Reviewerly/exHarmony huggingface dataset:
How AI Is Changing the Nature and Landscape of Student Learning and Outcomes Assessment Within Higher Education

Technologies have long changed the social lives and practices of users. While some argue that the effects have been positive and others have been a bit more skeptical to take this perspective, there is no question that they have altered the way that we engage in daily behaviors. They have changed the way that we work and the way that we communicate with others. And said technologies are, with little question, here to stay. This chapter introduces readers to the various strategies adopted by both students and assessors (e.g., instructors) regarding the use of ChatGPT within the world of higher education. ChatGPT, in short, is an artificial intelligence chatbot that aids the user in a multitude of tasks and mimics the human mind. Not only will the chapter expose readers to the ways in which students are using ChatGPT (e.g., as artificial tutors, to help with the construction of ideas), but also how instructors are beginning to notice the benefits of this AI tool for the classroom (e.g., lesson plans).
A hybrid combination of CNN Attention with optimized random forest with grey wolf optimizer to discriminate between Arabic hateful, abusive tweets

Arabic hateful speech recognition has long been a major area of focus in Natural Language Processing (NLP) research. In light of recent advancements in transformer models and deep learning, researchers are now turning to transfer learning techniques based on existing models such as BERT for Arabic hateful speech recognition. To detect Arabic hateful contexts, using advanced machine learning algorithms and NLP techniques is essential. These techniques can help to detect different forms of hateful contexts in Arabic by analyzing the text for lexical, semantic, and syntactic features. In this research, we proposed a new hybrid approach that combines deep and machine learning models to detect hateful and abusive content in Arabic. The proposed model consists of a combination of convolutional neural networks and attention layers that are trained to differentiate between normal, abusive, and hateful contexts in Arabic. In the first step, we used a pre-trained model to extract features from the hateful Arabic context. After that, we used an optimized random forest combined with particle swarm optimization and wolf optimizer to classify the extracted features. Finally, we evaluated the performance of the model to detect hateful Arabic contexts. To evaluate the proposed method we used 5846 and 6023 tweets with 3 categories of hateful, abusive, and normal Arabic contexts. The experimental result indicates sensitivity have been reported CNN Attention + optimized random forest by the wolf optimizer on 6023 tweets.
A taxonomy for Media Literacy in Portugal

Media literacy, which encompasses skills and knowledge considered essential for civic participation in social, political, and professional life, is an area that has been growing steadily. In Portugal, there is a wide range of entities that have developed media literacy initiatives, from public entities, universities, research centers, training centers, media organizations, among others. These actors are adopting different approaches and media literacy is being promoted through training, academic publications, social projects, and learning platforms. These actions, carried out in more or less formal environments, are aimed at different target audiences, from pre-school children to the elderly. Based on the multiplicity of theoretical approaches to the concept of media literacy, this study seeks to understand the extent to which this diversity is reflected in the multiplicity of actors, approaches and target audiences involved in promoting media literacy in Portugal. To achieve this goal, using two analysis grids, one dedicated to the actors and the other to their interventions, a mapping of Portuguese entities dedicated to media literacy was carried out, seeking to identify the types of organizations and the professional areas to which they belong, their target audiences, the incidence of literacy promoted, and the pedagogical tools used. The results are presented in the form of a taxonomy of the actors involved in the operationalization of media literacy initiatives in Portugal, making it possible to characterize the landscape of this literacy promotion and its constituent networks, as well as to identify areas to be explored and the types of target audiences in need of initiatives aimed at their needs. The identification of empirical practices to promote media literacy in Portugal also makes it possible to contribute to the global theoretical-conceptual debate on the subject, particularly in terms of the volatility of the concept, its propensity for change and its readaptation according to the contexts in which it is used.
Exploring emergent syllables in end-to-end automatic speech recognizers through model explainability technique

Abstract Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs’ internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems.
Generation Z’s Trust in The Chatbot of Mental Health Service

Generation Z is the age group that has experienced the most mental health-related issues due to the COVID-19 pandemic in Indonesia. Mental health information system services have become increasingly popular, so patient services are overwhelmed. Chatbot has emerged as one of the solutions to address this problem. However, conversations with them have led to social issues alongside the growing use of chatbots. This study aims to identify the factors influencing Generation Z's trust in patient service chatbots within mental health applications in Indonesia. Using the Interpretative Phenomenological Analysis method, this research analyses qualitative data from observations and interviews with five undergraduate students. Based on the analysis, the study identified seven factors that influence Generation Z's trust in chatbot customer service within mental health applications, with three being novel findings.
Medical Image Description Based on Multimodal Auxiliary Signals and Transformer

Medical image description can be applied to clinical medical diagnosis, but the field still faces serious challenges. There is a serious problem of visual and textual data bias in medical datasets, which are the imbalanced distribution of health and disease data. This can greatly affect the learning performance of data-driven neural networks and finally lead to errors in the generated medical image descriptions. To address this problem, we propose a new medical image description network architecture named multimodal data-assisted knowledge fusion network (MDAKF), which introduces multimodal auxiliary signals to guide the Transformer network to generate more accurate medical reports. In detail, audio auxiliary signals provide clear abnormal visual regions to alleviate the visual data bias problem. However, the audio modality signals with similar pronunciation lack recognizability, which may lead to incorrect mapping of audio labels to medical image regions. Therefore, we further fuse the audio with text features as the auxiliary signal to improve the overall performance of the model. Through the experiments on two medical image description datasets, IU-X-ray and COV-CTR, it is found that the proposed model is superior to the previous models in terms of language generation evaluation indicators.
Network Algorithms for Intelligent Evaluation of Composition in Middle School English Cloud Classrooms

This study combines link grammar (LG) detector with N-grammar model to analyze and evaluate grammar in compositions. And then the composition level is judged through information entropy. Finally, the composition score is calculated based on the overall composition level and grammar weight. The experimental results show that the combined weight of recall and accuracy of the proposed method in this study is 89.9%, which is 26.6% higher than LG and 9.7% higher than Grammarly. In the performance test of scoring the entire essay, the proportion of error between the proposed method and manual evaluation is 87.29%, with a lower overall mean square error of only 3.08 and a shorter average running time of only 22.69 seconds. The method proposed in this study has a high accuracy and strong applicability in the evaluation of English compositions for middle school students, providing a new approach for teaching English writing for middle school students.
Mapping Ni-Cu-Platinum Group Element-Hosting, Small-Sized, Mafic-Ultramafic Rocks Using WorldView-3 Images and a Spatial-Spectral Transformer Deep Learning Method

Abstract Mafic-ultramafic intrusive complexes possess a considerable capacity for hosting Ni-Cu-platinum group element (PGE) sulfide deposits. However, the mapping of small outcrops over large areas by field surveys is time-consuming. In this study, WorldView-3 (WV-3) data with moderate spectral and very high spatial resolution were employed for mapping mafic-ultramafic units using spectral indices and the spatial-spectral transformer (SSTF) deep learning method in the Luotuoshan area of Beishan, Gansu Province, China. Based on representative reflectance signatures extracted from imagery of known locations, false-color composites of three-band ratios and a newly proposed shortwave infrared (SWIR) spectral index provided reasonable delineation of mafic-ultramafic rocks. The SSTF method facilitated mapping the occurrence of small mafic-ultramafic outcrops and defining much clearer boundaries, particularly for tiny units at meter scale. Moreover, the SSTF method is not sensitive to the occurrence of carbonate lenses that may affect the reflectance of outcrops. Field investigation and laboratory sample analyses confirmed the occurrence of mafic and ultramafic rocks with substantial metallic mineral potential in this area. Seven prospects were confirmed to be related to mafic-ultramafic intrusions during field validation, four of which contained metallic minerals such as chalcopyrite, pentlandite, pyrite, and chromite in the samples observed by scanning electron microscopy and energy dispersive spectrometry. This study proves that the spectral indices and SSTF deep learning method applied on WV-3 multispectral data are useful for discriminating small-sized mafic-ultramafic intrusive rocks (&amp;lt;100 m) for prospecting of local mineralization.
Deep learning-based privacy-preserving recommendations in federated learning

Privacy preservation in recommendations has been increasingly garnering huge interest from the research community owing to the rapid rise in data security and privacy concerns among users. The computation overhead and attaining high recommendation accuracy remain the key issues in the existing methods. In this research, a course recommendation method using Federated Learning (FL) based on Deep Learning is presented. The course recommendation technique is carried out in the local nodes using multiple phases, like agglomerative matrix generation, course grouping, bi-level matching, retrieval of learner-preferred courses, and course recommendation. Here, course grouping is accomplished using Deep Fuzzy Clustering (DFC), and Deep Convolutional Neural Networks (DCNN) performs recommendation. The DFC-DCNN-FL is examined based on accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP) and is found to have attained values of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925.
Exploiting multi-scale hierarchical feature representation for visual tracking

Abstract Convolutional neural networks (CNNs) have been the dominant architectures for feature extraction tasks, but CNNs do not look for and focus on some specific image features. Correlation operations play an important role in visual tracking. However, the correlation operation reserves a large amount of unfavorable background information. In this paper, we propose an effective feature recognizer including channel and spatial attention modules to focus on important object feature information. Thus, the representation power of the feature extraction network is improved. Further, we design a multi-scale feature fusion network. The fusion network performs feature fusion on template feature and encoded feature branches to establish connections between features at different scales. Experiments on six benchmarks demonstrate that the proposed tracker outperforms the state-of-the-art trackers. In particular, the proposed tracker achieves an 80.4% AUC on TrackingNet and a 68.4% AUC on GOT-10k while running at a real-time speed.
ReLiCADA: Reservoir Computing Using Linear Cellular Automata design algorithm

Abstract In this paper, we present a novel algorithm to optimize the design of Reservoir Computing using Cellular Automata models for time series applications. Besides selecting the models’ hyperparameters, the proposed algorithm particularly solves the open problem of Linear Cellular Automaton rule selection. The selection method pre-selects only a few promising candidate rules out of an exponentially growing rule space. When applied to relevant benchmark datasets, the selected rules achieve low errors, with the best rules being among the top 5% of the overall rule space. The algorithm was developed based on mathematical analysis of Linear Cellular Automaton properties and is backed by almost one million experiments, adding up to a computational runtime of nearly one year. Comparisons to other state-of-the-art time series models show that the proposed Reservoir Computing using Cellular Automata models have lower computational complexity and, at the same time, achieve lower errors. Hence, our approach reduces the time needed for training and hyperparameter optimization by up to several orders of magnitude.
A lexicon-based diachronic comparison of emotions and sentiments in literary translation: A case study of five Chinese versions of David Copperfield

In the Chinese context, translations have served as a useful conduit for providing access to wider literature authored in other languages. A prominent question has been whether translators' linguistic choices are influenced by factors such as translators' social and cultural background and emotions towards the texts they are translating. When multiple translations of the same text over a span of time are produced, another layer of complexity is introduced, and research such as the present study, must examine how or whether variation in the expression of emotions within translations produced over a period of time is discernible. To this end, the present study made use of Lexicon-based Sentiment Analysis (LBSA), a common natural language processing (NLP) approach, to study people's attitudes, opinions or emotions towards a certain person or thing. LBSA has attracted much attention in the literary works or translated works for analyzing reader response and appraisal of the works themselves. The present study undertook a diachronic comparison of emotions and sentiments in five translations of David Copperfield based on the emotion lexicons. The corpus of the study comprised translations of five books and 3,084,599 tokens. We applied the computational method of emotion and sentiment analysis to the emotion words in the five translations. In addition, we used python and R package to analyze the positive and negative words in five versions. The study revealed that translators as social beings in the target world express unique reactions towards the same emotion in the original text as well as in literary translations. Yet, the modern vernacular Chinese versions also showcase a similarity in the expression of emotions thus demonstrating the decisive role of the overall flow of emotion in the original plays and in translation. The contribution of the study is significant as it is a pioneering investigation given that it undertakes a sentiment and emotion analysis of literary translations in Chinese.
Lightweight high-precision SAR ship detection method based on YOLOv7-LDS

The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly accurate SAR ship detection model built upon the YOLOv7 framework. In the core of YOLOv7-LDS's architecture, we introduce a streamlined feature extraction network that strikes a delicate balance between detection precision and computational efficiency. This network is founded on Shufflenetv2 and incorporates Squeeze-and-Excitation (SE) attention mechanisms as its key elements. Additionally, in the Neck section, we introduce the Weighted Efficient Aggregation Network (DCW-ELAN), a fundamental feature extraction module that leverages Coordinate Attention (CA) and Depthwise Convolution (DWConv). This module efficiently aggregates features while preserving the ability to identify small-scale variations, ensuring top-quality feature extraction. Furthermore, we introduce a lightweight Spatial Pyramid Dilated Convolution Cross-Stage Partial Channel (LSPHDCCSPC) module. LSPHDCCSPC is a condensed version of the Spatial Pyramid Pooling Cross-Stage Partial Channel (SPPCSPC) module, incorporating Dilated Convolution (DConv) as a central component for extracting multi-scale information. The experimental results show that YOLOv7-LDS achieves a remarkable Mean Average Precision (mAP) of 99.1% and 95.8% on the SAR Ship Detection Dataset (SSDD) and the NWPU VHR-10 dataset with a parameter count (Params) of 3.4 million, a Giga Floating Point Operations Per Second (GFLOPs) of 6.1 and an Inference Time (IT) of 4.8 milliseconds. YOLOv7-LDS effectively strikes a fine balance between computational cost and detection performance, surpassing many of the current state-of-the-art object detection models. As a result, it offers a more resilient solution for maritime ship monitoring.
Transformer-based time-to-event prediction for chronic kidney disease deterioration

Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture for electronic health records.The input for STRAFE is a sequence of visits with SNOMED-CT codes in OMOP-CDM format. A Transformer-based architecture was developed to calculate probabilities of the occurrence of the event in each of 48 months. Performance was evaluated using a real-world claims dataset of over 130 000 individuals with stage 3 chronic kidney disease (CKD).STRAFE showed improved mean absolute error (MAE) compared to other time-to-event algorithms in predicting the time to deterioration to stage 5 CKD. Additionally, STRAFE showed an improved area under the receiver operating curve compared to binary outcome algorithms. We show that STRAFE predictions can improve the positive predictive value of high-risk patients by 3-fold. Finally, we suggest a novel visualization approach to predictions on a per-patient basis.Time-to-event predictions are the most appropriate approach for clinical predictions. Our deep-learning algorithm outperformed not only other time-to-event prediction algorithms but also fixed-time algorithms, possibly due to its ability to train on censored data. We demonstrated possible clinical usage by identifying the highest-risk patients.The ability to accurately identify patients at high risk and prioritize their needs can result in improved health outcomes, reduced costs, and more efficient use of resources.
Synthesis of Hybrid Data Consisting of Chest Radiographs and Tabular Clinical Records Using Dual Generative Models for COVID-19 Positive Cases

To generate synthetic medical data incorporating image-tabular hybrid data by merging an image encoding/decoding model with a table-compatible generative model and assess their utility. We used 1342 cases from the Stony Brook University Covid-19-positive cases, comprising chest X-ray radiographs (CXRs) and tabular clinical data as a private dataset (pDS). We generated a synthetic dataset (sDS) through the following steps: (I) dimensionally reducing CXRs in the pDS using a pretrained encoder of the auto-encoding generative adversarial networks (αGAN) and integrating them with the correspondent tabular clinical data; (II) training the conditional tabular GAN (CTGAN) on this combined data to generate synthetic records, encompassing encoded image features and clinical data; and (III) reconstructing synthetic images from these encoded image features in the sDS using a pretrained decoder of the αGAN. The utility of sDS was assessed by the performance of the prediction models for patient outcomes (deceased or discharged). For the pDS test set, the area under the receiver operating characteristic (AUC) curve was calculated to compare the performance of prediction models trained separately with pDS, sDS, or a combination of both. We created an sDS comprising CXRs with a resolution of 256 × 256 pixels and tabular data containing 13 variables. The AUC for the outcome was 0.83 when the model was trained with the pDS, 0.74 with the sDS, and 0.87 when combining pDS and sDS for training. Our method is effective for generating synthetic records consisting of both images and tabular clinical data.