Reviewerly/exHarmony huggingface dataset:
Android Game-Based Learning Media Recognizes the Structure and Functions of Plant and Animal Parts for Elementary School

Teaching and learning activities in Indonesia still employ the lecture method which is considered traditional, therefore it is necessary to refresh it by utilizing learning media in teaching and learning activities. One of the methods applied is using learning media based on android game in which it puts forward user experience to its user. With this interesting educational game, children will not realize that what they are doing includes learning and children will feel happy and want to learn. This application is intended for the android smartphone platform. This development method uses Research and Development (R&amp;D) with an Analysis, Design, Development, Implementation, and Evaluation (ADDIE) development model. The results of the black box test from the research are that this android game-based learning media can run well.
Automatic Construction of Hardware Traffic Validators

This paper describes a fully automated process that creates a custom hardware traffic validator directly from a formal grammar and deploys it within a specialized network security appliance. The appliance appears as a hidden, all-hardware “bump-in-the-wire” that can be inserted within any network segment; it stores and validates messages on-the-fly, and either forwards or drops individual packets in real-time. Consequently, it serves to disrupt and mitigate stealthy remote attacks that leverage zero-day exploits and persistent implants. Allowed traffic, files, and mission payload formats are specified formally using a standard Look-Ahead, Left-to-Right (LALR) grammar that operates on ASCII and/or binary data. The grammars can be expressed either in Backus-Naur Form (BNF), used by industry standard tools such as Bison, or through state-of-the-art combinators, such as Hammer, under development within the DARPA SafeDocs program. Bison and Hammer compiler tools are used to generate standard shift/reduce parsing tables. These tables are post-processed to improve their compactness and practical viability. The optimized tables are then combined with a generic push-down automaton to form a complete parser. The parser is then automatically transformed into a hardware circuit using High-Level Synthesis (HLS). The result is a composable block of circuitry that can be directly inserted into a generic communications harness embedded within a Field Programmable Gate Array (FPGA) on the network appliance.
Learning Outcomes of Listening Comprehension Skills through Audio Visual and Interactive-Audio

This study aims to examine strategies and media bases in learning English-Listening Comprehension skills. The two strategies that are the subject of discussion are the Audio-Visual Media-based Strategy and the Interactive-Audio Strategy. Quantitative research was carried out by surveying the object of research and strengthened by interviews with several respondents at random to ensure that all respondents had received learning with two strategies. The object of this research is the students of the English Education Department at the State Islamic University of Maulana Malik Ibrahim Malang. All respondents in this survey are 34 respondents. Data analysis was carried out in a qualitative descriptive manner to interpret the percentage of the implementation of learning in the two strategies. The results showed that the Audio-Visual Strategy had a percentage point interval of 76.92%, which indicated that this strategy was in the "Good" category. While the Interactive-Audio Strategy has a percentage point interval of 50.48%, which indicates that this strategy is in the “Enough” category. From these results, it can be concluded that the Audio-Visual strategy provides maximum results when applied in Listening Comprehension Skill learning so that the Audio-Visual strategy is more effective
THE ROLE OF DIDACTIC TECHNOLOGIES IN THE PROCESS FORMATION OF PROFESSIONALLY IMPORTANT QUALITIES FUTURE SOCIOLOGISTS

In this article the special attention is paid to the necessity of innovative didactics technologies me in the process of professionally important qualities of future sociologists formation.
A SELF-SUPERVISED KEYPOINT DETECTION NETWORK FOR MULTIMODAL REMOTE SENSING IMAGES

Abstract. Currently, multimodal remote sensing images have complex geometric and radiometric distortions, which are beyond the reach of classical hand-crafted feature-based matching. Although keypoint matching methods have been developed in recent decades, most manual and deep learning-based techniques cannot effectively extract highly repeatable keypoints. To address that, we design a Siamese network with self-supervised training to generate similar keypoint feature maps between multimodal images, and detect highly repeatable keypoints by computing local spatial- and channel-domain peaks of the feature maps. We exploit the confidence level of keypoints to enable the detection network to evaluate potential keypoints with end-to-end trainability. Unlike most trainable detectors, it does not require the generation of pseudo-ground truth points. In the experiments, the proposed method is evaluated using various SAR and optical images covering different scenes. The results prove its superior keypoint detection performance compared with current state-of-art matching methods based on keypoints.
Wavelet Prior Attention Learning in Axial Inpainting Network

Image inpainting is the task of filling masked or unknown regions of an image with visually realistic contents, which has been remarkably improved by Deep Neural Networks (DNNs) recently. Essentially, as an inverse problem, the inpainting has the underlying challenges of reconstructing semantically coherent results without texture artifacts. Many previous efforts have been made via exploiting attention mechanisms and prior knowledge, such as edges and semantic segmentation. However, these works are still limited in practice by an avalanche of learnable prior parameters and prohibitive computational burden. To this end, we propose a novel model -- Wavelet prior attention learning in Axial Inpainting Network (WAIN), whose generator contains the encoder, decoder, as well as two key components of Wavelet image Prior Attention (WPA) and stacked multi-layer Axial-Transformers (ATs). Particularly, the WPA guides the high-level feature aggregation in the multi-scale frequency domain, alleviating the textual artifacts. Stacked ATs employ unmasked clues to help model reasonable features along with low-level features of horizontal and vertical axes, improving the semantic coherence. Extensive quantitative and qualitative experiments on Celeba-HQ and Places2 datasets are conducted to validate that our WAIN can achieve state-of-the-art performance over the competitors. The codes and models will be released.
Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Predictions of chemical reaction outcomes using machine learning (ML) has emerged as a powerful tool for advancing materials synthesis. However, this approach requires large and diverse datasets, which are extremely limited in the field of nanomaterials synthesis due to inconsistent and non-standardized reporting in the literature and a lack of understanding of synthetic mechanisms. In this study, we extracted parameters of InP quantum dot (QD) syntheses as our inputs, and resultant properties (absorption, emission, diameter) as our outputs from 72 publications. We “filled in” missing outputs using a data imputation method to prepare a complete dataset containing 216 entries for training and testing predictive ML models. We defined the descriptor space in two ways (condensed and extended) based on either chemical identity or the role of reagents to explore the best approach for categorizing input features. We achieved mean absolute errors (MAEs) as low as 20.29, 11.46, and 0.33 nm for absorption, emission, and diameter respectively with our best ML model across diverse synthetic methods. We used these models to deploy an accessible and interactive webapp for designing syntheses of InP (https://share.streamlit.io/cossairt-lab/indium-phosphide/Hot_injection/hot_injection_prediction.py). Using this webapp, we investigated the power of ML to uncover chemical trends in InP syntheses, such as the effects of common additives, like zinc salts and trioctylphosphine. We also designed and conducted new experiments based on extensions of literature procedures and compared our experimentally measured properties to predictions, thus evaluating the “real-life” accuracy of our models. Conversely, we used inverse-design to obtain InP QDs with specific properties. Finally, we applied the same approach to train, test, and launch predictive models for CdSe QDs by expanding a previously published dataset. Altogether, our data pre-processing method and ML implementations demonstrate the ability to design materials with targeted properties and explore underlying reaction mechanisms even when faced with limited data resources.
REFLECTIVE SKILLS AS A COMPONENT OF PROFESSIONAL PREPARATION OF STUDENT STUDENT STUDENTS

In the article the essential of the specialists training modernization in the conditions of the contemporary university is substantiated. The meaning of the concept “reflective skills ” as an important factor in the student”s training to their further professional activity in defened.
BREAST CANCER PREDICTION WITH HYBRID ML MODELS

In recent times various types of cancer propagation in humans are alarmingly increasing and especially women are prone to and threatened by breast cancer with high morbidity and mortality. The absence of robust prognosis models results in difficulty for physicians to prepare a treatment plan that may extend patient survival chances and time. Hence, the need of the time is to develop the technique which offers minimum error with increased accuracy. Different legacy algorithms like SVM, Regression, are compared with the proposed hybrid prediction model outcome. All experiments are executed within a parallel environment and conducted in anaconda python platform with relevant libraries. This is helpful in domains like. prediction of cancer before diagnosis, prediction of diagnosis and outcome during treatment. The proposed work combining detailed pre-prepressing stages over a deep neural network model with tuned hyper parameters, validated to yield needed accuracy. This can be used to derive and compare the outcome of different techniques and suitable one having max accuracy and stability, can be used depending upon requirement. Different data sets are tried and analysed for prediction with different parameters and results are compared. Keywords — Breast Cancer detection, machine learning, feature selection, classification, hybrid deep learning, image classification, KNN , Random Forest, ROC.
Exploring the Facilitator’ Roles and Successful Factors of International Youth Programs: A Grounded Theory Approach*

This study aimed to explore the identities, roles and factors contributing to successful international youth programs perceived by the facilitators who participated in Korea-ASEAN International Youth Summit. A grounded theory approach was utilized in this study to analyze experiential data provided by the facilitators as the study participants. Online interviews were conducted to 10 study participants. A total of 288 statements and 28 categories were extracted and the results using the framework model of causal, context, intervening condition, action/interaction, central phenomena and consequences were presented. The facilitators perceived themselves as “having a role of balancing the team,” “role of guaranteeing everyone’s right to speech,” “supportive assistant for the team,”accentuating their unique roles as opposed to being leaders of their groups. The successful factors were identified as “successful cultural exchange,” “collaboration from various countries,” “identical cultural values,” “adequate experience for the age group,” “cultivation of decision making through effective open discussion” As the limitations due to COVID-19 pandemic continues to exist for such large international event as well, additional reflections and recommendations were discussed in the study. Further implications for the international youth programs and study limitations were also stated.
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning

In this paper, we study a large-scale multi-agent minimax optimization problem, which models many interesting applications in statistical learning and game theory, including Generative Adversarial Networks (GANs). The overall objective is a sum of agents' private local objective functions. We first analyze an important special case, empirical minimax problem, where the overall objective approximates a true population minimax risk by statistical samples. We provide generalization bounds for learning with this objective through Rademacher complexity analysis. Then, we focus on the federated setting, where agents can perform local computation and communicate with a central server. Most existing federated minimax algorithms either require communication per iteration or lack performance guarantees with the exception of Local Stochastic Gradient Descent Ascent (SGDA), a multiple-local-update descent ascent algorithm which guarantees convergence under a diminishing stepsize. By analyzing Local SGDA under the ideal condition of no gradient noise, we show that generally it cannot guarantee exact convergence with constant stepsizes and thus suffers from slow rates of convergence. To tackle this issue, we propose FedGDA-GT, an improved Federated (Fed) Gradient Descent Ascent (GDA) method based on Gradient Tracking (GT). When local objectives are Lipschitz smooth and strongly-convex-strongly-concave, we prove that FedGDA-GT converges linearly with a constant stepsize to global $\epsilon$-approximation solution with $\mathcal{O}(\log (1/\epsilon))$ rounds of communication, which matches the time complexity of centralized GDA method. Finally, we numerically show that FedGDA-GT outperforms Local SGDA.
CONTENT OF A SPECIAL EDUCATION TEACHER’S PROFESSIONAL COMPETENCE

The article reveals the essence and content of professional competence of a special education teacher as an integral characteristic based on the presence of professional knowledge, skills and abilities, as well as personal qualities in the implementation of diagnostic, psychological, pedagogical, correctional, counselling support for children with special educational needs, their parents and teachers in educational institutions, and is manifested in the ability to carry out effective professional communication and interaction with participants in the educational process, as well as with members of the team of psychological and pedagogical support of a child with SEN.It is found that the formation of professional competence of a special education teacher is influenced by external and internal factors.Internal factors are personal (subjective) individual psychological qualities: responsibility for professional development and learning outcomes combined with a positive attitude to the profession and the desire for continuous self-improvement, focus on success in the chosen profession; objective self-esteem with a high level of normative behaviour and communication culture; ability to learn, intellectual skills.External determinants include: improving the content and organization of the educational process, providing opportunities for productive practical activities, favourable socio-psychological
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction

Machine learning models are often personalized with categorical attributes that are protected, sensitive, self-reported, or costly to acquire. In this work, we show models that are personalized with group attributes can reduce performance at a group level. We propose formal conditions to ensure the "fair use" of group attributes in prediction tasks by training one additional model -- i.e., collective preference guarantees to ensure that each group who provides personal data will receive a tailored gain in performance in return. We present sufficient conditions to ensure fair use in empirical risk minimization and characterize failure modes that lead to fair use violations due to standard practices in model development and deployment. We present a comprehensive empirical study of fair use in clinical prediction tasks. Our results demonstrate the prevalence of fair use violations in practice and illustrate simple interventions to mitigate their harm.
GateHUB: Gated History Unit with Background Suppression for Online Action Detection

Online action detection is the task of predicting the action as soon as it happens in a streaming video. A major challenge is that the model does not have access to the future and has to solely rely on the history, i.e., the frames observed so far, to make predictions. It is therefore important to accentuate parts of the history that are more informative to the prediction of the current frame. We present GateHUB, Gated History Unit with Background Suppression, that comprises a novel position-guided gated cross-attention mechanism to enhance or suppress parts of the history as per how informative they are for current frame prediction. GateHUB further proposes Future-augmented History (FaH) to make history features more informative by using subsequently observed frames when available. In a single unified framework, GateHUB integrates the transformer's ability of long-range temporal modeling and the recurrent model's capacity to selectively encode relevant information. GateHUB also introduces a background suppression objective to further mitigate false positive background frames that closely resemble the action frames. Extensive validation on three benchmark datasets, THUMOS, TVSeries, and HDD, demonstrates that GateHUB significantly outperforms all existing methods and is also more efficient than the existing best work. Furthermore, a flow-free version of GateHUB is able to achieve higher or close accuracy at 2.8x higher frame rate compared to all existing methods that require both RGB and optical flow information for prediction.
Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

We present Onesweep, a least-significant digit (LSD) radix sorting algorithm for large GPU sorting problems residing in global memory. Our parallel algorithm employs a method of single-pass prefix sum that only requires ~2n global read/write operations for each digit-binning iteration. This exhibits a significant reduction in last-level memory traffic versus contemporary GPU radix sorting implementations, where each iteration of digit binning requires two passes through the dataset totaling ~3n global memory operations. On the NVIDIA A100 GPU, our approach achieves 29.4 GKey/s when sorting 256M random 32-bit keys. Compared to CUB, the current state-of-the-art GPU LSD radix sort, our approach provides a speedup of ~1.5x. For 32-bit keys with varied distributions, our approach provides more consistent performance compared to HRS, the current state-of-the-art GPU MSD radix sort, and outperforms it in almost all cases.
Perturbation Augmentation for Fairer NLP

Unwanted and often harmful social biases are becoming ever more salient in NLP research, affecting both models and datasets. In this work, we ask whether training on demographically perturbed data leads to fairer language models. We collect a large dataset of human annotated text perturbations and train a neural perturbation model, which we show outperforms heuristic alternatives. We find that (i) language models (LMs) pre-trained on demographically perturbed corpora are typically more fair, and (ii) LMs finetuned on perturbed GLUE datasets exhibit less demographic bias on downstream tasks, and (iii) fairness improvements do not come at the expense of performance on downstream tasks. Lastly, we discuss outstanding questions about how best to evaluate the (un)fairness of large language models. We hope that this exploration of neural demographic perturbation will help drive more improvement towards fairer NLP.
EXPLORING QUANTUM COMPUTING POTENTIALS IN SOLVING A COMBINATORIAL OPTIMIZATION PROBLEM TO MINIMIZE EXPOSURE TO COVID-19 DURING A CITY JOURNEY

Abstract. COVID-19 is an airborne virus that can be spread directly or indirectly from one person to another. Spreading the virus strongly depends on the location and time and hence, a Spatio-temporal event. Moreover, traffic congestion will increase the spread of the virus not only because of the vicinity but also because of increased temperature and humidity in these spaces for a short or long time. This paper introduces a vehicle routing optimization model to reduce COVID-19 exposure risk during a city journey by solving it as a quadratic unconstrained binary optimization problem on a quantum annealing computer. Indeed, the objective of the COVID-19 prevention optimization problem is to minimize the risk of exposure for a given set of road users between origins and destinations. Microsoft Taxi data from the city of Beijing have been used to simulate road users’ movement. The problem has been run onto three different solvers. One of the solvers is executed on classical computers, and two other solvers are executed on hybrid quantum solvers. Hybrid solvers return the solution within less than 0.03 seconds on quantum processing unit time. However, the results will be returned at least 5 seconds after the execution in the classical solver. It is worth mentioning that, as there is no direct access to the quantum computers, it is hard to compare the results on the same scale as the queries will go on a queue in D-wave quantum computers. Applying the proposed model on the trajectory data shows a better distribution of the vehicles on the road network.
The Practices and Problems of Teaching English Grammar to the Young EFL Learners

English is the most widely used language (lingua franca) which is taught from primary to tertiary levels of education in Bangladesh education system. The secondary level is very much important for the young EFL learners to learn English language, especially grammar, as it prepares them and builds their foundation for the next levels of education. However, this study attempts to assess the practices and problems of teaching English grammar in our country, especially the teaching approaches and techniques, the roles of teacher and learners, the perception of needs of English grammar, the learners’ preferences about the learning strategies etc. The data, which was collected through the teacher and student questionnaire, interviews of the teachers and classroom observations, has unveiled the problems in teaching English grammar to the students of class IX-X. The findings of the study suggest that the teachers prefer applying the memorization techniques rather than the communicative approach. It suggests specific measures to solve the problems of teaching grammar at the end of the paper.