Reviewerly/exHarmony huggingface dataset:
Introduction to the Special Section

Economic Papers: A journal of applied economics and policyVolume 39, Issue 2 p. 101-103 Special Section: Economics of Blockchain Technology Introduction to the Special Section Ashton de Silva, Ashton de Silva RMIT University, Melbourne, VIC, AustraliaSearch for more papers by this authorMikayla Novak, Corresponding Author Mikayla Novak mikayla.novak@anu.edu.au Australian National University, Canberra, ACT, AustraliaSearch for more papers by this author Ashton de Silva, Ashton de Silva RMIT University, Melbourne, VIC, AustraliaSearch for more papers by this authorMikayla Novak, Corresponding Author Mikayla Novak mikayla.novak@anu.edu.au Australian National University, Canberra, ACT, AustraliaSearch for more papers by this author First published: 03 April 2020 https://doi.org/10.1111/1759-3441.12280Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume39, Issue2June 2020Pages 101-103 RelatedInformation
Recurrent Neural Networks with Longitudinal Pooling and Consistency Regularization

Most neurological diseases are characterized by gradual deterioration of brain structure and function. To identify the impact of such diseases, studies have been acquiring large longitudinal MRI datasets and applied deep-learning to predict diagnosis label(s). These learning models apply Convolutional Neural Networks (CNN) to extract informative features from each time point of the longitudinal MRI and Recurrent Neural Networks (RNN) to classify each time point based on those features. However, they neglect the progressive nature of the disease, which may result in clinically implausible predictions across visits. In this paper, we propose a framework that injects the extracted features from CNNs at each time point to the RNN cells considering the dependencies across different time points in the longitudinal data. On the feature level, we propose a novel longitudinal pooling layer to couple features of a visit with those of proceeding ones. On the prediction level, we add a consistency regularization to the classification objective in line with the nature of the disease progression across visits. We evaluate the proposed method on the longitudinal structural MRIs from three neuroimaging datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI, N=404), a dataset composed of 274 healthy controls and 329 patients with Alcohol Use Disorder (AUD), and 255 youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). All three experiments show that our method is superior to the widely used methods. The code is available at this https URL
The What, the Why and the Where of Emoji

explains what emoji are, the significance of their name and how they operate as a form of writing system - but a very different one from other established writing systems. It explains the distinction between standardised and non-standardised emoji (i.e. stickers) and how the standardised set work both from a technological and a semiotic perspective. In doing so it introduces the concepts of pictographic, ideographic and logographic writing.
INVESTIGATING UNIVERSITY STUDENTS’ MOTIVATION TOWARDS ENGLISH LANGUAGE LEARNING

This research aims at investigating the motivational factors of university students towards English Language learning. A quantitative method using a descriptive design was recommended for this study. A questionnaire was used as the data collection tool. The population comprises all males and females third year English language learner at colleges of education and languages at Sudan University of Science and Technology. Thus, the researcher used a sample size of 100 English language learners at colleges of Education and languages in Sudan University of Science and Technology being selected as participants in the current study, 99 valid responses were collected. Collected data is entered and treated by using the Statistical Package for Social Sciences (SPSS) version 20. The following are some of important findings based on data analysis and discussion of the collected data: 1. There are many factors that have positive effect on students' attitudes and motivation to learn English language. The most important ones include, having a good English language teacher positively affects their attitudes to learn English, and Learning English is important, as it is one of the requirements for high- education.2. Students were highly motivated to learn English language. There are many factors, motivates the students, on the top of the motivates factors is that, the need for travelling around the world, and the second motivator is that, almost students confirm that, learning English makes them more knowledgeable as they can be able to communicate. Finally, the results of the study, do not detect any statistically significant variations between students of the current study perceptions regarding learning English language related to students’ gender and age. Article visualizations:
LibGuides: Supplemental Teaching Materials for Transitioning from Face-to-Face Teaching to Online: Supplemental Materials

This guide provides information about providers & vendors who are working with universities & libraries to provide their resources for free or at a reduced cost. If there is a specific resource you need, please reach out to Kate Moore (kabmoore@ius.edu).
Construction of auditory media signal processing infrastructure to prevent media clone attacks

Understanding how auditory information is processed by the ear and the brain is no easy feat. The mechanics of basic sound signal transduction in the ear are fairly well characterised, however there is much that is still not well-known concerning aspects of the ear and the psychology of the process. Consequently, the way digital sound is stored, processed and delivered fails to take advantage of aspects of human hearing. Of particular concern is the ease with which digital sound can be virtually copied and distributed without the permission of the creator. Professor Masashi Unoki is an expert in auditory processing, his team is examining aspects of human auditory perception such as masking, cochlear delay and phase perception in order to explore the possibility of auditory information hiding and fingerprinting techniques.
ONLINE VOTING SYSTEM USING AES ALGORITHM

Electronic polling systems promise beneﬁts to voters like accessibility and convenience that modify them to solid their votes at any time, from any Internet-connected electronic computer anyplace within the world. However, not like ancient paper-based selection systems, associate e-polling system introduces many security risks like privacy of vote, unlikability of a citizen, citizen coercion, secrecy of partial election results, veriﬁability, and poll integrity. The legitimacy of a citizen is another security concern, i.e., a citizen should be identiﬁed through associate authentication mechanism that forestalls selection of unauthorized voters or vote from approved voters. Another security concern is that the manipulation of votes by associate infected (e.g., virus, malware, so on) selection device. A malware-hosted device may build unauthorized build to the voter’s selection selections.Many e-voting systems have been proposed however ,to date, all these schemes either fail to supply all the desired security properties or aren't much possible on light-weight computing devices. during this paper, we tend to gift a secure and veriﬁable polling system, Online Voting System using AES Algorithm, that employs well-known science primitives to supply vote and voter’s privacy,and pollintegrity, conﬁrms the identity of voters through a multifactor authentication theme, permits vote inside the allowed polling amount, prevents double selection, and achieves veriﬁability and uncoercibility within the presence of untrusted selection device.
The Validity of CET-6 among Chinese Students Studying Overseas

This paper focuses on the validity of College English Test Band 6 (CET-6) in oversea life among Chinese students to find out whether the scores of CET-6 can truly reflect students’ English language ability and whether it is possible to use the scores of CET-6 as a proof for English language proficiency. To do the survey, we conducted the survey by quantitative research methods with 50 samples in Universiti Putra Malaysia(UPM). After the collection and analysis of data, some current issues about the assessment standards of CET-6 are found, and suggestions are also given to improve the validity of CET-6.
Deploying Your App to a Server

In the previous chapters, we have learned how to build and test our React application. Once we have done that, we might want to know how our application performs in a production environment. That is when deployment comes into the picture.
IMPORTANCE AND SIGNIFICANCE OF READING READINESS  IN CHILDREN'S EDUCATIONAL LIVES

Learning to read is one of the most important goals of the first school years; reading is the process of understanding speech written down. In other words, the beginning reader must associate written words to their known lexicon of spoken words, must know that each word is composed of letters, and must understand that each letter represents individual sounds utilized within speech. The purpose of this study is to draw parents and teachers attention to the importance and significance of reading readiness for children and to answer the following questions: (1) when does reading begin, (2) how important is it to develop an awareness of print and (3) what are the components of reading. Article visualizations:
Information needs and strategies of older drivers for navigation on unfamiliar routes

In order to better inform the design of route guidance systems, a field study was carried out to explore the information needs and strategies of older drivers on unfamiliar routes. Previous analysis methods have been of too high a level to be of practical use for designers of such systems. Twelve older participants along with twelve younger participants (for comparative purposes), drove two routes in a city they were unfamiliar with. On one of the routes a map was given prior to driving, on the other route no map was given. ‘Question asking protocols’ were employed and all events recorded by video cameras located in the car to ensure a ‘rich’ data capture. This paper details the analysis methodology employed in the treatment of the data, allowing for the Information needs and strategies of older drivers to be identified in a manner more appropriate to the needs of designers.
Formation of Readership and Creative Development of Primary School Children Based on the Discrete Reading Method

The article discusses the method of discrete reading in literary reading lessons in primary school as an effective means of shaping readership, the ability to analyze the information contained in the texts, and the development of children’s creative abilities. The characteristic of the content and technology of discrete reading at the first stage of the development of reading activity by students of the first or second grades is given: intermittent reading; separate reading; fractional reading. The rules for implementing discrete reading are presented: “Imagine, Relive, Understand what you read.” The methodology of teacher guidance for schoolchildren performing creative tasks, features of the teacher’s work, which organizes creative interpretations of literary works in the form of illustrations, dramatization, retelling of the text with its optional addition-continuation, theatricalization, and staging games, are disclosed.
Video Deblocking for KMV-Cast Transmission Based on CNN Filtering

KMV-Cast is a pseudo analog video transmission scenario which is robust and scalable in multicast video transmission. However, the bias of received metadata in KMV-Cast may cause blocking artifact in the image. Instead of using the H.264 deblocking filter which is of high computational complexity and is of limited benefit, a CNN-based filtering method is proposed in this paper to correct the luminance of each block and remove the noise from each pixel. CNN filter can recognize the edge of the image, automatically distinguish the true and false edges of the image, and then smooth the image. Compared with traditional filtering methods, it can retain details intelligently. And experiment indicates our method performs well and can significantly increase the PSNR of reconstructed video by an average of 5 dB.
Library Guides: NSU Libraries Online Resources: Library Help

This guide highlights the research resources that are available remotely from the NSU Libraries.
Development of seismic fragility curves for high-speed railway system using earthquake case histories

Investigating damage potential of the railway infrastructure requires either large amount of case histories or in-depth numerical analyses, or both for which large amounts of effort and time are necessary to accomplish thoroughly. Rather than performing comprehensive studies for each damage case, in this study we collect and analyze a case history of the high-speed railway system damaged by the 2004 M6.6 Niigata Chuetsu earthquake for the development of the seismic fragility curve. The development processes are: 1) slice the railway system as 200 m segments and assigned damage levels and intensity measures (IMs) to each segment; 2) calculate probability of damage for a given IM; 3) estimate fragility curves using the maximum likelihood estimation regression method. Among IMs considered for fragility curves, spectral acceleration at 3 second period has the most prediction power for the probability of damage occurrence. Also, viaduct-type structure provides less scattered probability data points resulting in the best-fitted fragility curve, but for the tunnel-type structure data are poorly scattered for which fragility curve fitted is not meaningful. For validation purpose fragility curves developed are applied to the 2016 M7.0 Kumamoto earthquake case history by which another high-speed railway system was damaged. The number of actual damaged segments by the 2016 event is 25, and the number of equivalent damaged segments predicted using fragility curve is 22.21. Both numbers are very similar indicating that the developed fragility curve fits well to the Kumamoto region. Comparing with railway fragility curves from HAZUS, we found that HAZUS fragility curves are more conservative.
Precision Event Coreference Resolution Using Neural Network Classifiers

This paper presents a neural network classifier approach to detecting precise within-document (WD) and cross-document (CD) event coreference clusters effectively using only event mention based features. Our approach does not rely on any event argument features such as semantic roles or spatio-temporal arguments and uses no sophisticated clustering approach. Experimental results on the ECB+dataset show that our simple approach out performsstate-of-the-art methods for both within-document and cross-document event coreference resolution while producing clusters of high precision, which is useful for several downstream tasks.
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives

This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives. Initially, we analyze the limitations of the traditional CBIR systems that rely on the hand-crafted RS image descriptors. Then, we focus our attention on the advances in RS CBIR systems for which deep learning (DL) models are at the forefront. In particular, we present the theoretical properties of the most recent DL based CBIR systems for the characterization of the complex semantic content of RS images. After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives. Finally, the most promising research directions in RS CBIR are discussed.
Improving students vocabolary achievement by Using frayer model at the seventh grade student of SMPN 1 Kualuj Hilir

The aim of this research is to improve students’ vocabulary achievementafter being taught using Frayer model at SMPN 1 Kualuh Hilir. The research wasconducted with Classroom Action Research. The subject of study was students ofclass VII-1 Plus at SMPN 1 Kualuh Hilir which consisted of 23 students in theacademic year 2018/2019. The qualitative data were taken from observationsheet, interview and documentation. The quantitative data were taken fromtests, which was carried out in the end of every cycle. The test was given to thestudents in form of pre-test, post test in the first cycle and the post test in thesecond cycle.The result of the data analysis showed that the score of studentsincreased from the first meeting namely cycle I until the last meeting namelycycle II. It was showed from the mean of orientation test was 60.2,there was13.04%% (3 students) who got score 75 above. In the post test of cycle I, themean of the post test of cycle I was 72.70, it was higher than orientation test.There was 56.52%% (13 students) who got >75 score. The percentage of thestudents’ score in the post test of cycle I to the orientation test wasimprovement 43.48% . In the post test of cycle II the mean of the post test ofcycle II was 85.43, and the percentage was 91.30% ( 21 students) who got >75score. The improvement of the students score from the post test of cycle I to thepost test of cycle II was improvement 78.26%. In other words, the studentsachievement in vocabulary was improved. Based on the observation, it showedthat the excitement of the students were also improved.
Peak-locking minimization by three adjustment methods

A technique for obtaining subpixel resolution when tracking through cross-correlation consists of interpolating the obtained function and then refine the peak location. Although the technique provides accurate location results, the peak is always biased towards the closest integer coordinate. This effect is known as peak-locking error and is a major limit to the experimental accuracy of this calculation technique. This error may be different depending on the algorithm used to fit and interpolate the correlation peak but no systematic analysis was found in the literature. In our study we explore the three most common interpolation methods: thin-plate splines, second-order polynomial fit and Gaussian fit together with the influence of the extent of local interpolation area around the peak. Additionally, we have checked the influence of the image blurring on the results, since it is reported as one effective method to reduce the peak-locking error. Finally, the optimal adjustment found is the Gaussian fit with no blur and a neighborhood around the correlation peak of 11x11 pixels.
Fair Latency-Aware Metric for real-time video segmentation networks.

As supervised semantic segmentation is reaching satisfying results, many recent papers focused on making segmentation network architectures faster, smaller and more efficient. In particular, studies often aim to reach the stage to which they can claim to be real-time. Achieving this goal is especially relevant in the context of real-time video operations for autonomous vehicles and robots, or medical imaging during surgery. The common metric used for assessing these methods is so far the same as the ones used for image segmentation without time constraint: mean Intersection over Union (mIoU). In this paper, we argue that this metric is not relevant enough for real-time video as it does not take into account the processing time (latency) of the network. We propose a similar but more relevant metric called FLAME for video-segmentation networks, that compares the output segmentation of the network with the ground truth segmentation of the current video frame at the time when the network finishes the processing. We perform experiments to compare a few networks using this metric and propose a simple addition to network training to enhance results according to that metric.
THE DIGITAL ECONOMY AND ITS IMPACT ON THE FINANCIAL MARKET

The article discusses the relevance of the introduction of digital technologies in the financial sector. Special attention is paid to the evolution of the stock market due to its digitalization. Various options for the practical use of digital technologies in the described area are shown by analyzing big data and identifying significant patterns. The authors ' proposals will improve the efficiency and validity of decision-making in the financial sector.
Cross-Cultural Mentoring: What Education Needs Now

This conceptual analysis posits that cross cultural mentoring is one way toward mutual understanding and intellectual development in educational settings. By providing historical context, the author writes about the need for diverse people to open themselves to opportunities to learn from others who do not share any one of multiple identities.
A Survey of Pricing Aware Traffic Engineering in Cloud Computing

Cloud computing is the on-demand availability of computer resources and has drawn widely attention from industry. How to charge cloud resource usage to satisfy the customers’ requirements while considering a variety of parties’ interest is becoming increasingly important. Fair and effective pricing aware traffic engineering model could improve the resource utilization, attract more users and increase revenues for service providers. In this paper, we presents the basic core components and give a comparative review of latest and most appropriate pricing aware traffic engineering including network utility maximization based traffic control, game theory approaching bandwidth allocation and so on in cloud computing. The purpose of this paper is to bring out the important characteristics of pricing models with traffic engineering instead of survey all existing solutions. We hope to provide readers with a wide range of options and factors for designing better models in the future.
MAPAVERDE, herramienta educativa para dispositivos móviles sobre la metodología de Mapa Verde

Caring for and protecting the environment has become a pending issue for human beings. For years it has been projecting onto the planet the good and bad consequences of our development, and part of that development is reflected in new technologies, among which is the mobile phone. In the municipality of Consolación del Sur and from the Municipal University Center, work is being done on the project "Implementation of the Green Map Methodology in the management of environmental education in communities of Consolación del Sur" for the formation of an environmental culture for sustainable development creating awareness of the care and protection of the environment. The present work is given to solve the following problem: how to contribute in the construction of a computer application for the implementation of the Green Map methodology in the environmental management in the communities of Consolación del Sur for the development of the green maps of the communities of the Consolación del Sur municipality? The objective of this is to: Develop a computer application for mobile devices based on the Green Map methodology to educate future generations by raising awareness of the care and conservation of the environment. The methodologies, tools and technologies of software development to be used during the implementation to carry out the development of the application were studied. Finally, a system was obtained that responds to the stated objectives, allowing interaction with the user from any mobile device with the Android operating system.