Reviewerly/exHarmony huggingface dataset:
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects

Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging cases. To handle this problem, we draw inspiration from the prey-vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect. Camouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce a stronger detector. On the predator side, we introduce a novel COD method, called Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged feature coherence module to excavate the internal coherence of camouflaged objects, striving to obtain more complete segmentation results. Additionally, ICEG proposes a novel edge-guided separated calibration module to remove false predictions to avoid obtaining ambiguous boundaries. Extensive experiments show that ICEG outperforms existing COD detectors and Camouflageator is flexible to improve various COD detectors, including ICEG, which brings state-of-the-art COD performance.
Thermal local quantum uncertainty in a two-qubit-superconducting system under decoherence

By considering the local quantum uncertainty (LQU) as a measure of quantum correlations, the thermal evolution of a two-qubit-superconducting system is investigated. We show that the thermal LQU can be increased by manipulating the Hamiltonian parameters such as the mutual coupling and Josephson energies, however, it undergoes sudden transitions at specific temperatures. Furthermore, a detailed analysis is presented regarding the impact of decohering channels on thermal LQU. This controllable LQU in engineering applications can disclose the advantage enabled in the superconducting charge qubits for designing quantum computers and quantum batteries.
What do Twitter comments tell about news article bias? Assessing the impact of news article bias on its perception on Twitter

News stories circulating online, especially on social media platforms, are nowadays a primary source of information. Given the nature of social media, news no longer are just news, but they are embedded in the conversations of users interacting with them. This is particularly relevant for inaccurate information or even outright misinformation because user interaction has a crucial impact on whether information is uncritically disseminated or not. Biased coverage has been shown to affect personal decision-making. Still, it remains an open question whether users are aware of the biased reporting they encounter and how they react to it. The latter is particularly relevant given that user reactions help contextualize reporting for other users and can thus help mitigate but may also exacerbate the impact of biased media coverage. This paper approaches the question from a measurement point of view, examining whether reactions to news articles on Twitter can serve as bias indicators, i.e., whether how users comment on a given article relates to its actual level of bias. We first give an overview of research on media bias before discussing key concepts related to how individuals engage with online content, focusing on the sentiment (or valance) of comments and on outright hate speech. We then present the first dataset connecting reliable human-made media bias classifications of news articles with the reactions these articles received on Twitter. We call our dataset BAT - Bias And Twitter. BAT covers 2,800 (bias-rated) news articles from 255 English-speaking news outlets. Additionally, BAT includes 175,807 comments and retweets referring to the articles. Based on BAT, we conduct a multi-feature analysis to identify comment characteristics and analyze whether Twitter reactions correlate with an article’s bias. First, we fine-tune and apply two XLNet-based classifiers for hate speech detection and sentiment analysis. Second, we relate the results of the classifiers to the article bias annotations within a multi-level regression. The results show that Twitter reactions to an article indicate its bias, and vice-versa. With a regression coefficient of 0.703 (p<0.01), we specifically present evidence that Twitter reactions to biased articles are significantly more hateful. Our analysis shows that the news outlet’s individual stance reinforces the hate-bias relationship. In future work, we will extend the dataset and analysis, including additional concepts related to media bias.
Classification of Cervical Cell Images into Healthy or Cancer Using Convolution Neural Network and Linear Discriminant Analysis

Cancer of the cervix is the disease that accounts for the majority of deaths in women. This disease accounts for nearly 12% of all cancers and has a high risk of death for women worldwide. If precancerous lesions are found early, the disease can be cured. Pap smear screening is known for its reliability and effectiveness in detecting cervical cell abnormalities early, but there is a risk of errors in manual image analysis. Using deep learning approaches in the domains of medicine and healthcare can be used for decision support systems to remove bias from observations. This paper presents a framework that utilizes deep learning and techniques to reduce the dimensions of features. The suggested framework captures deep features from a convolutional neural network (CNN) model and employs a feature reduction approach using linear discriminant analysis (LDA) to ensure computational cost reduction. The feature dimension derived from the CNN model produces a huge feature space that requires a feature reduction to eliminate redundant features. The features that have been reduced by linear discriminant analysis are used for the training of three classifiers, namely SVM, MLP, and K-NN, to generate final predictions. The evaluation of the proposed framework involved the utilization of three datasets that are openly accessible: the Herlev dataset, the Mendeley dataset, and the SIPaKMeD dataset, which achieved classification accuracies of 95.65% (SVM and MLP), 100% (MLP), and 97.54 (K-NN), respectively.
STUDENTS’ SELF-CONFIDENCE IN SPEAKING FOR A LIVE PRESENTATION: A LITERATURE REVIEW

The ability to speak English is a challenge for students in Indonesia as users of English as a foreign language. This study aims to determine whether there is a relationship between students' confidence in their ability to speak English using a live presentation method. The researchers used the Prisma 2009 statement to spell out the process. Ten articles were presented to collect the data related to students' self-confidence and speaking skills. This paper finds that student self-confidence is an essential component that determines the mastery of speaking skills.
Digital inpainting of mural images based on DC-CycleGAN

Abstract Located in Dunhuang, northwest China, the Mogao Grottoes are a cultural treasure of China and the world. However, after more than 2000 years of weathering and destruction, many murals faded and were damaged. This treasure of human art is in danger. Mural inpainting through deep learning can permanently preserve mural information. Therefore, a digital restoration method combining the Deformable Convolution (DCN), ECANet, ResNet and Cycle Generative Adversarial Network (CycleGAN) is proposed. We name it DC-CycleGAN. Compared with other image digital inpainting methods, the proposed DC-CycleGAN based mural image color inpainting method has better inpainting effects and higher model performance. Compared with the current repair network, the Frechet Inception Distance (FID) value and the two-image structural similarity metric (SSIM) value are increased by 52.61% and 7.08%, respectively. Image color inpainting of Dunhuang murals can not only protect and inherit Chinese culture, but also promote academic research and development in related fields.
Earthquake Footprints for Predicting Events

This paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm averages the history of these events over preceding days, in particular the gaps when the events did not occur. The gaps are measured for the clustered events only and can be used to create a description that is quite unique. Results suggest that seismic events can in fact be traced using this key and subsequently recognised again, if the same conditions reoccur. They also suggest that force direction may be more important than magnitude, after the initial cluster selections are made. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention.
Teachers’ Perspectives on the Development of Augmented Reality Application in Geometry Topic for Elementary School

Augmented Reality (AR) is a new evolving technology that can enhance the learning experienceby making learning more engaging, has a beneficial influence on teaching and learning,and assists in comprehension, specifically when it comes to abstract concepts. Teachers playa pivotal role in accelerating the widespread use of AR in education, making it crucial toget their thoughts and perspectives. Based on the perspectives of mathematics teachers, thisresearch aims to determine the necessity for developing an augmented reality learning applicationin the geometry topic for elementary school students through a questionnaire that hasbeen answered by 52 elementary mathematics teachers from Northern State Malaysia. Thefindings of this study were analyzed using descriptive analysis with a value of Cronbach alpha(α = 0.98). The findings of the study indicate three main elements that need to be emphasizedfor the AR development process, namely learning strategy design, presentation design, andinteractivity design. Based on the mean of the research conducted, the findings of the threeelements indicate a significant necessity. The development of AR applications must incorporatethese three elements in order to assist elementary school mathematics teachers in designingAR learning applications that maximize student achievement.
Profile and Nature of Pedagogy as a Scientific Discipline

The purpose of this work is to describe and explain the characteristics of pedagogy as a science of education, as an object, concepts and research method. The methodology assumed was the review of the history of pedagogy, especially the classics of pedagogy, those who built it with their experiences in laboratory schools. Here are listed the main concepts of pedagogy, its laws and principles; she establishes the opposition that exists between teaching-learning and education as formation of the personality; she also delimits the fields of the pedagogical method, of didactics or teaching-learning, and the methodology of educational labor that organizes the process and the educational context, the regime and the lifestyle, such as those experienced in the "colonies" or "communes" of children and adolescents, organized and directed by Neill and Makarenko. Since the present article does not derive from a primary source or from field work, as an empirical or experimental research, the results constitute the content of the article, as conclusions.
SMART Chatbots in the E-learning Domain: A Systematic Literature Review

Integrating Artificial Intelligence (AI) technologies implied significant growth in variousdomains. Furthermore, many companies integrate AI technologies into their products toenhance the quality of their services. Chatbots are among the AI technologies widely used inseveral areas, especially E-learning. Chatbots support learners in their learning processes byhelping them to find the appropriate answers to their questions. We aim to conduct a systematicliterature review (SLR) to uncover the use of AI chatbots to offload teachers from repetitive andmassive tasks. This article surveys the literature over the period 2016–2022 on the use of AIchatbots in the E-learning domain as they automatically answer learners’ questions. Thus, weidentify, collect, and synthesize multiple research studies on the application of AI chatbots in theE-learning field. Based on the renowned frameworks, PRISMA and PICO, we have succeeded in(1) Developing our research questions and (2) Automatically implementing a solution based onPython language to analyze selected papers, highlighting research gaps, and opening new windowsto guide our future works. Our study shows that chatbots effectively interact with learners.However, there are some drawbacks: (1) Educational chatbots are still limited in their localKnowledge Base (KB), which makes them unable to answer students’ questions correctly. Thus,Chatbot’s KB needs to be extended through external sources, enabling the chatbot to update itsKB over time, making it rich and saving time. (2) Lack of reliable external sources to enrich thechatbot’s KB and make it up to date. (3) Lack of educational chatbots with smart services suchas speech recognition and sentiment analysis to boost the user experience and make learningeasier. In our SLR, we discuss these limitations and propose some solutions to fill the gap.
The power of time in lyrical poetry by Per Atterbom and Viktor Rydberg

The article focuses on the concept of time in human and global perspective in the poetry of two Swedish authors of the 19th century: Per Atterbom and Viktor Rydberg. These two poets belong to different branches of the Romantic movement. Atterbom represents the early mystic Romanticism and he is one of the first Romantic authors in Sweden. Rydberg is a post-Romantic poet: his poetic works contain features that are characteristic of the literature of Romanticism, but they were written in the last decades of the century. The theme of the power of time is one of the most important for both authors, it determines characters of lyrical heroes and poetics in many of their poems but in different ways. Atterbom represents the destructive power of time. Thus, the main characters of his fairy tale play “The Isle of Bliss”, Hyperborean king Astolf and nymph Felicia, live on a magical island and aspire to escape from Time, but Time overcomes them. Atterbom shows in his poetry that time hasn’t its limits in the world beyond. Rydberg represents the characters who try to apprehend the rules of time. The immortal mythological creatures in his poems (the Tomten, the elf of a river) cannot explain why time has such an influence on man and mankind. This theme corresponds with a philosophical matter of the meaning of human life. The humans in Rydberg’s lyrics do not try to overcome time as they perceive the nature and its rules throw their senses.
Generative Pretrained Transformers for Emotion Detection in a Code-Switching Setting

This paper describes the approach that we utilized to participate in the shared task for multi-label and multi-class emotion classification organized as part of WASSA 2023 at ACL 2023. The objective was to build mod- els that can predict 11 classes of emotions, or the lack thereof (neutral class) based on code- mixed Roman Urdu and English SMS text messages. We participated in Track 2 of this task - multi-class emotion classification (MCEC). We used generative pretrained transformers, namely ChatGPT because it has a commercially available full-scale API, for the emotion detec- tion task by leveraging the prompt engineer- ing and zero-shot / few-shot learning method- ologies based on multiple experiments on the dev set. Although this was the first time we used a GPT model for the purpose, this ap- proach allowed us to beat our own baseline character-based XGBClassifier, as well as the baseline model trained by the organizers (bert- base-multilingual-cased). We ranked 4th and achieved the macro F1 score of 0.7038 and the accuracy of 0.7313 on the blind test set.
Arithmetic-Based Pretraining Improving Numeracy of Pretrained Language Models

State-of-the-art pretrained language models tend to perform below their capabilities when applied out-of-the-box on tasks that require understanding and working with numbers.Recent work suggests two main reasons for this: (1) popular tokenisation algorithms have limited expressiveness for numbers, and (2) common pretraining objectives do not target numeracy.Approaches that address these shortcomings usually require architectural changes or pretraining from scratch.In this paper, we propose a new extended pretraining approach called Arithmetic-Based Pretraining that jointly addresses both in one extended pretraining step without requiring architectural changes or pretraining from scratch.Arithmetic-Based Pretraining combines contrastive learning to improve the number representation, and a novel extended pretraining objective called Inferable Number Prediction Task to improve numeracy.Our experiments show the effectiveness of Arithmetic-Based Pretraining in three different tasks that require improved numeracy, i.e., reading comprehension in the DROP dataset, inference-on-tables in the InfoTabs dataset, and table-to-text generation in the WikiBio and Sci-Gen datasets 1 .
Seeking Clozure: Robust Hypernym extraction from BERT with Anchored Prompts

The automatic extraction of hypernym knowledge from large language models like BERT is an open problem, and it is unclear whether methods fail due to a lack of knowledge in the model or shortcomings of the extraction methods.In particular, methods fail on challenging cases which include rare or abstract concepts, and perform inconsistently under paraphrased prompts.In this study, we revisit the long line of work on pattern-based hypernym extraction, and use it as a diagnostic tool to thoroughly examine the hypernomy knowledge encoded in BERT and the limitations of hypernym extraction methods.We propose to construct prompts from established pattern structures: definitional (X is a Y); lexico-syntactic (Y such as X); and their anchored versions (Y such as X or Z).We devise an automatic method for anchor prediction, and compare different patterns in: (i) their effectiveness for hypernym retrieval from BERT across six English data sets; (ii) on challenge sets of rare and abstract concepts; and (iii) on consistency under paraphrasing.We show that anchoring is particularly useful for abstract concepts and in enhancing consistency across paraphrases, demonstrating how established methods in the field can inform prompt engineering. 1
NCUEE-NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced RoBERTa Transformers

This paper describes our proposed system design for the WASSA 2023 shared task 1.We propose a unified architecture of ensemble neural networks to integrate the original RoBERTa transformer with two sentiment-enhanced RoBERTa-Twitter and EmoBERTa models.For Track 1 at the speech-turn level, our best submission achieved an average Pearson correlation score of 0.7236, ranking fourth for empathy, emotion polarity and emotion intensity prediction.For Track 2 at the essay-level, our best submission obtained an average Pearson correlation score of 0.4178 for predicting empathy and distress scores, ranked first among all nine submissions.
Text Summarization Quality Detection Based on GPT-3

To summarize lengthy text in a concise and accurate manner is called text summarization. With the increasing amount of information available, text summarization has become increasingly important in information retrieval, knowledge management, and sentiment analysis. The research on text summarization dates to the 1960s, and the methods have evolved from traditional template-based generation to statistical and neural network-based methods. Modern language model GPT-3 has demonstrated outstanding linguistic ability, including the ability to produce text that is cohesive and grammatically correct. However, evaluating the caliber of text produced by GPT-3 is challenging and requires careful evaluation criteria. This study evaluated the text summarization ability of GPT-3 using multiple evaluation models (ROUGE, BLEU, and CIDER), and found that the generated summaries exhibited high quality and accuracy.
FPGA realization of four chaotic interference cases in a terrestrial trajectory model and application in image transmission

Abstract This article presents a technique to integrate two dynamical models, a four-wing spherical chaotic oscillator and the elliptical path described by the planet Earth during its translation movement around the sun. Four application cases are derived from the system by varying the dynamics of the chaotic oscillator and these can be applied in information encryption to transmit RGB and grayscale images modulated by CSK. Consequently, the three main contributions of this work are (1) the emulation of the trajectories of the planet Earth with chaotic interference, (2) the CSK modulation and image encryption in a master-slave synchronization topology, and (3) the CSK demodulation for decryption without loss of information with respect to the original information. The three contributions are based on VHDL code implementation. The results of the synchronization, encryption and decryption technique were verified by means of time series and the encrypted images showed a correlation less than − 0.000142 and − 0.0003439 for RGB and grayscale format, respectively, while the retrieved image shows a complete correlation with the image original. In this work, the co-simulations were performed between MATLAB/Simulink and Vivado, using the VHDL language on two FPGA boards from different manufacturers, namely, Xilinx Artix-7 AC701 and Intel Cyclone IV.
The Role of Digital Transformation in Healthcare: A Sustainability Perspective, Design and Integration Challenges, Security and Privacy Challenges, Blockchain Technology, Applications, Future Research Directions

This study presents a detailed comparison of existing studies, mainly focusing on integrating ICT technologies in healthcare, an important sustainable development goal (SDG), in terms of sustainable aspects, security and privacy challenges, design and integration challenges, E-health-related applications, and future directions. We also present an overview of the need for digital transformation in healthcare, discuss its significant components, highlight E-health’s importance and benefits, explore its integration and design challenges and categorise the security and privacy challenges. Next, we present an in-depth discussion on the role of Blockchain technology in E-health, discussing Blockchain technology and its characteristics, highlighting its benefits, and describing the possible types of Blockchain-based E-health use cases. Furthermore, we discuss the positive and negative impact of ICT integration along with identifying open issues and challenges of integrating ICT technologies into the healthcare systems and discuss future research directions, which provide the strength for researchers to address the issues in future solutions.