Reviewerly/exHarmony huggingface dataset:
Learning Deep Latent Variable Models via Amortized Langevin Dynamics

How can we perform posterior inference for deep latent variable models in an efficient and flexible manner? Markov chain Monte Carlo (MCMC) methods, such as Langevin dynamics, provide sample approximations of such posteriors with an asymptotic convergence guarantee. However, it is difficult to apply these methods to large-scale datasets owing to their slow convergence and datapoint-wise iterations. In this study, we propose amortized Langevin dynamics, wherein datapoint-wise MCMC iterations are replaced with updates of an inference model that maps observations into latent variables. The amortization enables scalable inference from large-scale datasets. Developing a latent variable model and an inference model with neural networks, yields Langevin autoencoders (LAEs), a novel Langevin-based framework for deep generative models. Moreover, if we define a latent prior distribution with an unnormalized energy function for more flexible generative modeling, LAEs are extended to a more general framework, which we refer to as contrastive Langevin autoencoders (CLAEs). We experimentally show that LAEs and CLAEs can generate sharp image samples. Moreover, we report their performance of unsupervised anomaly detection.
Auto-Tuning by Using Double Extended Kalman-Bucy Filter: An Application to Dc Motor for Controlling Speed

In this study, a modified adaptive control algorithm is proposed and investigated. The algorithm consists of a controller, an estimator and an auxiliary model like in model reference adaptive control strategy. PID controller is used to provide controlling. The controller includes adjustable parameters. Traditional PID controller parameters are usually set to fulfil the reference behaviour criterion. In this study, minimum-time criterion is chosen. Extended Kalman-Bucy estimator is employed for estimating controller parameters to make system behave like auxiliary model. The estimator adjusts the controller parameters so that system output can catch the reference input at minimum time. The study may call as the minimization of the settling time problem. The controller and estimator of the system are operated simultaneously. The achievement of the proposed algorithm is proved by simulation results including a simple dc motor model.
A Simple and General Strategy for Referential Problem in Low-Resource Neural Machine Translation

This paper aims to solve a series of referential problems in sequence decoding caused by data sparsity and corpus scarce in low-resource Neural Machine Translation (NMT), including pronoun missing, reference error, bias and so on. It is difficult to find the essential reason of these problems because they are only shown in the prediction results and involve all aspects of the model. Different from the usual solutions based on complex mathematical rule setting and adding artificial features, we expect to turn the problems in the predictions into noise as much as possible, and use adversarial training to make the model find the balance between the noise and the golden samples, instead of exploring the reason of the problem during the complex training. In this paper, only a simple noise-based preprocessing operation and a slight modification of the adversarial training can make the model generalize to a series of referential problems in low-resource NMT task. On Korean-Chinese, Mongolian-Chinese and Arabic-Chinese tasks, the evaluation of BLEU score and the accuracy of pronouns in sequence have been significantly improved.
Identifying Non-Performing Students in Higher Educational Institutions Using Data Mining Techniques

The educational institutes are focusing on improving the performance of students by using several data mining techniques. Since there is an increase in the number of drop out students every year, if we are able to predict whether a student will complete the course or not, it is possible to take some preventive actions beforehand. The primary data set used for modelling has been taken from a reputed technical institute of Uttar Pradesh which consists of data of 6,807 students containing 20 academic and non-academic attributes. The most relevant attributes are extracted using CorrelationAttributeEval (in WEKA) technique using Ranker search method which ranks the attributes as per their evaluation. Synthetic minority oversampling technique (SMOTE) filter is applied to deal with the skewed data set. The models are built from eight classifiers that are analysed for predicting the most appropriate model to classify whether a student will complete the course or withdraw his/her admission.
Analisis Tingkat Kerusakan Jalan Menggunakan Sistem Informasi Geografis Di Kecamatan Sukolilo Kabupaten Pati

Sukolilo Subdistrict is a sub-district in Pati Regency which has suffered quite severe road damage from other sub-districts. The road damage is due to being a transportation route to the cement industry. Apart from that, supported by Sukolilo District, there are provincial roads which are alternative roads to big cities such as Semarang, Solo and Yogyakarta. The road is passed by a lot of heavy-duty transportation such as buses and trucks (carrying sand, stones and earth). This requires good and comfortable road handling for road users. This study has two objectives, namely to determine the level of potential road damage and to analyze the alignment of potential road damage and damage points in the field. The unit of analysis is a field unit. The sampling method in this research is stratified sampling and purposive sampling. The stratified sampling method is used to determine the sampling point while purposive sampling is used for the selection of terrain units that are traversed by roads. This study produces a map of the level of potential road damage in Sukolilo District which is classified as 2 levels of road damage, namely low and moderate damage levels, 4 terrain units are at low grade levels and 7 terrain units are at moderate damage levels. The level of road damage is evenly distributed with moderate levels of damage due to the influence of factors of soil type and traffic volume which can be classified as high. Alignment between the road damage potential diversion map and the road damage points in the field results in harmony.
ENHANCEMENT OF IMAGE STEGANOGRAPHY AND STEGANALYSIS USING WATER CYCLE OPTIMIZATION

Information security is on the list of chief challenge to be looked at when hidden information gets converted from one area to yet another. This process of data communication with its maintained confidentiality represents a typical task to deal with different issues of the security systems. The phrase Steganography typically describes a method of concealing the info or maybe information in which the term stego' implies a covering medium as well as graph denotes the type of writing jointly developing a covered writing procedure. A message based on cryptography is quite simple to identify though the idea based on steganography is actually difficult to identify. Thus, the primary goal of steganography is drive or maybe transmit the information from one stage to the next with a harmless or covered moderate. To us these kinds of an approach decreases the attacking capacity on a secret message as attackers don't get the info correctly. Data embedding with steganography process will surely produce a highly secured form of data which can be used for various types or carriers such as .txt,bmp, .gif, .wav, and .jpeg. Here, the proposed work highlights a technique of image-steganography to secure the data or message hidden behind an image using the wavelet transform method along with gradient optimization process. Using such an approach reduces the attacking ability on a hidden message as attackers do not get the information properly.
EngVallex - English Valency Lexicon 2.0

EngVallex 2.0 as a slightly updated version of EngVallex. It is the English counterpart of the PDT-Vallex valency lexicon, using the same view of valency, valency frames and the description of a surface form of verbal arguments. EngVallex contains links also to PropBank (English predicate-argument lexicon). The EngVallex lexicon is fully linked to the English side of the PCEDT parallel treebank(s), which is in fact the PTB re-annotated using the Prague Dependency Treebank style of annotation. The EngVallex is available in an XML format in our repository, and also in a searchable form with examples from the PCEDT. EngVallex 2.0 is the same dataset as the EngVallex lexicon packaged with the PCEDT 3.0 corpus, but published separately under a more permissive licence, avoiding the need for LDC licence which is tied to PCEDT 3.0 as a whole.
Magboard as Media to Teach English to Young Learner

The purposes of this research are to (1) develop the MagBoard, and (2) describe the strengths and weaknesses of the MagBoard for teaching English for young learners.To conduct this research, the researcher used an educational research and develop (R&D), that is, (1) Research and Information Collection, (2) Planning, (3) Product Development, (4) Final Product. The data taken from some interviews and documents were analyzed descriptive qualitatively. From the need analysis, the researcher found that the school goal toward English is to introduce English for children through science and art though the school has not yet owned complete media for teaching and learning English.In this research the researcher designed a Magnetic Board (MagBoard) by inviting supervisors and collaborators to assess the MagBoard. To conduct this research, the researcher did the need analysis. Need analysis is a procedure of what and how a course used to collect data information about teachers' goal, lack, necessity and want. Some strengths of the MagBoard were found that the current media can stimulate studentâ€™s curiosity and motivate the students, and make students more interested in learning process, so students can easily to explore their knowledge and interest. However, the MagBoard also had weaknesses on the numbers of magboard properties, and less space to apply the material or practice.
La yuxtaposición armónico-métrica y el bajo eléctrico

The electric bass is an essential instrument in most popular music formats nowadays. It is currently of such importance that, along with the double bass, it is difficult to think of any style within popular music that leaves aside the role which these instruments fulfill. Even styles that did not use these instruments in the past are using them today.During my experience as a college student and as a popular music bass player I have noticed that materials used by the electric bass vary rhythmically and melodically depending on the style. However, there is not much variation besides that. Additionally, the same behavior prevails in most basslines: to target the chord's root and the first pulse of the measure.Does this role imply a specific function upon the bass that distances it from other possible uses or creative possibilities? Why does the bass behave in such similar ways within so many musical styles? What incidence does this phenomenon have in music composition?Departing from these questions, this project aims to dwell over the origins and scope of this particular way of approaching the bass and how this impacts not only the discipline of bass playing but the practice of improvisation and musical composition itself. It also aims to present a set of musical pieces in which the bass behaves differently from its traditional role in popular music, and allow it to unfold its creative potential.
Review of Building teaching and learning communities: Creating shared meaning and purpose

Abstract: Review of Maron, N., Kennison, R., Bracke, P., Hall, N., Gilman, I., Malenfant, K., Roh, C., &amp; Shorish, Y. (2019). Open and equitable scholarly communications: Creating a more inclusive future. Chicago: Association of College and Research Libraries.
Mobile Applications for Students with Dyslexia: A Systematic Literature Review

Students with dyslexia often face many challenges in their learning process. Some of the challenges could be trying to understand the content when they lack the ability to read properly. Teachers, however, and all other professional stakeholders have a duty to find ways to help these students meet quality education and exploit their maximum potential despite their challenges. By incorporating technological solutions in the classrooms, academic challenges for learners can be reduced, ensuring that students with dyslexia get the most out of the education process. Therefore, this systematic literature review aims to explore what mobile applications are available that can be used to enhance the learning experience for students with dyslexia and what the literature says about how the overall learning experience of these students might change by providing mobile applications. Nine articles met the criteria for review and were included. The results showed that the introduction of mobile applications can effectively enhance the learning process, essentially pertaining to individuals with dyslexia. Furthermore, the various systems being developed in the form of m-learning applications are proving to be useful, especially on pre-literacy and dyslexic learning. The new developments are poised to increase students with dyslexia motivations for learning since they grant them an opportunity and avenue to make the learning process easy for them. The review concludes that there is a need for more research to determine what development components are needed to evaluate the mobile application that being used for students with dyslexia.
The Cultivation of Professional Skills of Mathematics Normal University Students

With the development of the basic education reform under the educational background of the times, the subject of mathematics also receives more and more attention and faces more challenges under the new curriculum reform. The level of professional quality of mathematics teachers is closely related to the pace of educational reform and development. In this paper, the following measures are taken to better carry out the teaching work and promote the development of national education: the cultivation of professional approval by the normal force, mastery of professional knowledge system, the cultivation of the professional teaching skills and professional affection skill analysis of mathematics students professional skills and practical skills training, establish and perfect the mathematics teachers professional skill training system, and strive to improve math students' professional skills and practical ability and make mathematics students more competitive in the employment.
Hybrid Neural Network And Rule-Induction Approach For The Subjective Cataloging and Classification of Document.(Dept.E)

The subjective cataloging p rocess of researches and books depends on the experience of the classifier. Although the index terms given by the authors at the end of their abstracts can guide the classifier to the proper subject; they are not quite enough to express the real content of the research. The Title and the abstract of a given research play an important role in the subjective cataloging. This paper utilizes the human index terms given in the papers published in the leading journals to build domain thesaurus Tries (advanced B-Tree). The Tries has the possibility to locate the index term and its occurrence. A rule induction system is used for the subjective cataloging be extracting the effective features index terms) from the title and abstract of a given research. The domain thesaurus' Tries and the rule induction system are used to classify new document by supervised artificial neural network (SANN). The training mode of the SANN is enhanced by three main algorithms, the genetic algorithm (GA), the conjugate gradient algorithm (CGA) and the simulated annealing algorithm (SAA). The processes of training and testing the SANN in the document classification are also presented.
Later Span Adaptation for Language Understanding

Pre-trained contextualized language models (PrLMs) broadly use fine-grained tokens (words or sub-words) as minimal linguistic unit in pre-training phase. Introducing span-level information in pre-training has shown capable of further enhancing PrLMs. However, such methods require enormous resources and are lack of adaptivity due to huge computational requirement from pre-training. Instead of too early fixing the linguistic unit input as nearly all previous work did, we propose a novel method that combines span-level information into the representations generated by PrLMs during fine-tuning phase for better flexibility. In this way, the modeling procedure of span-level texts can be more adaptive to different downstream tasks. In detail, we divide the sentence into several spans according to the segmentation generated by a pre-sampled dictionary. Based on the sub-token-level representation provided by PrLMs, we enhance the connection between the tokens in each span and gain a representation with enhanced span-level information. Experiments are conducted on GLUE benchmark and prove that our approach could remarkably enhance the performance of PrLMs in various natural language understanding tasks.
Exploring the Efficiency of the Combined Application of Connection Pruning and Source Data Preprocessing When Training a Multilayer Perceptron

A conventional scheme to operate neural networks until recently has been assigning the architecture of a neural network and its subsequent training. However, the latest research in this field has revealed that the neural networks that had been set and configured in this way exhibited considerable redundancy. Therefore, the additional operation was to eliminate this redundancy by pruning the connections in the architecture of a neural network. Among the many approaches to eliminating redundancy, the most promising one is the combined application of several methods when their cumulative effect exceeds the sum of effects from employing each of them separately. We have performed an experimental study into the effectiveness of the combined application of iterative pruning and pre-processing (pre-distortions) of input data for the task of recognizing handwritten digits with the help of a multilayer perceptron. It has been shown that the use of input data pre-processing regularizes the procedure of training a neural network, thereby preventing its retraining. The combined application of the iterative pruning and pre-processing of input data has made it possible to obtain a smaller error in the recognition of handwritten digits, 1.22 %, compared to when using the thinning only (the error decreased from 1.89 % to 1.81 %) and when employing the predistortions only (the error decreased from 1.89 % to 1.52 %). In addition, the regularization involving pre-distortions makes it possible to receive a monotonously increasing number of disconnected connections while maintaining the error at 1.45 %. The resulting learning curves for the same task but corresponding to the onset of training under different initial conditions acquire different values both in the learning process and at the end of the training. This shows the multi-extreme character of the quality function – the accuracy of recognition. The practical implication of the study is our proposal to run the multiple training of a neural network in order to choose the best result.
LISTENING AND SPEAKING SKILL, WHAT IS THE CORRELATION? (A CORRELATIONAL STUDY AT THE FIRST SEMESTER OF D3 HOSPITALITY STUDY PROGRAM STUDENTS OF INDONUSA SURAKARTA POLYTECHNIC, ACADEMIC YEAR 2019/2020)

This study aims to determine the correlation between listening and speaking skills ofstudents in D3 Hospitality Study Program. The sample is twenty-eight first semester studentsof the D3 hospitality study program for the 2019-2020 academic year. This type of researchis correlation with technical analysis data using Pearson Product Moment. The data weretaken from the final English examination one in the form of listening and speaking. Based onthe test of normality, the p value for each variable is more than 0.05 so that the data isnormally distributed. Based on the calculated data, it is found that the listening score andspeaking score variables have a strong correlation or correlation, indicated by the value of r= 0.644 and p value = 0.000 less than 0.005. This confirms that there is a relationshipbetween listening and speaking skills in learning English.
Is Similarity or Common Words Enough to Answer User's Legal Questions?

To provide search results for user queries, semantic and Lexical search platforms find documents that are more similar or have more common words to the user query. However, if we want to answer the user's question, a comprehension system is needed on top of the search and retrieval. Answer Finder which is BERT sequence classifier predicts how well a retrieved passage answers a specific user question. By leveraging this capability, among top k similar retrieved documents, the documents that answer the user's question could be shown to the users.In this presentation, creating the training data, fine-tuning a BERT sequence classifier, and evaluating the trained classifier (Answer Finder) will be demonstrated. Test results show using Answer Finder probability, the quality of the answers is improved significantly. Answer Finder is one of the components of open Q&A system which was released in Lexis+.
Adversarial Synthetic Datasets for Neural Program Synthesis

Program synthesis is the task of automatically generating a program consistent with a given specification. A natural way to specify programs is to provide examples of desired input-output behavior, and many current program synthesis approaches have achieved impressive results after training on randomly generated input-output examples. However, recent work has discovered that some of these approaches generalize poorly to data distributions different from that of the randomly generated examples. We show that this problem applies to other state-of-the-art approaches as well and that current methods to counteract this problem are insufficient. We then propose a new, adversarial approach to control the bias of synthetic data distributions and show that it outperforms current approaches.
남북한 언어 이질화 요인으로서의 언어 정책

This study aims to show that language purification policies are the most important factors that influence Korean language differentiation, based on...
LibGuides: Resource Hub for Thesis Writers: Citation Management

This guide provides all resources related to project, thesis, and dissertation writing and submission process at AUB.