Reviewerly/exHarmony huggingface dataset:
Assessing Transformer Oil Quality using Deep Convolutional Networks

Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment.
Encountering COUNTER 5

To make fiscally and academically responsible selection and deselection of subscribed content, it is critically important to have reasonably accessible usage data for those resources. To this end, the organizers of Project Counting Online Usage of NeTworked Electronic Resources (COUNTER) initiated the development of a standard known as the COUNTER Code of Practice. Beginning with the year 2019, COUNTER 5 standardized usage reports are available for most commercially published electronic information resources such as online journals, aggregated research databases, and ebooks. This column explores some of the differences between the COUNTER 5 Title Master Report-Journal Report (TR-JR1) report and the COUNTER 4 JR1 report for online journal usage and offers some assistance in extracting specific data from the TR-JR1 report that is not as readily visible as it was in COUNTER 4 reports.
Quantum Radar Research: A Snapshot in Time

Reports on the IQC Workshop on Quantum Illumination: From Theory to Practice. The workshop was held 3–4 December 2019 at the Institute of Quantum Computing (IQC), University of Waterloo, a well-known research center for quantum information as well as home to the world’s first microwave quantum radar experiment that was published in the literature.
Searching for Ambiguous Objects in Videos using Relational Referring Expressions.

Humans frequently use referring (identifying) expressions to refer to objects. Especially in ambiguous settings, humans prefer expressions (called relational referring expressions) that describe an object with respect to a distinguishing, unique object. Unlike studies on video object search using referring expressions, in this paper, our focus is on (i) relational referring expressions in highly ambiguous settings, and (ii) methods that can both generate and comprehend a referring expression. For this goal, we first introduce a new dataset for video object search with referring expressions that includes numerous copies of the objects, making it difficult to use non-relational expressions. Moreover, we train two baseline deep networks on this dataset, which show promising results. Finally, we propose a deep attention network that significantly outperforms the baselines on our dataset. The dataset and the codes are available at this https URL.
Investigating Agreement Among HCI Researchers About Human–Computer Interaction in CMMI-DEV Model: A Case Study

Abstract Do human–computer interaction (HCI) researchers agree about which HCI approaches should be used in the development of an interactive system? To address this question, we performed a study based on the engineering process areas (requirements development, technical solution, product integration, verification and validation) of the CMMI-DEV (Capability Maturity Model Integration for Development), a software engineering model commonly used in the industry. To carry out this study, a literature review was performed, a specific instrument was designed based on CMMI-DEV and interviews with 20 researchers from the HCI domain were conducted. Analyzing the interview data with monovariate and multivariate statistical approach (Multiple Correspondence Analysis - MCA), we find the greatest agreement occurred among researchers for HCI approaches that support verification and validation phases and the use of functional prototypes in some activities of the last phases of software development (technical solution and product integration). However, we identified lack of agreement among researchers regarding requirements development activities.
Methodologies for the design of application frameworks: systematic review

Framework design for application development allows to reuse design patterns and code, speeding up the production of software in a same domain. However, in order to design a framework, it is required a considerable time before a functional product is ready to use, and it is essential to design following the best practices. The lack of a guide with current methodologies for framework development was the main motivation to pursue this systematic review. In this paper, the results of the systematic review to find the methodologies for framework development in the context of software engineering are reported and analyzed. The initial search threw 13,624 documents, from which only 30 references were relevant after refinement. The analysis of these documents provided evidence of diverse methodologies for framework development, which adopt UML for their definition. On the other hand, whereas some works emphasize the structure to support the functionality, others prioritize the behavior of the classes. The analysis of the results suggests the use of UML in the design process, the implementation of design patterns, the identification of hot-spots, and the recognition of black-box and white-box frameworks.
The Effect of Text Preprocessing Strategies on Detecting Fake Consumer Reviews

Fake review detection is getting crucial due to rapid growth of internet purchases. Obviously, it is important to choose the most efficient algorithm in order to detect fake (deceptive, spam) reviews either positive or negative. On the other hand, it is also important to pre-process the textual content of the reviews for training and later for production environment. A number of text preprocessing methods are examined in this study, such as feature dimensionality, tokenization, removal of stop words, stemming and different term weighting schemes. Three well-known machine learning algorithms are used as benchmark classifiers, including Naïve Bayes, neural network and support vector machine. Here we show that text preprocessing strategies are important determinants of the classifiers' performance. We find that the classifiers perform better for high-dimensional datasets represented by bigrams or trigrams selected according to the non-binary weighting scheme. Stemming and stopword removal seem to be less important.
The Practical Training Development of Digital Electronic Technology Course

The practical training of digital electronic technology course is an important teaching link of applied electronic technology specialty in higher vocational education. The paper analyzes and studies the project selection and the course development of it. First of all, through enumerating the data obtained from practical training for several years, the relationship between the success rate of students' works, the content of knowledge points in the works and the situation of the students in the classes has been analyzed. An important conclusion has been drawn that the suitable training projects should be selected according to the students' situation. Secondly, the main steps of the project training are detailed, and the backward deduction method is put forward, which is the effective method of analyzing the cause of the fault. Finally, the paper not only points out the related factors that affect the training effect and the corresponding measures that should be taken, but also illustrates the cultivation of students' innovative ability by varying circuit design, so that the training course can produce satisfactory results.
Hidden Markov Models: Identification, Inverse Filtering and Applications

A hidden Markov model (HMM) comprises a state with Markovian dynamics that is hidden in the sense that it can only be observed via a noisy sensor. This thesis considers three themes in relation to ...
[Research on Improved Algorithm of DR Image Enhancement Based on Gauss-Laplacian Pyramid].

In order to obtain more decision information from Digital Radiography(DR) images, an improved image enhancement algorithm is proposed based on the algorithm of Gauss-Laplacian pyramid.The original algorithm is improved on the basis of the human visual characteristics and better enhancements, the low frequency components of the image is histogram equalized to make the image gray scale more balanced, and the high frequency component is enhanced by a hierarchical exponential enhancement to make the details of the image clearer.The improved algorithm improves the contrast of DR images in chest, pelvic and spine, and makes the image more layered and obtains good image enhancement effect.The results show that the improved algorithm is superior to the traditional algorithm in terms of image enhancement.
Assessing the pharmacy students’ knowledge of common medical terms after a curricular change in Saudi Arabia

Medical terminology is the vocabulary used to describe the human body and its conditions; fluency in this language is essential for health care professionals. We examined the level of basic medical terminology understanding among 347 pharmacy students in four different colleges of pharmacy in Saudi Arabia using a newly developed test of 30 multiple choice questions. Students in the relatively new colleges of pharmacy were more likely to have a higher score in the medical terminology test compared to their counterparts from the old college of pharmacy (β = 1.23, 95% CI = 0.16–2.30, P-value = 0.02). Female gender (β = 1.72, 95% CI = 0.57–2.88, P-value = 0.003), and advanced class level (β = 0.84, 95% CI = 0.36–1.32, P-value < 0.001) were also positively associated with high medical terminology test scores. The findings of this study reveal a deficiency in the pharmacy students’ level of understanding of basic medical terms which may necessitate a reintroduction of the medical terminology course into the pharmacy curriculum.
Self-Synchronized Encryption for Physical Layer in Gigabit Ethernet Optical Links

In this work a new self-synchronized symmetric encryption solution for high speed communication systems necessary to preserve the format of the plaintext is proposed, developed and tested. This new encryption mechanism is based on the block cipher operation mode called PSCFB (Pipelined Statistical Cipher Feedback) and the modulo operation. The confidentiality of this mode is analyzed in terms of its IND-CPA (Indistinguishability under Chosen-Plaintext Attack) advantage, concluding that it can be considered secure in the same way as traditional modes are. The encryption system has been integrated in the physical layer of a 1000Base-X Gigabit Ethernet Interface, where the 8b/10b symbol flow is encrypted at line rate. Moreover, an implementation of the proposed system has been carried out in an FPGA (Field Programmable Gate Array) device. Finally, an encrypted optical link has been tested with real Ethernet frames, getting maximum throughput and protecting the data traffic from passive eavesdroppers.
The decay and persistence of web references

Purpose The purpose of this study is to identify the persistence and decay of uniform resource locator (URLs) associated with Web references. The decaying of Web references is analyzed in relation to their age, domain, technical errors and error codes. Design/methodology/approach The Web references of the Journal of Informetrics were selected for analysis and interpretation to fulfill the set objectives. The references of all the scholarly articles, excluding editorials and reviews published in the Journal of Informetrics for five years from 2007 to 2011 were recorded in a text file. Later, the URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The collected data were then transferred into an excel file and tabulated for further analysis and interpretation using simple statistical techniques. Findings The results showed that of the total 7,409 citations retrieved from 221 articles, 358 citations (4.8%) were Web citations. These Web citations were assessed to find their persistence and decay. The results reveal that 115 (32.12%) Web references were missing or dead. The most common error associated with the missing Web citations was Error 404 Page not found, contributing 60% of the total missing citations, followed by 400 Bad Request Error (35.65%). The domain analysis of missing Web citations depicts that most of the missing URLs were associated with the .gov domain (40%), followed by .edu (29.58%) and .com (26.04%). Research limitations/implications The Web references of a single journal, namely, Journal of Informetrics , were analyzed for five years, and hence, the generalization of findings needs to be cautioned. Practical implications The URL decay is becoming a major problem in the preservation and citation of the Web resources, and collaborative efforts are needed to reduce the decaying of URLs. Originality/value A good number of studies have been conducted to analyze the persistence and decay of Web references, as it is the hot topic of research across disciplines, and this study is a step further in the same direction.
Predicting an Election’s Outcome Using Sentiment Analysis

Political debate - in its essence - carries a robust emotional charge, and social media have become a vast arena for voters to disseminate and discuss the ideas proposed by candidates.The Brazilian presidential elections of 2018 were marked by a high level of polarization, making the discussion of the candidates’ ideas an ideological battlefield, full of accusations and verbal aggression, creating an excellent source for sentiment analysis.In this paper, we analyze the emotions of the tweets posted about the presidential candidates of Brazil on Twitter, so that it was possible to identify the emotional profile of the adherents of each of the leading candidates, and thus to discern which emotions had the strongest effects upon the election results. Also, we created a model using sentiment analysis and machine learning, which predicted with a correlation of 0.90 the final result of the election.
MONK in Practice : Indexing Heterogeneous Handwritten Collections

This short paper describes how MONK, a machine-learning driven handwriting recognition system, can be used to rapidly index a heterogeneous handwritten collection with the help of volunteers. We discuss the setup and results of an event which saw volunteers come together to enrich a subset of the digitized Prize paper collection, a collection of historical handwritten documents of the High Court of Admiralty (1652-1815).
Australia’s COVIDSafe Experiment, Phase II: A Draft Law for Surveillance and Trust

The joint Australian governments’ coronavirus contact tracing app, marketed as ‘COVIDSafe’, was released on 26 April 2020 for public download by the federal government, together with an emergency Determination under the Biosecurity Act to govern its operation, a Privacy Impact Assessment (PIA), plus the App itself and its privacy policy. A week later the federal government released an Exposure Draft Privacy Amendment (Public Health Contact Information) Bill 2020 (‘the COVIDSafe Bill’), which it is expected will be debated and enacted in a Parliamentary sitting from 12-14 May.It is a package intended to create sufficient public confidence to result in downloads of the app by a sufficient percentage of the Australian mobile-phone-owning population, for it to have a significant effect on the tracing of persons infected with the COVID19 virus. In the first two weeks since its launch over 5 million Australian’s have downloaded the app, about 25% of those possible. This article analyses the extent to which the draft Bill provides the privacy protections that would help justify public trust, and aims to make a constructive contribution to the development of better legislation and greater transparency.The Draft Bill contains significant improvements on the previous Determination, particularly the inclusion of an individual right of enforcement action before the Privacy Commissioner for most breaches of the COVIDSafe requirements. This is particularly important as a means of stopping attempts to make use of the app de facto compulsory. It also adds valuable provisions concerning deletion of data, extension of Commonwealth jurisdiction to cover state and territory authorities, and sharing of information between Australian privacy authorities.We conclude that the conditions necessary to justify sufficient public trust in government for the Australian public to opt in voluntarily to the installation and use of the COVIDSafe app, and to not opt out, are lacking. Many of the main deficiencies we identify in this article are remediable: five deficiencies in transparency; and nine categories of improvements to the current Determination by the proposed COVIDSafe Act. However, the question of whether an individual Australian would be well advised to install and run the app remains a decision which depends on individual circumstances. Many other countries are developing contact tracing apps. Australia’s experiment is further advanced than most that are attempting to build a system on voluntary uptake, protected by legislation. The results of its experiment will be of interest to many.The Act referred to above, the Privacy Amendment (Public Health Contact Information) Act 2020 (Cth) (‘the COVIDSafe Act’) was enacted on 15 May 2020. The authors' analysis of that Act is G. Greenleaf & K. Kemp 'Australia’s COVIDSafe experiment, Phase III: Legislation for trust in contact tracing' at https://ssrn.com/abstract=3601730.
Corpus Augmentation for Improving Neural Machine Translation

The translation quality of neural machine translation (NMT) systems depends largely on the quality of large-scale bilingual parallel corpora available. Research shows that under the condition of limited resources, the per... | Find, read and cite all the research you need on Tech Science Press
Inverse Kinematics Solution of Manipulator Based on PSO-ELM

In this paper, an improved extreme learning machine (ELM) algorithm based on particle swarm optimization (PSO) is introduced, and the inverse kinematics solution of a six-degree-of-freedom manipulator is analyzed and solved by this algorithm. Firstly, PSO is used to optimize the input layer weights and hidden layer deviations of the ELM, and then the output layer weights are solved by ELM. The simulation results show that PSO-ELM has a good effect in solving the inverse kinematics of the manipulator.
A Study on Analysis of Noise with Filters for the MRI Images of Alzheimer’s Disease

The slow and progressive decline of brain disease is called the Alzheimer’s disease which affects the cognitive functions of the brain. To analysis the disease at the early stage it is difficult. In images processing dealing with the rebuilding of the primitive and aciculate images from the perverted image is strife for the researchers. Noise plays a crucial role in reducing the quality of images at various occurrences while conquering or transferring of the images. There exists algorithms and various filtering techniques with its limits and delimits with the prior knowledge of the noise. Filtering or enhancing of the images place a crucial role in bringing out the relevant information in the images. The work is with the T2 sequences MRI of the brain AD affected images. The work carried out in this paper discuss about different noises models like speckle noise, salt n pepper noise, Gaussian noise and periodic noise with different filtering techniques which includes Gaussian filter, median filter and wiener filter. To bring out the difference of what kind of the filters works better for the kind of noise applied on the MRI images of Alzheimer disease.
AI-based prediction of lesion occurrence in high-risk women based on anomalies detected in follow-up examinations

Breast Magnetic Resonance Imaging (MRI) is recognized as the most sensitive imaging method for the early detection of breast cancer in women who carry a lifetime risk for breast cancer higher than or equal to 20%. Given the aggressive biology of cancers in this population, early detection is crucial for a favorable prognosis. This study aimed to use artificial intelligence for the detection of lesions at the earliest stage in high-risk women. A Generative Adversarial Network (GAN) detected lesions in breast MR data by quantifying anomaly as divergence from healthy breast tissue appearance. First, follow-up images of patients were aligned and the breast was segmented automatically. Then, the GAN created a model of healthy variability of appearance change during follow-up in 64x64-sized image patches sampled only at healthy tissue locations in follow-up image sequences. During the assessment of new data, each image position was compared with the model yielding an anomaly score. On a image patch level, we evaluated if this anomaly score identifies confirmed lesions, as well as lesionfree regions, where lesions appear during later follow-up studies. In the first experiment of lesion detection, a mean sensitivity of 99.5% and a mean specificity of 84% was achieved. When applying the model to studies denoted as lesion-free, subsequently occurring lesions were predicted with a mean sensitivity of 92.7% and a mean specificity of 78.8%.