Reviewerly/exHarmony huggingface dataset:
Dynamic Inference with Neural Interpreters

Modern neural network architectures can leverage large amounts of data togeneralize well within the training distribution. However, they are lesscapable of systematic generalization to data drawn from unseen but relateddistributions, a feat that is hypothesized to require compositional reasoningand reuse of knowledge. In this work, we present Neural Interpreters, anarchitecture that factorizes inference in a self-attention network as a systemof modules, which we call \emph{functions}. Inputs to the model are routedthrough a sequence of functions in a way that is end-to-end learned. Theproposed architecture can flexibly compose computation along width and depth,and lends itself well to capacity extension after training. To demonstrate theversatility of Neural Interpreters, we evaluate it in two distinct settings:image classification and visual abstract reasoning on Raven ProgressiveMatrices. In the former, we show that Neural Interpreters perform on par withthe vision transformer using fewer parameters, while being transferrable to anew task in a sample efficient manner. In the latter, we find that NeuralInterpreters are competitive with respect to the state-of-the-art in terms ofsystematic generalization
An Optimized Path Planning for Wheeled Robot in Obstacle Environments

In this work, we have proposed in unknown setting, there is an impediment avoidance for a mobile robot based on the fuzzy logic control. Therefore, the wheeled mobile robot is equipped with 3 wheels, one steering wheel and two fixed wheels and mounted on the same axis. It functions to lift from a starting point to a target position. For this, our proposed algorithm creates one or more imaginary target and applies a fuzzy logic control system, which is adopted by a rule table that is induced from two inputs data (the distance and the angle between the robot and the target) and two outputs data (the angle orientation and velocity of the steer wheel). Experiential outcomes show how effective and functional the suggested algorithm it is.
Combining multiple measures into a summary index: A step toward more reliable measurement

see article on pages 171–179 in this issue .
Journalists Trends towards the Opinion Leaders' Tweets on Twitter Field Study on Sample of Journalists at the Gulf Digital Journalism: DOI: 10.46988/IJMMC.01.01.2019.006

The media has emerged as an important means of socialization conducive to rendering the dire need for opinion leaders from different cultures in diversified fields. Their tweets via Twitter represent a source of news and information. This study is descriptive. According to the statistics, 98.4% of the journalists working in the Gulf digital newspapers follow the social media websites incessantly. The most dominant social networking sites considered by the subjects as the source of electronic newspapers' content is Twitter, by 80.3%. (23.8%) is the percentage of those who trust the opinion leaders, but those who trustless, and neutrals are 32.8% to the effect that the indicator is those who do not trust the opinion leaders' tweets are 55.8%. The subjects follow the leaders of opinion via Twitter to get the news, and by 60.7%. The subjects view the opinion leaders' tweets as a source of information that strengthens the electronic press's credibility and was represented by 68.8%. The researcher found that 73.3% of the subjects agreed that the tweets contained topics worthy of pursuing.
M-Learning: Survey on Academic Performance of Undergraduate Students During the Covid-19 Pandemic Lockdown in Private Universities in Nigeria

The paper investigated mobile-learning and academic performance of undergraduate students in private universities in Nigeria during the COVID-19 pandemic lockdown. Tertiary education in Nigeria is largely dependent on the traditional face-to-face (F2F) classroom learning but with COVID-19 protocols, there was a significant paradigm shift of learning method. To evaluate students’ performance based on the shift of the learning pedagogy from F2F to m-learning, a social survey was employed and a descriptive research design was adopted which was basically a non-experimental quantitative design in which questionnaire as an instrument for data collection was used on simple random sampling technique to describe their opinions, experiences and attitude. The participants were made up of undergraduate students of the Faculty of Education, Veritas University Abuja and the Faculty of Arts and Social Sciences, Catholic Institute of West Africa Port Harcourt, Nigeria. The total population of study was 560 students while a sample of 233 was derived from the total population using Slovin’s statistical formula. A structured and self-administered questionnaire was employed as instrument for data collection. The questionnaire was based on a 4-point Likert type, ranked on the following scale: strongly agree =4; agree=3; strongly disagree=2 and disagree=1 respectively. The instrument’s reliability had a Cronbach alpha value of 0.70 which is acceptable. Two theories were used and three research questions were employed as a guide. Data analysed using frequencies and percentages, mean and Standard Deviations revealed that, the usage of m-learning did not have any significant relationship with the performance of the students but the impact was significantly correlated with the academic performance of students. Therefore, m-learning improved students’ performance. However, this can further be enhanced if stakeholders encourage maximum use of m-learning pedagogy; establishment of ICT hubs with e-learning facilities across the country and development of Nigerian prototype m-learning architecture.
THE IMPLEMENTATION OF PROJECT-BASED LEARNING TO IMPROVE RECOUNT ESSAY WRITING OF THE EIGHTH GRADERS OF SMPN 1 TENGGARONG SEBERANG

This study focuses on recount essay writing.  However, teaching writing needs specific attention, method, materials, and strategy.  To make students write a good writing, innovative teaching techniques are needed.  In this study, PjBL is selected as the alternative to teaching recount essayThe design of this study is classroom action research (CAR). The study was conducted at the eighth grader of SMPN 1 Tenggarong Seberang. The class consists of 30 students. The results of the research are: In the first cycle, there were 4 weaknesses found in different parts of techniques, in mechanics score, language score, vocabulary score and organization score. The weaknesses in the first and second cycles were the same, and in the third cycles all the weaknesses had been corrected.  The average score of students’ mastery changed in every cycle. The average scores of students’ mastery in cycle I, II and III were increased in series 28.33%; 55.00% and 73.33%.
Μελέτη και ανάπτυξη εφαρμογών στο νέφος με χρήση ελεύθερα διαθέσιμων υπηρεσιών: οι περιπτώσεις των Google Cloud Platform και IBM Cloud Computing

The object of the dissertation is the investigation, benchmarking and indicative utilization / demonstration of freely available services in the cloud. The freely available services of Google Cloud Platform and IBM Cloud Computing will be researched / recorded, compared to each other in terms of development of different categories of applications and will be developed / demonstrated indicative applications for their utilization and comparative conclusions will be drawn
Physical Data Extraction from Android mobile using Apeaksoft Android toolkit and Android Debug Bridge

Android plays a vital role in day-to-day life. Android acts like a mini brain in many human lives. Therefore, data stored in android is also very essential. Now a days it is mandatory to preserve the stored data or to extract the stored data and place it safely in any location. Data extraction plays a major role in mobile forensics also. Mobile forensics is a kind of digital forensics that deals with extracting, analyzing and generating reports. It extracts data such as WhatsApp, SMS, Call logs, MMS, images, videos, etc. that can be stored on devices. To extract data efficiently, many data extraction tools are available. In this paper, command line tool such as ADB (Android Debug Bridge) and fully automated tool, Aspeaksoft Android toolkit are taken into consideration for extracting data. Moreover, performance of both the tools for extracting physical data can also be compared.
Pengaruh Model Pembelajaran Hypnoteaching terhadap Kemampuan Menulis Cerpen Siswa Kelas XI SMAN 1 Cihara Tahun Pelajaran 2018/2019

This research aims to answer the formulation of question raised about whether there is an effect of Hypnoteaching learning model on short story writing ability of Class XI students of SMA Negeri 1 Cihara,  2018/2019 academic year.The method used in this research is experimental method conducted at SMA Negeri 1 Cihara, by taking students of class XI as population. The random results for the two existing classes obtained Class XI A as experimental class and class XIB as control class. Each class has 30 students, thus the number of samples in this research are 60 students. Class XIA gets treatment using Hypnoteaching learning model. Meanwhile, class XIB as control class using direct learning model (expository).Based on data analysis obtained that student’s short story writing ability of class XI SMA Negeri 1 Cihara, 2018/2019 academic year which uses Hypnoteaching learning model has good category with average value of 79,1. While, short story writing ability of students which uses direct learning model (expository) at class XIB has enough category with average value of 70,26. It means, there is a positive effect of Hypnoteaching learning model on short story writing ability of students at Class XI SMA Negeri 1 Cihara,  2018/2019 academic year. It can be seen from the result of hypothesis test using t-test with a significant level 0,05’ gained tcount 4,758 and  ttable 1,6723; obtained  tcount  4,758  ttable 1,6723, so  is rejected and  is accepted. In short,   states that there is no effect of Hypnoteaching learning model on short story writing ability and  states that there is an effect of Hypnoteaching learning model on short story writing ability, then   is rejected and is accepted.
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL problem, it is not clear, how to choose the WNs' and the server's update directions, the minibatch sizes, and the local update frequency, so that the WNs use the minimum number of samples and communication rounds to achieve the desired solution. This work addresses the above question and considers a class of stochastic algorithms where the WNs perform a few local updates before communication. We show that when both the WN's and the server's directions are chosen based on a stochastic momentum estimator, the algorithm requires $\tilde{\mathcal{O}}(\epsilon^{-3/2})$ samples and $\tilde{\mathcal{O}}(\epsilon^{-1})$ communication rounds to compute an $\epsilon$-stationary solution. To the best of our knowledge, this is the first FL algorithm that achieves such {\it near-optimal} sample and communication complexities simultaneously. Further, we show that there is a trade-off curve between local update frequencies and local minibatch sizes, on which the above sample and communication complexities can be maintained. Finally, we show that for the classical FedAvg (a.k.a. Local SGD, which is a momentum-less special case of the STEM), a similar trade-off curve exists, albeit with worse sample and communication complexities. Our insights on this trade-off provides guidelines for choosing the four important design elements for FL algorithms, the update frequency, directions, and minibatch sizes to achieve the best performance.
Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER

Biomedical Named Entities are complex, so approximate matching has been used to improve entity coverage.However, the usual approximate matching approach fetches only one matching result, which is often noisy.In this work, we propose a method for biomedical NER that fetches multiple approximate matches for a given phrase to leverage their variations to estimate entity-likeness.The model uses pooling to discard the unnecessary information from the noisy matching results, and learn the entity-likeness of the phrase with multiple approximate matches.Experimental results on three benchmark datasets from the biomedical domain, BC2GM, NCBI-disease, and BC4CHEMD, demonstrate the effectiveness.Our model improves the average Fmeasures by up to 0.21 percentage points compared to a BioBERT-based NER.
Discrete-Valued Neural Communication

Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes. In structured models, an interesting question is how to conduct dynamic and possibly sparse communication among the separate components. Here, we explore the hypothesis that restricting the transmitted information among components to discrete representations is a beneficial bottleneck. The motivating intuition is human language in which communication occurs through discrete symbols. Even though individuals have different understandings of what a cat is based on their specific experiences, the shared discrete token makes it possible for communication among individuals to be unimpeded by individual differences in internal representation. To discretize the values of concepts dynamically communicated among specialist components, we extend the quantization mechanism from the Vector-Quantized Variational Autoencoder to multi-headed discretization with shared codebooks and use it for discrete-valued neural communication (DVNC). Our experiments show that DVNC substantially improves systematic generalization in a variety of architectures -- transformers, modular architectures, and graph neural networks. We also show that the DVNC is robust to the choice of hyperparameters, making the method very useful in practice. Moreover, we establish a theoretical justification of our discretization process, proving that it has the ability to increase noise robustness and reduce the underlying dimensionality of the model.
THE ROLE OF BUDAPEST DISASTER CENTRAL MUSEUM IN TRAINING AND EDUCATING POPULATION

The authors of the article introduce the experience of cultural, educational work of Budapest Disaster Central Museum. The article presents forms and methods of educating population, primarily the younger generation, and the professional orientation of youth. The authors’ goal is to show the role of the museum in the patriotic education of the younger generation based on the conducted analysis.
Use of Mobile Assisted Language Learning (MALL) in Teaching Vocabulary to ESP Students

The rapid advancement of mobile technologies proffers new contingencies and perspectives in language teaching. The leverage of these technologies on education contributes to the expansion of the approach in language teaching. The research was conducted in the form of an experiment in a vocational school in Poland. 114 ESP students in number were randomly divided into equal-sized groups, ascertained as experimental and control groups. The obtained results illustrate the acceptance of mobile devices as a learning tool indicating that the process of learning does not have to be limited to classroom activities only. The results of our study are significant in at least two major aspects. Not only do they demonstrate that using MALL in the didactic process increases the students' knowledge of vocabulary but also that the learners' attitude towards using mobile devices is positive. Implementing mobile devices as learning tools reinforces students' motivation, the sense of belonging to a particular community, and of autonomy and flexibility of learning. Based on the received outcomes, it can be concluded that the use of WhatsApp affects the ESP learners' knowledge of vocabulary significantly. Furthermore, the questionnaire results reveal students' positive attitude toward WhatsApp instruction.
The Combinatorial Expressions and Probability of Random Generation of Binary Palindromic Digit Combinations

The aim of this paper is to obtain three types of expressions for calculating the probability of implementing palindromic digit combinations on a finite equally possible combination of zeros and ones.When calculating the probability of implementation of palindromic digit combinations, the classical definition of probability is applied.The main results of the paper are formulated in the form of three theorems.Moreover, the consequences of these theorems and typical examples of calculating the probability of implementing palindromic digit combinations in a data string of binary code are considered.All formulated theorems and their consequences are accompanied by proofs.The obtained numerical results of the paper can be used in the analysis of numerical computer data written as a binary code string in BIN format files.It should also be noted that the combinatorial expressions described in the article for calculating the number of palindromic combinations of digits in the binary number system can be used in number theory and in various branches of computer science.The development of these results from the point of view of obtaining an expression for calculating the number of palindromic combinations of digits in the binary number system contained in two-dimensional data arrays is also of immediate theoretical and practical interest.However, these results are not presented in this work, but they can be considered in subsequent publications.
STUDENT’S RESPONSES TO THE USE OF INSTAGRAM IN TEACHING READING NEWS ITEM TEXT

The students response is one tool in determining the methods and media of learning, so that it becomes a reference in achieving the success of a learning process. This research was conducted in order to determine the response of students in class XII TKJ 2 Smk Bina Insan Bangsa Ngamprah Bandung, on the use of Instagram in teaching reading news items texts. Thirsty one students were made researchers as research objects, consisting of 14 male students and 17 female students. The data obtained in this study were observation data and questionnaire data of student responses. the method used in this research is descriptive qualitative method, to get the answers to the problems studied. The results revealed  74.8% of students who gave positive responses to the use of Instagram in teaching reading news items texts, because learning activities with the use of Instagram were more interesting, interactive, and fun for students, and as many as 25.2% of students who gave negative responses in the use of Instagram in teaching reading text news items, because there are deficiencies in the use of Instagram, which is related to some students who need difficulties in terms of text content due to lack of vocabulary mastery. Keywords :  Students Responses, Instagram, Reading News Item Text
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance

We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space. We propose a Gaussian process-based algorithm and establish its order-optimal regret performance (up to a poly-logarithmic factor). This is the first GP-based algorithm with an order-optimal regret guarantee. The proposed algorithm is rooted in the methodology of domain shrinking realized through a sequence of tree-based region pruning and refining to concentrate queries in increasingly smaller high-performing regions of the function domain. The search for high-performing regions is localized and guided by an iterative estimation of the optimal function value to ensure both learning efficiency and computational efficiency. Compared with the prevailing GP-UCB family of algorithms, the proposed algorithm reduces computational complexity by a factor of $O(T^{2d-1})$ (where $T$ is the time horizon and $d$ the dimension of the function domain).
Quadcopter Design Presentation

Poster presentation describing work done to create a quadcopter drone for a student design project.
Label-Imbalanced and Group-Sensitive Classification under Overparameterization

The goal in label-imbalanced and group-sensitive classification is to optimize relevant metrics such as balanced error and equal opportunity. Classical methods, such as weighted cross-entropy, fail when used with the modern practice of training deep nets to the terminal phase of training(TPT), that is training beyond zero training error. This observation has motivated recent flurry of activity in developing heuristic alternatives following the intuitive mechanism of promoting larger margin for minorities. In contrast to previous heuristics, we follow a principled analysis explaining how different loss adjustments affect margins. First, we prove that for all linear classifiers trained in TPT, it is necessary to introduce multiplicative, rather than additive, logit adjustments so that the relative margins between classes change appropriately. To show this, we discover a connection of the multiplicative CE modification to the so-called cost-sensitive support-vector machines. Perhaps counterintuitively, we also find that, at the start of the training, the same multiplicative weights can actually harm the minority classes. Thus, while additive adjustments are ineffective in the TPT, we show numerically that they can speed up convergence by countering the initial negative effect of the multiplicative weights. Motivated by these findings, we formulate the vector-scaling(VS) loss, that captures existing techniques as special cases. Moreover, we introduce a natural extension of the VS-loss to group-sensitive classification, thus treating the two common types of imbalances (label/group) in a unifying way. Importantly, our experiments on state-of-the-art datasets are fully consistent with our theoretical insights and confirm the superior performance of our algorithms. Finally, for imbalanced Gaussian-mixtures data, we perform a generalization analysis, revealing tradeoffs between different metrics.
A Study of Water Intake Status for Korean Nursing Universty Students

This study is a descriptive correlation research that aims to inform the importance of water intake, and also to understand the relations of water intake, fatigue, life stress, and BMI of Korean nursing university students. The data was collected for a month from May 1st 2021. Targeting total 229 subjects, the data was analyzed by using the SPSS 23.0 Program. As the statistical methods, this study used the t-test, ANOVA, and correlation. The mean age of subjects was 23. The fatigue, life stress, and BMI related to the general characteristics are as follows. The fatigue showed differences according to the matter of exercise(t=-4.217, p<.001). The life stress showed differences according to the matter of smoking(t=3.110, p=.002) and coffee intake(t=3.404, p=.035). There were differences in BMI according to sex(t=4.719, p<.001), coffee intake(F=3.404, p=.035), and water intake(F=3.397, p=.035). Viewing the water intake, fatigue, and life stress of subjects, the water intake had a significant correlation with BMI(r=0.190, p=.004). The fatigue had a significant correlation with life stress(r=0.348, p<.001). Based on such results of this study, for the great health of nursing students, it would be essential to intake water correctly suitable for weight. Also, even under situations causing the fatigue and stress, the water intake should be recommended instead of coffee intake. In case when educating the water intake, the positive influence of water needs to be emphasized more.