Reviewerly/exHarmony huggingface dataset:
Image inpainting using directional wavelet packets

The paper presents a new algorithm for the image inpainting problem. The algorithm is using a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of the presented qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image processing applications, in particular, in dealing with the inpainting problem addressed in the paper. The obtained results for this problem are quite competitive with the best state-of-the-art algorithms. The inpainting is implemented by an iterative scheme, which, in essence, is the Split Bregman Iteration (SBI) procedure supplied with an adaptive variable soft thresholding based on the Bivariate Shrinkage algorithm. In the inpainting experiments, performance comparison between the qWP-based methods and the state-of-the-art algorithms is presented.
FACIAL INPAINTING IN UNALIGNED FACE IMAGES USING GENERATIVE ADVERSARIAL NETWORK WITH FEATURE RECONSTRUCTION LOSS

Facial inpainting or face restoration is a process to reconstruct some missing region on face images such that the inpainting results still can be seen as a realistic and original image without any missing region, in such a way that the observer could not realize whether the inpainting result is a generated or original image. Some of previous researches have done inpainting using generative network, such as Generative Adversarial Network. However, some problems may arise when inpainting algorithm have been done on unaligned face. The inpainting result show spatial inconsistency between the reconstructed region and its adjacent pixel, and the algorithm fail to reconstruct some area of face. Therefore, an improvement method in facial inpainting based on deep-learning is proposed to reduce the effect of the stated problem before, using GAN with additional loss from feature reconstruction and two discriminators. Feature reconstruction loss is a loss obtained by using pretrained network VGG-Net, Evaluation of the result shows that additional loss from feature reconstruction loss and two type of discriminators may help to increase visual quality of inpainting result, with higher PSNR and SSIM than previous result.
Usefulness of tissular heterogeneity analysis measured by elastography in the characterization of tyroid nodules

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
Performance Evaluation of Models Established for the Estimation of Diffused Solar Radiation: Case Study Lahore, Pakistan

A suitable design of solar power project requires accurate measurements of solar radiation for the site ofinvestigation. Such measurements play a pivotal role in the installation of PV systems. While conducting such studies,in general, global solar radiation (GSR) is recorded, whereas diffuse component of solar radiation on a horizontalsurface is seldom recorded. The objective of the present study is to assess diffuse solar radiation (DSR) on horizontalsurfaces by using polynomial models for Lahore, Pakistan (27.89 N, 78.08 E) and by correlating clearness index withdiffuse fraction. The established models are compared with some of the existing models from the literature.Performance of models is evaluated by employing five goodness-of-fit (GoF) tests that are, mean bias error (MBE),root mean square (RMSE), Coefficient of Determination (R2), Mean Absolute Percentage Error (MAPE) and Akaike’sInformation Criterion (AIC). The comparison of the results of goodness-of-fit tests with those of existing modelsindicate that the models established in the present study are performed better as compared to the existing models. Thevalues of statistical error analysis further suggested that a cubic model with a good accuracy of 97.5% and AIC of -22.8is relatively more suitable for this climatic region for estimating diffuse solar radiation. The study shows that the modeldeveloped is in good agreement with Elhadidy and Nabi model with an accuracy of 96.1% and AIC of 4.4 andsatisfactory results are obtained for Lahore. The findings can help to give a generous understanding of solar radiation inorder to optimize the solar energy conversion systems. The results of this study provide a better understanding of theassociations between global solar radiation, clearness index and diffused fraction for the region under study.
An Improved Algorithm for Fast K-Word Proximity Search Based on Multi-component Key Indexes

A search query consists of several words. In a proximity full-text search, we want to find documents that contain these words near each other. This task requires much time when the query consists of high-frequently occurring words. If we cannot avoid this task by excluding high-frequently occurring words from consideration by declaring them as stop words, then we can optimize our solution by introducing additional indexes for faster execution. In a previous work, we discussed how to decrease the search time with multi-component key indexes. We had shown that additional indexes can be used to improve the average query execution time up to 130 times if queries consisted of high-frequently occurring words. In this paper, we present another search algorithm that overcomes some limitations of our previous algorithm and provides even more performance gain.
Digitised Academic Credential Verification Using Blockchain

Blockchain is a digitized underlying technology having variety of applications that powers cryptocurrencies such as Bitcoin, Ripple and Ethereum. It has not been limited to just cryptocurrencies but has also been exploited and utilized in different domains such as education, healthcare, finance, electronics, commerce, Internet of Things, governance, etc.Some key attributes of this technology are security, decentralization, immutability, and transparency. In education sector, Blockchain technology can be used for various purposes. It can be used for issuing and verification of educational credentials and records digitally to prevent forgery. Counterfeiting academic certificates has been a longstanding obstacle in the academia. While it is an easy task to counterfeit credentials almost instantaneously, verification of authenticity is a complicated and challenging. Hence, we are proposing a blockchain-based solution to help resolve the problem, named ‘Academic credential verification’ in which we are using smart contract and Ethereum for awarding digital form of certificates to students for easy storage and verification of credentials. It also enables students to share their credentials and records to trusted third parties. Keywords:Blockchain technology, decentralized verification, graduation certificate forgery, graduation certificate verification, technology Cite this Article: Rohan Parekh, Pooja Pillai, Shambhavi Shukla, Suhasini Parvatikar.Digitised Academic Credential Verification Using Blockchain. Research & Reviews: A Journal of Embedded System & Applications. 2020; 8(2): 21–29p.
Holistic Resource Management for Sustainable and Reliable Cloud Computing: An Innovative Solution to Global Challenge

AMinimizing the energy consumption of servers within cloud computing systems is of utmost importance to cloud providers towards reducing operational costs and enhancing service sustainability by consolidating services onto fewer active servers. Moreover, providers must also provision high levels of availability and reliability, hence cloud services are frequently replicated across servers that subsequently increases server energy consumption and resource overhead. These two objectives can present a potential conflict within cloud resource management decision making that must balance between service consolidation and replication to minimize energy consumption whilst maximizing server availability and reliability, respectively. In this keynote talk, I shall discuss a energy-reliability aware resource scheduling approach for holistic management of cloud computing resources including servers, networks, storage, and cooling systems. This technique clusters and executes heterogeneous workloads on provisioned cloud resources and enhances the energy-efficiency and reduces the carbon footprint in datacenters without adversely affecting cloud service reliability.
Detecting Deepfakes and Forged Videos Using Deep Learning

Over just a few years, methods to manipulate videos have become so sophistica- ted that even someone without much expertise or computational resources can forge videos inseparable from pristine ones to the human eye. These methods can for instance insert a person in a video or manipulate their lip movements to make them say anything of the manipulator’s liking. Though there exist harm- less and constructive uses of these technologies, it is not hard to imagine the harm they could cause if put in the wrong hands. This report presents a model to detect forged manipulated videos, more specifically those where faces have been manipulated. Four kinds of manipu- lation videos were taken into consideration: FaceSwap, DeepFakes, Face2Face and Neural Textures. The model proposed consists of a feature extraction CNN followed by an LSTM network. The FaceForensics++ dataset was used, as well as the associated benchmark. The model, though not competing with the state- of-the-art detectors, was able to classify videos with an accuracy higher than or close to that of several models in the benchmark. (Less)
Data Scientist’s Toolbox

We love our tools! Each job eventually comes up with a set of tools that define the job. In medieval Europe, for example, tradesmen and merchants formed guilds that defined trades and crafts for centuries. Coats of arms of these organizations typically included tools of the trade. If we were to form a guild of data scientists, our coat of arms would include many tools: languages like Python, libraries for deep learning and modeling like PyTorch or Keras, computer vision libraries like OpenCV, data processing libraries, tools for visualization, model deployment, cloud tools that operationalize our data science projects, and more . Studying human movement is a field combining kinesiology, biomechanics, data science, and oftentimes sensors, so in this chapter, we’ll talk about our toolbox.
Slide: Timing

All great new companies have something in common; they were the right company, solving the right problem at the right time. For every colossal startup success, you’ll often hear, "Oh! That thing? Jenny was working on that like a decade ago." Jenny—whoever she is—is now working on a new startup, because she failed to find traction and product/market fit for her thing.
Guides: Check it Out! The University Library is Wherever You Are: Welcome!

WELCOME BRONCOS! The virtual library has many services to support your success. Click on the icons below to learn more!
AeMFace: Additive E-Margin Loss for Deep Face Recognition

With the rapid development of deep learning, deep face recognition models have far exceeded the traditional models. Recent models like CenterLoss, SphereFace, CosFace and ArcFace mainly design an appropriate loss following by the powerful Con-volutional Neural Networks, resulting in more powerful ability on identifying intra-class and inter-class examples. Inspired by that, we propose an Additive E-Margin Loss to obtain highly discriminative features for face recognition. Thus, the resulting AeMFace model can process the weight vector with L2 norm by introducing the E-Margin to reduce the power operations, leading to comparable accuracy and better convergence performance during the training process. Moreover, we study the effectiveness of $L_{2}$ regularization of feature vectors and show the further accuracy improvement on face recognition. The experimental results on three large-scale benchmarks demonstrate the effectiveness of our proposed model.
Developing The Professional Competencies In Preservice Teachers By Means Of Pedagogical Cases

The article reveals some aspects of the scientific and methodological support of the application of case technology in the learning process of teacher education to develop professional (gnostic, communicative, design and constructive, managerial) teacher competencies. Based on the analysis of foreign and national scientific views, approaches to the classification of cases are considered; a typology of cases is presented from the stand point of case study as analytical strategy and of that as pedagogical technology. In reliance on the content analysis of authentic scientific and pedagogical sources, the authors propose an integrative typology of pedagogical cases to define the specific features of case designing and the areas of effective case application in the training of preservice teachers. It is evidenced in the article that at present in pedagogical science there is no single complete classification of pedagogical cases for several reasons which are herein qualified. That absence significantly narrows the scope of case effective application in learning pedagogical disciplines. To enable scientific based full-fledged case design and implementation in practices, example integration facet patterns of the general and pedagogical classifications of cases are provided in the aspect of pedagogical goal setting. The authors propose original case varieties, as projecting, diagnosis and planning cases to be studied in the teacher preparation course. The educational potential of the case technology is testifyed to in the context of presser vice teachers gaining experience in solving known, partially similar or unprecedented (non-typical) educational and professional problems in the course of multipurpose pedagogical case studies.
Batten Down the Hatches! Collection Management in Rough Seas

Rising rates in publishing combined with flat funding can make it challenging to maintain the library’s collection within an academic library. Add in university-wide funding reductions, and libraries must further reduce expenditures to balance the budget. This article will present a case study on the steps and initiatives deployed at the William E. Laupus Health Sciences Library to address impacts to the library’s collection budget for fiscal year 2020. Issues covered will focus on the decision-making process, working with vendors, and communicating with library stakeholders.
LibGuides: Web Archiving: What Is Web Archiving?

This guide covers web archiving, UTSA's web archiving efforts, and considerations for researchers.
Bayesian Hierarchical Models for Count Data

Author(s): Shuler, Kurtis | Advisor(s): Lee, Juhee | Abstract: This dissertation focuses on the development of methodology for the analysis of multivariate count responses. Such contexts present a number of unique modeling challenges that are not well handled by standard models for count data which have restrictive mean-variance and correlation structures. In addition to being high-dimensional, sparse and overdispersed, multivariate count data often exhibits complicated dependencies across categories and samples that must be accounted for in order to obtain accurate inference. Three Bayesian modeling strategies are presented to handle these challenges and produce accurate, interpretable inference with uncertainty quantification. The first model incorporates novel nonlocal priors for variable selection which outperform existing alternatives, and introduces a process convolutions sub-model to handle temporally dependent responses taken over uneven sampling intervals. The second applies Bayesian nonparametric (BNP) methods based on a dependent Dirichlet process mixture to flexibly model how category abundance levels and zero inflation are related to covariates. The BNP approach facilitates community level comparisons across experimental conditions through density estimates that provide additional insights over simple statistical tests or ordination analysis. The third model employs a directed acyclic graph (DAG) to identify related response categories. The graphical model does a better job uncovering network relationships than alternatives based on simple marginal correlations, and, unlike simpler count data models, handles cross-category dependence in a principled manner.
LibGuides: Continuity of Services: Library Resources

A guide to Remote Services for the Ruby E. Dare Library during periods when campus is closed or on reduced staffing.
SURVEILLANCE CAMERA AND AUTO BACKUP CLOUD USING RASPBERRY PI

In the technological age 4.0 It should be trying to prevent crime. For that an a office must install a surveillance camera, so that theft can be known immediately by the office owner. for simplify in supervision this system using ipcamera , webcam and using mini computer (Raspberry Pi) which aims to secure evidence in the form of recorded images with way Surveillance camera the capture images when there is movement, then the results of the image recording automatically uploaded to cloud computing. so with there is system, then it will be more secure evidence if the machine than the camera record has been stolen or tampered with by the perpetrators of theft
Trio d'archi e pianoforte

Author(s): Vannoni, Giovanni | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
Gaussian Process-Based Spatiotemporal Modeling of Electrical Wave Propagation in Human Atrium

Rhythm regularity of the heart depends on how electrical impulses spread through the cardiac conduction system. Any abnormal activities in the electrical impulses can lead to serious cardiac disorders or sudden death. It is important to understand the electrical activities of the human heart in both healthy and diseased conditions to determine the cause of cardiac disorders and explore the best therapeutic designs. Mathematical models calibrated with clinical and/or in-vitro data are popularly used to study cardiac function and investigate treatment effects. Most of the current human heart models are highly integrated and couple over a hundred equations across different organizational scales of ion channel, cell, and muscle. The model complex poses a significant computational challenge on cardiac simulation. This study developed a metamodel to replace the time-consuming simulation model. Specifically, Gaussian Process (GP) is used to reconstruct the spatiotemporal variations of the cell membrane potential in left atrium. Four different covariance functions were used to infer the potential distributions. The GP model provides an accurate estimation of the spatiotemporal propagation of electrical waves with a small set of data and shows great advantage in computations as compared to traditional models.
LibGuides: Learn the Library: Guide to Videos

A resource for learning how to use library resources. This guide offers you an introduction to how to search within library resources.
A fast algorithm for finding square root modulo p

We propose a novel algorithm for finding square roots modulo prime . Taking Square root modulo prime of 3(mod 4) type is known to be straightforward. However, for odd primes of 1(mod 4) type , finding square root remains non-trivial. Tonelli-Shanks algorithm remains the most widely used and probably the fastest when averaged over all primes. This paper proposes a novel approach for finding square roots modulo odd primes, which turns out to be much faster than existing Tonelli-Shanks algorithms. Apart from faster computation time, the proposed method does not require availability of non-residue and can work with 'relative non-residue' also. The relative non-residues are much easier to find ( probability 2/3) compared to non-residues ( probability 1/2).
School Based Risk Factors Promoting Drug Abuse among Student in Public Secondary Schools in Molo Sub County, Kenya

The purpose of the study was to examine school based risk factors promoting drug among students in Public Secondary Schools in Molo Sub- County, Kenya. The study used a mixed methods embedded research design. The study used a sample of 120 participants from five schools sampled out of 26 schools through probability and non-probability sampling. The study used questionnaires and interview guide as the major instruments for data collection. The quantitative data were analyzed descriptively while qualitative data were transcribed, coded and analyzed in themes and narratives. The study identified peer pressure, poor communication, family stress, conflict with school management, social network sites and violence as the major risk factors for drug abuse among secondary school students. The research recommended further study to be conducted on factors contributing to the psychological well-being of students in schools in Molo Sub- County.
Poisson-model Analysis of Power Alternation in Africa

In Africa, there are many countries that rarely know the change of their president. The aim of this paper is to study the economic and non-economic determinants of number of power alternation in Africa over the period 1990-2015. The poisson regression is used. The results indicate that the growth of the gross domestic product per capita has a negative impact on the number of political change while the democracy index and the coup have a positive influence.
QE Viewer: an Open-Source Tool for Visualization of Machine Translation Quality Estimation Results.

QE Viewer is a web-based tool for visualizing results of a Machine Translation Quality Estimation (QE) system. It allows users to see information on the predicted post-editing distance (PED) for a given file or sentence, and highlighted words that were predicted to contain MT errors. The tool can be used in a variety of academic, educational and commercial scenarios.
A brief Review of Deep Learning Based Approaches for Facial Expression and Gesture Recognition Based on Visual Information

Abstract Psychological researchers and Many Other Researchers have found that body language of a human can provide substantial information in detecting and interpreting emotions. It could express explicit and implicit information mutually of one’s emotional state and intentions over multi-channel modalities. These imperative channels include eye gaze, head movement, facial expression, body posture and gesture and so on. This learning focuses on detecting emotional states from the body language of the hand and face using computer vision and soft computing techniques. The facial expression and gesture recognition have widely used and challenging task in the present scenario. This paper describes brief review of all approaches which are majorly categorized into two -categories where the first category belongs to Conventional approaches and other based on Deep-learning or Non - Conventional. This paper is basically used as a survey of deep-learning approaches for hand-gesture and Facial Expression Recognitions. We describe all review through the taxonomy of deep-learning approaches proposed architecture details, fusion strategies, datasets, way to treat temporal-dimensions of data, its main features, basic knowledge & general understanding of its challenges
Research Guides: Holding Space Community Learning Page: Facilitation Tools

In this guide, you will find resources and guidance on how to Hold Space in your classroom.