Reviewerly/exHarmony huggingface dataset:
Game-Based Learning Application in Chemistry Learning: A Systematic Literature Review

Game-based learning attention to the design of learning and game that can help the learning process be fun, creative, innovative, and not boring. This research aimed to review empirical research articles in chemistry learning using the Systematic Literature Review (SLR) method is to identify, analyze, and interpret the findings on the research topic published from 2017 to 2021. Twelve articles were identified through a search engine providing international and national journal pages that could be accessed using ERIC and Scopus databases. The articles studied in terms of objectives, learning design, and game implementation in learning. The results review focuses on increasing knowledge, understanding concepts, learning outcomes, critical thinking, motivation, emotions, literacy, and student independence. Using digital games is more widely applied in chemistry learning because of interest in educational technology having a better evaluation of the subject matter studied and the achievement of a deeper understanding. Keywords: game-based learning, chemistry learning, literature review. Abstrak: Pembelajaran berbasis game memperhatikan rancangan pembelajaran dan permainan yang dapat membantu proses pembelajaran yang menyenangkan, kreatif, inovatif, dan tidak membosankan. Penelitian ini bertujuan untuk meninjau artikel penelitian empiris tentang pembelajaran kimia berbasis permainan menggunakan metode Systematic Literature Review (SLR) yaitu mengidentifikasi, menganalisis dan menginterpretasi temuan-temuan pada topik penelitian tersebut yang diterbitkan dari tahun 2017 hingga 2021. Dua belas artikel diidentifikasi melalui mesin pencari penyedia laman jurnal Internasional dan nasional yang dapat diakses menggunakan database E RIC dan Scopus. Artikel dikaji dari segi tujuan, desain pembelajaran , dan implementasi game pada pembelajaran. Hasil review berfokus pada meningkatkan pengetahuan, pemahaman konsep, hasil belajar, berpikir kritis, motivasi, emosi, literasi , dan kemandirian siswa. P enggunaan permainan digital lebih banyak diterapkan dalam pembelajaran kimia karena minat teknologi pendidikan yang lebih besar dan memiliki evaluasi yang lebih baik mengenai materi pelajaran yang dipelajari dan ketercapaian pemahaman yang lebih mendalam. Kata kunci: p embelajaran b erbasis g ame , p embelajaran k imia , reviu literatur. DOI: http://dx.doi.org/10.23960/jpmipa/v23i1.pp01-12
Silhouette-Based 3D Human Pose Estimation Using a Single Wrist-Mounted 360° Camera

In this paper, we propose a framework for 3D human pose estimation using a single 360&#x00B0; camera mounted on the user&#x2019;s wrist. Perceiving a 3D human pose with such a simple setup has remarkable potential for various applications (e.g., daily-living activity monitoring, motion analysis for sports training). However, no existing method has tackled this task due to the difficulty of estimating a human pose from a single camera image in which only a part of the human body is captured, and because of a lack of training data. We propose a method for translating wrist-mounted 360&#x00B0; camera images into 3D human poses. Since we are the first to try this task, we cannot use existing datasets. To address this issue, we use synthetic data to build our own dataset. This solution, however, creates a different problem, that of a domain gap between synthetic data for training and real image data for inference. To resolve this problem, we propose silhouette-based synthetic data generation created for this task. Extensive experiments comparing our method with several baseline methods demonstrated the effectiveness of our silhouette-based pose estimation approach.
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer

Automatic transfer of text between domains has become popular in recent times.One of its aims is to preserve the semantic content of text being translated from source to target domain.However, it does not explicitly maintain other attributes between the source and translated text, for e.g., text length and descriptiveness.Maintaining constraints in transfer has several downstream applications, including data augmentation and de-biasing.We introduce a method for such constrained unsupervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models.Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooperate and reduce the same loss.The first is a contrastive loss and the second is a classification loss -aiming to regularize the latent space further and bring similar sentences across domains closer together.We demonstrate that such training retains lexical, syntactic, and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change.We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures. 1
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness

Data modification, either via additional training datasets, data augmentation, debiasing, and dataset filtering, has been proposed as an effective solution for generalizing to outof-domain (OOD) inputs, in both natural language processing and computer vision literature.However, the effect of data modification on adversarial robustness remains unclear.In this work, we conduct a comprehensive study of common data modification strategies and evaluate not only their in-domain and OOD performance, but also their adversarial robustness (AR).We also present results on a twodimensional synthetic dataset to visualize the effect of each method on the training distribution.This work serves as an empirical study towards understanding the relationship between generalizing to unseen domains and defending against adversarial perturbations.Our findings suggest that more data (either via additional datasets or data augmentation) benefits both OOD accuracy and AR.However, data filtering (previously shown to improve OOD accuracy on natural language inference) hurts OOD accuracy on other tasks such as question answering and image classification.We provide insights from our experiments to inform future work in this direction.
MPII: Multi-Level Mutual Promotion for Inference and Interpretation

In order to better understand the rationale behind model behavior, recent works have exploited providing interpretation to support the inference prediction.However, existing methods tend to provide human-unfriendly interpretation, and are prone to sub-optimal performance due to one-side promotion, i.e. either inference promotion with interpretation or vice versa.In this paper, we propose a multi-level Mutual Promotion mechanism for self-evolved Inference and sentence-level Interpretation (MPII).Specifically, from the model-level, we propose a Step-wise Integration Mechanism to jointly perform and deeply integrate inference and interpretation in an autoregressive manner.From the optimizationlevel, we propose an Adversarial Fidelity Regularization to improve the fidelity between inference and interpretation with the Adversarial Mutual Information training strategy.Extensive experiments on NLI and CQA tasks reveal that the proposed MPII approach can significantly outperform baseline models for both the inference performance and the interpretation quality. 1
Open Vocabulary Extreme Classification Using Generative Models

The extreme multi-label classification (XMC) task aims at tagging content with a subset of labels from an extremely large label set. The label vocabulary is typically defined in advance by domain experts and assumed to capture all necessary tags. However in real world scenarios this label set, although large, is often incomplete and experts frequently need to refine it. To develop systems that simplify this process, we introduce the task of open vocabulary XMC (OXMC): given a piece of content, predict a set of labels, some of which may be outside of the known tag set. Hence, in addition to not having training data for some labels–as is the case in zero-shot classification–models need to invent some labels on-thefly. We propose GROOV, a fine-tuned seq2seq model for OXMC that generates the set of labels as a flat sequence and is trained using a novel loss independent of predicted label order. We show the efficacy of the approach, experimenting with popular XMC datasets for which GROOV is able to predict meaningful labels outside the given vocabulary while performing on par with state-of-the-art solutions for known labels.
PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction

Neural models for distantly supervised relation extraction (DS-RE) encode each sentence in an entity-pair bag separately.These are then aggregated for bag-level relation prediction.Since, at encoding time, these approaches do not allow information to flow from other sentences in the bag, we believe that they do not utilize the available bag data to the fullest.In response, we explore a simple baseline approach (PARE) in which all sentences of a bag are concatenated into a passage of sentences, and encoded jointly using BERT.The contextual embeddings of tokens are aggregated using attention with the candidate relation as query -this summary of whole passage predicts the candidate relation.We find that our simple baseline solution outperforms existing state-of-the-art DS-RE models in both monolingual and multilingual DS-RE datasets.
An Ensemble Approach to Detect Emotions at an Essay Level

This paper describes our system (IREL, reffered as himanshu.1007on Codalab) for Shared Task on Empathy Detection, Emotion Classification, and Personality Detection at 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis at ACL 2022.We participated in track 2 for predicting emotion at the essay level.We propose an ensemble approach that leverages the linguistic knowledge of the RoBERTa, BARTlarge, and RoBERTa model finetuned on the GoEmotions dataset.Each brings in its unique advantage, as we discuss in the paper.Our proposed system achieved a Macro F1 score of 0.585 and ranked one out of thirteen teams (the current top team on leaderboard submitted after the deadline).The code can be found here
Conversation- and Tree-Structure Losses for Dialogue Disentanglement

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately.This task is referred as dialogue disentanglement.A significant drawback of previous studies on disentanglement lies in that they only focus on pair-wise relationships between utterances while neglecting the conversation structure which is important for conversation structure modeling.In this paper, we propose a hierarchical model, named Dialogue BERT (DIALBERT), which integrates the local and global semantics in the context range by using BERT to encode each message-pair and using BiLSTM to aggregate the chronological context information into the output of BERT.In order to integrate the conversation structure information into the model, two types of loss of conversation-structure loss and tree-structure loss are designed.In this way, our model can implicitly learn and leverage the conversation structures without being restricted to the lack of explicit access to such structures during the inference stage.Experimental results on two large datasets show that our method outperforms previous methods by substantial margins, achieving great performance on dialogue disentanglement.
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density

Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefore impair the subsequent analysis tremendously. In manifold learning, several studies indicate solutions for removing background noise or noise close to the structure when the density is substantially higher than that exhibited by the noise. However, in many applications, including astronomical datasets, the density varies alongside manifolds that are buried in a noisy background. We propose a novel method to extract manifolds in the presence of noise based on the idea of Ant colony optimization. In contrast to the existing random walk solutions, our technique captures points that are locally aligned with major directions of the manifold. Moreover, we empirically show that the biologically inspired formulation of ant pheromone reinforces this behavior enabling it to recover multiple manifolds embedded in extremely noisy data clouds. The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.
On Length Divergence Bias in Textual Matching Models

Despite the remarkable success deep models have achieved in Textual Matching (TM) tasks, it still remains unclear whether they truly understand language or measure the semantic similarity of texts by exploiting statistical bias in datasets.In this work, we provide a new perspective to study this issuevia the length divergence bias.We find the length divergence heuristic widely exists in prevalent TM datasets, providing direct cues for prediction.To determine whether TM models have adopted such heuristic, we introduce an adversarial evaluation scheme which invalidates the heuristic.In this adversarial setting, all TM models perform worse, indicating they have indeed adopted this heuristic.Through a well-designed probing experiment, we empirically validate that the bias of TM models can be attributed in part to extracting the text length information during training.To alleviate the length divergence bias, we propose an adversarial training method.The results demonstrate we successfully improve the robustness and generalization ability of models at the same time.
A Fuzzy-Based Approach for Cyberbullying Analysis

Social networks and new technologies are present today in almost all aspects of social relations. These technologies, which have so many advantages in the daily lives of young and older people, can become a double-edged sword. One of the most negative connotations created by the incursion of social networks and new technologies in the lives of children and adolescents is cyberbullying. Cyberbullying is the act of harassment using social networks, causing severe emotional problems for the victim and, in the worst-case scenario, even leading to suicide. Therefore, detecting and preventing this misuse of social networks and the internet is a task of significant interest for society. Considering the overlapping of different types of cyberbullying within the same document, in this paper, we propose the use of fuzzy logic for the study and modelling of cyberbullying from an explainable point of view. On a dataset containing five different types of cyberbullying, we demonstrate that fuzzy modelling is a promising technique.
Optimization of Small Sized File Access Efficiency in Hadoop Distributed File System by Integrating Virtual File System Layer

Storage for large datasets, handling data in different formats and data getting generated with high speed are the major highlights of the Hadoop because of which the Hadoop got invented.Hadoop is the solution for the big data problems as discussed above.In order to give the improved solution (in terms of access efficiency and time) for small sized files, this solution is proposed.A novel approach called VFS-HDFS architecture is designed in which the focus is on optimization of small sized files access problems with significant development compared with the existing solutions i.e.HDFS sequence files, HAR, NHAR.In the proposed work a Virtual file system layer has been added as a wrapper over the top of existing HDFS architecture.However, the research work is carried out without altering the existing HFDS architecture.In this paper drawbacks of existing techniques i.e.Flat File Technique and Table Chain Technique which are implemented in HDFS HAR, NHAR, sequence file is overcome by using Bucket Chain Technique.The files to merge in a single bucket are selected using ensemble classifier which is a combination of different classifiers.Combination of multiple classifiers gives the better accurate results.Using this proposed system, better results are obtained compared with the existing system in terms of access efficiency of small sized files in HDFS.
On the LWE Cryptosystem with More General Disturbance

The main purpose of this paper is to give an extension on learning with errors problem (LWE) based cryptosystem about the probability of decryption error with more general disturbance.In the first section, we introduce the LWE cryptosystem with its application and some previous research results.Then we give a more precise estimation probability of decryption error based on independent identical Gaussian disturbances and any general independent identical disturbances.This upper bound probability could be closed to 0 if we choose applicable parameters.It means that the probability of decryption error for the cryptosystem could be sufficiently small.So we verify our core result that the LWE-based cryptosystem could have high security.
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation

Back-translation is a critical component of Unsupervised Neural Machine Translation (UNMT), which generates pseudo parallel data from target monolingual data.A UNMT model is trained on the pseudo parallel data with translated source, and translates natural source sentences in inference.The source discrepancy between training and inference hinders the translation performance of UNMT models.By carefully designing experiments, we identify two representative characteristics of the data gap in source: (1) style gap (i.e., translated vs. natural text style) that leads to poor generalization capability; (2) content gap that induces the model to produce hallucination content biased towards the target language.To narrow the data gap, we propose an online self-training approach, which simultaneously uses the pseudo parallel data {natural source, translated target} to mimic the inference scenario.Experimental results on several widelyused language pairs show that our approach outperforms two strong baselines (XLM and MASS) by remedying the style and content gaps. 1
Analysis of human perception models for motion sickness in autonomous driving

Autonomous vehicle technologies are rapidly growing and are expected to change transportation habits radically. Autonomous cars increase the likelihood of motion sickness by allowing everyone in the vehicle to become passengers and perform non-driving tasks such as reading, working, and socializing. Comfort is one of the critical factors in the acceptance of autonomous vehicles. This makes accurate estimation of motion sickness a necessity in the development stages of autonomous vehicles. The sensory conflict theory is a widely accepted theory that explains the mechanism of motion sickness. Computational models based on the sensory conflict theory are used to predict motion sickness and contain two main parts: a human perception model and a nonlinear fitting function to the subjective feeling of motion sickness. Models of the human perception, including the dynamics of the vestibular system, are used to calculate the difference between sensory inputs and the predicted motions in the brain, i.e. the conflict signal, which is the primary cause of motion sickness. One of the main limitations of motion sickness prediction is how to mathematically model human perception because of the complexity of the psychophysiological systems. The aim of this work is to implement and analyse different human perception modelling techniques, such as observer framework in the control theory and optimal estimator approach using Kalman filters, to evaluate their abilities to integrate with motion sickness prediction. In this study, the different human perception models are implemented and analysed using MATLAB / Simulink and the advantages, as well as disadvantages of the models, are discussed.
Non-Autoregressive Sequence Generation

State-of-the-art sequence generation models are mostly autoregressive (AR, Vaswani et al., 2017;Brown et al., 2020) where each generation step depends on the previously generated tokens.However, such models are inherently sequential, leading to high latency at inference time and suffering label bias (Lafferty et al., 2001) problem due to the locally normalized searching steps and exposure bias (Bengio et al., 2015) problem due to mismatch between training and inference.Recently, increasing attention has been paid to modeling sequence generation in a non-or semiautoregressive manner, which attempts to generate the entire or partial output sequences in parallel to speed up the decoding process and avoid potential issues (e.g., label bias, exposure bias) in autoregressive generation.In this tutorial, for simplicity, we summarize both approaches as non-autoregressive (NAR) sequence generation models.NAR models have been explored in many sequence generation tasks for text (e.g.
IoT Based Blockchain Temperature Monitoring and Fake Medicine Prevention

he Target of today’s healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves distributors, manufacturers, wholesalers, and customers. The biggest challenge related to this supply chain is temperature monitoring also as counterfeit drug prevention. Many drugs and vaccines remain viable within a selected range of temperatures. If exposed beyond this temperature range, the drugs do not work as intended. Many of the world’s top-selling medicines are sensitive and need cold-chain shipping, i.e., storing goods during a temperature-controlled environment. Keeping the drugs cold is an important a part of the availability chain that connects drug producers to patients. But storing the essential information associated with cold-chain shipping in centralized databases is often susceptible to manipulations or data hacks. Also, the present software doesn't have tracking capability of maintaining a full chain of identity. One of the most reliable solutions would be the integration of IoT sensors with blockchain. To store the essential information related to cold-chain shipping in a blockchain network so, it becomes impossible for the data to get manipulate by anyone unlike centralized databases.
Thai Nested Named Entity Recognition Corpus

This paper presents the first Thai Nested Named Entity Recognition (N-NER) dataset.Thai N-NER consists of 264,798 mentions, 104 classes, and a maximum depth of 8 layers obtained from news articles and restaurant reviews, a total of 4894 documents.Our work, to the best of our knowledge, presents the largest non-English N-NER dataset and the first non-English one with fine-grained classes.To understand the new challenges our proposed dataset brings to the field, we conduct an experimental study on (i) cutting edge N-NER models with the state-of-the-art accuracy in English and (ii) baseline methods based on wellknown language model architectures.From the experimental results, we obtain two key findings.First, all models produce poor F1 scores in the tail region of the class distribution.There is little or no performance improvement provided by these models with respect to the baseline methods with our Thai dataset.These findings suggest that further investigation is required to make a multilingual N-NER solution that works well across different languages.