Reviewerly/exHarmony huggingface dataset:
On the Intersection of Context-Free and Regular Languages

Clemente Pasti, Andreas Opedal, Tiago Pimentel, Tim Vieira, Jason Eisner, Ryan Cotterell. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Employing Semi-Supervised and Supervised Learning to Discover False Online Ratings

Today's modern industry and trade, internet evaluations matter a lot. Buying web items is often influenced by the opinions of other customers. Because of this, unscrupulous folks or organisations attempt to rig customer evaluations to their personal advantage. Using a lodging rating database, this research examines the performance of semi-supervised (SSVD) and supervised (SVD) word extraction methods for detecting false ratings.
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training

To mitigate the impact of the scarcity of labelled data on fact-checking systems, we focus on few-shot claim verification. Despite recent work on few-shot classification by proposing advanced language models, there is a dearth of research in data annotation prioritisation that improves the selection of the few shots to be labelled for optimal model performance. We propose Active PETs, a novel weighted approach that utilises an ensemble of Pattern Exploiting Training (PET) models based on various language models, to actively select unlabelled data as candidates for annotation. Using Active PETs for few-shot data selection shows consistent improvement over the baseline methods, on two technical fact-checking datasets and using six different pretrained language models. We show further improvement with Active PETs-o, which further integrates an oversampling strategy. Our approach enables effective selection of instances to be labelled where unlabelled data is abundant but resources for labelling are limited, leading to consistently improved few-shot claim verification performance. Our code is available.
Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities

Several proposals have been put forward in recent years for improving out-of-distribution (OOD) performance through mitigating dataset biases. A popular workaround is to train a robust model by re-weighting training examples based on a secondary biased model. Here, the underlying assumption is that the biased model resorts to shortcut features. Hence, those training examples that are correctly predicted by the biased model are flagged as being biased and are down-weighted during the training of the main model. However, assessing the importance of an instance merely based on the predictions of the biased model may be too naive. It is possible that the prediction of the main model can be derived from another decision-making process that is distinct from the behavior of the biased model. To circumvent this, we introduce a fine-tuning strategy that incorporates the similarity between the main and biased model attribution scores in a Product of Experts (PoE) loss function to further improve OOD performance. With experiments conducted on natural language inference and fact verification benchmarks, we show that our method improves OOD results while maintaining in-distribution (ID) performance.
Bridging the Gap between Pre-Training and Fine-Tuning for Commonsense Generation

Commonsense generation aims to generate a plausible sentence containing all given unordered concept words. Previous methods focusing on this task usually directly concatenate these words as the input of a pre-trained language model (PLM). However, in PLMs’ pre-training process, the inputs are often corrupted sentences with correct word order. This input distribution discrepancy between pre-training and fine-tuning makes the model difficult to fully utilize the knowledge of PLMs. In this paper, we propose a two-stage framework to alleviate this issue. Firstly, in pre-training stage, we design a new format of input to endow PLMs the ability to deal with masked sentences with incorrect word order. Secondly, during fine-tuning, we insert the special token [MASK] between two consecutive concept words to make the input distribution more similar to the input distribution in pre-training. We conduct extensive experiments and provide thorough analysis to demonstrate the effectiveness of our proposed method.
Longtonotes: OntoNotes with Longer Coreference Chains

Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan. Findings of the Association for Computational Linguistics: EACL 2023. 2023.
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization

Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews. However, generating aspect-specific and general summaries is challenging due to the lack of annotated data. In this work, we propose two simple yet effective unsupervised approaches to generate both aspect-specific and general opinion summaries by training on synthetic datasets constructed with aspect-related review contents. Our first approach, Seed Words Based Leave-One-Out (SW-LOO), identifies aspect-related portions of reviews simply by exact-matching aspect seed words and outperforms existing methods by 3.4 ROUGE-L points on Space and 0.5 ROUGE-1 point on Oposum+ for aspect-specific opinion summarization.Our second approach, Natural Language Inference Based Leave-One-Out (NLI-LOO) identifies aspect-related sentences utilizing an NLI model in a more general setting without using seed words and outperforms existing approaches by 1.2 ROUGE-L points on Space for aspect-specific opinion summarization and remains competitive on other metrics.
Quantum Computing – Real-Time Data Processing from a Dilution Refrigerator

This poster presents a real-time data visualization system for monitoring and displaying data from a Bluefors control unit connected to a dilution refrigerator. The goal is to provide an aesthetically pleasing and user-friendly web-based interface that continuously updates and displays critical data such as temperature and flow rates at various points within the refrigerator. Leveraging WebSocket technology, the system establishes multiple connections to the dilution refrigerators, enabling simultaneous monitoring of several units. The interactive webpage dynamically updates the data, providing researchers and operators with instant insights into the system's performance. The system's ability to stream and visualize data in real-time enhances the understanding of the dilution refrigerator's behavior, aiding in optimizing its operation and ensuring efficient scientific experiments. The application's web-based nature makes it easily accessible from any device with internet connectivity, promoting seamless collaboration and remote monitoring capabilities. Overall, this poster offers a comprehensive solution for real-time data visualization and analysis of dilution refrigerators, catering to the needs of researchers and scientists working in low-temperature physics and quantum computing fields.
Novel Fast Qubit Readout Approaches Enabled By Qubit Cloaking

Instrumentation Control Kit (QICK). Integrating machine learning methods on QICK opens several pathways for efficient real-time processing of quantum circuits.
Pedagogical conditions for the development of cognitive independence in physical education lessons

Cognitive Independence (CI), which physical education has a high potential for fostering in students, is characterized by a developed set of cognitive skills, an ideal level of knowledge, a strong value attitude towards cognition, and competencies related to the ability and willingness to take the initiative and engage in professionally responsible activities. However, the biggest challenges for physical education teachers stem from a lack of program material that clearly defines the content of lessons for the development of students’ CI as well as the stages of their entrance into the educational process. The purpose of the study is to theoretically justify and methodologically support the process of developing students’ CI in physical education lessons. The methodology used in the study comprises a quasi-experimental pretest-posttest research design. The study participants included 210 students in grades 10 and 11 from two secondary schools in the city of Atyrau (the Republic of Kazakhstan) and teachers acting as experts. The study findings revealed substantial variations in the dynamics of students’ CI in the control and experimental groups, confirming the efficiency of the application of the built-in model and technology. Despite the limitations, which lie in the fact that not all aspects of the development of students’ CI in physical education lessons were considered, this study has provided the theoretical justification and methodological support for development of students CI in physical education lessons that might contribute to efficient specialists’ training.
Causal Inference

In an attempt to define causationCausation, we came to realize that there are levels to consider. Necessary causes are those that must be present to see the effect. Sufficient causes, those that can produce the effect are almost always a collection of component causesComponent causes acting together. Most frequently, there are different collections of component causes that are sufficient to produce the effect. The frequency with which a particular component cause occurs in sufficient causes influences the apparent strength of association between that component cause and the effect. When interpreting an observed association, we realize there are three possible explanations. One is that it reflects a causal relationship, but there are two other possible explanations. These are that the association reflects a biased relationship or that the association reflects a chance relationship. To decide if an association is due to a causal relationship, we eliminate bias and chance as likely explanations. Cause is concluded through the process of elimination. To assist in this evaluation, Sir Austin Bradford Hill suggested nine criteria for causationCausation. For the most part, these help to evaluate bias and chance as likely explanations for an observed association.
Youth Homelessness: Researching VR as a Tool for Empathy

poster Free Access Share on Youth Homelessness: Researching VR as a Tool for Empathy Authors: Edith Ameh Earlham College, USA Earlham College, USA 0009-0002-0051-379XSearch about this author , Anne Bowen University of Texas at Austin, USA University of Texas at Austin, USA 0009-0009-8565-353XSearch about this author Authors Info & Claims PEARC '23: Practice and Experience in Advanced Research ComputingJuly 2023Pages 433–436https://doi.org/10.1145/3569951.3597576Published:10 September 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Learning Deixis In Class Using A Case Study Of Bambang Yudhoyono’s Speech On Climate Change Conference

This writing is entitled “Deixis of Bambang Yudhoyono’s Speech in Climate Change Conference”. It is aimed at finding the types of deixis that has in two speeches of Bambang Yudhoyono in climate change conference, to analyze how the deixis expressions and the deictic markers are presented in the speech.&#x0D; This speech also has discourse deixis elements such as: it, this/ these and that/those. Social deixis in these speeches only used Arabic term. The percentages of deictic markers in these speeches are dominated by the deictic markers of person and deictic marker of discourse.
Self Patch Labeling Using Quality Distribution Estimation for CNN-Based 360-IQA Training

In this study, we propose a methodology for estimating quality score distribution (QSD) for 360-IQA patch labeling. A collection of 2D-IQA models is used to generate a QSD for patches, inspired by how subjective quality ratings are gathered and handled. The proposed framework is first benchmarked on a subjectively annotated dataset, namely KonPatch-32k, in terms of patch quality classification. The best composition of QSD is then used to derive quality labels for patches sampled from 360-degree images. Furthermore, the quality labels are used in a multi-regression training strategy of CNN models. The ResNet-50 and EfficientNet-B5 are used to test the effectiveness of the proposed labeling framework on two publicly available 360-IQA datasets, namely OIQA and MVAQD. The experimental results demonstrated the efficacy of jointly using local and global qualities. The multi-regression proved to be a bit challenging on OIQA compared to MVAQD, reflecting the necessity to accurately regulate the training process.
UT-GAN: A Novel Unpaired Textual-Attention Generative Adversarial Network for Low-Light Text Image Enhancement

How to balance lighting and texture details to achieve the desired visual effect remains the bottleneck of existing low-light image enhancement methods. In this paper, we propose a novel Unpaired Textual-attention Generative Adversarial N network (UT-GAN) for low-light text image enhancement task. UT-GAN first uses the Zero-DCE net for initial illumination recovery and our TAM module is proposed to translate text information into a textual attention mechanism for the overall network, emphasizing attention to the details of text regions. Moreover, the method constructs an AGM-Net module to mitigate noise effects and fine-tune the illumination. Experiments show that UT-GAN outperforms existing methods in qualitative and quantitative evaluation on the widely used the low-light datasets LOL and SID.
Adversarial Defense via Perturbation-Disentanglement in Hyperspectral Image Classification

In recent years, deep neural networks (DNNs) have been widely used in hyperspectral image (HSI) classification. However, it has a strong vulnerability to crafted adversarial examples. Therefore, defense against adversarial examples is an urgent problem to be solved. To date, most defense methods are difficult to defend against unknown attacks. In this paper, we propose a perturbation-disentanglement-based adversarial defense method (PD-Defense) to protect HSI classification networks from unknown attacks. In the proposed method, the adversarial examples are decoupled into attack-invariant features and perturbation features, and the defense is conducted on the attack-invariant feature to defend against unknown attacks. Extensive experiments are performed on two benchmark HSI datasets, including PaviaU and HoustonU 2018. The results indicate that the proposed PD-Defense method achieves an excellent defense performance compared to four state-of-the-art defense methods.
Unsupervised Domain Adaptive Learning for Image Desnowing with Real-World Data

Snow images usually contain snow grains, snow streaks, and mist, which greatly affect the visibility of images. Currently, supervised learning with synthetic data often faces limitations when it comes to handling real-world snow images. To address this crucial issue, this work proposes an unsupervised domain adaptation image snow removal framework. The framework improves the performance on real-world images by learning a domain classifier in adversarial training manner. Additionally, considering the diversity of snowflake shapes and sizes in real-world snow images, we design a multiple-kernel dilated convolution module. Extensive experiments on three representative datasets have validated that our model can achieve better results than existing desnowing methods. More importantly, experiments on real datasets show that the proposed method obtains state-of-the-art performance in real-world desnowing.
Multi-Task Model Based on Vision Task Level for Saliency Object Detection in Foggy Conditions

In recent years, saliency object detection methods based on convolutional neural networks have been widely studied, and have achieved excellent performance in clear images. However, due to the low visibility of images in foggy conditions, the existing saliency object detection methods will be seriously affected or even ineffective. To address this problem, we introduce an end-to-end multi-task learning network. We design two subetworks for depth estimation and image restoration as auxiliary tasks to improve saliency object detection in foggy conditions. According to different characteristics of vision tasks, different shared layers are assigned to improve the performance of saliency object detection. Experiments show that our method has been greatly improved on both synthetic foggy datasets and real-to-world foggy datasets, outperforming many state-to-the-art saliency object detection methods.
Implementation of Digital Education Tools in the Pedagogical Community

This article is devoted to solving the problem of accumulation, systematization, and translation of the practices of using digital educational technologies in municipal educational organizations. The solution to this problem was implemented by implementing the project “Organization of a system of digital education practices in the urban district of the City of Yelets” in 2022. The project was aimed at involving at least 350 managing and teaching staff, 4000 students in the active practice of using digital educational technologies in educational organizations of the urban district of Yelets (Lipetsk region, Russian Federation) by the end of 2022. To achieve the goal, the implementation of a system of digital education practices in educational organizations of the urban district of the city of Yelets was determined. Continuity and concession are key features of this system.&#x0D; Consequently, the stakeholders are preschool educational organizations, public educational organizations, organizations of additional education, secondary vocational education, and higher education. This system involved the use of such organizational forms as a resource center, a municipal network platform, a pivotal school, and an author’s school, within which approbation, tuning, and dissemination of the experience of using software products, the LECTA digital educational platform, the digital educational environment “Mobile e-education” and directions 3D modeling, digital video and robotics. The key results of the conducted research were quantitative and qualitative indicators. The main problem that has blocked the adoption of digital education tools among the pedagogical community is the belief that they are ineffective compared to full-time education.
From Pixels to Predictions Handwritten Digit Recognition using Deep Learning Techniques

Abstract: Handwritten digit recognition is a fundamental problem in the field of deep learning, with applications ranging from postal services to finance. In this work, we explore the implementation of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to predict and classify handwritten digits using the MNIST (Modified National Institute of Standards and Technologies) dataset, a well-known collection of handwritten digits. Our primary goal is to determine which of these methods offers higher accuracy in digit recognition. We started by preparing the MNIST dataset. Using this dataset, we construct two separate models: a CNN-based model and an RNN-based model. Both models were trained extensively to learn the intricate patterns and structures within the handwritten digits. This project lets us see which model works better, helping us make smarter choices when we want to predict numbers in different situations
Comparative Analysis of Indian Sign Language Recognition System

Sign language is a way of communication among Hearing and Speech Impaired Persons. Normal peoples can not understand sign language and it is not feasible for deaf-dumb people to bring translator with them in every place. So, for bridging this gap many systems have been developed. Sign Language recognition systems which can convert Sign into text or Speech and vice-versa. Sign language recognition system work in five steps are: data acquisition, pre-processing, feature extraction, classification and recognition. This paper discussed the Indian sign language recognition system. In this paper, a comparative analysis of various gesture recognition techniques involving Artificial Neural Network, Convolutional Neural Networks Hidden Markov Model and PCA has been discussed with its accuracy. This comparative study came across that much work has been done in alphabet and numeric level but work in word and sentence level is less. Sign language recognition for static signs has been done by many researchers but dynamic sign recognition systems have scope of development. A Comparative study is utilized to find out research gaps in existing systems and give inspiration to develop interpreters for Indian Sign Languages.
Combining FAIR principles and long-term archival of 3D data

This paper presents an update of the French solution for long-term archiving combined with online publication of 3D research data in the humanities. The choice of data organisation, metadata, standards and infrastructure is in line with the FAIR principles of the semantic web. Since 2017, our consortium 3D for Humanities offers this service through the French National 3D Data Repository for Humanities. This was the start of our collaboration with CINES (Centre Informatique National de l’Enseignement Supérieur), the standard Open Archival Information System (OAIS) infrastructure for research data in France. In 2021, with more than one thousand (1000) projects registered, driven by laboratories from all over the country, open questions were posed to the community. In response, we have developed a new metadata schema that allows a more precise description of the research content, greater openness to other non-archaeological humanities fields, and better FAIR compliance. This metadata schema is aligned with standard vocabularies and mapped to the Europeana Data Model (EDM). Among other online features, a 3D viewer has been implemented to meet the needs of researchers and public communication. As designed, aLTAG3D, the desktop UI software we developed to help research labs create Submission Information Packages (SIPs), has adapted itself to the new schema through the XSD content.
Ensuring Privacy and Security on Banking Websites in Malaysia: A Cookies Scanner Solution

In this new era of science and technology, data can be said to be an extremely valuable asset for individuals, corporations, and even countries. Different parties attempt to obtain users' data occasionally, and the collection of web cookies is a prominent example. When users use a computer network, their data will be saved by the web server as cookies, including their private information. As people with bad intentions obtain this information, they can use it to commit cybercrimes and cause losses to the information owners. Thus, cookies management is vital for web users to protect their data. This paper proposes a cookies scanner for banking websites in Malaysia to help web users manage cookies. The scope is focused on banking websites as it is the most targeted website by cybercriminals. The proposed scanner will help users identify, understand, and manage cookies to keep their banking information safe. This paper explores existing cookie scanners to determine the proposed system's design and identify improvement areas. In this paper, we proposed a framework to develop the cookie scanner in the browser extension format. The system's access mode, workflow, functionalities, technical specifications, and requirements are discussed throughout the paper. To show our contribution, a copy of the proposed code implementation has been made available at https://github.com/gnohiy/cookies-scanner-for-banking-websites-in-malaysia.git.