNN) was studied. Metrics such as accuracy, precision, recall and F1-score (Hossin et al. 2011) w ...
Proposta de Curso Introdutório de Serviço de Referência em Bibliotecas

The educational product is a proposal for an introductory Reference Service course to be offered to library assistants at the Federal Institute of Sergipe - IFS. The objective of thecourse is to train assistants so that they can collaborate more actively withlibrarians in serving the users of the libraries where they work. The Introductory CourseReference Service in Libraries (figure 01) was organized in order to demonstrate theimportance of satisfying users' information needs through goodattendance. In addition to the theoretical content, strategies were used to provoke reflection andresearch within the scope of actions developed in libraries.
A Method of Trajectory Anonymization with Adjustable Usefulness

Movement trajectories can provide useful information for all fields. However, they may have high privacy parameters, sharing trajectory data with other operators without anonymization carries the risk of linking movement trajectories to individuals. Therefore, it is necessary to consider applying privacy protection to trajectory data. Anonymization indicators, such as k-anonymity are generally adopted for anonymization of trajectory data. Some studies modify position information to achieve anonymity. This modification sometimes produces inaccuracies in data sets. Moreover, they cannot decide on which balance to change location information and timestamp. In this study, to reduce the modification distance of the position, we propose an algorithm that allows the mismatch of time when the position information is acquired within a certain range. Further, we define indicators that represent distortions of position and time information. As a result of comparing the proposed method and the existing method, the usefulness of the proposed method is shown.
Using Bidirectional Encoder Representations from Transformers for Conversational Machine Comprehension

Bidirectional Encoder Representations from Transformers (BERT) is a recently proposed language representation model, designed to pre-train deep bidirectional representations, with the goal of extra ...
Pieces for Duo Pianos

Author(s): Hilditch, Francesca | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
Abstracting common errors in the learning of time intervals via cognitive diagnostic assessment / Phei-Ling Tan, Liew-Kee Kor and Chap-Sam Lim

This study engaged the cognitive diagnostic assessment (CDA) to abstract the common errors inthe learning of time intervals based on pupils’ knowledge states. CDA is a feasible testing toolthat can inform us where a test taker may be prone to making errors in the tests. In this study, acognitive diagnostic model with six attributes and 12 test items was created to evaluate pupils’performance in a diagnostic test on “duration of two inclusive dates”. A total of 269 primary sixpupils from 11 elementary schools participated in the study. The diagnostic test scores wereanalyzed using the Artificial Neural Network which generated 12 knowledge states (KS). Resultshows that “100000” was the leading KS. The common errors associated with this KS inhierarchical order of prominence were: (i) exclude starting date as a day in duration; (ii) errorin regrouping; (iii) compute incorrectly the sum of the two given dates; and, (iv) expressincorrectly the time measurement in months and days. These identified common errors wouldprovide a valuable basis for remedial teaching of the topic “Time”. It also allows mathematicsteachers to identify the inadequacy of an earlier teaching strategy and to engender an improvedapproach to help struggling learners shore up their basic skills.
Study Mode and System Design of Mobile Knowledge Sharing Based on Micro Video.

With the development of mobile Internet, students tend to use mobile phone software to obtain information. Most knowledge learning software is dedicated to sharing and disseminating knowledge by video. The form of video enables learners to better understand knowledge. With the rise of mobile short video, most mobile users will watch short video in their spare time. This paper combines micro video and knowledge sharing, and constructs a knowledge sharing platform based on micro video, where users can use short micro video to share knowledge and learn knowledge. In this paper, firstly, the knowledge sharing mode based on micro video is constructed, the user integration system is implemented, and the mode of user internal evaluation video is designed. Secondly, the requirements of the platform are defined and analyzed. Finally, the logic model of the platform is designed, and the overall design and detailed design scheme are proposed. The construction of this platform promotes the further development of mobile learning, which is conducive to users' autonomous and fragmented learning anytime and anywhere.
Techniques of Classical Mechanics

This book serves as an excellent stepping stone from introductory physics to graduate-level physics, it provides a level field for the various techniques used to solve problems in classical mechanics, it explains the Lagrangian and Hamiltonian methods more simply, and is a must for junior and senior physics undergraduates.
CCS Libraries: Liberal Arts: Overview

Resources and help for the Liberal Arts undergraduate department broadly, with links to more specific guides for sub-disciplines within the department.
Edmodo: An Instructional Media to Teach English Grammar

This report aimed at measuring the effectiveness of Edmodo to teach English grammar. Edmodo as an online instructional media was used to teach English grammar. This research used quantitative approach with a pre experimental research design. The second semester students of English Department STKIP PGRI Tulungagung were taken as the sample of this study. Since there is no control group, the second semester students were assigned as the experimental group. One group pretest-posttest research design was used. From the analysis, it was found that the mean of the pretest was 59.47 and the mean of the posttest was 73.48. Based the test of assumptions, the data was found to be not parametric. So, the analysis using Wilcoxon test was used to test the hypothesis. The result the significant value (.027) was lower than level of significant .05. Therefore, it can be concluded that the students have significantly better grammatical mastery after being taught using Edmodo.
Client-Oblivious OPRAM

Oblivious Parallel RAM (OPRAM) enables multiple clients to synchronously make read and write accesses to shared memory (more generally, any data-store) whilst hiding the access patterns from the owner/provider of that shared memory. Prior work is best suited to the setting of multiple processors (or cores) within a single client device, and consequently there are shortcomings when applying that work to the multi-client setting where distinct client devices may not trust each other, or may simply wish to minimise – for legal reasons or otherwise – the volume of data that is leaked to other client devices. In prior constructions, obliviousness from the storage provider is achieved by passing accesses between the clients in one or more sorting networks, both before and after the logical access is made to the shared memory: this process inherently leaks the contents of the accesses to those other clients. In this paper we address this issue by introducing the notion of client obliviousness for OPRAM, which asks that clients should only learn as much as is necessary for the scheme to function correctly. We provide an instantiation using established tools, with careful analysis to show that our new notion and regular OPRAM security are met. In the process, we give new insight into the use of the OPRAM model in the context of outsourced storage.
Deep Learning Approach to Material Properties

In this thesis, we consider a deep learning approach to predict material properties. Primarilywe study artificial neural networks (ANN), which predict the energy distance to the convexhull (measure of stability) of perovskites. Further, we explore if the networks can begeneralised to predict band gaps and unit cell volume. We also demonstrate total energy calculationsusing density functional theory as it is essential for building the datasets requiredfor ANN studies.More detailed, we re-implement an ANN architecture proven successful at predicting formationenergies and reproduce the corresponding results. This ANN model is then directlyapplied to predict the distance to the convex hull for the perovskites. We explore furtherimprovements to the input representation and the network itself. In the following we usetransfer learning to generalise the model to predict band gaps and unit cell volume.While reproducing the results of the re-implemented model, the results are considerably improved.For the perovskites, we find that our deep learning model achieves significantly betterperformance than conventional machine learning methods. This improved model allowed fora high-throughput search of tetragonal quaternary perovskites, which resulted in identifying437 new potentially stable quaternary perovskites. In line with previous research, we findthat the models performance varies over the periodic table. While the transfer learning forthe unit cell volume is successful, band gaps were more difficult to predict.
Context matters : Classifying Swedish texts using BERT's deep bidirectional word embeddings

When classifying texts using a linear classifier, the texts are commonly represented as feature vectors. Previous methods to represent features as vectors have been unable to capture the context of ...
Developing Relationship Among Risk Factors and Project Factors for Large Scale Healthcare Applications

The impact and probability of healthcare software risks are widely discussed in software risk management. The identification of healthcare software risk factor will not be useful without measuring its impact and probability. The impact filed determines the damage level of the risk factors if it appears, while the probability determines how probable is the existence of that risk. The risks of having high probability and higher impact levels have to be addressed urgently. The orientation of the risks takes place in response to unbalanced resource allocation. Establishing an association among the project factors and risk factors helps in gaining more effective control of the resource allocation to the healthcare application development. This paper identifies the conceptual and numerical association between the project factors and risk factors by conducting a mixed-method research analysis to optimize the resource allocation and reduce (eliminate) the software risks in large scale healthcare applications. The resultant associations between the project factors and risk factors are categorized as weak or strong based on the resultant data of this study and can be used as a guideline document for developing the large scale healthcare systems.
Image Denoising using Improved Non-Local Means Filter

Local filters used for denoising images do not account for nonlinear structure within the image, where similarities can be shared between spatially distant pixels which are overcome by nonlocal filters by allowing for the use of self-predictions . In this method, an improved version of nonlocal denoising filtering has been presented using quaternion non coupled means filter. The proposed method has been applied to various noisy images and shows good results in terms of performance characteristics.
Practice of Introductory Education for Department of Mechanical Engineering in College of Technology Using Wind Turbine Paper Model

In recent years, a decrease in motivation of learning, has become a problem in the College of Technology. In order to resolve this problem, measures are needed from the lower grades of the higher education institutions. However, to educate both theory and actual practice at the same time for student who have less machining and processing technical skills is difficult in introductory education. In this study, we developed the experimental teaching tools for college student, using wind turbine paper model and wind turbine analysis software. The teaching tool provides the both theory and actual practice for introductory education. Currently, we are using the tools in the experiment of Mechanical Engineering first and second grade in the College of Technology.
Object-Oriented Programming (OOP)

Delphi supports full object-oriented programming (OOP). In OOP, the most fundamental entity is an object. An object is characterized by its state (implemented by data) and what sort of capabilities it has (functionality). For example, a computer can be described as an object in a particular state (e.g., available memory, screen resolution, computational power, etc.) being able to provide several functions (e.g., connect to the Internet, edit a text file, convert an image, etc.). In the rest of the chapter, we look at the fundamentals of OOP in Delphi.