Reviewerly/exHarmony huggingface dataset:
From Research to Production: Fine-Grained Analysis of Terminology Integration

Dynamic terminology integration in neural machine translation (NMT) is a sought-after feature of computer-aided translation tools among language service providers and small to medium businesses. Despite the recent surge in research on terminology integration in NMT, it still is seldom or inadequately supported in commercial machine translation solutions. In this presentation, we will share our experience of developing and deploying terminology integration capabilities for NMT systems in production. We will look at the three core tasks of terminology integration: terminology management, terminology identification, and translation with terminology. This talk will be insightful for NMT system developers, translators, terminologists, and anyone interested in translation projects.
AUTOSUMM: Automatic Model Creation for Text Summarization

Recent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets. However, obtaining the best model configuration for a given dataset requires an extensive knowledge of deep learning specifics like model architecture, tuning parameters etc., and is often extremely challenging for a non-expert. In this paper, we propose methods to automatically create deep learning models for the tasks of extractive and abstractive text summarization. Based on the recent advances in Automated Machine Learning and the success of large language models such as BERT and GPT-2 in encoding knowledge, we use a combination of Neural Architecture Search (NAS) and Knowledge Distillation (KD) techniques to perform model search and compression using the vast knowledge provided by these language models to develop smaller, customized models for any given dataset. We present extensive empirical results to illustrate the effectiveness of our model creation methods in terms of inference time and model size, while achieving near state-of-the-art performances in terms of accuracy across a range of datasets.
The Effect of College Students' Learning Immersion and self-Direction on Non-Face-to-Face Learning Performance and Class Satisfaction^

This study aims to analyze the status of distance learning in higher education and explore the effects of learning engagement and self-directed learning readiness among variables related to learners on academic performance and course satisfaction in distance learning. It seeks to provide a baseline for other studies to develop strategies for quality online courses in preparation for a prolonged COVID-19 pandemic. An online survey was conducted on 708 students from a four-year University E located in Seongnam, Gyeonggi-do. Each student completed a self-administered questionnaire on a web page, which was created for the survey. The survey used a multiple regression analysis, with the independent variables being student engagement and self-directed learning readiness. With the aim of explaining academic performance and satisfaction for distance learning, the study also adopted a stepwise selection method to select the independent variables. It was found that learner engagement has a greater influence on academic performance than self-directed learning readiness among the independent variables. Additionally, it was found that learners’ self-directed readiness did not impact course satisfaction; only learner engagement influenced student satisfaction for online courses. In conclusion, the survey showed that learner engagement and self-directed learning readiness are significant predictor variables of academic performance in online courses. It also found that learner engagement is a significant predictor variable of student satisfaction with online courses. In other words, teaching strategies are important for the promotion of student engagement to improve academic performance and student satisfaction in distance learning.
Hubungan Disiplin Belajar dan Motivasi Belajar dengan Hasil Belajar Matematika

The purpose of this study was to determine the significant relationship between learning discipline with learning result, learning motivation with learning result, and learning discipline and learning motivation together with mathematics learning result of students in class V SD Negeri 6 Kelapa Tujuh . This type of research was ex-postfacto correlation. Data collection techniques carried out were observation, interviews, questionnaires, and documentation studies. Data analysis was done by using product moment correlation and multiple correlation. The results showed there was a significant relationship between the discipline of learning with mathematics learning result (0.448 / moderate), learning motivation with mathematics learning result (0.433 / moderate) and discipline learning and motivation learning together with mathematics learning result (0.531 / moderate). Tujuan penelitian ini untuk mengetahui hubungan yang signifikan antara disiplin belajar dengan hasil belajar, motivasi belajar dengan hasil belajar, dan disiplin belajar dan motivasi belajar secara bersama-sama dengan hasil belajar matematika peserta didik kelas V SD Negeri 6 Kelapa Tujuh. Jenis penelitian yaitu ex-postfacto korelasi. Teknik pengumpulan data yang dilakukan yaitu observasi, wawancara, angket, dan studi dokumentasi. Analisis data menggunakan korelasi product moment dan multiple correlation . Hasil penelitian terdapat hubungan yang signifikan antara disiplin belajar dengan hasil belajar matematika (0.448 / sedang), motivasi belajar dengan hasil belajar matematika (0.433 / sedang) dan disiplin belajar dan motivasi belajar secara bersama-sama dengan hasil belajar matematika (0.531 / sedang). Katakunci: disiplin, motivasi, hasil belajar matematika.
Design Principles for Learning Environment based on STEAM Education

In this study, a learning environment based on STEAM theory was proposed to support and improve learners’ activities and achievements for convergent design education. The learning environment design influence STEAM education with intentional design and schedule coordination, schools can create informal environments that are crucial to STEAM education. The physical surroundings of the learning space should be applied to teaching methods and learning activity, especially for STEAM-based education, physical space conditions should support the learner’s design thinking and process. Furthermore, STEAM-based education environment should support a vast array of experiences that allow students to learn the context around ideas and skills. For spaces for learning environment based on STEAM, common design principles should be considered such as technology integration, safety and security, transparency, multipurpose space, and outdoor learning. Therefore, the learning environment based on STEAM needs flexible and mobile, connected, integrated, organized, flipped, and team-focused surroundings to support the learners understand, participate, cooperate, and accomplish the design process.
Investigating Various Approaches and Ways to Detect Cybercrime

Cyber security is one of the major concepts of cyber world which provides protection to the cyberspace from various sorts of cybercrimes. This research paper provides detailed information of cybersecurity. We conduct the survey of security of recent prominent researches and categorise the recent incidents in context to varied fundamental principles of cybersecurity. We have proposed a new taxonomy of cybercrime which can cover all types of cyberattacks. We have analysed multiple cyberattacks in accordance with the updated cybercrime taxonomy to highlight the challenges in the field of cybersecurity and future research directions added in this field. In India, most of the cases are found where crimes are committed due to lack of data or by mistake. In this paper, I even have discussed various categories and cases of cybercrime which is committed thanks to lack of data or sometimes thanks to intention behind. In India also, suggested various preventive measures against these unlawful acts in day-to-day life. Now day-by-day cybercrime has increased, and criminal finds new ways of crime, use new techniques for crime completion. Here, we have mentioned techniques to solve cases. We have researched various approaches for detection of cybercrime. In this paper, we have provided the details.
Implementasi Implentasi Metode Tartili Dalam Pembelajaran Membaca Al-Qur'an di TPQ Nurul Hikmah Kertonegoro Jenggawah Jembar

Abstract: The purpose of this study is to describe the implementation of the Tartili method in learning to read the Qur'an at TPQ Nurul Hikmah Kertonegoro Jenggawah Jember. This thesis is a descriptive qualitative field research, namely a method that describes the results of research in the field by means of objective rational thinking, therefore the data is collected through observation, interviews, and documentation, while the data analysis uses data reduction. The general conclusion in writing this thesis is that the implementation of the Tartili method in TPQ Nurul Hikmah Kertonegoro Jenggawah Jember has produced children who are able to read the Koran fluently, well and correctly, and students are able to understand the rules of reading the Koran that is read.Keywords: Tartili Method, al-Qur’an Learning
A Research on the Teaching Mode of General Fundamental Courses for the Training of Students' Practical Skills

Abstract:In the present college curriculum system, the general fundamental course is as important as professional course and required course. But it can't give its real effect with present teaching model, teaching evaluating method, students' attention value and other influence factors. On the other hand, with development of the times, it has changed for human talent resources. The talents no only need to have more book learning, but also need to have practical and operational ability. So, it is become an important problem to develop students' practical and operational ability by general fundamental courses. In past 7 years, a long time experiment has been done. With discussions and analysis, a new teaching model and teaching evaluation method has been advanced in general fundamental course. With the result of experiment, we found that using different teaching methods in course, it not only can be useful to improve students' practical ability, and also be useful to add comprehension of course and enhance students' learning quality.
Mitigating Divergence of Latent Factors via Dual Ascent for Low Latency Event Prediction Models

Real-world content recommendation marketplaces exhibit certain behaviors and are imposed by constraints that are not always apparent in common static offline data sets. One example that is common in ad marketplaces is swift ad turnover. New ads are introduced and old ads disappear at high rates every day. Another example is ad discontinuity, where existing ads may appear and disappear from the market for non negligible amounts of time due to a variety of reasons (e.g., depletion of budget, pausing by the advertiser, flagging by the system, and more). These behaviors sometimes cause the model's loss surface to change dramatically over short periods of time. To address these behaviors, fresh models are highly important, and to achieve this (and for several other reasons) incremental training on small chunks of past events is often employed. These behaviors and algorithmic optimizations occasionally cause model parameters to grow uncontrollably large, or diverge. In this work present a systematic method to prevent model parameters from diverging by imposing a carefully chosen set of constraints on the model's latent vectors. We then devise a method inspired by primal-dual optimization algorithms to fulfill these constraints in a manner which both aligns well with incremental model training, and does not require any major modifications to the underlying model training algorithm.We analyze, demonstrate, and motivate our method on OFFSET, a collaborative filtering algorithm which drives Verizon Media (VZM) native advertising, which is one of VZM's largest and faster growing businesses, reaching a run-rate of many hundreds of millions USD per year. Finally, we conduct an online experiment which shows a substantial reduction in the number of diverging instances, and a significant improvement to both user experience and revenue.
GraphMR: Graph Neural Network for Mathematical Reasoning.

Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics questions. Previous natural language processing researches have proven the effectiveness of sequence-to-sequence (Seq2Seq) or related variants on mathematics solving. However, few works have been able to explore structural or syntactic information hidden in expressions (e.g., precedence and associativity). This dissertation set out to investigate the usefulness of such untapped information for neural architectures. Firstly, mathematical questions are represented in the format of graphs within syntax analysis. The structured nature of graphs allows them to represent relations of variables or operators while preserving the semantics of the expressions. Having transformed to the new representations, we proposed a graph-to-sequence neural network GraphMR, which can effectively learn the hierarchical information of graphs inputs to solve mathematics and speculate answers. A complete experimental scenario with four classes of mathematical tasks and three Seq2Seq baselines is built to conduct a comprehensive analysis, and results show that GraphMR outperforms others in hidden information learning and mathematics resolving.
Development of Resilient Measurement Instruments for High School Students in Denpasar

The purpose of this development research is to determine the value of the validity and reliability of the tough character instrument. This study was designed with research and development methods. Analysis of the data used in the CVR and CVI tests from Lawshe, the empirical test with the product moment formula, while to test the reliability using Cronbach's alpha. The instrument validation test was carried out by experts. The results of data analysis using the Lawshe CVR test obtained an item value (CVR&gt; 0), then the CVI value was obtained at 0.96, this value means that the content validity index of 45 items in the tough character instrument is very good. The results of the calculation of the validity of the item validity test using the Product Moment formula according to the valid category criteria. this shows that the level of reliability of the tough character instrument is very high, so this tough character instrument product is very important to be developed for students in high school.
PENGARUH PROMOSI, CITRA MEREK DAN KUALITAS PRODUK TERHADAP KEPUTUSAN PEMBELIAN BATIK TENUN IKAT (Studi Kasus CV Paradila Parengan Maduran)

This study aims to determine the effect of Promotion, Brand Image and Product Quality on Purchasing Decisions of woven batik. The pupulation in this study is a questionnaire calculation report. This study uses the 2015-2019 observation period. With purposive sampling technique with 75 respondents survived the last 5 years. Data analysis method used in this study is multiple linier regression test. The variables used are Promotion, Brand Image and Product Quality as the independent variable, while the Purchase Decision variable as the dependent variable.the results of this study produce findings that Promotion, Brand Image and Product Quality significantly influence purchasing decisions. Simultaneously, Promotion, Brand Image and Product Quality significantly influence Purchasing Decision of batik woven on CV Paradila Lamongan.
ANALISIS SISTEM INFORMASI AKUNTANSI PENERIMAAN DAN PENGELUARAN KAS PADA RUMAH SAKIT MUHAMMADIYAH KOTA METRO

This study aims to find out the Accounting Information System for Cash Receipts and Expenditures at Muhammadiyah Hospital in Metro City. The data is obtained from interviews, observations, and documentations. Research data analysis was conducted using qualitative descriptive data analysis. The results show that the procedure of receipt and expenditure of cash has been implemented properly where the evidence of receipt and expenditure of cash are reported and stored by the authorities. In addition, records are begun from journals, ledgers to financial statements.
MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity

Similarity is a comparative-subjective measure that varies with the domain within which it is considered. In several NLP applications such as document classification, pattern recognition, chatbot question-answering, sentiment analysis, etc., identifying an accurate similarity score for sentence pairs has become a crucial area of research. In the existing models that assess similarity, the limitation of effectively computing this similarity based on contextual comparisons, the localization due to the centering theory, and the lack of non-semantic textual comparisons have proven to be drawbacks. Hence, this paper presents a multi-layered semantic similarity network model built upon multiple similarity measures that render an overall sentence similarity score based on the principles of Network Science, neighboring weighted relational edges, and a proposed extended node similarity computation formula. The proposed multi-layered network model was evaluated and tested against established state-of-the-art models and is shown to have demonstrated better performance scores in assessing sentence similarity.
Machine Learning Based Human Activity Recognition in Video Surveillance

&#x0D; &#x0D; &#x0D; In the current time there is a massive shift of technology from analog to digital. According to previous facts, three quarters of world data was in analog form. But now, approximately it is preserved in digital form. Due to this rapid growth of technology there is a massive growth of image data being generated by surveillance cameras. Automated anomaly detection has become necessary in order to detect the presence of any kind of dangerous activities such as robbery, road accidents and many more. Recently, machine Learning approaches have achieved the state of the art results in many tasks related to the automated anomaly detection process. The objective of this paper is to propose one such efficient method for anomaly detection. The proposed approach works with multiple instance learning with I3D feature extractor and crop augmented images. The obtained AUC prove the proposed approach to be superior of previous models.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Telemedicina e COVID-19: uma revisão de literatura

In March 2020, the pandemic state caused by Sars-Cov-19 (COVID-19) was declared by the World Health Organization. Due to social isolation, medical care by telemedicine became an essential tool. This article will analyze scientific productions and the legislation in force about telemedicine in the pandemic. This is a systematic review of the literature, which considered 15 original articles published between 2019 and 2021. The searches were conducted in the “Biblioteca Virtual em Saude” (Public Health Virtual Library) and SciELO databases. The selected articles address two thematic aspects: the advances in telemedicine at the time of COVID-19 and the doctor-patient relationship in telemedicine. In conclusion, regulations on the use of telemedicine are an advance in society, despite the existence of certain difficulties. In the doctor-patient relationship, challenges can be overcome using an objective and explanatory language.
AdaCN: An Adaptive Cubic Newton Method for Nonconvex Stochastic Optimization

In this work, we introduce AdaCN, a novel adaptive cubic Newton method for nonconvex stochastic optimization. AdaCN dynamically captures the curvature of the loss landscape by diagonally approximated Hessian plus the norm of difference between previous two estimates. It only requires at most first order gradients and updates with linear complexity for both time and memory. In order to reduce the variance introduced by the stochastic nature of the problem, AdaCN hires the first and second moment to implement and exponential moving average on iteratively updated stochastic gradients and approximated stochastic Hessians, respectively. We validate AdaCN in extensive experiments, showing that it outperforms other stochastic first order methods (including SGD, Adam, and AdaBound) and stochastic quasi-Newton method (i.e., Apollo), in terms of both convergence speed and generalization performance.
Post-synthesis manipulation of field-coupled nanocomputing circuits for fundamental energy reduction

Progress in computer performance is promoted on several fronts. Initiatives such as the implementation of optimal algorithms, parallelization of algorithms and architectures, and, at a lower level, Moore's Law, have guaranteed performance evolution. The latter, in particular, already shows clear signs of exhaustion, imposing on industry and academia the search for alternatives. The reason is heat generation, which has been one of the main obstacles to reducing digital devices' dimensions. Landauer investigated the origin of this problem and demonstrated analytically in 1961 that the cause is the erasure of information in each logic gate. This fundamental energy limit, named Landauer's Principle, was experimentally proven in 2012, indicating that there is indeed a limit on energy scalability for conventional computer systems. In this work, we introduce approaches to reduce those losses by applying post-synthesis methods. First, we propose a novel method to divide Partially Reversible Pipelined circuits into stages, computing the optimal energy vs. throughput tradeoff. Specifically, we develop an algorithm that, given the circuit, it returns the division that yields the minimum possible energy dissipation while considering a minimum throughput restriction. We also developed an optimal algorithm that, given the maximum energy restriction, finds the maximum possible throughput. Therefore, the designer can have the best partially reversible pipelined circuit configuration according to its project requirements. Computational experiments show that our algorithm outperforms the state-of-the-art algorithm from the literature in solution's quantity and quality, being able to dominate about 56% of its best solutions and to generate 11X more solutions, on average. The other strategy is a novel technique that identifies exploitable fan-outs and uses them to reduce energy losses in FCN circuits, thus enabling the design of new types of partially reversible systems. Moreover, we propose an algorithm that creates partially reversible systems while allowing the designer to choose between energy reduction with no effect on the delay or focus solely on energy and accepting a potential delay penalty. Simulation results for state-of-the-art benchmarks indicate an average reduction of the fundamental energy limit by 44% without affecting the delay. If delay is not the main concern, the average reduction reaches even 77%. Finally, we present a unified analysis of both partially reversible systems. These efforts open new perspectives in designing FCN systems where energy efficiency is mandatory.
Research Guides: University Archives Digital Collections: Digitized Yearbooks

Guide to using digitized University Archives Resources. Guide is co-created by Graduate Student Assistants, Casey Hughes and Sara Ludewig.