Reviewerly/exHarmony huggingface dataset:
Fact-Preserved Personalized News Headline Generation

Personalized news headline generation, aiming at generating user-specific headlines based on readers’ preferences, burgeons a recent flourishing research direction. Existing studies generally inject a user interest embedding into an encoder-decoder headline generator to make the output personalized, while the factual consistency of headlines is inadequate to be verified. In this paper, we propose a framework Fact-Preserved Personalized News Headline Generation (short for FPG), to prompt a tradeoff between personalization and consistency. In FPG, the similarity between the candidate news to be exposed and the historical clicked news is used to give different levels of attention to key facts in the candidate news, and the similarity scores help to learn a fact-aware global user embedding. Besides, an additional training procedure based on contrastive learning is devised to further enhance the factual consistency of generated headlines. Extensive experiments conducted on a real-world benchmark PENS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> validate the superiority of FPG, especially on the tradeoff between personalization and factual consistency. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://msnews.github.io/pens.html
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts

The paper describes the RuSentNE-2023 evaluation devoted to targeted sentiment analysis in Russian news texts. The task is to predict sentiment towards a named entity in a single sentence. The dataset for RuSentNE-2023 evaluation is based on the Russian news corpus RuSentNE having rich sentiment-related annotation. The corpus is annotated with named entities and sentiments towards these entities, along with related effects and emotional states. The evaluation was organized using the CodaLab competition framework. The main evaluation measure was macro-averaged measure of positive and negative classes. The best results achieved were of 66% Macro Fmeasure (Positive+Negative classes). We also tested ChatGPT on the test set from our evaluation and found that the zero-shot answers provided by ChatGPT reached 60% of the F-measure, which corresponds to 4th place in the evaluation. ChatGPT also provided detailed explanations of its conclusion. This can be considered as quite high for zero-shot application.
Fine-tuning Text Classification Models for Named Entity Oriented Sentiment Analysis of Russian Texts

The paper presents an approach to named entity oriented sentiment analysis of Russian news texts proposed during the RuSentNE evaluation. The approach is based on RuRoBERTa-large, a pre-trained RoBERTa model for Russian. We compared several types of entity representation in the input text, and evaluated strategies for handling class imbalance and resampling entity tags in the training set. We demonstrated that some strategies improve the results of pre-trained models obtained on the dataset presented by the organizers of the evaluation.
The Design of Skynesia Adventure Mobile Game Application Using Game Development Life Cycle Method as an Alternative Learning Media for High School Students

Skynesia Adventure is a mobile-based gaming application designed to make the learning process more enjoyable and interactive, while also generating revenue for the startup Skynesia. The results of the questionnaire indicate that, on average, potential users prefer educational games that focus on science or STEM subjects, and some users believe that educational games can enhance their knowledge of science or STEM while having fun. This research utilized the game development life cycle theory in the development of the gaming application, and questionnaires were used as a method for designing and testing the application to ensure its usability by users. After the completion of the gaming application design, it went through two phases of testing and evaluation: alpha testing through Unit Testing, and beta testing through the User Experience Questionnaire - Short Version. The results of these tests indicated that Skynesia Adventure achieved a score of 2.29 for both pragmatic and hedonic qualities. Therefore, the Skynesia Adventure gaming application can be considered to provide users with a highly satisfactory user experience. The benefits of Skynesia Adventure include providing a more enjoyable and interactive learning experience by combining gaming elements with scientific or STEM knowledge.
Simultaneous Piezoelectrocatalytic Hydrogen-Evolution and Degradation of Water Pollutants By Quartz Microrods@Few-Layered MoS<sub>2</sub> Hierarchical Heterostructures

Intense light attenuation in water/wastewater results in photocatalysts exhibiting a low quantum efficiency. This study develops a novel piezoelectrocatalysis system, which involves quartz microrods (MRs) abundantly decorated with active edge-sites MoS 2 nanosheets to form quartz microrods@few-layered MoS 2 hierarchical heterostructure (QMSH). Through theoretical calculations, quartz MR serves as a parallel-plate capacitor, which is self-powered to provide an internal electric field to the few-layered MoS 2 nanosheets surrounding the quartz MR surfaces, and the piezoelectric potential (piezopotential) effectively facilitates redox reactions with the free carrier in MoS 2 . The self-powered quartz MRs in QMSH present an internal bias to the MoS 2 nanosheets, thus yielding a piezoelectrocatalysis system. An efficient piezoelectrocatalytic hydrogen evolution reaction (HER) and decomposition of wastewater without light irradiation could achieve simultaneously. The second-order rate constant of QMSH is approximately 0.631 L mg -1 min -1 , which was 650-fold that of quartz MRs, indicating that the piezoelectric heterostructural catalysts display exceptionally high efficiency on piezo-electrocatalytic redox reactions rather than in the peizocatalytic process. The H 2 -production rate of QMSH catalysts approached approximately 6456 μmo1·g −1 h −1 and peaked at approximately 16.8 mmol g −1 in 8 h. The piezoelectrocatalytic process may be a promising method for treating industrial wastewater and producing clean energy.
Advanced Encryption Standard on a Quantum System

Quantum computers have shown their capability to outperform the world's fastest supercomputers despite their nascent stage. This poses questions about the security of existing encryption schemes and offers exciting new possibilities to the field of cryptography. Extension of existing cryptographic algorithms and development of novel cryptographic protocols exploiting the physics of the quantum realm are unique prospects for the industry moving forward. This work implemented a hybrid classical-quantum AES-128 circuit on the open-source Qiskit simulator and explored the possibilities of reducing the qubit count necessary for implementing the same. Multiple classical bits were encoded into a single qubit for processing, and a machine learning model was trained to decode the output data back to classical bits. The circuit size was halved by changing the encoding form of classical bits into qubits. Our hybrid circuit also achieved significant speed up over the existing hybrid circuit for AES encryption and decryption.
Multivariate Fast Iterative Filtering Algorithm for Denoising of Ocular Artifacts in EEG

The elimination of artifacts including ocular movements in multichannel long-standing scalp EEG recordings has been the goal of extensive research with the aim to distinguish epileptic seizure episodes in EEG signals. The current research introduces a method for effectively eliminating ocular artifacts (OA) from extended EEG recordings by employing multivariate fast iterative filtering (MVFIF) in conjunction with a total variation (TV) denoising technique. The current approach initially employs the MVFIF for signal decomposition and then filters the selected noisy IMFs with a TV denoising scheme. The evaluation of the denoising impact involves mean absolute error (MAE), the power spectral density distortion (PSDD), and mutual information (MI) metrics. Applied to actual EEG data, the proposed methodology successfully eliminates OA components and yields a minimal MAE of 0.0020.
Optimizing Gas Fees for Cost-Effective E-voting Smart Contracts on the Ethereum Blockchain

E-voting utilizing blockchain technology is an emerging solution that has the potential to transform the electoral process due to its features like anonymity, privacy protection, and non-repudiation. Integrating voting applications into smart contracts on the blockchain introduces a computational expense known as gas. This research paper focuses on presenting methodologies to reduce gas fees associated with contract deployment and execution. The experimentation is conducted using Ethereum blockchain and Solidity smart contract, with Remix IDE serving as the testing environment. Our proposed solution includes a simplified voting contract facilitating candidate registration, vote storage, and preventing multiple votes from the same user. The findings demonstrate an overall reduction of 19.18% in deployment costs and 0.95% for single vote execution costs notably, as the number of transactions increases. the overall impact of these optimizations is anticipated to produce a much greater reduction in gas fees for single transactions. These results offer insights into optimizing gas fees in e-voting applications, enhancing the viability and efficiency of blockchain-driven voting systems.
A Critical Evaluation of Political Incorrectness in Pakistani Political Discourse

This study examines instances of political incorrectness in the context of contemporary Pakistani politics. It investigates the strategies employed by certain Pakistani politicians and political activists to advance their political objectives with political incorrectness. The research population consists of individuals from the online community on Twitter, specifically political activists and politicians. The data collection process was conducted by the researchers, ensuring accuracy and reliability and then was carefully examined and analyzed in detail with the help of the three-dimensional model devised by Norman Fairclough (2015). The research methodology used was descriptive and qualitative, focusing on providing a clear understanding of the subject. The findings of the study highlight that utilizing political incorrectness can be an effective means of belittling, ridiculing, and mocking political adversaries. Additionally, it revealed that political incorrectness could sway less educated individuals in the desired direction. This strategy proves to be quite shrewd, allowing political parties to accomplish their desired political objectives by defaming opponents.
Evaluarea în era digitală la Anul pregătitor. Platforma Exam.net, un instrument util în evaluarea de la cursurile de română ca limbă străină

The aim of this paper is to describe the process of evaluation which occurs in a digital environment using Exam.net, an examination platform developed by a group of Swedish teachers. The mechanism behind Exam.net was analysed during the numerous evaluation activities which took place in May and June 2020, as a part of the second semester examination at the Preparatory Year of Romanian (a full-time academic programme organised by the University of Bucharest). Based on the cloud-computing system, Exam.net is an easy-to-use and secure evaluation tool, with several technical and didactic advantages, which are explained in the present paper. The platform was created in accordance with the requirements set out by the National Agency for Education in Sweden regarding data handling and anonymisation. Nevertheless, the main advantage of Exam.net consists in the two different levels of security: the low-security mode and the high-security mode, the latter being associated with Safe Exam Browser (SEB), a type of software which prevents unauthorized resources being used during an exam. As a result, both layers of security considerably reduce the risk of examination fraud. In addition to this, Exam.net is highly reliable, given its infrastructure that automatically handles a large number of concurrent users.
A pilot study of data-driven learning approach in teaching Chinese vocabulary

The Data-Driven Learning (DDL) approach advocates a shift from passive knowledge recipients to active researchers among learners. This is achieved by utilizing abundant and context-rich target language inputs in a bottom-up learning process (Johns, 1990). Despite the scarcity of empirical research on the implementation of DDL in Chinese as a Second Language (CSL) classrooms, this study conducted a teaching experiment focused on confusable words to explore the approach's effects and learners' attitudes. Five advanced-level CSL learners from diverse native language backgrounds participated in the study, being taught ten sets of confusable words over a five-week period. The first five lessons employed an indirect DDL method, while the latter five utilized a direct DDL method with Sketch Engine. To evaluate the effectiveness of the DDL approach, a pre-test was administered prior to the course, followed by a post-test, a questionnaire, and one-on-one interviews after the completion of the course. The results of the post-test revealed an average improvement of 24% compared to the pre-test with statistical significance. Additionally, learners exhibited a positive attitude towards the course, expressing a preference for learning vocabulary through collocations and showing a desire to observe pre-selected concordance lines under the guidance of the instructor.
Exploring users’ perceptions of ASR for writing narrative texts

This study examines how users perceive Automatic Speech Recognition (ASR) as a tool for writing narrative texts, and compares the perceptions of two groups of users: native and non-native English writers. As such, this study aimes to answer the following questions: (1) How do English writers perceive the use of ASR as a writing tool?; and (2) How do native and non-native English writers’ perceptions compare in terms of using ASR as a writing tool? To answer these questions, we employed the Technology Acceptance Model 2 (TAM2) to investigate 60 participants’ perceptions of utilizing ASR for producing narrative texts. Our findings from analyzing seven components of TAM2 show that writers express a positive attitude towards utilizing ASR as a tool for composing texts. Our findings also indicate no noticeable differences between how native and non-native English writers perceive the usefulness of ASR for creating texts. This is contrary to our hypothesis that native speakers, owing to their more advanced pronunciation skills in English, might have a more favorable attitude towards using ASR.
Generative AI tools in CALL: what are the options for teachers and language practitioners?

We present an exploration of generative artificial intelligence's potential in Computer-Assisted Language Learning (CALL), describing recent and expected near-future developments, tools, practical applications, and ethical considerations in the field. We trace different stages of development in CALL and argue that the advent of generative AI has inaugurated a new stage of Intelligent CALL. We consider the possible capabilities and limitations of AI as found in recent studies, compare with our personal, hands-on experience of using generative AI in CALL, and suggest ways to successfully utilise this technology. In particular, we briefly present the C-LARA project, which is a current focus of our activities.
Machine translation in language education: Perspectives from advanced language learners

This paper explores the experiences and perspectives of advanced language learners on the use of Machine Translation Tools (MTTs). The study focuses on how learners navigate their use of tools like Google Translate, DeepL, and Reverso, and how their usage is influenced by instructional recommendations. Nine learners, studying different languages at an advanced level, were interviewed in focus groups. Thematic analysis of the interview transcripts revealed key themes related to the use of Online Language Learning Tools (OLLTs) and MTTs, students' uncertainty about MTT functionalities, policies regarding MTT use, and suggestions for MTT training. The findings highlight the significant impact of initial encounters with MTTs on learners' long-term perceptions and use of these tools. Participants demonstrated strategic use of MTTs to support their reading, writing, listening, and speaking skills, while being aware of their limitations. Although learners found MTTs helpful, they also emphasised the importance of authentic communication in language learning. The study concludes with recommendation for a collaborative approach between teachers and learners to effectively integrate MTTs into the language learning curriculum.
An investigation of the efficacy of a ChatGPT-powered chatbot

One of the newest tools for language learning is AI-powered chatbots which allow students to engage in interactive conversations with a chatbot. English Central is a digital language learning platform that allows students to watch videos, study vocabulary, and produce spoken language through short interactive videos. In addition to these three core components, English Central has recently added a chatbot powered by ChatGPT. This paper shares the results of a pilot study that analysed data from Japanese university students who engaged in interactions with this chatbot. The authors introduce quantitative data showing the quantity and quality of the interactions and discuss the strategies they implemented to encourage longer interactions. A student feedback survey was conducted and the results of this are also shared. The results suggested that although many students reported benefits to using a chatbot, the strategies implemented did not lead to significantly longer interactions, and that many students felt frustrated when the chatbot did not understand their utterances.
Persian FLAIR: grammatically intelligent web search for language learning

We describe our work on the development of a new Persian module for FLAIR, a Java-based grammatically intelligent web search engine (Chinkina et al., 2016; Chinkina &amp;amp; Meurers, 2016). This website allows Persian teachers and Persian language learners to search for texts about any desired topic, prioritizing documents that are rich in selected grammatical constructions, and filtering them based on text difficulty. Currently, the Persian module includes 56 unique grammatical constructions. Our Persian FLAIR module allows for a more self-guided approach to language learning and provides a level of flexibility to both language learners and teachers. Students are able to read accessible texts that interest them, while teachers are able to find real world examples of grammatical concepts used together in the same text. FLAIR can be used to create custom learning materials. Teachers can use FLAIR to search for texts containing particular grammar concepts being focused on in the classroom, which can then be used as relevant topics of discussion or reading. We developed FLAIR in a response to the limited availability of resources for learning and teaching Persian, particularly when it comes to its colloquial forms. With FLAIR, we hope to provide a valuable tool for students and teachers alike.
Attitudes of part-time Japanese university teachers on technology use after emergency remote teaching

With the onset of the COVID-19 pandemic in 2020, many Japanese universities made the decision to stop on-campus classes. This generally meant teaching remotely, either synchronously or asynchronously. Online courses are usually planned in advance with a curriculum and materials designed for that mode of teaching. The situation in 2020 was different, most teachers had only a few weeks to adapt their teaching materials and methods for Emergency Remote Teaching (ERT). The aim of this research was to investigate the experiences of university teachers during and after the period of ERT and to investigate how we can learn from this experience to avoid what Vegas (2022) refers to as a ‘missed opportunity’. This was achieved through an initial pilot survey and five semi-structured follow-up interviews conducted in 2023. The results indicated that despite some reporting high levels of initial anxiety, most teachers adapted, learning new skills and developing their technical expertise, which is now being used in face-to-face classes. As most teachers reported support in the period of ERT as having been uncoordinated – being provided by colleagues rather than through official workshops organized by institutions - this is an area that could be developed in the future.
Learner corpora and the design of data-driven learning activities

This paper seeks to continue the conversation on an underused resource in Data-Driven Learning (DDL), namely learner corpora. It first explores the potential of learner corpora in data-driven learning activity design, focusing on the advantages claimed by a number of scholars throughout the years and mainly associated with the field of Learner Corpus Research (LCR). It then illustrates the status that learner corpus use has in DDL activity design, on the basis of data drawn from the most recent and comprehensive review of DDL research, covering a timespan of 30 years. After describing the main qualitative and quantitative design features of a learner corpus of Italian (i.e. the CELI corpus), the paper shows how learner corpora containing texts produced at different proficiency levels can be used as graded corpora for both target-oriented and error-oriented activities. The sample illustrated activities can thus cater to learners at different proficiency levels, including lower-intermediate and intermediate levels, which are still under-represented in DDL research. Some of the main pedagogical and scientific advantages of using level-specific DDL materials in a paper-based format are also presented and briefly discussed.
Data-driven learning beyond English: Insights and implications from three monographs

This paper stems from the 2023 CorpusCALL SIG Symposium on Data-Driven Learning (DDL) for Languages Other Than English (LOTE). Three monographs on DDL for LOTE were presented and are briefly illustrated in this paper (Forti, 2023; Vyatkina, 2024; Schaeffer-Lacroix, 2019). Despite the growth of DDL in second language (L2) research and education in many languages, its focus has largely been on English. As a result, the knowledge about the applicability of DDL for learning LOTE remains limited. This hinders the validity and generalizability of DDL as a whole, and conceals the important implications related to bridging the research vs. practice gap in such contexts where the focus is on LOTEs. The monographs introduced in this paper demonstrate the relevance of DDL for learning LOTEs by discussing corpus-based resources, pedagogical applications, and empirical research from three perspectives.