Reviewerly/exHarmony huggingface dataset:
SoftMemoryBox II: A Scalable, Shared Memory Buffer Framework for Accelerating Distributed Training of Large-Scale Deep Neural Networks

Distributed processing using high-performance computing resources is essential for developers to train large-scale deep neural networks (DNNs). The major impediment to distributed DNN training is the communication bottleneck during the parameter exchange among the distributed DNN training workers. The communication bottleneck increases training time and decreases the utilization of the computational resources. Our previous study, SoftMemoryBox (SMB1) presented considerably superior performance compared to message passing interface (MPI) in the parameter communication of distributed DNN training. However, SMB1 had disadvantages such as the limited scalability of the distributed DNN training due to the restricted communication bandwidth from a single memory server, inability to provide a synchronization function for the shared memory buffer, and low portability/usability as a consequence of the kernel-level implementation. This paper proposes a scalable, shared memory buffer framework, called SoftMemoryBox II (SMB2), which overcomes the shortcomings of SMB1. With SMB2, distributed training processes can easily share virtually unified shared memory buffers composed of memory segments provided from remote memory servers and can exchange DNN parameters at high speed through the shared memory buffer. The scalable communication bandwidth of the SMB2 framework facilitates the reduction of DNN distributed training times compared to SMB1. According to intensive evaluation results, the communication bandwidth of the proposed SMB2 is 6.3 times greater than that of SMB1 when the SMB2 framework is scaled out to use eight memory servers. Moreover, the training time of SMB2-based asynchronous distributed training of five DNN models is up to 2.4 times faster than SMB1-based training.
Copula-based Correlation Structure for Multivariate Emission Distributions in Hidden Markov Models

The hardware in the UMBC High Performance Computing Facility (HPCF) is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. HMM estimation has been carried out using the MVNHMM toolbox (http://www.sergeykirshner.com/software/mvnhmm) developed by Sergey Kirshner; ancillary scripts developed by Jonathan N. Basalyga and Gerson C. Kroiz have also been used. Reetam Majumder was supported by JCET and as HPCF RA.
Investigating the Use of Project-Based Learning in Enhancing Indonesian University Students’ Cultural Knowledge

The research investigated the use of email interview to develop the critical thinking and creativity of the university students in American culture classes. The students conducted email interview with Americans for group project and had group discussion with the Americans. Learning culture was an integral part of learning a language, which was crucial for the Englsih as a Foreign Language (EFL) learners to develop their awareness and understanding of the rich and dynamic cultural aspects of the target language. Thus, providing cultural experiences through project-based learning (PBL) would be motivating for the language learners in exploring the target culture. The data were collected from students’ open-ended online questionnaire and observation notes, which contain students’ interview result and their presentation slides. Descriptive statistics and thematic analysis were used in analyzing the data. The findings indicate that the use of project-based approach is helpful to enhance the students’ understanding and critical thinking skills. Moreover, it is found that the students’ positive attitude has increased due to the use of PBL. They are more critical towards the cultural phenomenon since their communication skill has developed.
Second-order interference of true thermal light from a warm atomic ensemble in two independent unbalanced interferometers

We report the demonstration of a second-order interference experiment by use of thermal light emitted from a warm atomic ensemble in two spatially separated unbalanced Michelson interferometers (UMIs). This novel multipath correlation interference with thermal light has been theoretically proposed by Tamma [ New J. Phys. 18 , 032002 ( 2016 ) NJOPFM 1367-2630 10.1088/1367-2630/18/3/032002 ]. In our experiment, the bright thermal light used for second-order interference is superradiantly emitted via collective two-photon coherence in Doppler-broadened cascade-type <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mmultiscripts> <mml:mrow> <mml:mi>Rb</mml:mi> </mml:mrow> <mml:mprescripts /> <mml:none /> <mml:mrow> <mml:mn>87</mml:mn> </mml:mrow> </mml:mmultiscripts> </mml:mrow> </mml:math> atoms. Owing to the long coherence time of the thermal light from the atomic ensemble, we observe its second-order interference in the two independent UMIs by means of time-resolved coincidence detection. The temporal waveforms of the interfering thermal light in the two spatially separated UMIs exhibit similarities with the temporal two-photon waveform of time–energy entangled photon pairs in Franson interferometry. Our results can contribute toward a better understanding of the relation between first- and second-order interferences that are at the heart of photonics-based quantum information science.
A Study of University Students’ Academic Self-efficacy, Problem Solving &amp; Self-directed Learning Abilities in an Online General Education Class

This study investigated Korean university students" academic self-efficacy, problem solving and self-directed learning abilities by pre & post self-evaluation questionnaires in an online general education class. At the beginning of the class, 261 students participated in the pre-survey and at the end of the class, 230 students participated in the post-survey. Pearson correlation and visualization of a correlation matrix of “corrplot” show that problem solving and self-directed learning abilities are very significantly (p=0) correlated to each other and that self-confidence and self-regulation efficacy in academic self-efficacy are significantly (p<0.05) correlated with both problem solving and self-directed learning abilities. Also, the study results show that the students have slightly improved their abilities in all the three variables at the end of the class: in particular, planning power in problem solving ability and 6 items in academic self-efficacy, e.g., challenging power, connecting old and new knowledges, etc. significantly increased. However, the students still need to increase divergent thinking, information gathering ability and challenge spirit against difficulties. Thus, it is necessary to develop teaching methods which can help students to solve their shortage in online general education classes.
Enhanced adaptive partitioning in a distributed graph database

Nowadays, open-source graph databases do not include an inherent mechanism for data relocation that would be based on their usage. They often do not offer even appropriate monitoring that could help to make such a decision. Information about data utilization could, however, work as an input to some decision-making process about more suitable data regrouping that could be much more efficient in terms of intra-network communication. Therefore, we created a module for the graph computational framework TinkerPop that logs traffic generated by the user queries. These logged records serve as an input for the algorithm of Adaptive Partitioning that we enhanced with better balancing, avoidance of local optima and the notion of weighted graphs. This approach yields a 70–80% improvement in intra-network communication, which is comparable to other methods, namely Ja-be-Ja, that offers similar results but has higher computational demands.
Meta-trained agents implement Bayes-optimal agents

Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution. A previous theoretical study has argued that this remarkable performance is because the meta-training protocol incentivises agents to behave Bayes-optimally. We empirically investigate this claim on a number of prediction and bandit tasks. Inspired by ideas from theoretical computer science, we show that meta-learned and Bayes-optimal agents not only behave alike, but they even share a similar computational structure, in the sense that one agent system can approximately simulate the other. Furthermore, we show that Bayes-optimal agents are fixed points of the meta-learning dynamics. Our results suggest that memory-based meta-learning might serve as a general technique for numerically approximating Bayes-optimal agents - that is, even for task distributions for which we currently don't possess tractable models.
Guidance of a person by scaling the image projected from a mobile robot

In this paper, we propose a new method to guide a person to an exhibition using a mobile robot with a projector. It has been reported that conventional guide robots cannot control the position of people around the robot; for example, when people gather around a robot, it becomes difficult to move. Robots had to explicitly instruct people to move by voice or display, saying, "Please make way." or "Please move there." However, repeated such explicit instructions are not comfortable for us. In this study, we propose a method to guide a person by scaling projected images using a mobile robot with a projector. Based on the tendency that we naturally move to the place where the projected explanation is easy to see, the proposed guiding behaviors can guide a person away from the robot or move the robot while keeping the person in the place. Preliminary experimental results showed that the person tends to be guided by the proposed method without explicit indication.
Evaluation of anisotropic correction models for diffuse solar irradiance measured by the MEO shadow ring method

Diffuse solar radiation is measured using a pyranometer positioned under the shadow of a ring. The function of the ring is to block direct solar radiation. However, the ring also blocks a portion of the diffuse solar radiation, and therefore, the diffuse radiation needs corrections. The most well-known assembly in the literature on the shading ring method is the Drumond assembly. This study aims to determine the effectiveness of three methods of correction of diffuse irradiance measured with shadow bands: the LeBaron method, the Battles method, and the Dal Pai method. The Laboratory of Solar Radiometry of Botucatu provided global, direct, and diffuse solar irradiances. The laboratory measures diffuse solar radiation using the Melo–Escobedo–Oliveira (MEO) shading ring method, a different setup compared to Drummond's setup. The period used for the study comprised the years 1996 to 2005. The results showed that LeBaron and Battles correction models with the original coefficients are not suitable for correction of diffuse irradiance in Botucatu. According to MBE (Mean Bias Error) and RMSE (Root Mean Square Error), the statistical indicators, LeBaron and Battles with adjusted coefficients, and Dal Pai correction models are indicated to correct the diffuse irradiance in Botucatu measured under an MEO shading ring. For monthly applications, the LeBaron model with adjusted coefficients is recommended. Although this model is more complex from the operational point of view, it better explains anisotropic effects, especially for the winter months.
Comparison of Machine Learning Methods for Multi-label Classification of Nursing Education and Licensure Exam Questions

In this paper, we evaluate several machine learning methods for multi-label classification of text questions.Every nursing student in the United States must pass the National Council Licensure Examination (NCLEX) to begin professional practice.NCLEX defines a number of competencies on which students are evaluated.By labeling test questions with NCLEX competencies, we can score students according to their performance in each competency.This information helps instructors measure how prepared students are for the NCLEX, as well as which competencies they may need help with.A key challenge is that questions may be related to more than one competency.Labeling questions with NCLEX competencies, therefore, equates to a multilabel, text classification problem where each competency is a label.Here we present an evaluation of several methods to support this use case along with a proposed approach.While our work is grounded in the nursing education domain, the methods described here can be used for any multi-label, text classification use case.
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?

Recent latent tree learning models can learn constituency parsing without any exposure to human-annotated tree structures.One such model is ON-LSTM (Shen et al., 2019), which is trained on language modelling and has nearstate-of-the-art performance on unsupervised parsing.In order to better understand the performance and consistency of the model as well as how the parses it generates are different from gold-standard PTB parses, we replicate the model with different restarts and examine their parses.We find that (1) the model has reasonably consistent parsing behaviors across different restarts, (2) the model struggles with the internal structures of complex noun phrases, (3) the model has a tendency to overestimate the height of the split points right before verbs.We speculate that both problems could potentially be solved by adopting a different training task other than unidirectional language modelling.
Subject Review:Authentication Techniques for Smartphone

The rapid development of smart phone devices and users' satisfaction with them, also have storage and accounting capabilities that compete with computers.Smart phones carry a lot of personal information and important financial information, so they are vulnerable to theft or access by unauthorized persons to that information.Increased need for ways to protect that information.Smartphones have several built-in sensors that facilitate the capture of biometrics and behavioral traits This paper reviews the authentication techniques for mobile, as it covers a comprehensive survey on authentication methods and comparison between these techniques. .
THE EFFECTIVENESS OF MEMRISE APPLICATION AND TINYCARDS BY DUOLINGO APPLICATION TO TEACH ADJECTIVE (A QUASY EXPERIMENTAL RESEARCH AT THE 11TH GRADE STUDENTS OF SMA KARTIKA III-1 BANYUBIRU IN THE ACADEMIC YEAR OF 2019/2020)

Arifin, Iqbal. 2020. The Effectiveness of Memrise Application to Teach Adjective (A Quasy Experimental Research at the Eleventh Grade Students of SMA Kartika III-1 Banyubiru in the Academic Year of 2019/2020). A Graduating Paper. English Education Department. Teaching Training and Educational Faculty. State Institute for Islamic Studies of Salatiga. Counselor: Dr. Ruwandi, S. Pd., M. A.This research population was intended to find out the effectiveness or the significant difference of using Memrise application and Tinycards by Duolingo application towards students’ adjective mastery of the eleventh grade students of SMA Kartika III-1 Banyubiru in the academic year of 2019/2020.This reseach is a quasy experimental research. The population of this research was the eleventh grade students of SMA Kartika III-1 Banyubiru that consist of 197 students. The sample was 65 students chosen based on their class; experiment and comparison classes. The instrument for collecting data was tests, which were given at the beginning before the treatment and the last meeting after the treatment. The data of pre-test and post-test were analyzed by using t-test. At the end of this research, the researcher took some conclusions as follows: after analyzing data, (1) the mean score of pre-test of students who were taught using Memrise application is 70.47, and the mean score from students who were taught using Tinycards by Duolingo application is 65.61. There is difference Ma (70.77) > Mb (65.61). Based on the independent sample test result p-value or sig (2 – tailed) = 0.118. The coefficient of t-test is 1.583, than it is compared with t-table. The coefficient of t-table at real level ?= 0.05 with df= 63 gained the coefficient of t-table = 2.656. In fact, the coefficient of t-test (1.583) Mb (72.27) which shows that the teaching and learning activity by using Memrise application is more effective to increase students’ mastery of adjective. Based on independent sample t-test result, p-value (2 tailed) = 0.113 is higher than sig ?= 0.05 (5%). It shows that the hypothesis which proposed by the researcher that there is no significant difference of using Memrise application and using Tinycards by Duolingo application toward students’ mastery on adjective is acceptable. There is no significant difference of t-test (1.609) < t-table (2.656). It shows that t-test is in alternative hypothesis rejection (Ha1). Thus, null hypothesis (Ha2) is accepted. It means that the null hypothesis proposed by the researcher that there is no significant different effectiveness is accepted.Keyword: Memrise Application, Tinycards by Duolingo Application, Students’ Adjective Mastery
Evolution in image steganography; an efficacious route for handling areas of colour noise in digital photography

Steganography has attracted an outstanding area of research nowadays. Being the science of hiding information,it focuses on the concept of hiding a message in plain sight [1]. It can be confusing and frustrating to pointedlyunderstand how image quality is influenced by digital camera noise. The state of art analysis is relying onartificial intelligence and machine language techniques aiming to endow with better capability to differentiatebetween a carrier image from a hidden message image. Researches conducted in this field manifested that noisyareas in a picture is harder to construct, making it more difficult to detect an embedded message. Herein, thisarticle shed the light on a novel steganography route for hiding information in these areas and bestow morecomplexation on their detection. The validation of the adopted method has been assessed through harnessingtwo imaging databases and two recent Steganalysis. The experiments disclosed that the proposed algorithm hassignificantly improved the statistical undetectability with respect to the LSB matching system for the samecapacity of incrustation.
Word class flexibility: A deep contextualized approach

Word class flexibility refers to the phenomenon whereby a single word form is used across different grammatical categories.Extensive work in linguistic typology has sought to characterize word class flexibility across languages, but quantifying this phenomenon accurately and at scale has been fraught with difficulties.We propose a principled methodology to explore regularity in word class flexibility.Our method builds on recent work in contextualized word embeddings to quantify semantic shift between word classes (e.g., noun-to-verb, verb-to-noun), and we apply this method to 37 languages 1 .We find that contextualized embeddings not only capture human judgment of class variation within words in English, but also uncover shared tendencies in class flexibility across languages.Specifically, we find greater semantic variation when flexible lemmas are used in their dominant word class, supporting the view that word class flexibility is a directional process.Our work highlights the utility of deep contextualized models in linguistic typology.
Collaborative Preference Learning: A Case Study

In the scope of marketing operations, the transformation of transactional data into customer-item preference indicators is a critical process. As in the case of fast food sector, one needs to estimate preferences or ratings based on that sales data. The ratings can then be used to estimate customer similarities and utilised in recommendation systems. In this study, we elaborately explain the process of sales data transformation into preferences and propose a trend concerning collaborative filtering method. In the experiments, we highlight the domain specific data processing requirements and demonstrate that our method makes significanly better predictions.
Discourse on The Right Guaranteed by the Constitution of Korea and Improvements of The Sports Class System

Introduction: For college students, the right to study means a right guaranteed by the Constitution and is understood as one of the fundamental educational rights that guarantee the right to learn about knowledge. However, it is difficult to understand that the definition of this right to learn is properly observed when looking at the sports class system of the university in Korea. It is because the sports class system is overly concentrated in the practical subjects centered on sports. A significant problem arises in equity with non-major students. The system reduces competitiveness for physical education students.BR Purpose: This study is aimed of redefining college students’ right to study based on the Constitution of the Republic of Korea, and proposing improvements for the university’s sports class system based on this.BR Results: The research results derived from this study are as follows. First, the concept of the right to learn should include not only the right to learn, but also the right to teach, the right to reject knowledge, and the right to request the disclosure of knowledge-related information. Second, in terms of the right to learn guaranteed by the constitution, the current sports class system could be an infringement against the right to learn. Third, in relation to the current sports class system, there is a need for an increase in theories-oriented sports class, the opening of sports class to cultivate the competitiveness of university students, allowance of sports class for university students in physical education, and reflection of the realistic demands of opening sports classes proposed.BR Conclusion: In summary, this study approached and interpreted the sports class system of the university in Korea based on the positive laws of Korea, and derived the results. It is hoped that the results of the research drawn in this study will greatly contribute.
Robust Multi-object Matching via Iterative Reweighting of the Graph Connection Laplacian

We propose an efficient and robust iterative solution to the multi-object matching problem. We first clarify serious limitations of current methods as well as the inappropriateness of the standard iteratively reweighted least squares procedure. In view of these limitations, we suggest a novel and more reliable iterative reweighting strategy that incorporates information from higher-order neighborhoods by exploiting the graph connection Laplacian. We demonstrate the superior performance of our procedure over state-of-the-art methods using both synthetic and real datasets.