Reviewerly/exHarmony huggingface dataset:
Designing irregular verb game as teaching tool for learning irregular verbs

This research aimed to validate the authenticity and usefulness of Irregular Verb Game as a teaching tool in learning past tense irregular verbs in English language class. This research involved an English teacher and students of grade eight at SMPN 3 Salatiga, Kabupaten Sambas. The research method was development which consists of three phases: analyzing, designing, and developing. For the analysis phase, the researcher used interview sheets to obtain the data that highlights teacher and students’ needs in teaching and learning. The design phase focused on designing the contents and layout of the product. The development phase generated the product, tried it out, and got teacher’s validation by using assessment rubric. The first part of the result of this research was a complete design of the product. The layout of the design had 3 main parts: intro, inside house, and outside house. The contents included a visual dictionary, pronunciation function, and simple quizzes. The second part of the result was the teacher’s validation for the product. The percentage of teachers’ validation for the product’s effectiveness was 94.44%. Based on this data, the researchers ensured that Irregular Verb Game can be used as a teaching tool to teach irregular verbs in the form of past tense for eighth grade students of Junior High School.
Bug Detection and Report: A Case Study on Dataset for Software Management using Security Bug Report

Abstract: In order to build a prediction model we must label a large amount of data in order to mine software repositories. The accuracy of the labels has a significant impact on a model's performance. However, there have been few research that have looked into the influence on a prediction model, there are occurrences that have been mislabeled.. To close the gap, we conduct a research project on how to report a security bug (SBR)prediction in this paper. Furthermore, it has the potential to mislead SBR prediction research. We first enhance the label validity of these 5 datasets by personally evaluating each and every bugcomplaint in this study, and we discover 749 SBRs that were previously Non-SBRs have been mislabeled(NSBRs). We then examine the performance of the classification models both on messy (before the alteration) and cleaner (after our reconfiguration) datasets , impact of dataset label correctness. The results suggest that cleaning the datasets improves the performance of classification models. Index Terms: Prediction of security bug reports, data quality, software detection and report
IMPROVING 3D PEDESTRIAN DETECTION FOR WEARABLE SENSOR DATA WITH 2D HUMAN POSE

Abstract. Collisions and safety are important concepts when dealing with urban designs like shared spaces. As pedestrians (especially the elderly and disabled people) are more vulnerable to accidents, realising an intelligent mobility aid to avoid collisions is a direction of research that could improve safety using a wearable device. Also, with the improvements in technologies for visualisation and their capabilities to render 3D virtual content, AR devices could be used to realise virtual infrastructure and virtual traffic systems. Such devices (e.g., Hololens) scan the environment using stereo and ToF (Time-of-Flight) sensors, which in principle can be used to detect surrounding objects, including dynamic agents such as pedestrians. This can be used as basis to predict collisions. To envision an AR device as a safety aid and demonstrate its 3D object detection capability (in particular: pedestrian detection), we propose an improvement to the 3D object detection framework Frustum Pointnet with human pose and apply it on the data from an AR device. Using the data from such a device in an indoor setting, we conducted a comparative study to investigate how high level 2D human pose features in our approach could help to improve the detection performance of orientated 3D pedestrian instances over Frustum Pointnet.
Sequential algorithms for testing identity and closeness of distributions

What advantage do \emph{sequential} procedures provide over batch algorithms for testing properties of unknown distributions? Focusing on the problem of testing whether two distributions $\mathcal{D}_1$ and $\mathcal{D}_2$ on $\{1,\dots, n\}$ are equal or $\epsilon$-far, we give several answers to this question. We show that for a small alphabet size $n$, there is a sequential algorithm that outperforms any batch algorithm by a factor of at least $4$ in terms sample complexity. For a general alphabet size $n$, we give a sequential algorithm that uses no more samples than its batch counterpart, and possibly fewer if the actual distance $TV(\mathcal{D}_1, \mathcal{D}_2)$ between $\mathcal{D}_1$ and $\mathcal{D}_2$ is larger than $\epsilon$. As a corollary, letting $\epsilon$ go to $0$, we obtain a sequential algorithm for testing closeness when no a priori bound on $TV(\mathcal{D}_1, \mathcal{D}_2)$ is given that has a sample complexity $\tilde{\mathcal{O}}(\frac{n^{2/3}}{TV(\mathcal{D}_1, \mathcal{D}_2)^{4/3}})$: this improves over the $\tilde{\mathcal{O}}(\frac{n/\log n}{TV(\mathcal{D}_1, \mathcal{D}_2)^{2} })$ tester of \cite{daskalakis2017optimal} and is optimal up to multiplicative constants. We also establish limitations of sequential algorithms for the problem of testing identity and closeness: they can improve the worst case number of samples by at most a constant factor.
Cross-Language Text Document Plagiarism Detection System Using Winnowing Method

Currently, there are many text documents such as journals scattered on the internet, both Indonesian and English-language journals. With this, it is possible to act plagiarism by copying from foreign journals that are translated into other languages or copying directly without being changed from the original language. One way that can suppress these actions is to build a plagiarism detection system for cross-language text documents. The method that can be used to detect document plagiarism is the Winnowing method. Winnowing method is a method where text input will be processed to produce a hash value called a fingerprint. This study aims to build a system that can detect plagiarism of text documents in different languages using the Winnowing method. Text documents that can be tested are input text and PDF files. Documents used in system testing are journals that have the same topic. The results of the highest level of accuracy produced between the calculation of the Jaccard Coefficient with the Plagiarism Checker X application are in the fourth scenario with an average percentage value of 84.7%.
ICT SCAFFOLDING FOR SCHOOL EDUCATION IN COVID-19 LOCKDOWN

The COVID-19 situation, where the educational process had to be as flexible as possible, revealed that the use of information and communication technologies (ICT) has become a challenge for Lithuanian schools. In early 2021, we conducted focus group discussions in four Lithuanian schools to investigate how educational technology can enable an inclusive educational process. The analysis of the key determinants of pedagogical success and schools’ decisions on the unified use of ICT to include all students in learning, the scaffolding of the educational process using ICT revealed the two main factors. These factors ensuring the scaffolding process are: (1) how teachers remove the learning barriers and support students by use of educational technologies; (2) what helps teachers to transfer responsibility to students and enables their participation in the educational process. When we examined the potential of ICT to engage students, we found several differences between schools, namely: technology provision, the teachers’ ICT skills, the experience of the school community itself, the readiness of the school, teachers, and students to teach and to learn by distance or blended learning applying new ICT-based educational scenarios. The study results showed that the gains made during the COVID-19 lockdown in Lithuania in combining teaching solutions with the use of technology can become the ‘new normal’ and can help schools address the issue of inclusion for all students.
Klasifikasi Wilayah Potensi Risiko Kerusakan Lahan Akibat Bencana Tsunami Menggunakan Machine Learning

Indonesia is an archipelagic country with a long coastline where some areas are prone to tsunami waves which can result in land damage. Tsunamis occur due to earthquakes or volcanic eruptions under the sea that cause movement of the seabed and then create strong waves. The Special Region of Yogyakarta, precisely in Bantul Regency, is one of the areas that have a high risk of a tsunami disaster because the area is located in the expanse of the Indian Ocean which has quite impulsive plate movements. This study aims to find out information about the level of risk of land damage due to the tsunami using vegetation index data from OLI 8 Landsat imagery. Classification or prediction using the Artificial Neural Network (ANN) method. The vegetation index used is NDVI, NDWI, NDBI, SAVI, and MNDWI. The relationship between SAVI and NDVI has a positive correlation coefficient with the highest value of 0.962 where the potential risk of low damage is 0.933 and the potential risk of high damage is 0.856. Classification of potential areas of high risk of damage to tsunami land (High Risk) using the ANN method resulted in 7 villages with high risk. The ANN algorithm is the most accurate method for classification predictions between the Random Forest and SVM methods which get an accuracy of 95.45% and get a Kappa value of 86.08%. Spatial prediction using IDW produces a map of the distribution of the potential risk area for land damage caused by the tsunami.
The Relationship of Indonesian Language Learning and the Environment

The environment has a very important influence on language learning, with environment based Indonesian language learning, students can think critically about the things that are around them, and positive traits emerge from students. Because learning Indonesian is taught how to use words in sentences to be more effective and polite. Then it is applied by students in the surrounding environment. It is not surprising that students who study environmental-based Indonesian will tend to have a polite attitude in speaking.
Prediksi Kinerja Pegawai sebagai Rekomendasi Kenaikan Golongan dengan Metode Decision Tree dan Regresi Logistik

Employee performance is one element that greatly determines the quality of an organization, both government and private. Employee performance appraisal has become a routine for most companies. Performance appraisal is required for the process of salary increases, promotions, and demotions. Until this research was carried out, the processing of employee performance appraisal and evaluation at Prasama Bhakti Foundation was still done manually, so that sometimes employee promotions were carried out late or even on an inconsistent basis for each employee. Therefore, it is necessary to group data with the help of machine learning that can help predict the eligibility of an employee to get a promotion based on his performance. Classification is one method for classifying or classifying data that are arranged systematically. Decision tree and logistic regression methods are classification or grouping methods that have been widely used for solving classification problems. In this study, it will be explained how the process of processing employee performance appraisal data starts from data preparation to determine the accuracy of the decision tree model and logistic regression that is formed. The two classification models are used to predict employee performance as a recommendation for employee promotion at the Prasama Bhakti Foundation.
An efficient supervised deep hashing method for image retrieval

Abstract Recently, retrieving and searching for relevant images from large datasets has become increasingly challenging for researchers. Hashing methods that mapped raw data into a short binary code have attracted increasing attention from the researcher. Most existing hashing approaches map samples to a binary vector with a single linear projection. This paper introduces a CNN-based hashing method that uses multiple nonlinear projections to produce additional short-bit binary code. Applying a convolutional neural network, we obtain an end-to-end. In order to maintain similarity between images, a loss function was also designed. Moreover, it minimized the quantization error by providing a uniform distribution of the hash bits. Extensive experimental results from various image retrieval benchmarks show the superiority of the proposed method and demonstrate its efficient performance compared with other state-of-art methods.
Prosody and cognitive accessibility in left-detached topics: lessons from Nigerian Pidgin

In Nigerian Pidgin, or Naija, a language spoken by some 100 million people in Nigeria, the role of prosody in topic-marking remains an understudied question, particularly with regards to the way in which speakers distinguish between topics which are readily present in the minds of their interlocutors, and less cognitively accessible ones such as those being introduced for the first time.Advances in the field of natural language processing have yielded effective methods for the automatic extraction of stylized prosodic contours from aligned sound files, facilitating the wide-scale study of syntactic units and their melodic properties.Through a combination of manual pragmatic annotation, automatic prosodic stylization, and exploratory statistical analysis, we aim to better understand the prosodic differences between categories of left-detached topics.We hypothesize that there exists a clear relationship between the cognitive accessibility of topics and their associated prosodic contours.More precisely, the most cognitively accessible topics will be characterized by falling F0 contours with low fundamental frequency ranges.Conversely, we consider that less accessible topics will be associated with rising contours and ones covering wide frequency ranges.We also postulate that contours ending in a very high final pitch will be almost exclusive to less accessible topics.
Bridging Sim2Real Gap Using Image Gradients for the Task of End-to-End Autonomous Driving

We present the first prize solution to NeurIPS 2021 - AWS Deepracer Challenge. In this competition, the task was to train a reinforcement learning agent (i.e. an autonomous car), that learns to drive by interacting with its environment, a simulated track, by taking an action in a given state to maximize the expected reward. This model was then tested on a real-world track with a miniature AWS Deepracer car. Our goal is to train a model that can complete a lap as fast as possible without going off the track. The Deepracer challenge is a part of a series of embodied intelligence competitions in the field of autonomous vehicles, called The AI Driving Olympics (AI-DO). The overall objective of the AI-DO is to provide accessible mechanisms for benchmarking progress in autonomy applied to the task of autonomous driving. The tricky section of this challenge was the sim2real transfer of the learned skills. To reduce the domain gap in the observation space we did a canny edge detection in addition to cropping out of the unnecessary background information. We modeled the problem as a behavioral cloning task and used MLP-MIXER to optimize for runtime. We made sure our model was capable of handling control noise by careful filtration of the training data and that gave us a robust model capable of completing the track even when 50% of the commands were randomly changed. The overall runtime of the model was only 2-3ms on a modern CPU.
A Quantitative Symbolic Approach to Individual Human Reasoning

Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.
Communication and Decision Support Tool for an In-Hospital 3D Printing Service

Effective communication is a key factor in healthcare, essential for improving process efficiency and quality of care. This is particularly true in new services, e.g., the 3D printing service inside the hospital.A web platform, called 3DSCT, has been developed to act as an interface between the three categories of operators involved in 3D printing: physicians, radiologists and engineers.The 3DSCT platform has been designed using Microsoft Visual Studio Code, enclosing .js scripts and HTML pages with the relative CSS formats.When applied to a real 3D printing service, the 3DSCT platform provided an effective solution that streamlined the process of designing and manufacturing 3D-printed artifacts, from physician's request through development to printing.By incorporating the platform into the hospital management system, it will be possible to reduce the overall lead time and decrease the waste of time for the operators involved in 3D printing inside the hospital.
Norm-Scaling for Out-of-Distribution Detection

Out-of-Distribution (OoD) inputs are examples that do not belong to the true underlying distribution of the dataset. Research has shown that deep neural nets make confident mispredictions on OoD inputs. Therefore, it is critical to identify OoD inputs for safe and reliable deployment of deep neural nets. Often a threshold is applied on a similarity score to detect OoD inputs. One such similarity is angular similarity which is the dot product of latent representation with the mean class representation. Angular similarity encodes uncertainty, for example, if the angular similarity is less, it is less certain that the input belongs to that class. However, we observe that, different classes have different distributions of angular similarity. Therefore, applying a single threshold for all classes is not ideal since the same similarity score represents different uncertainties for different classes. In this paper, we propose norm-scaling which normalizes the logits separately for each class. This ensures that a single value consistently represents similar uncertainty for various classes. We show that norm-scaling, when used with maximum softmax probability detector, achieves 9.78% improvement in AUROC, 5.99% improvement in AUPR and 33.19% reduction in FPR95 metrics over previous state-of-the-art methods.
STUDENTS' READINESS FOR CREATIVE ACTIVITY AS FACTOR OF PERSONALITY DEVELOPMENT IN THE PROCESS MUSIC EDUCATION

In clause, the contents of concept of “readiness ” to various kinds of activity and in particular to creative activity are considered (examined). The specific features of concept “readiness ” of creative activity with reference to process of musical training come to light. The structural components of readiness to creative activity of the pupils are defined(determined). The generated attributes of readiness under influence of the model, developed by us are stated.
Improving Sequential Query Recommendation with Immediate User Feedback

We propose an algorithm for next query recommendation in interactive data exploration settings, like knowledge discovery for information gathering. The state-of-the-art query recommendation algorithms are based on sequence-to-sequence learning approaches that exploit historical interaction data. Due to the supervision involved in the learning process, such approaches fail to adapt to immediate user feedback. We propose to augment the transformer-based causal language models for query recommendations to adapt to the immediate user feedback using multi-armed bandit (MAB) framework. We conduct a large-scale experimental study using log files from a popular online literature discovery service and demonstrate that our algorithm improves the per-round regret substantially, with respect to the state-of-the-art transformer-based query recommendation models, which do not make use of immediate user feedback. Our data model and source code are available at https://github.com/shampp/exp3_ss
Proactive Robot Movements in a Crowd by Predicting and Considering the Social Influence

Dense crowds are challenging scenes for an autonomous mobile robot. Planning in such an interactive environment requires predicting uncertain human intention and reaction to future robot actions. Concerning these capabilities, we propose a probabilistic forecasting model which factorizes the human motion uncertainty as follows: 1) A (conditioned) normalizing flow (CNF) estimates the densities of human goals. 2) We forecast the crowd’s trajectory density towards the goals by autoregressively (AR) inferring the individual social action densities simultaneously for a dynamic number of persons. We extend the underlying Gaussian AR framework with our SocialSampling to counteract collisions during sampling. Based on this, a model-predictive policy that infers about the social influence of a controlled robot on neighboring humans is created. For this, we formulate a novel Social Influence (SI) objective that measures the divergence between a scene prediction conditioned by the robot plan and a scene prediction independent of the robot plan. The experiments with our metrics on real datasets show that the model achieves stateof-the-art accuracy in predicting pedestrian movements and remains collision-free, which is still often neglected in current methods. Furthermore, our evaluations show that robot policy with our SI objective produces safe and proactive behaviors, such as taking evasive action at the right time to avoid conflicts.