Reviewerly/exHarmony huggingface dataset:
Improving very large vocabulary language modeling and decoding for speech recognition in morphologically rich languages

In the automatic speech recognition of agglutinative and morphologically rich languages, the recognition vocabulary may, in many tasks, need to cover several millions of word forms. This poses challenges for the search component of the speech recognizer, as in many cases, real-time recognition speed would be preferred, and the number of possible recognition hypotheses is large. A typical modern large vocabulary speech recognizer utilizes a probabilistic language model to assign prior probabilities for the word sequences. Estimating accurate language models from a text corpus also becomes harder due to increased data sparsity. So far, the most successful approach for the speech recognition of morphologically rich languages has been to segment the words to shorter, more frequently occurring units, thus alleviating the estimability problems. Also, if all concatenations of subwords are allowed, the recognition vocabulary is unlimited. This thesis concentrates on different approaches where a limited but very large recognition vocabulary is used. This type of recognizer can, in addition to the subword-based language models, also use language models trained over words and word classes to reach improved modeling accuracy. For the case where only a subword language model is used, the thesis shows a novel way of constructing the recognition graph. In this case, the recognition vocabulary is easy to augment with new word forms by utilizing resources like dictionaries and morphological analyzers. The constrained recognition vocabulary approaches are shown to be viable choices in many speech recognition use cases. Additionally, in this case, it is shown that the search may also operate in real time and even faster than the case where the recognition vocabulary was unlimited. Also, the recognition of non-words is avoided, and the recognition accuracy may exceed the unlimited vocabulary approach if a low enough out-of-vocabulary rate is reached. In one part of the thesis, human word recognition performance is analyzed using statistical morphological models in a visual lexical decision task where the participants' eye movements were also recorded using eye tracking. Morfessor Baseline -method, which segments only the infrequent words, predicted the observations well in most of the experiments. This finding supports the corresponding model of word recognition in humans.; Morfologisesti rikkaiden ja agglutinatiivisten kielten laajan sanaston puheentunnistuksen yksi haasteista on sanaston suuri koko. Tunnistussanaston taytyy monissa tunnistustehtavissa sisaltaa miljoonia sanamuotoja. Tama on ongelmallista puheentunnistimelle, koska useissa kayttotapauksissa tavoitteena olisi reaaliaikainen tunnistus ja eri hypoteesien maara on suuri. Tyypillinen moderni puheentunnistin kayttaa todennakoisyyksiin perustuvaa kielimallia eri tunnistushypoteesien pisteytykseen. Tarkkojen kielimallien estimointi tekstikorpuksesta on myos haastavaa datan harvuuden vuoksi. Tahan asti yleisimmin kaytetty lahestymistapa agglutinatiivisten kielten…
Analysis of the Effectiveness of Online Lectures Against Student Achievements Using the C4.5 Method (Case Study: Faculty of Communication and Information Technology National University): Analysis of the Effectiveness of Online Lectures Against Student Achievements Using the C4.5 Method (Case Study: Faculty of Communication and Information Technology National University)

This research is aimed at studying the effectiveness of online lectures on student achievement at the Faculty of Technology and Communication and Information at the National University using the C4.5 Algorithm method. The factors that must be considered in measuring the level of effectiveness of online lectures, namely from the income of parents, attendance and GPA (Grade Point Average) to every student. In the third factor has not been measured with certainty so that the Faculty of Communication and Information and Technology found it difficult to determine what can be changed and improved in quality, which will use the C4.5 method, in the case the author tries to measure these three factors so that eventually a Decision Tree. After doing calculations using the application it will know the level of short comings. Educational quality is something that needs to be considered because it is very influential in the process of student learning, therefore it needs an effort to improve effectiveness in online lectures.
Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model

We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically developed from a data stream with focus on memory and data coverage. The fuzzy controller also evolves, based on the data instances and fuzzy model parameters. Its local gains are redesigned in real-time -- whenever the corresponding local fuzzy models change -- from the solution of a linear matrix inequality problem derived from a fuzzy Lyapunov function and bounded input conditions. We have shown one-step prediction and asymptotic stabilization of the Henon chaos.
LibGuides: Annotated Bibliographies and Literature Reviews: Define Your Research Question

This guide is intended for students who are seeking help on writing annotated bibliographies and literature review
Comparison of Density-Based and Distance-Based Outlier Identification Methods in Fuzzy Clustering

Outlier identification is a process of identification of any kind of abnormality in the data regarding to context or behavior of data objects. In literature, various outlier identification methods such as statistical methods, clustering-based methods, and proximity-based methods have been proposed. However, in fuzzy clustering, mainly proximity-based methods (density-based and distance-based) are used for outlier identification. In this paper, we have compared a density-based outlier identification method and a distance-based outlier identification method in context of fuzzy clustering. Experimental results of these outlier identification methods show higher stability and accuracy of distance-based method over density-based method.
Facemask Detection using Inception V3 Model and Effect on Accuracy of Data Preprocessing Methods

Abstract Nowadays, image classification done by Machine Learning can classify images within an instant after an efficient model is built. Such techniques can help identify whether a person correctly puts a mask on. During the Covid situation, it is important to ensure the people in public areas put on a mask correctly so it can cut off the route of mass infection. In this paper, three classes of image classification were tested: with mask, without mask, and mask-weared-incorrectly. Based on the Google Colaboratory platform and Inception V3 model, a three-classes-detect-model with 94.52% testing accuracy was built. In addition to building the models, the effectiveness of data preprocessing has also been tested. After applying different methods for data preprocessing, the testing accuracy for the two-classes-detector model improved to 97.11% but the three-classes-detector model decreased to 94.26%.
Research on Conceptual Evolution Model of Developmental Psychology Based on Fuzzy Clustering Algorithm

Evolutionary development psychology uses the basic principles of evolution to explain contemporary human development, including the study of genetic mechanism and environmental mechanism, because genetic mechanism and environmental mechanism are the basis of the universal development of social and cognitive abilities, and also the basis of the extraevolutionary genetic process to adapt these abilities to local conditions. According to the complexity of trust network, the hierarchical reduction of trust network is put forward, the fuzziness of behavior trust is deeply studied, and the trust management problem is modeled by using fuzzy set theory. A complete conceptual evolution model of developmental psychology is constructed by constructing fuzzy similarity matrix based on rough set, determining fuzzy similarity clustering analysis method of role attributes and giving trust evaluation mechanism. Experimental results show that the clustering results of this algorithm are better than Ward clustering algorithm based on genetic algorithm.
Design and Implementation of Mobile Platform for Online Recommendation and Comment for British and American Literary Works

This paper first introduces the characteristics of online review information. With the help of the comment data filled in by users after browsing behavior, the user's preferences and the inherent characteristics of the product are explained by mining the description of all aspects of the product in the user's comment data. Then, combined with the topic model and collaborative filtering model, the online recommendation technology based on mobile platform is proposed, and the feature matching degree table is established. By calculating the feature matching value between the user and the product to predict the user's score on the product, to realize the commodity recommendation which is more in line with the consumers' thinking habits of network retrieval information. The experimental results on a real data set constructed on a book e-commerce mobile website show that the platform has a great improvement in the accuracy of the recommendation system compared with the traditional methods, and it has a better interpretation compared with the traditional model.
Effects of Symmetry and Apparent Distance in a Parasagittal-Mirror Variant of the Rubber Hand Illusion Paradigm

When I see my face in a mirror, its apparent position (behind the glass) is not one that my own face could be in. I accept the face I see as my own because I have an implicit understanding of how mirrors work. The situation is different if I look at the reflection of my right hand in a parasagittal mirror (parallel to body midline) when my left hand is hidden behind the mirror. It is as if I were looking through a window at my own left hand. The experience of body ownership has been investigated using rubber hand illusion (RHI) paradigms, and several studies have demonstrated ownership of a rubber hand viewed in a frontal mirror. Our “proof of concept” study was the first to combine use of a parasagittal mirror and synchronous stroking of both a prosthetic hand (viewed in the mirror) and the participant’s hand, with a manipulation of distance between the hands. The strength of the RHI elicited by our parasagittal-mirror paradigm depended not on physical distance between the hands (30, 45, or 60 cm) but on apparent distance between the prosthetic hand (viewed in the mirror) and the participant’s hand. This apparent distance was reduced to zero when the prosthetic hand and participant’s hand were arranged symmetrically (e.g., 30 cm in front of and behind the mirror). Thus, the parasagittal-mirror paradigm may provide a distinctive way to assess whether competition for ownership depends on spatial separation between the prosthetic hand and the participant’s hand.
From the Pleasure of Schooling to the Joy of Schooling

The past academic year of 2018 was a significant one for education. We celebrated the 100th birthday of Vasily Sukhomlinsky. Unfortunately, it was met with little fanfare and in hushed tones: There were two or three conferences held in Sukhomlinsky’s memory and a few separate mentions in the media, despite the fact that UNESCO declared this year the Year of Sukhomlinsky. It really did, and we did not even petition them to do so.
MiniTrack: Advances in trust, identity, and trusted systems in technology-mediated environments

Within the 52nd Hawaiian International Conference on System Sciences (HICSS), we organize for the third time a minitrack on Trust, Identity, and Trusted Systems in Technology-mediated Environments. Trust is a pervasive concern not just with new technologies but also with established technologies as they become more complex and interdependent. Through five papers and an open discussion, the minitrack will discuss and debate when and to what degree trust matters, in what form(s), and with which consequences in the context of existing and emerging hardware and software technologies, e.g., biometric technologies, cryptocurrencies, artificial intelligence, peer-to-peer networked platforms, and autonomous vehicles.
Research on the Improvement of College Students’ Entrepreneurial Ability under the Background of Big Data

The cultivation of entrepreneurial ability is a key issue that colleges and universities need to pay attention to. Because the accumulation of talents in the society is approaching saturation at this stage, the competition for laid-off positions is becoming more and more fierce in this situation. Improving students’ entrepreneurial ability can effectively alleviate the phenomenon of occupational tension in the current society, and at the same time, it can guarantee students’ future development and social and economic development. This paper also focuses on the improvement of college students’ entrepreneurial ability, and analyzes how to effectively improve students’ entrepreneurial ability under the background of big data.
Research Guides: Get the Ask A Librarian Mobile App: Guide

This guide provides information about downloading and using an app that will access CCGA library information and resources.
Upper Bounds on the Generalization Error of Private Algorithms for Discrete Data

In this work, we study the generalization capability of algorithms from an information-theoretic perspective. It has been shown that the expected generalization error of an algorithm is bounded from above by a function of the relative entropy between the conditional probability distribution of the algorithm’s output hypothesis, given the dataset with which it was trained, and its marginal probability distribution. We build upon this fact and introduce a mathematical formulation to obtain upper bounds on this relative entropy. Assuming that the data is discrete, we then develop a strategy using this formulation, based on the method of types and typicality, to find explicit upper bounds on the generalization error of stable algorithms, i.e., algorithms that produce similar output hypotheses given similar input datasets. In particular, we show the bounds obtained with this strategy for the case of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -DP and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> -GDP algorithms.
Analisis Kemampuan Penulisan Huruf Kapital dan Tanda Baca Siswa Kelas IV Gugus I

This research is a quantitative descriptive study that aims to determine the ability to use capital letters and punctuation marks of class IV students of group I, Takkalalla District, Wajo Regency. The sample in this study were all fourth grade students in cluster I, Takkalalla District, Wajo Regency with a total of 56 students. The data collection technique used a validated test. Data analysis used descriptive statistical analysis and inferential statistics. The results showed that the average value of the use of capital letters was in the medium category and the average value of the use of punctuation was in the medium category. Meanwhile, for students who scored 75 on the use of capital letters, there were 24 out of 56 students (42.9%) and 34 out of 56 students (60.7%). The conclusion of this research is that the ability to write capital letters and punctuation marks of fourth grade students in Group I, Takkalalla District, Wajo Regency is under KKM.
Strategi Penelitian yang Efektif Dimasa Pandemi Covid-19

The Covid 19 pandemic, which is an epidemic for the world, has a significant effect not only on the industrial sector, the economy, but also on the education sector. With the limited communication and learning media that existed at the beginning of the pandemic, it created a series of problems that had an impact on the quality and timeliness of study completion, not only at the undergraduate level but also at the masters and doctoral levels who experience delays due to not comprehensively understanding the strategy of writing scientific papers correctly. This activity is carried out with the aim of helping all parties, especially students who are in the process of preparing their final project. The results of the activities have a positive influence in helping students understand the writing of the final project. The results of the activity have a positive influence in helping students understand the writing of the final project, as evidenced by the enthusiasm and interest of the participants so that this kind of activity can be carried out on an ongoing basis and held again at another time.
The Effects of Using a Mobile Vocabulary Application on EFL College Learners’ English Vocabulary Learning and Perceptions

This study investigates the effects of a mobile application on Korean EFL college students’ vocabulary learning and perceptions of the mobile application. Eighty undergraduates were randomly divided into three groups; (a) a mobile application group with teacher-directed vocabulary lists, (b) a mobile application group with student-directed vocabulary lists, and (c) a control group based on paper handouts. Major results are as follows. First, the mobile application groups performed significantly better in the post vocabulary test than the control group. Second, in regard to the effect of self-directed learning, the test scores between the two mobile application groups didn’t show significant differences. Third, the students using the mobile application responded positively to their experience of learning vocabulary through the device. Based on the findings, the study discusses pedagogical implications towards the use of mobile applications in teaching vocabulary.
Application of Video-to-Video Translation Networks to Computational Fluid Dynamics

In recent years, the evolution of artificial intelligence, especially deep learning, has been remarkable, and its application to various fields has been growing rapidly. In this paper, I report the results of the application of generative adversarial networks (GANs), specifically video-to-video translation networks, to computational fluid dynamics (CFD) simulations. The purpose of this research is to reduce the computational cost of CFD simulations with GANs. The architecture of GANs in this research is a combination of the image-to-image translation networks (the so-called "pix2pix") and Long Short-Term Memory (LSTM). It is shown that the results of high-cost and high-accuracy simulations (with high-resolution computational grids) can be estimated from those of low-cost and low-accuracy simulations (with low-resolution grids). In particular, the time evolution of density distributions in the cases of a high-resolution grid is reproduced from that in the cases of a low-resolution grid through GANs, and the density inhomogeneity estimated from the image generated by GANs recovers the ground truth with good accuracy. Qualitative and quantitative comparisons of the results of the proposed method with those of several super-resolution algorithms are also presented.
Analysis of Sexual Harassment Tweet Sentiment on Twitter inIndonesia using Naïve Bayes Method through National Instituteof Standard and Technology Digital Forensic AcquisitionApproach

In this era, the internet is commonly used in society, especially for social media. Social media is an open and unlimited medium of communication where people can freely express their opinions. However, nowadays, many people are abusing social media to do negative things; online sexual harassment is an example. Thisresearch was conducted based on previous research or literature studies; the research results showed various sentiment analysis results using the Naive Bayes classifier method through the digital forensic acquisition approach. The study aims to identify sexual harassment by using sentiment analysis from Twitter andmeasuring grouping results' accuracy with the Naive Bayes method. In this study, sexual harassment was identified using analytical sentiment by testing 300 tweets data consisting of 30 queries from Twitter. The result of sentiment analysis from300 Twitter scraping data shows that the value of negative sentiment is higher than positive sentiment with an average total of 69.7%. From this sentiment, Twitter becomes one of the free communication media and vulnerable to verbal sexual harassment through tweets in Indonesia.
Quantum Money Scheme

Quantum computing has the power to break current cryptographic systems, disrupting online banking, shopping, data storage and communications. However, quantum mechanics can also be used to make these systems stronger and more resilient. In this paper we describe the transmissibility of a quantum money scheme, which was proposed by Dmitry Gavinsky and implemented by the authors, and discuss some of its benefits and limitations.
Counting Ternary Square-Free Words Quickly

An efficient, when compared to exhaustive enumeration, algorithm for computing the number of square-free words of length n over the alphabet \(\{a, b, c\}\) is presented.
Twisted mathematics: How subtle microaggressions in math movies shape our opinion of mathematicians (2019-04-23)

Presented by Shah Roshan Zamir, Applied and Computational Mathematics MS Candidate; Tuesday, April 23, 4 - 5pm; Library Rotunda (4th Floor); The event gets underway at 4pm with light refreshments, followed by a 15 - 20-minute talk, and concludes with 15 - 20 minutes of informal discussion. The goal of UMD GRAD Talks is to create an opportunity for interactions among faculty, staff, and graduate students in disparate disciplines that could grow into meaningful new trans-disciplinary collaborations. Presentations in this series are designed to be accessible to a non-specialist audience, and to give students an opportunity to present their work to the broader University community.
Intellectual property and digital technology in Thailand

Chapter 4 investigates intellectual property rights (IPR) protection and the related issues in the context of digital transformation in Thailand. It overviews Thailand's intellectual property (IP) laws and analyses from a legal perspective the key factors that Thailand adopted to protect digital products. From a socio-economic perspective, it reviews case studies covering product categories such as virtual reality, social media, e-commerce, and online services. It suggests the country to eye the European Union (EU) and the US and learn from their experience in regulating the IP of digital products, such as virtual reality, social media, e-commerce, and online services. A big challenge in making IPR protection effective in supporting Thailand 4.0 is to balance the legal and socio-economic concerns on IP and digital technology.