Reviewerly/exHarmony huggingface dataset:
Echoes of Murlough

Echoes of Murlough is an electroacoustic composition presented in VR. The listener is enveloped in a virtual space that explores the intersection of musical and environmental sonic materials gathered from Murlough Beach, Co. Down in Northern Ireland. The user is a passive observer in the piece which unfolds around them in a spatially head tracked experience. The auditory virtual environment (AVE) has an authentic approach that moves into the creational, as described by Novo [1]. Taking the listener on a journey from the real to the abstract but where the sounds retain a connection to place. All sounds where gathered at the beach using a combination ambisonic, MS stereo, mono and contact microphones including the electric guitar except for two parts recorded in the studio. The music was composed of improvised guitar parts using an “Ebow” and recorded on the beach using an ambisonic microphone in conjunction with a lavalier wireless system to allow freedom of movement that would be captured and become integral to the piece. The contrast and interplay between environmental and instrumental sources are explored in relation to space. The intention is that the listener and composition itself will be rooted in a sense of place that will provide a foundation for immersion.
Faces 2D-Recognition and Identification Using the HOG Descriptors Method

The article describes the development of a face recognition system for university security services. Recent facial recognition solutions were analyzed. The software and hardware architecture, the developed system, the methods used in the recognition program are described. Examples of the implementation of two-dimensional recognition of faces with HOG descriptors are given.
Machine Learning: The Way for Improving Law Application

The study was carried out to show how the Precautionary Principle is better applied with Machine Learning techniques. The development of Machine Learning techniques and the wider application for different disciplines, such as Biotechnology, Nanotechnology, and Science of Materials, has been increasing in recent years.Modelling and simulation techniques should be promoted for regulation assessment, taking into consideration the uncertainty of new compounds product of biotechnology and nanotechnology applications.
CORAL User Group Meeting at NC Serials 2019

This article reports on the CORAL User Group meeting at the NC Serials 2019 conference. This was the first CORAL user group meeting to take place at the NC Serials conference. The speakers provided updates from CORAL committees and highlighted new features in the most recent CORAL release. They also shared how CORAL has been used in NC State University Libraries and East Carolina University to support their e-resource workflow management.
Kinerja Tenaga Kesejahteraan Sosial Kecamatan di Kecamatan Pasirwangi Kabupaten Garut

The research entitled “The Correlation between Students’ Habit in Writing Diary and Students’ Ability in Writing Recount Text”. It was aimed to find out the significant correlation between students’ habit in writing diary and students’ ability in writing recount text. Quantitative method was used to find out the correlation between two variables. To find out the significant correlation between students’ habit in writing diary and students’ ability in writing recount text, this paper used ex post facto. The data of writing recount text result was counted with Pearson Product Momment. The result of Pearson test showed tobserved was 0.17and ttable was 2.1604. The writer used significant value 5% or 0.05. It means that H0 was accepted and Ha was rejected. Based on the result of Pearson Product Momment, there were no significant correlation between students’ habit in writing diary and students’ ability in writing recount text.
New Developments in Electronic Measurement Technology - Virtual Instruments

Under the rapid development of China's current information technology and science and technology, various advanced instruments and equipment have been widely used, and power measurement technology has been further developed. New electronic measurement technology—virtual instrumentation has emerged, and has been established in different fields. More applications provide greater support for the further development of power measurement technology. This paper will introduce the development history of virtual instruments, analyze the composition and application of each system of virtual instruments, and improve the application range of electronic measurement technology-virtual instruments in various fields.
An Analysis of Public Secondary School English Teacher Employment Exams: Focusing on Items in Phonology and Phonetics from 2014 to 2018

Youn-Kyoung Lee. 2019. An Analysis of Public Secondary School English Teacher Employment Exams: Focusing on Items in Phonology and Phonetics from 2014...
Sistem Temu Kembali Informasi Pada Gejala Autisme Dengan Metode Vector Space Model

Information is now very easy to get anywhere. Information technology, especially the internet, strongly supports the exchange of information very quickly. The internet has become an information and communication media that has been used by many people with many interests, especially in taking large-scale information data, Unfortunately the information presented is sometimes less relevant. Quality information is influenced by relevance, accuracy and on time. However, there are not many effective search systems available. This study discusses the implementation of an information retrieval system to find and find symptoms of autism disorders using the Vector Space Model (VSM) method. Vector Space Model (VSM) is a model used to measure the similarity between a document and a query. In this model, queries and documents are considered vectors in n dimensional space. Where n is the number of all terms listed. The purpose of this study was to design an information retrieval software to find and match the symptoms of autism disorders. By using Vector Space Model, it is hoped that it can provide a solution to the search engine to provide text matching information in the database using certain keywords, the results of the matching are presented in the form of ranks.
Bringing Research Data Management to Academic Libraries

Modern research institutions are faced with rapidly increasing challenges imposed by data-driven science and have to deal with new demands from funding agencies and publishers regarding open access to research data as well as from the Code of Conduct for Research Integrity on reproducibility of results. In reply to these requirements, the Technical University of Denmark (DTU) has adapted a two-fold strategy of bringing together decision-makers, relevant support units in the administration and researchers from all departments for the task of designing policies and services in alignment with common traditions in the different fields of research. This mixed “bottom-up” / “top-down” approach ensures that best practices for research data management can be implemented in the daily routines of employees in a practical and supportive way while ensuring that the researchers can live up to all relevant administrative, legal and ethical standards. &#x0D; In this presentation, we will show how DTU Library has taken over a central position in this process by investigating the needs and demands from researchers in the different fields, drafting procedures and guidelines for research data management and establishing support functions and training for all aspects of the data life cycle. Within this new and complex area, the creation of new positions for research data management at the library in addition to an optimal utilization of existing resources and knowledge has been crucial for building up the necessary expertise and thus for the success of the strategy. &#x0D; AuthorsFalco Jonas Hüser, PhDProject Officer – Research Data Managementemail: falh@dtu.dkORCiD 0000-0001-9645-6691Paula Maria Martinez Lavanchy, PhDProject Officer – Research Data Managementemail: paml@dtu.dkORCiD 0000-0003-1448-0917
[PDF] NCERT Solutions for class 3 English Unit-5 Poem The Balloon Man

NCERT Solutions for Class 3 English Unit-5 Poem The Balloon Man are part of NCERT Solutions for Class 3 English. Here we have given NCERT Solutions for ...
User Authentication Secure by Randomly Cubic Spline Curve and Blum Blum Shub Algorithm

With the tremendous development witnessed by the world in recent times, computer security significance expanded as insurance procedure of registering systems and authentication the user with scientific and technological developments are increasing every day. This assurance procedure is connected for touchy information such of electronic business, electronic learning, and electronic saving money exchange, online networking and more administrations over the web by utilizing login application to distinguish the character for doing this authentication. The problem by using some application login how to detected the user authentic and experimental data collection and separated the duplicated name that may be Confusion the system with speed detected and difficult attack. This paper solve this problem by used Cubic spline to easy represented name and easy detected by distance Euclidean equation, authentication techniques utilized as insurance process by securing name mix with Blum Blum Shub (BBS) algorithm that have randomly number. In this work, the application is founded on type name that the combine with (BBS) and represented by cubic spline, to detected user. The result of proposed work require randomly number to drawing the curve to detect and how much the different shape in cubic spline curve that can be detected. Assurance gives affirmation distinguished suitable security and all the more effortlessly least complex that developing popular in the online application that shows in result some portion of the paper
A Robot’s Street Credibility: Modeling authenticity judgments for artificially generated Hip-Hop lyrics

This study aims to advance and enhance our understanding of the properties that contribute to the perceived authenticity of a specific art form: Hip-Hop lyrics. The basis of our study is an experiment carried out in the context of a large, mainstream contemporary music festival. We crowdsourced a large dataset of authenticity judgements for both authentic and neurally generated Hip-Hop lyrics, which enable us to quantitatively assess human biases toward artificially generated text as well as which linguistic characteristics are perceived as authenticity cues. Additionally, the dataset provides solid ground for evaluating different neural language generation systems with respect to their perceived credibility. We compare a variety of language models and techniques. Our experiments contribute equally to improving the credibility of generated text and enhancing our understanding of the cognitive processes at play in the perception of authentic and artificial art.
[PDF] NCERT Solutions for Class 4 English Unit-8 Poem A watering rhyme

NCERT Solutions for Class 4 English Unit-8 Poem A watering rhyme, are part of  NCERT Solutions for Class 4 English. Here we have given NCERT Solutions ...
[PDF] NCERT Solutions for Class 3 English Unit-10 Poem How creatures move?

NCERT Solutions for Class 3 English Unit 10 Poem How creatures move? are part of NCERT Solutions for Class 3 English. Here we have given NCERT Solutions ...
Robust Tracking via Dual Constrained Filters

In this paper, we propose a novel correlation filter framework constrained by dual filters. The Minimum Output Sum of Squared Error (MOSSE) filter is the unbiased estimate of the filter which easily to cause overfitting. The trained filter by linear ridge regression is the biased estimate of the filter which can deal with the overfitting. We combine the advantages of the two filters to constrain the trained filter which optimizes our model. To deal with background clutter, clipping background patches around the target position up, down, left, and right, we add the cropped background patches to the learning filter. To overcome the challenge of occlusion, we introduce a novel criterion, Average Peak-to-Correlation Energy (APCE). Extensive experiments on the CVPR 2013 Benchmark well demonstrate that our tracker can effectively solve the background clutter and occlusion. Both quantitative analysis and qualitative analysis show that our tracker outperforms some state-of-the-art trackers.
Knowledge Discovery and the Aesthetics of Big Data: Simulating the Herschel Observatory

Abstract The authors, an astrophysics student and a humanities researcher, developed a simulation of William Herschel's visual experience during his observational runs, bringing to life archival data produced by William and his sister Caroline Herschel, who are credited with having invented modern cosmology. From a media studies perspective, the use of intensive computational resources to produce boring, accurate, realtime simulations of William Herschel's observations helps us to confront our conflation of visual complexity with reality in the era of big data. At the intersection of data science, the history of science and media studies, the project proposes the aesthetics of boredom as a means of dwelling with the sense of big data as “big” relative to modes of knowledge production.
Design of a Computationally Economical Image Classifier using Generic Features

In this paper, we propose an image classification technique which uses a simple autoencoder with a regularizer. Nowadays, Convolutional Neural Networks (CNN) are primarily used for image classification. Our method can be used for image classification with much reduced requirement of computational capability than a complex CNN which has a huge number of degrees of freedom. Here, the terms simple and complex, respectively, correspond to the simplicity and the complexity of a network in terms of the number of learnable parameters (degrees of freedom) and the number of hidden layers. This technique uses features extracted from a pretrained CNN, trained on a completely different dataset. Genetic algorithm solves for the optimal hyperparameters of the pretrained CNN. It is observed that these features serve as important and robust parameters for the training of the autoencoder, as a final average image classification accuracy improvement of about 17.45% is observed with the inclusion of these features. We use a pretrained CNN on MNIST dataset and classify images of several other benchmark datasets. We utilize different classifiers for image classification based on features extracted from the autoencoder and repeat each of the experiments a number of times with different random initialization of the classifier and the weight matrix of the autoencoder. We also perform experiments by pretraining the CNN with different datasets. Our results show a notable image classification accuracy and a significant reduction of training time with respect to a complex CNN.
Estudio de toma de apuntes en un centro de profesores uruguayo

Notes taken by students in class have always been important for education, independently of the form used to take and keep them. Taking this fact into account, a research of the antecedents of the importance and evolution of this practice was carried out so that the reality of a Regional Centre of Teachers could be known. In 2018 two thirds (over 500) of the students of this center from different areas and grades were surveyed using a written form which was voluntarily and anonymously completed. Among the general aspects, it can be stated that this practice is very frequent as more than the 90 % of the surveyed students admit taking notes recognizing its importance. Besides, students are convinced that this practice is closely linked to good academic performance. In general, they state they take notes to understand topics better and keep a record of what is taught in class to help them remember the topics of the lessons afterwards. Classifying the answers according to the different subject areas of teacher training (Language, Arts and Science), no relevant differences are found. As a consequence, it can be stated that note-taking is a current practice among students who consider it positive for their learning. Other aspect that can be considered is the fact that the use of digital media is not so popular for note-taking among students, contrary to previous expectations of the researchers. It is interesting that the majority of students has not had any form of formal instruction related to note-taking and encounters the difficulty of slow writing speed while taking notes, both aspects which would require some special attention.
Prediction Constrained Factor Analysis

Author(s): Abdrakhmanova, Madina | Advisor(s): Sudderth, Erik B | Abstract: This work is explores linear dimensionality reduction techniques that preserve information relevant for specific classification tasks. We propose a Gaussian latent variable model that is tuned to maximize the likelihood of the observed data, subject to a constraint that a prediction loss based on the lower dimensional representation meets a chosen threshold. We augment a log-likelihood objective with auxiliary losses that enforce the prediction constraint via Lagrange multipliers. Our prediction-constrained training objective effectively integrates supervisory information even when only a small fraction of training samples are labeled. We analyzed the performance of our PC approach for predicting emotions from face images. We improved prediction quality compared to a multinomial logistic regression model fitted on the output of standard linear dimension reduction techniques. We also achieved competitive performance against a multinomial logistic regression model trained on full resolution images. We were able to learn parameters that can project images to a low-dimensional space and capture the defining feature of each class. In particular, reconstructed images with these parameters were significantly better in capturing the facial expressions compared to Factor Analysis and Probabilistic Principal Component Analysis.
Complexity Approximation of Classification Task for Large Dataset Ensemble Artificial Neural Networks

In this paper, operational and complexity analysis model for ensemble Artificial Neural Networks (ANN) multiple classifiers are investigated. The main idea behind this, is lie on large dataset classification complexity and burden are to be moderated by using partitioning for parallel tasks and combining them to enhance the capability of a classifier. The complexity of the single ANN and ensemble ANN are obtained from the estimates of upper bounds of converged functional error with the partitioning of dataset. The estimates derived using Apriori method shows that the use of an ensemble ANN with different approach is feasible where such problem with a high number of inputs and classes can be solved with time complexity of $$ {\text{O}}\left( {{\text{n}}^{\text{k}} } \right) $$ for some $$ {\text{k}} $$ , which is a type of polynomial. This result is in line with the importance of good performance achieved by diversity rule applied with the use of reordering technique. As a conclusion, an ensemble heterogeneous ANN classifier is practical and relevance to theoretical and experimental of combiners for ensemble ANN classifier systems for large dataset.
Recognizing Faces in Shades of Gray

Face recognition depends on relatively few distinguishing features, when compared with common facial features. This gives color information greater value to recognition and identification processes. However, dealing with grayscale facial images is a must in some cases, e.g. legacy images. In this paper, we investigate the effect of losing color information on face recognition. We propose a novel framework, which utilizes CNN-based colorization before a CNN classifier. The proposed framework is tested on LFW benchmark dataset. The evaluation results prove the success of the proposed framework in reducing the negative effect of dropping the color information on face recognition performance.
Predicting Virtual Machine Resource Consumption Based on Optimized Grey Model

Abstract In order to relatively change the computing specifications of virtual machines (VMs for short) for the changing resource consumption of VMs, and reduce the impact of the changes, this paper proposes the optimized grey GM(1, 1) model to predict the resource consumption of VMs. Because VM resources may fluctuate greatly in a short time, the initial data are smoothed first, and then modeled, the model edge value is optimized according to the characteristics of cloud environment. Experiments show that the accuracy and stability of the prediction model can be improved by sorting out the initial data and optimizing the model. The prediction value is helpful to design the allocation scheme of VM resources, improve the utilization rate of physical resources in cloud environment.
DeepCleanse: A Black-box Input SanitizationFramework Against Backdoor Attacks on DeepNeural Networks

We propose Februus; a new idea to neutralize highly potent and insidious Trojan attacks on Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a backdoor crafted in a deep neural network model using a secret trigger, a Trojan, applied to any input to alter the model's decision to a target prediction---a target determined by and only known to the attacker. Februus sanitizes the incoming input by surgically removing the potential trigger artifacts and restoring the input for the classification task. Februus enables effective Trojan mitigation by sanitizing inputs with no loss of performance for sanitized inputs, Trojaned or benign. Our extensive evaluations on multiple infected models based on four popular datasets across three contrasting vision applications and trigger types demonstrate the high efficacy of Februus. We dramatically reduced attack success rates from 100% to near 0% for all cases (achieving 0% on multiple cases) and evaluated the generalizability of Februus to defend against complex adaptive attacks; notably, we realized the first defense against the advanced partial Trojan attack. To the best of our knowledge, Februus is the first backdoor defense method for operation at run-time capable of sanitizing Trojaned inputs without requiring anomaly detection methods, model retraining or costly labeled data.
Multi-pose Face Recognition Based on Contour Symmetric Constraint-Generative Adversarial Network

In order to address the impact of large-angle posture changes on face recognition performance, we propose a contour symmetric constraint-generative adversarial network (CSC-GAN) for the multi-pose face recognition. The method employs the convolutional network as the generator for face pose recovery, which introduces the global information of the constrained pose recovery of positive face contour histogram. Meanwhile, the original positive face is used as the discriminator, and the symmetric loss function is added to optimize the learning ability of the network. The positive face with gesture recovery is obtained by striking the balance between training of the generator and discriminator. Then we employed the nearest neighbor classifier to identify. The experimental results show that CSC-GAN obtained good posture reconstruction texture information on the multi-pose face reconstruction. Compared with the traditional deep learning method and 3D method, it also achieves higher recognition rate.