Reviewerly/exHarmony huggingface dataset:
Motivation for Self-learning as a Condition for the Professional Competencies Formation

The purpose of the article is to consider the main components of motivation in learning and identify ways to increase the cognitive motivation of students to self-education; to characterize the process of improving the training of future professionals in modern education and the concept of motivating students to self-education. The research methodology consists of a systematic approach, principles of scientific and pedagogical research and pedagogical observation, as well as methods of systematization and generalization (to understand the set of factors, mechanisms and processes, covering the concept of motivation; analysis of basic concepts of motivating students to self-education). The scientific novelty is to determine ways to form the motivation of cognitive independence as a dynamic process that determines human behaviour. Conclusions. The concept of motivation covers a set of factors, mechanisms, and processes that stimulate the emergence of real or potentially directed activity, particularly the formation of motivation for self-learning. It is emphasized that the scientific-cognitive motive contributes to a high level of independence in a person who does not need external stimuli. At the same time, it is necessary to take into account the methodology of increasing students’ cognitive motivation for self-education, considering motivation as a process of motivating action to achieve a certain goal. It is proved that the ways of forming cognitive independence can be ways of mastering educational material, the order of staffing students in groups, the relationship between motive and purpose, the availability and accessibility of educational and methodological support, the use of interactive technologies, creating a problem situation and finding solutions, procedural theory of expectation, organization of motivational effect, as well as introspection methods, game technologies, psychological and pedagogical training, methodical means of communicative attack, proof and persuasion, suggestion, overcoming obstacles, delegation, consolidating positive impressions, solving problems through dialogic conversation, the use of interactive technologies.
Mining Web User Behavior: A Systematic Mapping Study

Nowadays, the number of people using the internet online increases day by day. Therefore, there is a growing need to analyze user behavior trends using navigational clickstream data these days. User behavior can be defined as the collection of the user’s click sequence and transition movements between pages within site. Application areas of user behavior play an important role in many areas, from e-commerce to the banking sector. User behavior is a good argument for analyzing users’ interests. Although there are many research studies in the literature related to user behavior mining, we could not identify a comprehensive literature review on this topic. This paper gives a systematic map of the analysis and mining of web domain users’ activity in this research. We give exact user behavior measures, and publications that we anticipate will help other researchers discover study gaps and possibilities in this paper.
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization

As the complexity of neural network models has grown, it has become increasingly important to optimize their design automatically through metalearning. Methods for discovering hyperparameters, topologies, and learning rate schedules have lead to significant increases in performance. This paper shows that loss functions can be optimized with metalearning as well, and result in similar improvements. The method, Genetic Loss-function Optimization (GLO), discovers loss functions de novo, and optimizes them for a target task. Leveraging techniques from genetic programming, GLO builds loss functions hierarchically from a set of operators and leaf nodes. These functions are repeatedly recombined and mutated to find an optimal structure, and then a covariance-matrix adaptation evolutionary strategy (CMA-ES) is used to find optimal coefficients. Networks trained with GLO loss functions are found to outperform the standard cross-entropy loss on standard image classification tasks. Training with these new loss functions requires fewer steps, results in lower test error, and allows for smaller datasets to be used. Loss-function optimization thus provides a new dimension of metalearning, and constitutes an important step towards AutoML.
Interpersonal Function in Martin Luther King Jr’s Speech

This research aims was to discover the interpersonal function types and to explores their realization within the speech. This research was conducted by using qualitative research design. The data was taken from the website which are readily accessible to the public. After that, the data is divided into clauses form. Next, the data was analyzed based on Halliday’s theory through many steps of data analysis by Cresswell. The results of data analysis showed that Martin Luther King Jr applied: (1) Speech Function (statement, command, and question); Mood (declarative, imperative and interrogative); Modality (high, median, and low); Tense Shift (present, future, and past) and Personal Pronoun (First Personal Pronoun, Second Personal Pronoun and Third Personal Pronoun). (2) speech function (statement was a most dominant, command was the second dominant and question was less); Mood (declarative was most dominant, imperative was the second dominant and interrogative was less); modality (median was most dominant, high was the second, and low was rare); personal pronoun (we was used frequently) and tense shift (present tense was majority, future was a second place, and past tense was less). So based result above, it was concluded that Martin Luther King Jr gave much infomation. Therefore, this speech was called as an informative speech. Considering the result above, the researcher suggested orator use many pronoun “we” when delivering a speech and the university students learn interpersonal function.
Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

There have been a lot of interest in the scaling properties of Transformer models. However, not much has been done on the front of investigating the effect of scaling properties of different inductive biases and model architectures. Do model architectures scale differently? If so, how does inductive bias affect scaling behaviour? How does this influence upstream (pretraining) and downstream (transfer)? This paper conducts a systematic study of scaling behaviour of ten diverse model architectures such as Transformers, Switch Transformers, Universal Transformers, Dynamic convolutions, Performers, and recently proposed MLP-Mixers. Via extensive experiments, we show that (1) architecture is an indeed an important consideration when performing scaling and (2) the best performing model can fluctuate at different scales. We believe that the findings outlined in this work has significant implications to how model architectures are currently evaluated in the community.
EFFECTIVENESS OF WRITING STRATEGIES IN TEACHING PARAGRAPH WRITING

This study applied the concept of a descriptive type of research.Likewise, 80 Grade 11 students of Don Manuel Rivera Memorial Integrated National High School, Pila, Laguna, S.Y. 2020-2021 were selected as the respondents of the study.Similarly, the study aimed to identify the status of the learners' performance in pre-writing; the learners' performance in post-writing following the concept of the three paragraph strategies such as clustering, outlining and freewriting; lastly, the significant difference in the status of the learners' paragraph writing performance after being introduced to writing strategies.Weighted Average Mean and Standard Deviation was used to determine the level of the learners' writing performance both in pre-writing and post-writing.While, Anova was used to identify if there was difference in the status of the learners' performance after being introduced to the writing strategies.As the result shows, the learners' performance in pre-writing was interpreted as Average.Meanwhile, during the post-writing, the learners performed Very Good using the clustering writing strategy, Good following the concept of outlining and also Good applying the freewriting writing strategy respectively.Likewise, based on the computation, the learners' performance in the pre-writing and post-writing was significantly different which explains that there was an improvement in their writing performance after being introduced to paragraph strategies.Thus, there was significant difference in the learners' writing performance after being introduced to the writing strategies; therefore, the hypothesis stating that there is no significant difference in the learners' writing performance after being introduced to paragraph writing strategies is rejected.Based on the results and findings of the study, it is highly recommended that Learners from all grade levels shall be introduced to the paragraph writing strategies.
Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory

Machine-learning tasks performed by neural networks demonstrated useful capabilities for producing reliable, and repeatable intelligent decisions. Integrated photonics, leveraging both component miniaturization and the wave-nature of the signals, can potentially outperform electronics architectures when performing inference tasks. However, the missing photon-photon force challenges non-volatile photonic device-functionality required for efficient neural networks. Here we present a novel concept and optimization of multi-level discrete-state non-volatile photonic memory based on an ultra-compact (<4um) hybrid phase change material GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), to serve as node in a photonic neural network. An optimized electro-thermal switching mechanism, induced by Joule heating through tungsten contacts, is engineered. This operation allows to change the phase of the GSST film thus providing weight updating functionality to the network. We show that a 5 V pulse-train (<1 us, 20 pulses) applied to a serpentine contact produces crystallization and a single pulse of longer duration (2 us) amorphization, used to set the analog synaptic weights of a neuron. Emulating an opportunely trained 100x100 fully connected multilayered perceptron neural network with this weighting functionality embedded as photonic memory, shows up to 93% inference accuracy and robustness towards noise when performing predictions of unseen data
Tutorial pembuatan media pembelajaran berbasis kuis oodlu

The purpose of this study can be used to determine the extent to which the development of the Oodlu quiz media on the eye sense material for 4th grade elementary school students. To determine the feasibility of Oodlu learning media on the sensory material of 4th grade elementary school students. primary school. In this study, not only are my goals described, but there are also benefits that underlie this research. This research is expected to be especially useful for writers and readers both theoretically and practically. This quiz media development research has gone through a validation process. based on validation that has been tested by experts, the research developed has results that are quite feasible to use during learning and have been declared valid by experts. This research is about the development of oodlu quiz learning media. Researchers raised quiz-based learning media to refresh the previously boring classroom atmosphere to become more fun. In the development of this OODLU quiz media, researchers used the 4D development stages or processes, namely Define (define), Design (design), Develop (development), Disseminate (Deployment). Definition of Stage (Definition) The definition related to development needs is the initial stage in the 4D model. At the stage of needs analysis, it is necessary to collect information data about the media to be developed. The last stage is dissemination, so that the media that has been developed will be distributed to students and teachers.
Pengembangan Bahan Ajar Berbasis E-Modul pada Pembelajaran Komunikasi Perkantoran Program Studi Pendidikan Administrasi Perkantoran

The development research aims to develop E-Module teaching materials in office communication learning, office administration education study programs. In addition, research was conducted to determine the feasibility of e-modules and student responses to the developed e-modules. Thiagarajan's 4-D development model (Define-Design-Develop Desseminate) is used in the development of this e-module. This research was piloted on 20 students of the office administration education study program. The data collection instrument used a material expert validation sheet, a linguist, a media expert and a student response questionnaire sheet. Expert validation sheets and student response questionnaires were analyzed quantitatively using a Likert scale. The results showed the feasibility of expert validation of the e-module teaching materials developed, namely material validation obtained 84.34%, language validation 82.85%, and media validation 86.67% in the very strong interpretation category so that it can be used for learning activities. And the results of student responses obtained an assessment of 84.15% with very strong interpretation criteria.
IT Humanities Education Program to Improve Digital Literacy of the Elderly

Due to the influence of the COVID-19 pandemic, more older people are exposed to Information Technology(IT) in their daily lives. However, due to the lack of digital literacy capabilities of the elderly, it is difficult to use digital devices, making it difficult to live. Therefore, this paper outlined the impact of the digital divide on daily life and the ability of the elderly to use digital information. Through this, we propose an educational program that combines IT and humanities to improve digital literacy in the elderly. IT and humanities do not seem to match, but the convergence of the two is essential. Hardware and software, technology and technology, technology and design are being combined. Accordingly, technology and humanities can combine competency is becoming more critical. Therefore, the educational program proposed in this thesis combines a decision tree with a game and allows the elderly to acquire IT knowledge while playing a game naturally. This educational program was conducted for 23 older adults in their 60s in J city, South Korea. The average satisfaction of the study participants in education was 4.13(±0.65). In addition, the post-test mean of the recognition area in digital literacy was 3.02(±0.64) (p&lt;.05), and the post-test mean of the behavior area in digital literacy was 3.67(±0.59) (p&lt;.01), which was statistically significant compared to the pre-test.
Сonceptual Models of Computer Games in the Tasks of Managing the Involvement of Students in the Learning Process

The article considers the opportunities of managing the student engagement in the study process by using gamification technologies. The elements, mechanics, gamification models and their influence on the learners’ involvement in the acquisition of new knowledge and skills are analyzed. The existing gamification models are considered. The algorithm of teacher's actions when introducing gamification technologies into the educational process is presented. The students’ preferences for gamification technologies implemented in the academic process are revealed. Their interest in such technologies is shown.
Cloud Computing and Docker Containerization: A Survey

Business today has gradually moved the cloud and serverless computing has become a trend for IT services. With current cloud computing offerings, businesses in nearly any market segment perform at levels once only accessible to multimillion dollar companies. In addition to putting advanced infrastructure and applications within reach of all companies, cloud computing utilizes a payment structure where utility companies have adopted in the very beginning: you pay only for what you use. Cloud Computing is more cost effective and provides better flexibility and scalability. It is ideal for companies such as online stores who need to serve significantly more customers in Thanksgiving shopping season compared to regular season. Traditionally, cloud computing service providers rely on virtualization, a technique to create virtual servers and computing resources on a single hardware set, to provide a variety of services for customers. The heavy virtualization has led to the newer architectural approach to create cloud application: Microservices. In Microservices architecture, cloud application is built as a set of services. Each service can be developed, tested and deployed independently. Containerization is one of the enabling techniques in Microservices architecture and Docker is one of the leading implementations of containerization. This poster presents a survey of Docker containerization.
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism

Scaling CNN training is necessary to keep up with growing datasets and reduce training time. We also see an emerging need to handle datasets with very large samples, where memory requirements for training are large. Existing training frameworks use a data-parallel approach that partitions samples within a mini-batch, but limits to scaling the mini-batch size and memory consumption makes this untenable for large samples. We describe and implement new approaches to convolution, which parallelize using spatial decomposition or a combination of sample and spatial decomposition. This introduces many performance knobs for a network, so we develop a performance model for CNNs and present a method for using it to automatically determine efficient parallelization strategies. We evaluate our algorithms with microbenchmarks and image classification with ResNet-50. Our algorithms allow us to prototype a model for a mesh-tangling dataset, where sample sizes are very large. We show that our parallelization achieves excellent strong and weak scaling and enables training for previously unreachable datasets.
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions. Methods for uncertainty estimation in the output of deep object detectors (DNNs) have been proposed in recent works, but have had limited success due to 1) information loss at the detectors non-maximum suppression (NMS) stage, and 2) failure to take into account the multitask, many-to-one nature of anchor-based object detection. To that end, we introduce BayesOD, an uncertainty estimation approach that reformulates the standard object detector inference and Non-Maximum suppression components from a Bayesian perspective. Experiments performed on four common object detection datasets show that BayesOD provides uncertainty estimates that are better correlated with the accuracy of detections, manifesting as a significant reduction of 9.77\%-13.13\% on the minimum Gaussian uncertainty error metric and a reduction of 1.63\%-5.23\% on the minimum Categorical uncertainty error metric. Code will be released at {\url{https://github.com/asharakeh/bayes-od-rc}}.
A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarios

Ultra reliable low latency communications (URLLC) is a new service class introduced in 5G which is characterized by strict reliability $(1-10^{-5})$ and low latency requirements (1 ms). To meet these requisites, several strategies like overprovisioning of resources and channel-predictive algorithms have been developed. This paper describes the application of a Nonlinear Autoregressive Neural Network (NARNN) as a novel approach to forecast interference levels in a wireless system for the purpose of efficient resource allocation. Accurate interference forecasts also grant the possibility of meeting specific outage probability requirements in URLLC scenarios. Performance of this proposal is evaluated upon the basis of NARNN predictions accuracy and system resource usage. Our proposed approach achieved a promising mean absolute percentage error of 7.8 % on interference predictions and also reduced the resource usage in up to 15 % when compared to a recently proposed interference prediction algorithm.
Quantum simulation of molecules in solution

Quantum chemical calculations on quantum computers have been focused mostly on simulating molecules in gas-phase. Molecules in liquid solution are however most relevant for Chemistry. Continuum solvation models represent a good compromise between computational affordability and accuracy in describing solvation effects within a quantum chemical description of solute molecules. Here we report on the extension of the Variational Quantum Eigensolver to solvated systems, using the Polarizable Continuum Model. We show that accounting for solvation effects does not impact the algorithmic efficiency. Numerical results of noiseless simulations for molecular systems with up to twelve spin-orbitals (qubits) are presented. Furthermore, calculations performed on a simulated quantum hardware (IBM Q Mumbai), thus including noise, yield computed solvation free energies in fair agreement with the classical calculations without the inclusion of any error mitigation protocol.
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet

In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest. To reduce training costs and annotation efforts needed to build a classifier for a specific label set, we present and evaluate several entailment-based zero-shot approaches to suggestion classification in a label-fully-unseen fashion. In particular, we introduce the strategy of assigning target class labels to sentences in English language with user intentions, which significantly improves prediction quality. The proposed strategies are evaluated with a comprehensive experimental study that validated our results both quantitatively and qualitatively.
The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse

In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the "type" of discursive role that the comment is performing with respect to the previous comment. The initial dataset, being discussed here (and made available as part of the ComMA@ICON shared task), consists of a total 15,000 annotated comments in four languages - Meitei, Bangla, Hindi, and Indian English - collected from various social media platforms such as YouTube, Facebook, Twitter and Telegram. As is usual on social media websites, a large number of these comments are multilingual, mostly code-mixed with English. The paper gives a detailed description of the tagset being used for annotation and also the process of developing a multi-label, fine-grained tagset that can be used for marking comments with aggression and bias of various kinds including gender bias, religious intolerance (called communal bias in the tagset), class/caste bias and ethnic/racial bias. We also define and discuss the tags that have been used for marking different the discursive role being performed through the comments, such as attack, defend, etc. We also present a statistical analysis of the dataset as well as results of our baseline experiments with developing an automatic aggression identification system using the dataset developed.