Reviewerly/exHarmony huggingface dataset:
A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semi-supervised approach with minimal human supervision to extract aspect terms by detecting the aspect categories. Hence, the study deals with two main sub-tasks in ABSA, named Aspect Category Detection (ACD) and Aspect Term Extraction (ATE). In the first sub-task, aspects categories are extracted using topic modeling and filtered by an oracle further, and it is fed to zero-shot learning as the prompts and the augmented text. The predicted categories are the input to find similar phrases curated with extracting meaningful phrases (e.g., Nouns, Proper Nouns, NER (Named Entity Recognition) entities) to detect the aspect terms. The study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment (MAMS) dataset along with SemEval-2014 Task 4 sub-task 1 to show that the proposed approach helps detect aspect terms via aspect categories.
The Implementation of Media Sharing Sites in Learning French: A Systematic Literature Review

Several challenges hinder French language learning, hence diminishing its effectiveness. Consequently, there is a demand for educational tools that are accommodating and efficient, such as media-sharing sites (MSS). MSS has several positive and negative effects. Minimizing adverse effects requires knowledge of factors that can make learning effective. Consequently, this research aims to fulfill this requirement by identifying the variables that affect learning outcomes and students' responses to the implementation of MSS in learning French. This study used a systematic literature review (SLR). Research data was collected from several databases, including Taylor &amp; Francis Online (tandfonline.com), Research Gate (researchgate.net), and RDiscovery (discovery.research.life). Twenty-two articles were selected, extracted, and analyzed for this study. The findings are grouped into two main issues: factors affecting learning outcomes and students' responses. Factors affecting student learning outcomes are grouped into four areas: learning models, systems and features, audio-visual content, and teacher contributions. Thus, students' responses are categorized into three types: cognitive, affective, and conative responses. The results showed that implementing MSS in learning French assisted educators and learners more effectively and efficiently. Furthermore, it was found that there is a correlation between student responses and the future vision of education.
Research on the Application Service System of BeiDou Navigation Satellite System

As BDS-3 satellite navigation system has been built and put into use for more than two years, the application of BeiDou satellite navigation system has gradually presented a prosperous trend. In order to further promote the marketization, industrialization and internationalization of BeiDou application, this paper conducts research on the application service system of BeiDou Navigation Satellite system. Through making a conceptual analysis of BDS scale application and public application service, this paper lays a theoretical foundation for BDS large-scale application. A definition of BDS application service system is demonstrated in the perspective of BDS users. The BDS application service system architecture is designed at the same time, which clarifies the function of each component. This paper also discusses the progress at this stage, and puts forward suggestions for further improvement.
Deep Regression Network with Prediction Confidence in Time Series Application for Asset Health Estimation

Many works have been focused in developing detection, monitoring and prediction routines for asset health estimation system. Classic machine learning based models benefit from quality of physics-informed features available from domain knowledge. This, however, can be labor intensive and is limited by quality of features developed through available knowledge. Deep learning based approach, if successful, can alleviate this laborious step. On the other hand, users often need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. In this work, we propose a deep learning based regression network that output both prediction value and confidence score for asset health estimation in short intermittent transients time series application. In the experimental study, we show that our model has low prediction error given short intermittent transients multivariate time series as input. Furthermore, our model also provides a confidence score for each prediction that is highly negatively correlated with true prediction error. Experiments show that by setting an acceptance threshold on confidence score, our model can reach an averaged improvement of 20% on the prediction quality with 90% coverage.
Effect of Mobile Instructional Design on Student Perception of Distance Learning

Learning through mobile applications has become more relevant as mobile tools have evolved. The COVID-19 outbreak that spread across the globe in 2019 promoted to a brief period of distance learning. The purpose of the study was to investigate how the students' perceptions of distance learning were affected by the use of a mobile application designed for a seventh-grade mathematics course. The research was performed using quasi-experimental design. The study was conducted in the distance education process with 7th grade students studying in a secondary school in the 2020-2021 academic year. The mobile application was used to deliver the instruction to the students in the experimental group, whereas the control group received instruction based on the textbook. To collect the data for the study, Yıldırım et al. (2014) designed the "Student Opinions Scale for Distance Education". T-tests were used to assess the data for dependent and independent samples, respectively. The study revealed a substantial difference in the post-test results of the students in the experimental and control groups. It was discovered that there was no significant difference between the pre and posttest scores of the control group students' opinions on distance education and that there was a substantial increase in the experimental group students' attitudes on distance education between the two assessments. According to the findings, it has been suggested that by creating mobile applications in various ways and including them into the distance learning process, the impacts of using them for mathematics classes on distance learning can be studied.
Disentangled Contrastive Learning for Knowledge-Aware Recommender System

Knowledge Graphs (KGs) play an increasingly important role as useful side information in recommender systems. Recently, developing end-to-end models based on graph neural networks (GNNs) becomes the technical trend of knowledge-aware recommendation. However, we argue that prior methods are insufficient to discover multi-faceted user preferences based on diverse aspects of item attributes, since they only learn a single representation for each user and item. To alleviate this limitation, we focus on exploring user preferences from multiple aspects of item attributes, and propose a novel disentangled contrastive learning framework for knowledge-aware recommendation (DCLKR). Technically, we first disentangle item knowledge graph into multiple aspects for the knowledge view, and user-item interaction graph for the collaborative view, equipped with attentive neighbor assignment and embedding propagation mechanisms. Then we perform intra-view contrastive learning to encourage differences among disentangled representations in each view, and inter-view contrastive learning to transfer knowledge between the two views. Extensive experiments conducted on three benchmark datasets demonstrate the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/Jill5/DCLKR. .
Mapping and Cleaning Open Commonsense Knowledge Bases with Generative Translation

Structured knowledge bases (KBs) are the backbone of many knowledge-intensive applications, and their automated construction has received considerable attention. In particular, open information extraction (OpenIE) is often used to induce structure from a text. However, although it allows high recall, the extracted knowledge tends to inherit noise from the sources and the OpenIE algorithm. Besides, OpenIE tuples contain an open-ended, non-canonicalized set of relations, making the extracted knowledge’s downstream exploitation harder. In this paper, we study the problem of mapping an open KB into the fixed schema of an existing KB, specifically for the case of commonsense knowledge. We propose approaching the problem by generative translation, i.e., by training a language model to generate fixed-schema assertions from open ones. Experiments show that this approach occupies a sweet spot between traditional manual, rule-based, or classification-based canonicalization and purely generative KB construction like COMET. Moreover, it produces higher mapping accuracy than the former while avoiding the association-based noise of the latter. Code and data are available. ( https://github.com/Aunsiels/GenT , julienromero.fr/data/GenT )
EDUCATIONAL MULTIMEDIA AS A MEANS TO CONTRIBUTE TO THE FORMATION OF VALUES

The formation of values is a recurring theme in pedagogical research. Among the new realities in which learners fi nd themselves are ICTs that, using their different resources appropriately, are means of support for the formation of values. The objective of this research is to demonstrate the use of educational multimedia with the thought of José Martí in the formation of values. The research methods are Historical-logical, Analytical-synthetic, Inductive-Deductive, Documentary analysis, Modeling, Problem inventory, Survey, Sentence completion, Interview, Observation. The sample is formed of  students corresponding to the fourth grade of the Jesús Hernández Alfonso Primary School. The four dimensions worked on (behavioral, affective, cognitive and instructional) result in () students learning theoretical aspects about values in the vision of José Martí; () the teacher considering new technologies as an educational means that contributes to exposing knowledge of values from the thought of José Martí and to the motivation of students. There are % of students who, when faced with some of the values worked on, do not show a change in their behavior. Students have greater sensitivity towards the modes of action associated with the values worked on in the research. The formation of values as a process supports its methodological theoretical foundations from axiology, pedagogy and psychology linked to the student’s approach to the thought of José Martí. With the application of multimedia, signifi cant advances were achieved in the subject and other areas remain open that can be the subject of future research.
Detecting and Unmasking AI-Generated Texts through Explainable Artificial Intelligence using Stylistic Features

In recent years, Artificial Intelligence (AI) has sig-nificantly transformed various aspects of human activities, including text composition. The advancements in AI technology have enabled computers to generate text that closely mimics human writing which is raising concerns about misinformation, identity theft, and security vulnerabilities. To address these challenges, understanding the underlying patterns of AI-generated text is essential. This research focuses on uncovering these patterns to establish ethical guidelines for distinguishing between AI-generated and human-generated text. This research contributes to the ongoing discourse on AI-generated content by elucidating methodologies for distinguishing between human and machine-generated text. The research delves into parameters such as syllable count, word length, sentence structure, functional word usage, and punctuation ratios to detect AI-generated text. Furthermore, the research integrates Explainable AI (xAI) techniques—LIME and SHAP—to enhance the interpretability of machine learning model predictions. The model demonstrated excellent efficacy, showing an accuracy of 93%.Leveraging xAI techniques, further uncovering that pivotal attributes such as Herdan’s C, MaaS, and Simpson’s Index played a dominant role in the classification process.
Quantum Steganography: Hiding Secret Messages in Images using Quantum Circuits and SIFT

In today’s era of escalating digital threats and the growing need for safeguarding sensitive information, this research strives to advance the field of information concealment by introducing a pioneering steganography methodology. Our approach goes beyond the conventional boundaries of image security by seamlessly integrating classical image processing techniques with the cutting-edge realm of quantum encoding. The foundation of our technique lies in the meticulous identification of distinctive features within the cover image, a crucial step achieved through the utilization of SIFT (Scale-Invariant Feature Transform). These identified key points are further organized into coherent clusters employing the K-means clustering algorithm, forming a structured basis for our covert communication process. The core innovation of this research resides in the transformation of the concealed message into a NEQR (Novel Enhanced Quantum Representation) code, a quantum encoding framework that leverages the power of quantum circuits. This transformative step ensures not only the secrecy but also the integrity of the hidden information, making it highly resistant to even the most sophisticated decryption attempts. The strategic placement of the quantum circuit representing the concealed message at the centroids of the clusters generated by the K-means algorithm conceals it within the cover image seamlessly. This fusion of classical image processing and quantum encoding results in an unprecedented level of security for the embedded information, rendering it virtually impervious to unauthorized access. Empirical findings from extensive experimentation affirm the robustness and efficacy of our proposed strategy.
Secure Teleoperated Vehicles in Augmented Reality of Things: A Multichain and Digital Twin Approach

The integration of artificial intelligence-powered automotive electronics has revolutionized teleoperated vehicles through the adoption of Augmented Reality of Things (ARoT). This transformation is facilitated by Augmented Reality-based Heads-Up Displays (AR-HUDs) connected to vehicle sensors, which improve driver visibility with immersive overlays, taking in-vehicle human-computer interaction to new heights. These developments include Advanced Driver-Assistance Systems (ADAS) that aid drivers whether they are physically present in the vehicle or remotely controlling it. However, integrating ARoT and ADAS raises security and privacy concerns, especially regarding false data injection attacks. This paper analyzes security vulnerabilities in AR-HUDs, focusing on forged frames in shared AR-HUD video between teleoperated vehicles and their Digital Twins (DT). Critical vulnerabilities are identified through DT, necessitating effective mitigation for Metaverse access. In addition, the proposed solution is a decentralized data security framework based on multiple blockchains, overcoming latency and scalability issues with a Multichain scaffold. Furthermore, the ARoT heavily relies on a deep neural network for computer vision-based obstacle detection, with the results displayed on the AR-HUD. This helps secure the entire ARoT ecosystem, ensuring the integrity and safety of teleoperated vehicles.
Environmental Benefits of Electronic Table of Contents of Journals Over Print

Recent discussion has driven debate on the best format for journals to deliver content to their readers. Traditional dogma necessitated a physical print copy, which was sent to subscribers automatically and came with the benefits of ease of use and familiarity. With the passage of time, electronic tables of contents, with or without the option for a print copy, have been used in lieu to save cost and environmental concerns and to allow content to be consumed in a more convenient, tidier way.
Development of a Piano Practice System for Beginners Using Mixed Reality Technology

In this study, we have developed a piano practice system for beginners by fusing mixed reality and augmented reality technologies. This system allows piano learners to practice at their leisure. In this system, a virtual piano is superimposed on a real world piano, and users can practice the piano along with the virtual notes. The operability, visibility, functionality, relevance, effectiveness, and applicability of this system have been evaluated on 30 subjects. As a result, we were able to obtain high evaluations in many items.
Empowering Recommendations with NLP: Exploiting Textual Reviews for Enhanced Rating-Based Systems

This research paper proposes a rating-based recommender system that leverages Natural Language Processing (NLP) techniques to enhance the accuracy and effectiveness of recommendations. Traditional recommender systems primarily rely on numerical ratings provided by users to make predictions. However, these ratings often lack detailed information about user preferences and suffer from sparsity and inconsistency issues. By incorporating NLP, we aim to extract valuable insights from textual reviews and improve the recommendation process. Our system utilizes sentiment analysis, topic modelling, and text embeddings to capture the implicit information in reviews and generate more personalized and context-aware recommendations. The experimental results demonstrate the superior performance of the proposed rating-based recommender system compared to conventional approaches.
Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Quantum cryptography has emerged as a promising paradigm for secure communication, leveraging the fundamental principles of quantum mechanics to guarantee information confidentiality and integrity. In recent years, the field of quantum cryptography has witnessed remarkable advancements, and the integration of machine learning techniques has further accelerated its progress. This research paper presents a comprehensive review of the latest developments in quantum cryptography, with a specific focus on the utilization of machine learning algorithms to enhance its capabilities. The paper begins by providing an overview of the principles underlying quantum cryptography, such as quantum key distribution (QKD) and quantum secure direct communication (QSDC). Subsequently, it highlights the limitations of traditional quantum cryptographic schemes and introduces how machine learning approaches address these challenges, leading to improved performance and security. To illustrate the synergy between quantum cryptography and machine learning, several case studies are presented, showcasing successful applications of machine learning in optimizing key aspects of quantum cryptographic protocols. These applicatiocns encompass various tasks, including error correction, key rate optimization, protocol efficiency enhancement, and adaptive protocol selection. Furthermore, the paper delves into the potential risks and vulnerabilities introduced by integrating machine learning with quantum cryptography. The discussion revolves around adversarial attacks, model vulnerabilities, and potential countermeasures to bolster the robustness of machine learning-based quantum cryptographic systems. The future prospects of this combined field are also examined, highlighting potential avenues for further research and development. These include exploring novel machine learning architectures tailored for quantum cryptographic applications, investigating the interplay between quantum computing and machine learning in cryptographic protocols, and devising hybrid approaches that synergistically harness the strengths of both fields. In conclusion, this research paper emphasizes the significance of machine learning-enhanced advancements in quantum cryptography as a transformative force in securing future communication systems. The paper serves as a valuable resource for researchers, practitioners, and policymakers interested in understanding the state-of-the-art in this multidisciplinary domain and charting the course for its future advancements.
Deep Learning Driven Web Security: Detecting and Preventing Explicit Content

In today's digital age, the vast expanse of online content has made it increasingly accessible for users to encounter inappropriate text, images and videos. The repercussions of such exposure are concerning, impacting individuals and society adversely. Exposure to violent content can lead to undesirable human emotions, including desensitization, aggression, and other harmful effects. We utilize a machine learning approach aimed at real-time violence detection in text, images and videos embedded in the website. The foundation of this approach lies in a deep learning model, highly trained on a vast dataset of manually labeled images categorized as violent or non-violent. The model boasts exceptional accuracy in identifying violence in images, subsequently filter out violent content from online platforms. By performing all processing intensive tasks in the Cloud, and storing the data in a database, an improved user experience is achieved by completing all the necessary detection processes at a lower time frame, and also reducing the processing load on the user’s local system. The detection of the violent videos is done by a CNN model, which was trained on violent and non-violent video data, and the detection of emotions in the text is taken in by a NLP based algorithm. By implementing this highly efficient approach, web safety can undergo a significant improvement. Users can now navigate the web with confidence, free from concerns about accidentally encountering violent content, fostering improved mental health, and cultivating a more positive online environment. We are able to achieve 67% accuracy in detecting violent content at approximately 2.5 seconds at its best scenario.