Sentence Encoder Research Articles

UniEmbed: A Novel Approach to Detect XSS and SQL Injection Attacks Leveraging Multiple Feature Fusion with Machine Learning Techniques

Abstract Web applications are essential in the digital age, but their security vulnerabilities expose sensitive data and organizational integrity to sophisticated attacks. Among the most prevalent and damaging vulnerabilities in web applications are cross-site scripting (XSS) and SQL injection attacks. In this paper, we introduce UniEmbed, a unified approach for detecting XSS and SQL injection attacks using machine learning classifiers. This novel approach leverages natural language processing techniques, combining features from Word2Vec, the Universal Sentence Encoder (USE), and FastText to extract meaningful data from web applications. Extensive experiments were conducted using various machine learning classifiers on three benchmark datasets to evaluate the performance of the unified detection approach, demonstrating exceptional results. Experimental results demonstrate the superior performance of the MLP classifier. For the XSS attack dataset, the MLP classifier achieved an accuracy of 0.9982 and an F1-score of 0.9983, with minimal false positives and false negatives. Similarly, the hard voting classifier yielded the same outstanding results. For SQL injection attacks, the MLP classifier maintained exceptional performance, achieving an F1-score of 0.9980 and accuracy rates exceeding 0.9980 across two datasets. The classifier effectively minimized false positives and false negatives. The ROC curves further corroborate the effectiveness of the proposed method, indicating high true positive rates and low false positive rates. Furthermore, comparative analysis showed that the UniEmbed method consistently outperformed individual feature extraction methods across all classifiers. These findings indicate that the proposed UniEmbed method, particularly when combined with the MLP classifier, is highly effective in detecting both XSS and SQL injection attacks, making it a promising approach for enhancing web application security.

Soft cosine and extended cosine adaptation for pre-trained language model semantic vector analysis

Semantic textual analysis is a natural language processing task that has enjoyed several research contributions towards solving diverse real-life problems. Vector comparison is a core subtask in semantic textual similarity analysis. A plethora of solutions including recent state-of-the-art transformer-based pre-trained language models for transfer learning have focused on using only cosine similarity for embedding evaluation in downstream tasks and ignored other vector comparison methods. To investigate the relative performance of some such ignored measures, this work proposes novel adaptations for soft cosine and extended cosine vector measures. We investigate their performance against the conventional cosine measure, distance-weighted cosine, vector similarity measure, negative Manhattan, and Euclidean distances on downstream semantic textual similarity tasks, under same conditions, for the first time in literature. Adopting transformer-based Universal sentence encoder, SBERT, SRoBERTa, SimCSE, and ST5 for text encoding; the performances of the adapted measures are evaluated on diverse real world datasets using Pearson, Spearman, accuracy and F1 evaluation metrics. Results obtained show that the adapted measures significantly surpass previously reported state-of-the-art cosine similarity-based correlations in several test cases considered.

Developing Semantic Textual Similarity for Guragigna Language Using Deep Learning Approach

Semantic Similarity is one of the highest levels of NLP. STS has significant advantages in NLP applications like information retrieval, information extraction, text summarization, data mining, machine translation, and other tasks. This research aims to present a deep learning approach for capturing semantic textual similarity (STS) in the Guragigna. The methodology involves collecting a Guragigna language corpus and preprocessing the text data and text representation is done using the Universal Sentence Encoder (USE), along with word embedding techniques including Word2Vec and GloVe and mean Square Error (MSE) is used to measure the performance. In the experimentation phase, models like LSTM, Bidirectional RNN, GRU, and Stacked RNN are trained and evaluated using different embedding techniques. The results demonstrate the efficacy of the developed models in capturing semantic textual similarity in the Guragigna language. Across different embedding techniques, including Word2Vec, GloVe, and USE, the Bidirectional RNN model with USE embedding achieves the lowest MSE of 0.0950 and the highest accuracy of 0.9244. GloVe and Word2Vec embedding also show competitive performance with slightly higher MSE and lower accuracy. The Universal Sentence Encoder consistently emerges as the top-performing embedding across all RNN architectures. The research results demonstrate the effectiveness of LSTM, GRU, Bi RNN, and Stacked RNN models in measuring semantic textual similarity in the Guragigna language.

Improvement for deep learning for suicide and depression identification with unsupervised label correction

Abstract. Our study try to address the challenge of accurately identifying depression and suicidal ideation on social media platforms by introducing an enhanced novel methodology for unsupervised feature selection and label correction. Utilizing advanced word embedding models like BERT and the Universal Sentence Encoder, we transform textual content into dense numerical vectors that capture the nuanced emotional context of online discussions. Our approach enhances these embeddings with a deep neural network (DNN) to extract distinctive features, reducing the dimensionality of the data through Principal Component Analysis (PCA). For label correction, we employ clustering techniques including OPTICS, K-medoids, and hierarchical clustering, which are robust against noisy data points. We then train classifiers using CNN, DNN, logistic regression, and random forest algorithms, evaluated with metrics such as accuracy, precision, recall, F1 score, and AUC. This methodology improves the accuracy of classifying depressive and suicidal sentiments to some extent, assisting to utilize the vast data available on social media to advance mental health diagnostics and interventions.

A Chinese Nested Named Entity Recognition Model for Chicken Disease Based on Multiple Fine-Grained Feature Fusion and Efficient Global Pointer

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species.

Indonesian-English Textual Similarity Detection Using Universal Sentence Encoder (USE) and Facebook AI Similarity Search (FAISS)

The tremendous development in Natural Language Processing (NLP) has enabled the detection of bilingual and multilingual textual similarity. One of the main challenges of the Textual Similarity Detection (TSD) system lies in learning effective text representation. The research focuses on identifying similar texts between Indonesian and English across a broad range of semantic similarity spectrums. The primary challenge is generating English and Indonesian dense vector representation, a.k.a. embeddings that share a single vector space. Through trial and error, the research proposes using the Universal Sentence Encoder (USE) model to construct bilingual embeddings and FAISS to index the bilingual dataset. The comparison between query vectors and index vectors is done using two approaches: the heuristic comparison with Euclidian distance and a clustering algorithm, Approximate Nearest Neighbors (ANN). The system is tested with four different semantic granularities, two text granularities, and evaluation metrics with a cutoff value of k={2,10}. Four semantic granularities used are highly similar or near duplicate, Semantic Entailment (SE), Topically Related (TR), and Out of Topic (OOT), while the text granularities take on the sentence and paragraph levels. The experimental results demonstrate that the proposed system successfully ranks similar texts in different languages within the top ten. It has been proven by the highest F1@2 score of 0.96 for the near duplicate category on the sentence level. Unlike the near-duplicate category, the highest F1 scores of 0.77 and 0.89 are shown by the SE and TR categories, respectively. The experiment results also show a high correlation between text and semantic granularity.

Towards a cyberbullying detection approach: fine-tuned contrastive self-supervised learning for data augmentation

AbstractCyberbullying on social media platforms is pervasive and challenging to detect due to linguistic subtleties and the need for extensive data annotation. We introduce a Deep Contrastive Self-Supervised Learning (DCSSL) model that integrates a Natural Language Inference (NLI) dataset, a fine-tuned sentence encoder, and data augmentation to enhance the understanding of cyberbullying's nuanced semantics and offensiveness. The DCSSL model effectively captures contextual dependencies and the varied semantic implications inherent in cyberbullying instances, addressing the limitations of manual data annotation processes when compared against established models such as BERT and Bi-LSTM. Our proposed model registers a significant improvement, achieving a macro average F1 score of 0.9231 on cyberbullying datasets, highlighting its applicability in environments where manual annotation is impractical or unavailable.

Extracting Key-phrase Embedding using Deep Average Network and Maximal Marginal Relevance to Enhance Information Retrieval

Background: Automatic keyphrase extraction (AKE) is essential to many NLP and information retrieval tasks. Extracting high-quality key phrases is difficult due to technological advancements and the exponential growth ‎of ‎textual data and digital sources. Unsupervised keyphrase extraction with cheap computing cost that relies ‎on ‎heuristic notions of phrase importance such as embedding similarities but their development necessitates in-depth subject expertise. Materials and Methods: This paper presents a method to obtain a semantic understanding of the query ‎and index documents by using ‎the embedding technique(universal Sentence ‎encoder (USE) ) while keeping the most informative ‎using Maximal Marginal Relevance (MMR) and then scoring(an ‎inverted index) the most documents relevant to the query ‎vector to improve the ‎performance of IR systems.‎ Results: The proposed retrieval model implement on the (Fire2011) dataset. The final ‎stage was evaluating the results of the baseline and the results (indexing and ranking) by using mean average precision (MAP). The ‎result of the baseline was 0.61, while the result ‎inverted index was 0.6277519 .‎ Conclusions: In this paper, we have discussed document retrieval using keep key phrases that ‎have ‏informativeness ‎properties by ‎using maximal ‎marginal ‎relevance, since if we extract a fixed number of top ‎keyphrases, ‎redundancy ‎hinders the diversification of the ‎extracted keyphrases.

Automatic metaphor processing in developmental dyslexia

IntroductionPrevious research found metaphor impairments with dyslexia; however, it is unclear if difficulties are due to initial activation of the metaphorical meaning or to subsequent discourse integration processes. The study examines the presence of early automatic processing of metaphors in adults with developmental dyslexia, considering the role of executive functions and metaphor familiarity. MethodsUsing a sentence recall task and a semantic judgment task from the Metaphor Interference Effect (MIE) paradigm, we evaluated two early stages of metaphor comprehension, namely the generation of the figurative meaning and the suppression of the literal meaning. High and low familiar metaphors, and their scrambled counterparts, were aurally presented to participants, who were asked to judge whether sentences were literally true or literally false. Afterwards, they were provided ten minutes to recall the sentences they heard to verify the depth of processing for each type of stimulus. A total of 26 participants with dyslexia were included in the experimental group, and 31 in the control group. ResultsIndividuals with dyslexia showed a MIE and an accuracy rate that are similar to participants without dyslexia. Inhibition correlated with the MIE size only for high familiar metaphors, and working memory seemed to play no role in the process. In the recall task, both groups demonstrated a better encoding of the metaphorical sentences compared to scrambled metaphors, but participants with dyslexia recalled less metaphors than did the control group, showing that metaphors are no exception to the limitations in sentence retrieval typically found in dyslexia. ConclusionsOur findings suggest that individuals with dyslexia are comparable to participants without dyslexia in their ability to automatically compute metaphorical meanings. Thus, difficulties in metaphor comprehension in people with dyslexia that have been detected in previous studies might depend on meaning construction in context rather than online semantic processing.

Bias Detection in Media: An NLP-Based Approach using Corpus Statistics and Sentence Embeddings

In this paper, we implement a Natural Language Processing (NLP) solution for binary classification to categorize a sentence as biased or unbiased. Detecting bias is a challenge in the media today, but can be utilized to help readers identify which sources portray bias. The general approach to classifying a sentence as biased or unbiased involves representing words and sentences using probability or pretrained vectorization models. Our final model only contained probabilistic data about the connection between words, sentences, and each class. We used Pointwise Mutual Information (PMI) and Term Frequency Inverse Document Frequency (TF-IDF) as heuristics for finding the relationship between sentences and the biased and unbiased classes. We also leveraged Google’s Universal Sentence Encodings (USE) to capture the meaning of the sentences. Our results revealed a possible limitation in USE’s training data in terms of bias detection. Through topic analysis, we were able to uncover insights surrounding which topics are characterized by minimal bias. We were able to use these discoveries to contextualize the model’s performance.

Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts

Tasks such as semantic search and clustering on crisis-related social media texts enhance our comprehension of crisis discourse, aiding decision-making and targeted interventions. Pre-trained language models have advanced performance in crisis informatics, but their contextual embeddings lack semantic meaningfulness. Although the CrisisTransformers family includes a sentence encoder to address the semanticity issue, it remains monolingual, processing only English texts. Furthermore, employing separate models for different languages leads to embeddings in distinct vector spaces, introducing challenges when comparing semantic similarities between multi-lingual texts. Therefore, we propose multi-lingual sentence encoders (CT-XLMR-SE and CT-mBERT-SE) that embed crisis-related social media texts for over 50 languages, such that texts with similar meanings are in close proximity within the same vector space, irrespective of language diversity. Results in sentence encoding and sentence matching tasks are promising, suggesting these models could serve as robust baselines when embedding multi-lingual crisis-related social media texts. The models are publicly available at: https://huggingface.co/crisistransformers.

CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts

Social media platforms play an essential role in crisis communication, but analysing crisis-related social media texts is challenging due to their informal nature. Transformer-based pre-trained models like BERT and RoBERTa have shown success in various NLP tasks, but they are not tailored for crisis-related texts. Furthermore, general-purpose sentence encoders are used to generate sentence embeddings, regardless of the textual complexities in crisis-related texts. Advances in applications like text classification, semantic search, and clustering contribute to the effective processing of crisis-related texts, which is essential for emergency responders to gain a comprehensive view of a crisis event, whether historical or real-time. To address these gaps in crisis informatics literature, this study introduces CrisisTransformers, an ensemble of pre-trained language models and sentence encoders trained on an extensive corpus of over 15 billion word tokens from tweets associated with more than 30 crisis events, including disease outbreaks, natural disasters, conflicts, and other critical incidents. We evaluate existing models and CrisisTransformers on 18 crisis-specific public datasets. Our pre-trained models outperform strong baselines across all datasets in classification tasks, and our best-performing sentence encoder improves the state-of-the-art by 17.43% in sentence encoding tasks. Additionally, we investigate the impact of model initialization on convergence and evaluate the significance of domain-specific models in generating semantically meaningful sentence embeddings. The models are publicly available at: https://huggingface.co/crisistransformers.

A Sentence-Embedding-Based Dashboard to Support Teacher Analysis of Learner Concept Maps

Concept mapping is a valuable method to represent a domain of knowledge, also with the aim of supporting educational needs. Students are called upon to construct their own knowledge through a meaningful learning process, linking new concepts to concepts they have already learned, i.e., connecting new knowledge to knowledge they already possess. Moreover, the particular graphic form of a concept map makes it easy for the teacher to construct and interpret both. Consequently, for an educator, the ability to assess concept maps offered by students, facilitated by an automated system, can prove invaluable. This becomes even more apparent in educational settings where there is a large number of students, such as in Massive Open Online Courses. Here, we propose two new measures devised to evaluate the similarity between concept maps based on two deep-learning embedding models: InferSent and Universal Sentence Encoder. An experimental evaluation with a sample of teachers confirms the validity of one such deep-learning model as the baseline of the new similarity measure. Subsequently, we present a proof-of-concept dashboard where the measures are used to encode a concept map in a 2D space point, with the aim of helping teachers monitor students’ concept-mapping activity.

Code Comments: A Way of Identifying Similarities in the Source Code

This study investigates whether analyzing the code comments available in the source code can effectively reveal functional similarities within software. The authors explore how both machine-readable comments (such as linter instructions) and human-readable comments (in natural language) can contribute towards measuring the code similarity. For the former, the work is relying on computing the cosine similarity over the one-hot encoded representation of the machine-readable comments, while for the latter, the focus is on detecting similarities in English comments, using threshold-based computations against the similarity measurements obtained using models based on Levenshtein distances (for form-based matches), Word2Vec (for contextual word representations), as well as deep learning models, such as Sentence Transformers or Universal Sentence Encoder (for semantic similarity). For evaluation, this research has analyzed the similarities between different source code versions of the open-source code editor, VSCode, based on existing ESlint-specific directives, as well as applying natural language processing techniques on incremental releases of Kubernetes, an open-source system for automating containerized application management. The experiments outlines the potential for detecting code similarities solely based on comments, and observations indicate that models like Universal Sentence Encoder are providing a favorable balance between recall and precision. This research is integrated into Project Martial, an open-source project for automatic assistance in detecting plagiarism in software.

Revisiting clustering for efficient unsupervised dialogue structure induction

In the development of a task-oriented dialogue system, defining the dialogue structure is a time-consuming task. Hence, several works have looked into automatically inferring it from data, e.g., actual conversations between a customer and a support agent. To recover such dialogue structure, recent methods based on discrete variational models learn to jointly encode and cluster utterances in dialogue states, but (i) represent utterances by only considering preceding dialogue context, and (ii) are slow to train since they are optimized with a compute-expensive decoding objective. We revisit and improve upon an existing efficient pipeline approach, commonly adopted as a baseline, that first encodes utterances and then clusters them with k-means to induce the dialogue structure. However, the existing approach represents utterances as bag-of-words or skip-thought vectors, which have been shown to perform poorly in semantic similarity tasks, and without considering dialogue context. We therefore first investigate the use of more powerful transformer-based encoders for encoding utterances. Next, we propose ellodar, a method for learning representations that capture both preceding and subsequent dialogue context, inspired by word-to-vec training strategies. ellodar is efficient since representations are learned directly in the encoding space by finetuning just a single linear layer on top of a frozen sentence encoder with a vector-to-vector regression training objective. Extensive experiments on representative datasets for dialogue structure induction (SimDial, Schema Guided Dialogues, DSTC2, and CamRest676) demonstrate that in terms of effectiveness to induce the correct dialogue structure, (i) clustering utterances represented by transformed-based encoders improves recent joint models by 13%–32% on standard cluster metrics, and (ii) clustering ellodar’s representations yields additional improvements ranging from +20% to +26%, with speedups of ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{10}$$\\end{document}–104\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{10}^{\ extbf{4}}$$\\end{document} compared to the recent joint models.

Unveiling Business Activity Patterns of Digital Transformation through K-Means Clustering with Universal Sentence Encoder in Transport and Logistics Sectors

Digital transformation brings several key benefits to transportation and logistics firms, such as operational efficiency, improved customer experience, and supply chain visibility. This study seeks to identify and categorize business activities within the transportation and logistics sector, focusing on themes related to digital transformation. Additionally, it analyses the impact of firm size and sales performance. Data were sourced from 24 annual reports of four firms listed in the SET, covering the study period of 2017-2022. The study used context-free grammar-based verb patterns and a custom-made dictionary to extract business activities. The study used the Universal Sentence Encoder to represent the text and relied on the Dunn-Index and Silhouette score to determine the optimal number of clusters (both 8 clusters). Subsequently, K-means clustering was performed using these 8 clusters, leading to the categorization of eight digital transformation themes. Among these themes, profit management and regulation compliance are interesting discoveries, as they are rarely mentioned in previous studies. A total of 104 business activities were extracted as a result. The study also found that Online-to-Offline activity is an effective method for business model innovation, as well as a contemporary marketing tool.

Unsupervised Extractive Summarization with Learnable Length Control Strategies

Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-labelled summaries for training and thus can be easily applied for documents with different types, domains or languages. Most of existing unsupervised methods including TextRank and PACSUM rely on graph-based ranking on sentence centrality. However, this scorer can not be directly applied in end-to-end training, and the positional-related prior assumption is often needed for achieving good summaries. In addition, less attention is paid to length-controllable extractor, where users can decide to summarize texts under particular length constraint. This paper introduces an unsupervised extractive summarization model based on a siamese network, for which we develop a trainable bidirectional prediction objective between the selected summary and the original document. Different from the centrality-based ranking methods, our extractive scorer can be trained in an end-to-end manner, with no other requirement of positional assumption. In addition, we introduce a differentiable length control module by approximating 0-1 knapsack solver for end-to-end length-controllable extracting. Experiments show that our unsupervised method largely outperforms the centrality-based baseline using a same sentence encoder. In terms of length control ability, via our trainable knapsack module, the performance consistently outperforms the strong baseline without utilizing end-to-end training. Human evaluation further evidences that our method performs the best among baselines in terms of relevance and consistency.

Gated Recursive and Sequential Deep Hierarchical Encoding for Detecting Incongruent News Articles

With the increase in misinformation across digital platforms, incongruent news detection is becoming an important research problem. Earlier, researchers have exploited various feature engineering approaches and deep learning models with embedding to capture incongruity between news headlines and their respective bodies. Studies have broadly considered different combinations of bag-of-words-based features, sequential encoding, hierarchical encoding, headline-guided attention-based encoding, and so on, of the text in headlines and bodies. In this article, we focus on addressing two important limitations observed with hierarchical encoding and headline-guided attention-based encoding methods. The existing hierarchical encoding-based studies limit the hierarchical structure of the body of a news article to paragraph level only, undermining the importance of incorporating long-term dependence from word level to sentence, paragraph, and body. Furthermore, the existing headline-guided attention-based encoding focuses on contextually similar contents in the body of the headline, undermining the importance of incorporating contextually dissimilar contents. Motivated by the above observations, this article proposes a gated recursive and sequential deep hierarchical encoding (GraSHE) method for detecting incongruent news articles by extending the hierarchical structure of the news body from the body to the word level and incorporating incongruity weight. From various experimental setups over three publicly available benchmarks datasets, the experimental results indicate that the proposed model outperforms baseline models with bag-of-word-based features, sequential, hierarchical, and headline-guided attention-based encoding methods. To further validate the performance of the proposed model, we conduct several ablation studies. The following key observations can be made from the ablation study: 1) models with hierarchical encoding outperform models with nonhierarchical encoding; 2) recursive encoding of sentences boosts the performance of models as compared with sequential encoding of sentences within paragraphs; and 3) incongruent news article detection is domain-dependent. Incorporating explicit features further boosts the performance of proposed model and also decreases the domain dependence of models.

Novelty Evaluation using Sentence Embedding Models in Open-ended Cocreative Problem-solving

AbstractCollaborative creativity (cocreativity) is essential to generate original solutions for complex challenges faced in organisations. Effective cocreativity requires the orchestration of cognitive and social processes at a high level. Artificial Intelligence (AI) techniques, specifically deep learning sentence embedding models, have emerged as valuable tools for evaluating creativity and providing feedback to improve the cocreation process. This paper examines the implications of sentence embedding models for evaluating the novelty of open-ended ideas generated within the context of real-life project-based learning. We report a case study research design involving twenty-five secondary students, where a cocreative process was developed to solve a complex, open-ended problem. The novelty of the co-generated ideas was evaluated using eight pre-trained sentence embedding models and compared with experts’ evaluations. Correlation and regression analyses were performed to examine the reliability of the sentence embedding models in comparison to the experts’ scoring. Our findings disclose that sentence embedding models can solve the challenge of evaluating open-ended ideas generated during the cocreative process. Moreover, the results show that two-sentence embedding models significantly correlate better with experts- Universal Sentence Encoder Transformer (USE-T) and USE Deep Averaging Network (USE-DAN). These findings have a high pedagogical value as they successfully evaluate the novelty generated in a real problem-based environment that uses technology to promote key cocreative processes. Furthermore, the real-time evaluation facilitated by these models can have a strong pedagogical impact because it can provide valuable feedback to teachers and students, thereby optimising collaborative ideation processes and promoting effective cocreative teaching and learning methodologies.

Can Generative AI be used to improve doctor/patient relationship?

The aim of this paper is to see how Generative AI can be used in the doctor/patient relationship. We have studied the most frequently technical asked questions by second trimester pregnant patients. We have addressed these questions to ChatGPT-3.5, an OB-GYN resident, and an OB-GYN specialist. Using the Universal Sentence Encoder and the cosine similarity, we have compared the answers. We have proved that Generative AI is a wonderful tool that can be used to improve the doctor/patient relationship, by making the diagnosis and treatment more comprehensive and interpretable for the average patient.