Reviewerly/exHarmony huggingface dataset:
Enriching datasets for sentiment analysis in tweets with instance selection

Sentiment analysis in tweets is a research field of great importance, mainly due to the popularity of Twitter. However, collecting and annotating tweets is an expensive and time-consuming task, making that some domains have only a limited set of labeled data. A promising strategy to handle this issue is to leverage labeled domains rich in data to select instances that enrich target datasets. This paper proposes different strategies for selecting instances from a set of labeled source datasets in order to improve the performance of classifiers trained only with the target dataset. Different approaches are proposed, including similarity metrics and variations in the number of selected instances. The results show that the size of the training set plays an essential role in the predictive capacity of the classifier. Furthermore, the results point out the importance of taking into account diversity criteria when selecting the instances.
Multimodal Phased Transformer for Sentiment Analysis.

Multimodal Transformers achieve superior performance in multimodal learning tasks. However, the quadratic complexity of the self-attention mechanism in Transformers limits their deployment in low-resource devices and makes their inference and training computationally expensive. We propose multimodal Sparse Phased Transformer (SPT) to alleviate the problem of self-attention complexity and memory footprint. SPT uses a sampling function to generate a sparse attention matrix and compress a long sequence to a shorter sequence of hidden states. SPT concurrently captures interactions between the hidden states of different modalities at every layer. To further improve the efficiency of our method, we use Layer-wise parameter sharing and Factorized Co-Attention that share parameters between Cross Attention Blocks, with minimal impact on task performance. We evaluate our model with three sentiment analysis datasets and achieve comparable or superior performance compared with the existing methods, with a 90% reduction in the number of parameters. We conclude that (SPT) along with parameter sharing can capture multimodal interactions with reduced model size and improved sample efficiency.
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features.

We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.
Hand model of the brain training as emotion regulation method for elementary school students

In the era of the COVID-19 pandemic, learning from home comes with positive and negative consequences. The sense of security that children get from distance learning brings negative consequences where children need to adjust to a new paradigm. Problems around learning from home included mental health and emotional problems for students. This psychoeducation aimed to explain the emotion regulation process and how the brain worked in emotion processing. We conducted the program with a pre-survey and interviews with teachers and school principals. Furthermore, online mentoring and surveys to students accompanied by their parents. It is hoped that students could understand their emotions and the basic steps to regulate. The hand model of the brain is the model of the brain in hands to explain concretely the hierarchy and connection between different parts of the brain concerning emotions. This program got a positive response from the participants. Psychoeducation results show an increase in understanding and gave positive feedback that this program is effective and fit to what they needed.
Salience-Aware Event Chain Modeling for Narrative Understanding

Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process. It is desirable to extract the event chains that represent such processes. However, this extraction remains a challenging problem. We posit that this is due to the nature of the texts from which chains are discovered. Natural language text interleaves a narrative of concrete, salient events with background information, contextualization, opinion, and other elements that are important for a variety of necessary discourse and pragmatics acts but are not part of the principal chain of events being communicated. We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. We demonstrate the effectiveness of our methods at isolating critical event chains by comparing their effect on downstream tasks. We show that by pre-training large language models on our extracted chains, we obtain improvements in two tasks that benefit from a clear understanding of event chains: narrative prediction and event-based temporal question answering. The demonstrated improvements and ablative studies confirm that our extraction method isolates critical event chains.
Internet of Things & Cyber Security:

In today's world, the internet has become the necessity of life. The rapid advancement in technology and inexpensive access to the internet has increased the use of IoT devices in our everyday life. On the positive side, these devices have made our life luxurious by removing the human intervention, but they have also significantly increased the attack vectors in the cyber world. The purpose of this research paper is to look into the effects of the Internet of Things (IoT) devices in our cyberspace and to see how vulnerable we are, in internet society.
Relationship Between People Social Support and Achievement Motivation With Reading Interest in Junior High School Students in D.I. Yogyakarta

This study aims to determine; 1) the relationship between peer social support and students' reading interest; 2) the relationship between achievement motivation and students' reading interest; 3) the relationship between peer social support and achievement motivation with students' reading interest. This study involves one dependent variable, namely reading interest and two independent variables that influence it, namely peer social support and achievement motivation. The research subjects were 92 junior high school students in the D.I. Yogyakarta, which consists of 41 male students and 51 female students. The research method used is a quantitative approach with a measuring instrument for reading interest scale, peer social support scale, and achievement motivation scale. The data analysis used assumption test and hypothesis test. The research hypothesis test used product-moment correlation to test the first and second hypotheses, while the third hypothesis was analyzed using multiple linear regression. The results of the product-moment correlation analysis showed that there was a positive relationship between peer social support and student interest in reading (r=0.472; p&lt;0.01) and there was a positive relationship between achievement motivation and student interest in reading (r=0.680; p&lt;0.01). Based on multiple linear regression analysis, there is a positive relationship between peer social support and achievement motivation together with reading interest in students (r=40,835; p&lt;0.01). The contribution of peer social support and achievement motivation with reading interest in junior high school students in Yogyakarta is ​​0.479 or 47.9% explained by the variables of peer social support and reading interest, while the remaining 52.1% is explained by the other variable.
Distributed Online Computer-Based Test System for English Exam

Nowadays, computer-based tests are commonly used since a pandemic made educational institutions hold exams from home and it made computer-based tests application demand increasing time by time. This situation leads to the need for applications that can run on any device, secure from cheating and compatible with poor internet connection. Our proposed system able to improve security and deception issues in the examination. In this paper we also introduce distributed online computer-based test for English test that made our software can share questions-data to another instance securely.
Education as a Means, Not End: Perspectives from Nepal

The issue includes nine research articles from Nepali scholars who have explored the current status and key issues in Nepali higher education, seeking to offer broader perspectives. The articles have highlighted social disparities in the Nepali society reflected in its higher education, influence and domination of the western knowledge, professional development of teachers and shifting practices in teaching learning due to the recent global pandemic of Covid-19.
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training

Existing deep learning real denoising methods require a large amount of noisy-clean image pairs for supervision. Nonetheless, capturing a real noisy-clean dataset is an unacceptable expensive and cumbersome procedure. To alleviate this problem, this work investigates how to generate realistic noisy images. Firstly, we formulate a simple yet reasonable noise model that treats each real noisy pixel as a random variable. This model splits the noisy image generation problem into two sub-problems: image domain alignment and noise domain alignment. Subsequently, we propose a novel framework, namely Pixel-level Noise-aware Generative Adversarial Network (PNGAN). PNGAN employs a pre-trained real denoiser to map the fake and real noisy images into a nearly noise-free solution space to perform image domain alignment. Simultaneously, PNGAN establishes a pixel-level adversarial training to conduct noise domain alignment. Additionally, for better noise fitting, we present an efficient architecture Simple Multi-scale Network (SMNet) as the generator. Qualitative validation shows that noise generated by PNGAN is highly similar to real noise in terms of intensity and distribution. Quantitative experiments demonstrate that a series of denoisers trained with the generated noisy images achieve state-of-the-art (SOTA) results on four real denoising benchmarks. Part of codes, pre-trained models, and results are available at https://github.com/caiyuanhao1998/PNGAN for comparisons.
Analysis of Sales Vitamins Drugs Covid-19 Pandemic with the Random Forest Method

During the covid-19 pandemic that occurred in Indonesia until the new normal period, consumer demand for a vitamin drug was fast and unpredictable, thus making pharmacies must be able to plan the optimal supply of vitamin drugs based on the graph of public demand during the pandemic and new normal. This means that the pharmacy does not experience a shortage of stock or excess stock. Therefore, all systems that can predict sales of vitamin drugs are needed during the new normal pandemic. To be able to make predictions, an appropriate method is needed to get accurate results. One of them is the Random Forest method. With Random Forest, the data is predicted based on attribute data so as to produce a conclusion about the value of the desired attribute. Based on this study, 200 sales data of vitamin drugs were taken during the pandemic and new normal with categories of vitamin B complex drugs, vitamins C, D, E, K and Multivitamins and the attributes used were vitamin brand, category, sales conditions, price and sales category. and doesn't work. The test results from this study, it was found that the prediction of vitamin drug sales during the new normal pandemic using the Random Forest method obtained 100% accuracy for testing data with a composition of 80:20, 70:30 and 60:40, so that vitamin drugs would still be sold in new normal.
A Comparative Study of Detection and Classification of Emotions on Social Media Using SVM and Näıve Bayes Techniques

The rapid rate of innovations and dynamics of technology has made humans life more dependent on them. In today’s synopsis Microblogging and Social networking sites like Twitter, Facebook are a part of our lives that cannot be detached from anyone. Through these social media each one of them carry their emotions and fix their opinions based on a particular situations or circumstances. This paper presents a brief comparison about Detection and Classification of Emotions on Social Media using SVM and Näıve Bayesian classifier. Twitter messages has been used as input dataset because they contain a broad, varied, and freely accessible set of emotions. The approach uses hash-tags as labels to train supervised classifiers to detect multiple classes of emotion on potentially large data sets without the need for manual intervention. We look into the usefulness of a number of features for detecting emotions, including unigrams, unigram symbol, negations and punctuations using SVM and Näıve Bayesian Classifiers.
Foreshadowing the Benefits of Incidental Supervision

Real-world applications often require improved models by leveraging *a range of cheap incidental supervision signals*. These could include partial labels, noisy labels, knowledge-based constraints, and cross-domain or cross-task annotations – all having statistical associations with gold annotations but not exactly the same. However, we currently lack a principled way to measure the benefits of these signals to a given target task, and the common practice of evaluating these benefits is through exhaustive experiments with various models and hyperparameters. This paper studies whether we can, *in a single framework, quantify the benefits of various types of incidental signals for a given target task without going through combinatorial experiments*. We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals. We demonstrate PABI’s effectiveness by quantifying the value added by various types of incidental signals to sequence tagging tasks. Experiments on named entity recognition (NER) and question answering (QA) show that PABI’s predictions correlate well with learning performance, providing a promising way to determine, ahead of learning, which supervision signals would be beneficial.
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling

Integrating physics models within machine learning models holds considerable promise toward learning robust models with improved interpretability and abilities to extrapolate. In this work, we focus on the integration of incomplete physics models into deep generative models. In particular, we introduce an architecture of variational autoencoders (VAEs) in which a part of the latent space is grounded by physics. A key technical challenge is to strike a balance between the incomplete physics and trainable components such as neural networks for ensuring that the physics part is used in a meaningful manner. To this end, we propose a regularized learning method that controls the effect of the trainable components and preserves the semantics of the physics-based latent variables as intended. We not only demonstrate generative performance improvements over a set of synthetic and real-world datasets, but we also show that we learn robust models that can consistently extrapolate beyond the training distribution in a meaningful manner. Moreover, we show that we can control the generative process in an interpretable manner.
Achieving Model Robustness through Discrete Adversarial Training

Discrete adversarial attacks are symbolic perturbations to a language input that preserve the output label but lead to a prediction error. While such attacks have been extensively explored for the purpose of evaluating model robustness, their utility for improving robustness has been limited to offline augmentation only. Concretely, given a trained model, attacks are used to generate perturbed (adversarial) examples, and the model is re-trained exactly once. In this work, we address this gap and leverage discrete attacks for online augmentation, where adversarial examples are generated at every training step, adapting to the changing nature of the model. We propose (i) a new discrete attack, based on best-first search, and (ii) random sampling attacks that unlike prior work are not based on expensive search-based procedures. Surprisingly, we find that random sampling leads to impressive gains in robustness, outperforming the commonly-used offline augmentation, while leading to a speedup at training time of ~10x. Furthermore, online augmentation with search-based attacks justifies the higher training cost, significantly improving robustness on three datasets. Last, we show that our new attack substantially improves robustness compared to prior methods.
Security Analysis Of DGM and GM Group Signature Schemes Instantiated With XMSS-T

Group Merkle (GM) (PQCrypto 2018) and Dynamic Group Merkle (DGM) (ESORICS 2019) are recent proposals for post-quantum hash-based group signature schemes. They are designed as generic constructions that employ any stateful Merkle hash-based signature scheme. XMSS-T (PKC 2016, RFC8391) is the latest stateful Markle hash-based signature scheme where (almost) optimal parameters are provided. In this paper, we show that the setup phase of both GM and DGM does not enable drop-in instantiation by XMSS-T which limits both designs in employing earlier XMSS versions with sub-optimal parameters which negatively affects the performance of both schemes. Thus, we provide a tweak to the setup phase of GM and DGM to overcome this limitation and enable the adoption of XMSS-T. Moreover, we analyze the bit security of DGM when instantiated with XMSS-T and show that it is susceptible to multi-target attacks because of the parallel Signing Merkle Trees (SMT) approach. More precisely, when DGM is used to sign \(2^{64}\) messages, its bit security is 44 bits less than that of XMSS-T. Finally, we provide a DGM variant that mitigates multi-target attacks and show that it attains the same bit security as XMSS-T.
Delexicalized Crosslingual Dependency Parsing for Xibe

Manually annotating a treebank is time consuming and laborintensive.We conduct delexicalized crosslingual dependency pars ing experiments, where we train the parser on one language and test on our target language.As our test case, we use Xibe, a severely underresourced Tungusic language.We as sume that choosing a closely related language as the source language will provide better re sults than more distant relatives.However, it is not clear how to determine those closely re lated languages.We investigate three differ ent methods: choosing the typologically clos est language, using LangRank, and choosing the most similar language based on perplexity.We train parsing models on the selected lan guages using UDify and test on different gen res of Xibe data.The results show that lan guages selected based on typology and per plexity scores outperform those predicted by LangRank; Japanese is the optimal source lan guage.In determining the source language, proximity to the target language is more impor tant than large training sizes.Parsing is also influenced by genre differences, but they have little influence as long as the training data is at least as complex as the target.
Provably Efficient Black-Box Action Poisoning Attacks Against  Reinforcement Learning

Due to the broad range of applications of reinforcement learning (RL), understanding the effects of adversarial attacks against RL model is essential for the safe applications of this model. Prior theoretical works on adversarial attacks against RL mainly focus on either observation poisoning attacks or environment poisoning attacks. In this paper, we introduce a new class of attacks named action poisoning attacks, where an adversary can change the action signal selected by the agent. Compared with existing attack models, the attacker's ability in the proposed action poisoning attack model is more restricted, which brings some design challenges. We study the action poisoning attack in both white-box and black-box settings. We introduce an adaptive attack scheme called LCB-H, which works for most RL agents in the black-box setting. We prove that the LCB-H attack can force any efficient RL agent, whose dynamic regret scales sublinearly with the total number of steps taken, to choose actions according to a policy selected by the attacker very frequently, with only sublinear cost. In addition, we apply LCB-H attack against a popular model-free RL algorithm: UCB-H. We show that, even in the black-box setting, by spending only logarithm cost, the proposed LCB-H attack scheme can force the UCB-H agent to choose actions according to the policy selected by the attacker very frequently.
포스트드라마 연극텍스트(Postdramatik)의 무대화 특징 -엘프리데 옐리네크(Elfriede Jelinek) 작 ≪겨울 나그네≫ 번역 공연을 중심으로-

The purpose of the study was to investigate the relationships between postdramatic play text, in other word ‘postdramati’c and postdramatic theater performance in Winterreise[Winter Journey] written by an Austrian playwright, Elfriede Jelinek.One of the important features of Jelinek’s playtext is non-reproducibility. Text created by intertextuality consists of segmental and fragmentary preceding text, like a collage. In the text, specific characters and stories are not reproduced clearly. Regardless of what character says, his (her) long lasting text strongly reflects the non-reproducibility. The postdramatic intertextuality of Jelinek’s playtext makes the characters and objects unclear, causing non-reproducibility. The director utilizes the text as a source material to complete his own postdramatic performance.Discussing how the characteristics of postdramatic text affect the postdramatic theatrical performance, this study considers Korean adaptation of Winterreise, as an example of postdramatic performance. One of the features of postdramatic performance is reproducibility of non-reproducibility. In Jelinek’s postdramatic performance, character’s words and actions rarely match one another. The existence of non-representative language created by Jelinek's playtexts clashes with the reproduction of the character’s behavior. In this process, non-representation of the playtext draws an image that audiences may stimulate unique associations in his (her) mind. The other feature is polyphony that exists in the Jelinek’s postdramatic playtexts and it makes a hybrid form of monologue and chorus in the scene. The polyphony of playtexts derived from intertextuality can diversify the directions of the postdramatic performance with the director’s choice.The important significance of this study was to examine the basic tendencies of postdramatic playtext and get a glimpse of its impact on the relationship with its postdramatic performance. In particular, this study can dedicate to enhancing the practical development of postdramatic performances and expanding the theoretical horizon to the theater study in Korea by utilizing Korean adaptation of the postdramatic playtext abroad.