Reviewerly/exHarmony huggingface dataset:
Information Extraction and Human-Robot Dialogue towards Real-life Tasks A Baseline Study with the MobileCS Dataset

Recently, there have merged a class of taskoriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games.However, the Wizard-of-Oz data are in fact simulated data and thus are fundamentally different from real-life conversations, which are more noisy and casual.Recently, the SereTOD challenge is organized and releases the MobileCS dataset, which consists of real-world dialog transcripts between real users and customerservice staffs from China Mobile.Based on the MobileCS dataset, the SereTOD challenge has two tasks, not only evaluating the construction of the dialogue system itself, but also examining information extraction from dialog transcripts, which is crucial for building the knowledge base for TOD.This paper mainly presents a baseline study of the two tasks with the MobileCS dataset.We introduce how the two baselines are constructed, the problems encountered, and the results.We anticipate that the baselines can facilitate exciting future research to build human-robot dialogue systems for real-life tasks.
A Report on the Euphemisms Detection Shared Task

This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022.Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism.The input data is a corpus of sentences containing potentially euphemistic terms (PETs) collected from the GloWbE corpus (Davies and Fuchs, 2015), and are human-annotated as containing either a euphemistic or literal usage of a PET.In this paper, we present the results and analyze the common themes, methods and findings of the participating teams.
Unsupervised Text Deidentification

Deidentification seeks to anonymize textual data prior to distribution.Automatic deidentification primarily uses supervised named entity recognition from human-labeled data points.We propose an unsupervised deidentification method that masks words that leak personallyidentifying information.The approach utilizes a specially trained reidentification model to identify individuals from redacted personal documents.Motivated by K-anonymity based privacy, we generate redactions that ensure a minimum reidentification rank for the correct profile of the document.To evaluate this approach, we consider the task of deidentifying Wikipedia Biographies, and evaluate using an adversarial reidentification metric.Compared to a set of unsupervised baselines, our approach deidentifies documents more completely while removing fewer words.Qualitatively, we see that the approach eliminates many identifying aspects that would fall outside of the common named entity based approach. 1
Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation

Despite strong performance in many sequenceto-sequence tasks, autoregressive models trained with maximum likelihood estimation suffer from exposure bias, i.e. the discrepancy between the ground-truth prefixes used during training and the model-generated prefixes used at inference time.Scheduled sampling is a simple and empirically successful approach which addresses this issue by incorporating modelgenerated prefixes into training.However, it has been argued that it is an inconsistent training objective leading to models ignoring the prefixes altogether.In this paper, we conduct systematic experiments and find that scheduled sampling, while it ameliorates exposure bias by increasing model reliance on the input sequence, worsens performance when the prefix at inference time is correct, a form of catastrophic forgetting.We propose to use Elastic Weight Consolidation to better balance mitigating exposure bias with retaining performance.Experiments on four IWSLT'14 and WMT'14 translation datasets demonstrate that our approach alleviates catastrophic forgetting and significantly outperforms maximum likelihood estimation and scheduled sampling baselines.
NLP4ITF @ Causal News Corpus 2022: Leveraging Linguistic Information for Event Causality Classification

We present our submission to Subtask 1 of the CASE-2022 Shared Task 3: Event Causality Identification with Causal News Corpus as part of the 5th Workshop on Challenges and Applications of Automated Extraction of Sociopolitical Events from Text (CASE 2022) (Tan et al., 2022a).The task focuses on causal event classification on the sentence level and involves differentiating between sentences that include a cause-effect relation and sentences that do not.We approached this as a binary text classification task and experimented with multiple training sets augmented with additional linguistic information.Our best model was generated by training roberta-base on a combination of data from both Subtasks 1 and 2 with the addition of named entity annotations.During the development phase we achieved a macro F1 of 0.8641 with this model on the development set provided by the task organizers.When testing the model on the final test data, we achieved a macro F1 of 0.8516.
Entity Embedding Completion for Wide-Coverage Entity Disambiguation

Entity disambiguation (ED) is typically solved by learning to classify a given mention into one of the entities in the model's entity vocabulary by referring to their embeddings.However, this approach cannot address mentions of entities that are not covered by the entity vocabulary.Aiming to enhance the applicability of ED models, we propose a method of extending a state-of-the-art ED model by dynamically computing embeddings of out-of-vocabulary entities.Specifically, our method computes embeddings from entity descriptions and mention contexts.Experiments with standard benchmark datasets show that the extended model performs comparable to or better than existing models whose entity embeddings are trained for all candidate entities as well as embeddingfree models.We release our source code and model checkpoints at https://github.com/studio-ousia/steel.
Bayes at FigLang 2022 Euphemism Detection shared task: Cost-Sensitive Bayesian Fine-tuning and Venn-Abers Predictors for Robust Training under Class Skewed Distributions

Transformers have achieved a state of the art performance across most natural language processing tasks.However, the performance of these models often degrades when being trained on data that exhibits skewed class distributions (class imbalance) common social media data.This is because training tends to be biased towards head classes that have majority of the data points .Most of the classical methods that have been proposed to handle this problem like re-sampling and re-weighting often suffer from unstable performance, poor applicability and poor calibration.In this paper, we propose to use Bayesian methods and Venn-Abers predictors for well calibrated and robust training against class imbalance.Our proposed approach improves f 1-score over the baseline RoBERTa (A Robustly Optimized Bidirectional Embedding from Transformers Pretraining Approach) model by about 6 points (79.0%against 72.6%) when training with class imbalanced data.
Gender Bias in Meta-Embeddings

Different methods have been proposed to develop meta-embeddings from a given set of source embeddings.However, the source embeddings can contain unfair gender-related biases, and how these influence the metaembeddings has not been studied yet.We study the gender bias in meta-embeddings created under three different settings: (1) meta-embedding multiple sources without performing any debiasing (Multi-Source No-Debiasing), (2) meta-embedding multiple sources debiased by a single method (Multi-Source Single-Debiasing), and (3) metaembedding a single source debiased by different methods (Single-Source Multi-Debiasing).Our experimental results show that metaembedding amplifies the gender biases compared to input source embeddings.We find that debiasing not only the sources but also their meta-embedding is needed to mitigate those biases.Moreover, we propose a novel debiasing method based on meta-embedding learning where we use multiple debiasing methods on a single source embedding and then create a single unbiased meta-embedding.
Assessing Inter-metric Correlation for Multi-document Summarization Evaluation

Recent advances in automatic text summarization have contemporaneously been accompanied by a great deal of new metrics of automatic evaluation.This in turn has inspired recent work to re-assess these evaluation metrics to see how well they correlate with each other as well as with human evaluation, mostly focusing on single-document summarization (SDS) paradigm.Although many of these metrics are typically also used for evaluating multi-document summarization (MDS) tasks, so far, little attention has been paid to studying them under such a distinct scenario.To address this gap, we present a systematic analysis of the inter-metric correlations for MDS tasks, while comparing and contrasting the results with SDS models.Using datasets from a wide range of domains (news, peer reviews, tweets, dialogues), we thus study a unified set of metrics under both the task setups.Our empirical analysis suggests that while most reference-based metrics show fairly similar trends across both multi-and single-document summarization, there is a notable lack of correlation between reference-free metrics in multidocument summarization tasks.
The Impact of Virtual Reality and Artificial Intelligence on a Self-Guided Online Course in Oral English for Undergraduates

Past few years were characterized by the COVID-19 epidemic, saw a significant increase in the implementation of important new technologies in classrooms. This is especially because of the online higher education environment, where the integration of technology and education has strengthened and significantly impacted the learning process despite significant obstacles. Artificial intelligence (AI) and virtual reality (VR)'s interactive technology gives art education and psychology a new impetus for growth. The first part of this thesis examines the shortcomings and current issues in conventional art education. In particular, the art education teaching model may inform the design of a discipline-specific, student-oriented digital platform. We employed virtual reality (VR) equipment to provide undergraduates unrestricted access to the virtual environment; this was followed by an analysis of the factors that influence students' ability to focus and create. Finally, the information from the model was used in art classes to help pupils focus and be more imaginative. The researcher then used questionnaires to compare and analyses information gathered from pupils exposed to various pedagogical approaches. This effort, in comparison to other state-of-the-art research methodologies, improves students' learning outcomes by modifying oral tests by analysis and comparison with the system database. Lastly, some recommendations are made based on the findings to offer an experimental reference for future linguistics instruction and English language instructors.
Q-CASA Keynote Speaker Quantum Simulation from Quantum Chemistry to Quantum Field Theory

Quantum simulation proposes to use future quantum computers to calculate properties of quantum systems. In the context of chemistry, the target is the electronic structure problem: determination of the electronic energy given the nuclear coordinates of a molecule. Since 2006 we have been studying quantum approaches to quantum chemical problems, and such approaches must face the challenges of high, but fixed, precision requirements, and fermion antisymmetry. I will describe algorithmic and experimental developments in this area. I will also review work using some techniques developed for quantum chemistry on quantum computers for light front simulations of quantum field theory.
Multivariate TVLA - Efficient Side-Channel Evaluation using Confidence Intervals

Securing cryptographic hardware designs and software implementations against side-channel attacks that leverage the power consumption or electromagnetic emanations of a device is an active topic of research. Different countermeasures against these attacks have been published, many of which rely on masking where sensitive information is split into multiple shares. Here, the information is hidden in higher statistical moments of the leakage if processed at the same time (univariate) or in combinations of side-channel information from different points in time (multivariate) if processed sequentially. Test Vector Leakage Assessment (TVLA) is a common evaluation technique to address the growing number of specific attacks. However, the assessment of multivariate leakage requires the evaluation of all possible combinations of sample points, massively slowing down the evaluation and in turn the development of countermeasures due to computational complexity. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this work, we develop and compare techniques to determine clock cycle combinations that leak information in a multivariate setting. We develop an efficient multivariate assessment framework and show how this approach can be used to generate evaluation results that satisfy a desired confidence level. Eventually, we demonstrate the practical relevance of our approach by applying it to two masked implementations of block ciphers.
MACRO VBA-BASED MAIL SERVICE ADMINISTRATION INFORMATION SYSTEM IN VILLAGES AND SATISFACTION ANALYSIS USING NAÏVE BAYES CLASIFICATION

Based on the Law of the Republic of Indonesia Number 25 of 2009 concerning Services, public services must achieve good and clean governance, have less time discipline and tend to slow down the process, as well as a lack of professionalism from employees who are not responsible for their work. In addition to providing benefits and information to the village government, administrative services for research letters also affect quality and performance. The stages that will be carried out in this study include observation, interviews, and literature study from previous research. The Naïve Bayes classification method is used in this study, where the basic calculation is based on the probability of each attribute. If an attribute with a certain class has a value of zero, then the probability of that class will also be zero, and this will affect the final result of the Naïve Bayes calculation. Questionnaire result data is divided into 60% training data, 40% testing data, and 10% validation data. The evaluation results show an accuracy rate of 98.6% for training data, 100% for testing data, and 97.2% for validation data. The comparison shows that there is only a change of 2% after using the mail service administration information system
Optimization of Modeling Techniques for Economic Rstoration information systems after the Covid-19 pandemic by Simple Additive Weighting

The Covid-19 pandemic had a major impact on the world economy, including Indonesia. Rp. 677.2 Trillion is prepared by the Indonesian government for the recovery of this condition by providing assistance to business actors. Then eligibility of beneficiaries based on certain criteria is a complicated process, the aid process has an opportunity to make inaccurate decisions because the process of determining aid is based on subjectivity. It is likely that the selected business actors do not achieve the standards desired by the government. For this reason, a decision support system was developed that could perform the calculation process against all the criteria for determining beneficiaries. The Simple Additive Weighting (SAW) method which can be used in determining the eligibility of beneficiaries as a decision support system for the government. Business actors who will be selected to be eligible aid recipients have the criteria that have the highest ranking value that hasm been determined. The criteria needed are the criteria for not receiving people's business credit (KUR), the criteria for starting business capital, the criteria for having business information (SKU) and the criteria for not being a civil servant. Due to the large number of business actors who received this assistance, it made it difficult for the government to mminimize errors in providing assistance, so that quite a number of business actors received this assistance but did not comply with government criteria. The final results of this study found that the decision support system with the SAW method problem in determining the provision of method can produce the largest value with a weight of 1.00 which is selected as an alternative "Acceptable" and the smallest value is chosen as an alternative " Not Eligible to Receive "assistance with a calculation result of less than 0.500.
PENGEMBANGAN SUMBER DAYA KEUANGAN BERDASARKAN PERATURAN GUBERNUR NOMOR 97 TAHUN 2022 DI SMA NEGERI 1 PURWAKARTA

Peraturan Gubernur Nomor 97 Tahun 2022 provides an opportunity for the development of financialresources that do not only come from the government, this regulation regulates education funding thatcomes from the community. By using a qualitative approach and a descriptive study design throughobservation, interview and documentation data collection techniques in this study it shows that the initialsteps taken by SMA Negeri 1 Purwakarta in responding to Peraturan Gubernur Nomor 97 Tahun 2022are conducting socialization and formulating the Rencana Kegiatan dan Anggaran Sekolah (RKAS) bysorting out the budget originating from BOS (School Operational Assistance) and BOPD (RegionalEducation Operational Assistance) funds as well as shortages of school operational costs which will bemet through fundraising by the Komite Sekolah. After the funds have been collected, these funds arehanded over to the school principal as the manager of the institution to finance school operations inaccordance with the plan contained in the RKAS. The school principal as the manager of the budget isobliged to report the budget that has been used to the Komite Sekolah and then the Komite Sekolah reportsit to the parents of students through the Rapat Komite Sekolah.
Optimal control with a multidimensional quantum invariant

Optimal quantum control of continuous variable systems poses a formidable computational challenge because of the high-dimensional character of the system dynamics. The framework of quantum invariants can significantly reduce the complexity of such problems, but it requires the knowledge of an invariant compatible with the Hamiltonian of the system in question. We explore the potential of a Gaussian invariant that is suitable for quadratic Hamiltonians with any given number of motional degrees of freedom for quantum optimal control problems that are inspired by current challenges in ground-state to ground-state shuttling of trapped ions.
ZNO Exam Preparation Course: Implications of Needs Analysis for Instructional Design

The paper proposes a practical framework of conducting the needs analysis as a constituent of exam teaching and discusses how the findings of these surveys can translate into an actual course design. The participants of the study are a group of five Ukrainian school-leavers, studying on an exam preparation course to take the ‘external independent evaluation’ (ZNO exam). The procedure correlated with the ‘triangulation’ theory, which envisages the use of several instruments to analyse learner needs to enhance the validity of their results. A combination of quantitative and qualitative methods (questionnaires, diagnostic testing, observations and informal talks) has been applied to assess the outcomes of the needs analysis, which reveals the learners’ motivation, learning needs, as well as the areas of grammar, vocabulary, and subskills that requires further training. The study has showed that in case the instructional design is based on the outcomes of needs analysis, the teacher can take more informed decisions as to have the course tailored to the actual needs of its participants. Such individualized approach turns learners into co-creators of the course and fosters a more effective and purposeful preparation on their part. Further research could focus on fine-tuning the framework of analysing learner needs to the requirements of any specific exam.
Kernel-wise difference minimization for convolutional neural network compression in metaverse

Convolutional neural networks have achieved remarkable success in computer vision research. However, to further improve their performance, network models have become increasingly complex and require more memory and computational resources. As a result, model compression has become an essential area of research in recent years. In this study, we focus on the best-case scenario for Huffman coding, which involves data with lower entropy. Building on this concept, we formulate a compression with a filter-wise difference minimization problem and propose a novel algorithm to solve it. Our approach involves filter-level pruning, followed by minimizing the difference between filters. Additionally, we perform filter permutation to further enhance compression. Our proposed algorithm achieves a compression rate of 94× on Lenet-5 and 50× on VGG16. The results demonstrate the effectiveness of our method in significantly reducing the size of deep neural networks while maintaining a high level of accuracy. We believe that our approach holds great promise in advancing the field of model compression and can benefit various applications that require efficient neural network models. Overall, this study provides important insights and contributions toward addressing the challenges of model compression in deep neural networks.
Fake Image Detection Using Deep Learning

With the emergence of numerous electronic communication programs and image processing programs, as well as an increase in the number of people who use them with a zeal for publishing everything related to their lives and their special pictures and their fear of those who might use these pictures for malicious or humorous purposes, it has become necessary to have specialized and precise systems to determine whether a picture is real or fake. Our work aims to detect real and fake faces by using and modifying one of the most efficient CNN architectures, EfficientNetB0, after improving the architecture with additional fully connected layers and efficiently training the model by using the Adam optimizer and a scheduler learning rate technique. Our findings on the well-known 140k-real-and-fake-faces Kaggle dataset showed state-of-the-art accuracy with the lowest error rate. We achieved 99.06% accuracy, and 0.0569 error rate respectively.