Reviewerly/exHarmony huggingface dataset:
No-reference image quality assessment based on residual neural networks (ResNets)

Image Quality assessment (IQA) is a tricky field to master, as it attempts to measure the quality of an image with reference to the complex human visual system. In IQA, there are three dominant strands of research, namely: fullreference, reduced-reference and no-reference image quality assessment. No-reference image quality assessment is the hardest one to achieve, as the reference images required for determining the quality of the given images are not available. In one of our previous papers, we quantified no-reference IQA, using state-of-the-art multitasking neural networks, particularly the VGG-16 and shallow neural networks. We achieved good accuracy for the classification of most distortions. However, one of the drawbacks of the networks used was that the classification accuracy was not good for JPEG2000 compressed images. These images were classified incorrectly as blurry or noisy images. In this paper, we try to classify compressed images more accurately using residual neural networks (ResNets). These deep learning models were built based upon micro-architecture modules and are specific task-focused entities, each one determining the distortion type and distortion level of an artifact present in the image. The test images were obtained from the LIVE II, CSIQ, and TID2013 databases for comparison with previous work. In contrast to our previous approach, where the training was limited to one specific distortion at a time, we train the collection of ResNets with all the possible distortion types present in the test databases. Preprocessing of the images is done using local contrast normalization and global contrast normalization methods. All the hyper-parameters in the ResNets collection, such as activation functions, dropout regularizations, optimizers are tuned to produce optimal classification accuracy. The results are evaluated with different methods such as PLCC, SROCC and MSE and high linear correlation is achieved using the ResNets collection and compared to previous results.
Working with Text

Working with text will be one of the most regular tasks a C++ programmer will have to deal with. You are likely to need to read in user input, write out messages to the user, or write logging functionality for other programmers to more easily debug running programs. Unfortunately, working with text is not an easy or straightforward task. All too often, programmers rush into the job and make fundamental errors with their text handling which become major issues later into their projects. The worst of these is not properly accounting for localized versions of text strings. Working with English character sets is generally easy as all English characters and punctuation fit into the ASCII character set. This is convenient as every character needed to represent the English language can fit into a single 8-bit char variable. Things become problematic as soon as you are required to support foreign languages with your programs. Every character which you need to support will no longer fit into a single 8-bit value. C++ can handle non-English languages in a number of ways which will be covered in this chapter.
Libraries: Submission Workflow in OJS 3: Publishing a Full Issue in OJS

This guide will help work you through the four stages; Submission, Review, Copyediting, and Production of the OJS submission workflow.
Research Guides: Using HathiTrust: What is HathiTrust?

Description of how to find and use Hathi Trust resources at the University of New Hampshire
Cooperative Locality of MacDonald Codes

맥도날드 부호(MacDonald code)는 잘 알려진 부분접속 복구 부호(locally repairable code)이다. 지금까지 이 부호의 부분접속수(locality)는 2 또는 3임이 잘 알려져 있다. 이는 단일 소실(single failure) 심볼을 복구하기 위한 부분접속수 이다. 최근 여러 소실 심볼을 복구하기 위한 협력 부분접속수(cooperative locality)에 대한 논의가 활발하다. 본 논문에서는 맥도날드 부호에서 두 소실(double failures) 심볼을 복구하기 위한 협력 부분접속수가 3 또는 4임을 찾고 이를 증명한다.
ANALISIS PENGARUH PARTISIPASI, TRANSPARANSI, DEMOKRATIS DAN SALING PERCAYA TERHADAP PENGELOLAAN DANA BANTUAN OPERASIONAL SEKOLAH (BOS) (STUDI KASUS DI SMA NEGERI 1 RUMBIA)

The purpose of this study was to determine the effect of participation, transparency, democracy and mutual trust in the management of school operational assistance funds (BOS) in Rumbia 1 Public High School. This type of research is a case study. The data used are primary data obtained from the distribution of questionnaires. The analysis technique used is multiple linear regression analysis. The results of data analysis show that 1). participation does not have a positive effect on the management of BOS funds, 2). Transparency has a positive effect on the management of BOS funds, 3). Democracy has a positive effect on the management of BOS funds, 4). Mutual trust has a positive effect on the management of BOS funds. Keywords: participation, transparency, democratic, mutual trust and management of BOS funds
LibGuides: COVID-19 Educational Resources for home use: Resources from School Library System partners

These are curated resources for the School Librarian to utilize as they see fit. Site went live on March 23, 2020.
LibGuides: Reading Digital Content: Magazines

This guide will help you identify different types of information you are likely to find online
A novel SARMA-ANN hybrid model for global solar radiation forecasting

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 – 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.
Sistem Informasi Program Kreativitas Mahasiswa berbasis Web Service dan Microservice

The information system is faced with a significant development, it needs to have not only the policy changes but also the features as an effort update, according to necessity both internal and external business rule systems. This research aims to form a microservice infrastructure for updating changes in flow of business process to the development of information systems. This Research methods come under both field studies and literature to the subject matter related with the development of Information Systems in managing data of program kreativitas mahasiswa (PKM), Web services and Microservice Architecture. The design is using Prototype model to represent graphically the workflow of the system Model which is using the Unified Modeling Language (UML) tool, Managing the Data Base Management System (DBMS) application using GORM, Go Validator as a data validation tool, and Gorilla Mux as the REST rooter. The application runs by utilizing the Advanced Message Queuing protocol. In conclusion, the implementation of webservice-based university internal information system development with microservice architecture for PKM is reporting activities in where the relationship between units in data transactions (publish, send automatically) is carried out with a duration of time 0,00 / sec.
LibGuides: COVID-19 Resource Guide for Students: Employment

Fun and helpful resources aimed to assist those in home quarantine during the coronavirus pandemic of 2020
Assessing Discourse Relations in Language Generation from Pre-trained Language Models.

Recent advances in NLP have been attributed to the emergence of large-scale pre-trained language models. GPT-2, in particular, is suited for generation tasks given its left-to-right language modeling objective, yet the linguistic quality of its generated text has largely remain unexplored. Our work takes a step in understanding GPT-2's outputs in terms of discourse coherence. We perform a comprehensive study on the validity of explicit discourse relations in GPT-2's outputs under both organic generation and fine-tuned scenarios. Results show GPT-2 does not always generate text containing valid discourse relations; nevertheless, its text is more aligned with human expectation in the fine-tuned scenario. We propose a decoupled strategy to mitigate these problems and highlight the importance of explicitly modeling discourse information.
Serials Availability and Use in Nigeria Academic Libraries By The Postgraduate Students Of Osun State University, Osogbo, Nigeria

In the face of the eminence position of serials usage amongst the resources acquired in the library most especially academic libraries despite their exceptional values as a reliable and dependent for researchers and the increasing cost of their acquisition, serials have not been satisfactorily utilized by the postgraduate students in tertiary institutions. This study, therefore, was undertaken to find out the availability and use of serials among postgraduate students in Osun State University and how this affects their academic performance. The population of the study consisted of 140 postgraduate students, with an ultimate high return rate. The findings of the study revealed that postgraduate students acknowledged the existence of serials. However, the study identified problems that are against the effective use of serials which need to be tackled to ensure that patrons are satisfied. Hence, the recommendations made consist of the provision of necessary infrastructural facilities that will make the library conducive for the users. Similarly, the management should organize information literacy skills seminar for the library users to help them to acquire skills on how to search for and locate the information resources needed in the library.
Motivation for Teaching Students at a University: The Ecologization of Pedagogical Communication as a Methodological Technique

One of the ways of increasing the educational motivation of students called by the authors the method of pedagogical discourse «greening», which means the ability of the pedagogue to organize the meaning part of his speech, giving it the ability to awaken the student's propensity to develop an optimistic attribute style.The verbalization of some pedagogical tactics, considered in the scientific literature as tactics increasing the educational motivation of studentspraise, censure, encouragement etc. are assessed from the point of view of compliance with the optimistic standard.The Pedagogical Speech Assessment Method has been applied for the first time in this work and includes the evaluation of the life events attribution of M. Seligman (personalization/control locus, stability/permanence and generalization).Emotional features that make it difficult to communicate between teachers and students -the prevalence of negative emotions, low emotional controlare considered in the context of the theory of learned helplessness and learned optimism by M. Seligman.The demotivational potential of learned helplessness and the motivational potential of optimism are described in the paper.
Formal Context Representation and Calculus for Context-Aware Computing

Context is a rich concept that is mostly understood and used with different representations and interpretations in many different fields. This variety of usage adds both richness and vagueness, thus creating more complexity to comprehension, interpretation, and reasoning with contexts. As pervasive computing technology becomes more and more intrusive there is a need to construct formally verifiable context-aware computing environment, in which human dignity is preserved through safety, security, and privacy. These features cannot be ensured unless context notion is formalized, both in representation and reasoning. Motivated by this concern this paper introduces a formal context representation and a context calculus which can be used to build context models for many applications.
LibGuides: SUBR Free Online Course Materials: Home

In response to the COVID-19 outbreak, John B. Cade Library worked with the LOUIS Library Consortium to ensure that all students have access to course materials. RedShelf has partnered with publishing communities to provide up to 7 free EBooks
LibGuides: Virtual Reality: Videos

A guide to assist with the use of VR headsets and in finding excellent resources Videos on why using VR in education is important for our students.
An open speech resource for Tibetan multi-dialect and multitask recognition

This paper introduces a Tibetan multi-dialect data resource for multitask speech research. It can be used for Tibetan multi-dialect speech recognition, Tibetan speaker recognition, Tibetan dialect identification, and Tibetan speech synthesis. The resource consists of 30 hours Lhasa-U-Tsang dialect; 8.7 hours Kham dialect, including 3.4 hours Yushu dialect, 3.3 hours Dege dialect and 2 hours Changdu dialect; 10 hours Amdo pastoral dialect. Other resources are also provided for Lhasa-U-Tsang dialect including phoneme set, pronunciation dictionary and the codes for constructing the Lhasa-U-Tsang speech recognition baseline system. Meanwhile, for Tibetan multi-dialect and multitask speech recognition, the codes and recognition results based on WaveNet-connectionist temporal classification (WaveNet-CTC) are provided. All the resources are free for researchers and publicly available, which effectively compensates for the shortage of public Tibetan multi-dialect speech resources in order to promote the development of Tibetan multi-dialect speech processing technology.
Språklig tillgänglighet i de nationella proven kontra läromedelsprov i matematik : En studie om grammatiska strukturer i matematikupggifter

This study investigates the linguistic accessibility of the national tests in mathematics for grade 9 in Sweden, and compares it with tests from a textbook in mathematics used in grade 9. The study ...
The Effects of Test Mode and Contiguity of Material on Geometry Test Scores, Cognitive Load, and Self-Efficacy

In recent years, the development and usage of computer-based tests for educational assessment has grown.The computer-based tests are typically derived from paper-based tests, with the assumption the tests being administered in different modes are equivalent.Studies examining this test mode effect have mainly focused on the test scores, but few have examined other factors important to test performance.The current study examines the test mode effect for geometry test problems, while also putting in perspective the factors of self-efficacy and cognitive load as both are significant components in performance.The results suggest test scores and cognitive load for geometry problems are similar across the test modes, however learners' self-efficacy significantly decreases when performing the geometry test problems in computer-based test mode.The findings provide insight into the test mode literature and give direction for future lines of research.
Saphiraweb: An open-source cloud platform for E-health analysis

Here we present a cloud platform for e-health analysis. The goal of this research is to offers a standardized way to secure registry and share biometric impedance data from many volunteers of distinct locations. We perform the corporal analysis of a dataset from elderly volunteers who perform regular gym exercises. Our goal is to follow the evolution of health quality according to the percentage of body fat, hydration, and corporal lean mass. These factors can potentially indicate how physical exercises can contribute to improving the health quality of this population. Our result is two-fold: (a) a qualitative overview of the development of an open-source system for e-health analysis has many issues to host a useful large biometric dataset and (b) our preliminary analysis indicates that regular physical exercises contribute reducing corporal body fat, and improve the quality of health.
The mediating effects of career aspiration level between planned happenstance skills and the career decision-making self-efficacy perceived by pre-service early childhood teachers

본 연구의 목적은 예비유아교사를 대상으로 계획된 우연기술, 진로결정자기효능감, 진로포부수준의 관계를 확인하고, 계획된 우연기술과 진로결정자기효능감의 관계에서 진로포부수준의 매개효과를 검증하고자 하였다. 이를 위해 3년제 전문대학교 유아교육과에 재학중인 227명을 대상으로 연구를 수행하였다. 연구결과 첫째, 예비유아교사의 계획된 우연기술은 진로결정자기효능감에 영향을 미쳤다. 둘째, 계획된 우연기술은 진로포부수준에 영향을 미쳤으며, 계획된 우연기술의 하위요인 중 호기심, 인내심, 낙관성, 위험감수 능력이 높을수록 진로포부수준이 높았다. 셋째, 진로포부수준은 진로결정자기효능감에 영향을 미쳤으며, 진로포부수준의 하위요인 중 성취포부, 교육포부가 높을수록 진로결정자기효능감이 높았다. 넷째, 계획된 우연기술이 진로결정자기효능감에 미치는 영향에서 진로포부수준은 부분매개 효과를 나타내었다. 본 연구결과를 통하여 예비유아교사의 진로결정자기효능감 증진에 영향을 미치는 주요 진로 변인을 밝힘으로써 예비유아교사의 진로상담 및 프로그램 개발의 방향에 시사점을 제공하였다.The aim of this study is to examine the relationship among planned happenstance skills, career decision-making, self-efficacy, and career aspiration levels, investigating whether career aspiration level plays a mediating role between the two skills. For this purpose, the subjects were 227 students attending a college which offers three-year degree program, with a major in early childhood education. The results of this study were as follows: First, planned happenstance skills of pre-service early childhood teachers influenced the career decision-making self-efficacy. Second, planned happenstance skills affected the career aspiration level. Specifically among the sub-factors of planned happenstance skills, the higher the level of curiosity, patience, optimism and risk-taking ability, the higher the level of career aspiration level. Third, the level of career aspiration had an impact on the career decision-making self-efficacy, and among the sub-factors of career aspiration level, the higher the achievement aspiration and education aspiration, the higher the career decision-making self-efficacy. Fourth, the career aspiration level partially mediated between planned happenstance skills and the career decision-making self-efficacy. The findings of this research provide implications for career counseling and program development for pre- service early childhood teachers by clarifying the main variable affecting their career self-efficacy.
How Affordances of Immersive Visualization Systems Affect Learning Outcomes through Aesthetic Experience.

Virtual reality has received attention as an environment for learning, yet little is known about the effectiveness of bringing the immersive visualization systems into the university classrooms. Building upon prior literature on immersive technology and the theory of affordance, we develop a model investigating how the features afforded by an immersive visualization system escalate users’ engagement, which in turn increases their learning outcomes. We will test the model with undergraduate students who have experienced with an immersive visualization system in the classroom setting. We believe that our work will enrich the existing literature on virtual reality in education and provide insight into the design of immersive representations and the structure ofimmersive learning paradigm. Full paper available at https://aisel.aisnet.org/pacis2019/153.
LibGuides: Your Library @ Home!: The HBPL Quaranzine

Digital access to everything that Huntington Beach Public Library has to offer you! HBPL's Quaranzine is an online blog of local art and writing created about / during COVID-19, submitted by the local Huntington Beach community.
Can users infer missing information in conversational case-based reasoning: The effects of providing context

Fault diagnosis can be supported by conversational case-based reasoning, but case descriptions are often incomplete. However, users might be able to infer missing information when the system provides context information. We investigated how such information affects ratings, solution times, and learning. Using a simulated case-based reasoning system, participants could ask about the presence of symptoms to find out how well each case matched the current situation. The system answered with yes or no, simply stated that it did not know the answer, or provided three or 15 pieces of context information (suggesting similarity, dissimilarity, or including no relevant information). Only context suggesting similarity increased accuracy, while all types of context increased subjective certainty. The amount of context had little impact: a high amount decreased speed and learning, but much less than expected. Taken together, context information can be helpful, but performance outcomes depend on the type of context provided.
A Continuous Decision Based Multi Kernel Median Filter For Noise Removal on Brain MRI Images

Impulse noises in images are caused by bit errors in transmission and signal acquisition.  Salt and pepper noise and random noise are also known as Impulse Noise.    As per the statistical analysis of noise in Brain MRI image shows salt and pepper noise is one of the most common which affect the accuracy of the tumor detection.  Many nonlinear algorithms have been proposed to remove salt and pepper noise. But without damaging the edges is the difficult Task. Noise removal without damaging the edges is proposed in this paper.   If the noise density increases, the effectiveness of the filter will be decreased. This is the major drawback of the existing algorithms.  This paper discusses many noise removal techniques and proposes a novel noise removal technique using Continuous Decision Based Multi Kernel Median Filter (CDBMKMF). The proposed CDBMKMF algorithm attempts to eliminate noise in high noise density images with better  PSNR values.   Image pixels are checked for the occurrence of salt and pepper noise and removed effectively.  Using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR), the proposed methods are validated and compared with existing algorithms.  This paper also evaluates the proposed algorithm with standard and unsymmetrical median filters.
Support an S-duct optimization design study using state-of-the-art Machine Learning techniques

Manage the state-of-the-art method and tools in computational engineering design area, including stochastic optimisation, machine learning, computational fluid dynamics, and flexible geometry management algorithms
Selecting Electrical Billing Attributes: Big Data Preprocessing Improvements

The attribute selection is a very relevant activity of data preprocessing when discovering knowledge on databases. Its main objective is to eliminate irrelevant and/or redundant attributes to obtain computationally treatable issues, without affecting the quality of the solution. Various techniques are proposed, mainly from two approaches: wrapper and ranking. This article evaluates a novel approach proposed by Bradley and Mangasarian (Machine learning ICML. Morgan Kaufmann, Sn Fco, CA, pp. 82–90, 1998 [1]) which uses concave programming for minimizing the classification error and the number of attributes required to perform the task. The technique is evaluated using the electric service billing database in Colombia. The results are compared against traditional techniques for evaluating: attribute reduction, processing time, discovered knowledge size, and solution quality.
Investigating Deep Learning algorithms for end-to-end language-based interaction with domestic robots

A socially assistive robot capable of helping with domestic work through understanding natural language instructions is still considered a difficult challenge. This work investigates how deep learn ...