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Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Automatic image description, also known as image captioning, aims to describe the elements included in an image and their relationships. This task involves two research fields: computer vision and natural language processing; thus, it has received much attention in computer science. In this review paper, we follow the Kitchenham review methodology to present the most relevant approaches to image description methodologies based on deep learning. We focused on works using convolutional neural networks (CNN) to extract the characteristics of images and recurrent neural networks (RNN) for automatic sentence generation. As a result, 53 research articles using the encoder-decoder approach were selected, focusing only on supervised learning. The main contributions of this systematic review are: (i) to describe the most relevant image description papers implementing an encoder-decoder approach from 2014 to 2022 and (ii) to determine the main architectures, datasets, and metrics that have been applied to image description. [ABSTRACT FROM AUTHOR]

Job seekers find themselves increasingly duped and misled by fraudulent job advertisements, posing a threat to their privacy, security and well-being. There is a clear need for solutions that can protect innocent job seekers. Existing approaches to detecting fraudulent jobs do not scale well, function like a black-box, and lack interpretability, which is essential to guide applicants' decision-making. Moreover, commonly used lexical features may be insufficient as the representation does not capture contextual semantics of the underlying document. Hence, this paper explores to what extent different categorizations of fraudulent jobs can be classified. In addition, this paper seeks to find what type of features are most relevant in classifying the type of fraudulent job. In this paper, we develop and validate a machine learning system for identifying identity theft, corporate identity theft and multi-level marketing amongst fraudulent job advertisements. We utilized four classes of features: empirical rule set-based features, bag-of-word models, most recent state-of-the-art word embeddings and transformer models for various machine learning classifiers. The machine learning models were validated by evaluating them on a publicly available job description dataset. Our results indicate that the word embeddings and transformer-based features consistently outperformed the handcrafted rule-set based features class. Ultimately, a Gradient Boosting classifier with a combination of empirical rule-set based features, parts-of-speech tags and bag-of-words vectors achieved the best performance with an F1-score of 0.88. [ABSTRACT FROM AUTHOR]

The maturation of artificial intelligence technologies, such as Machine Learning algorithms, Natural Language Processing (NLP), Automatic Speech Recognition (ASR), and Natural Language Generation, is transforming the way users interact with technology. With the increasing prevalence of voice interactions, it is crucial to understand the process of training conversational agents. This systematic review examines how human data is collected for training these agents, with a specific focus on datasets obtained directly from human participation in real-life contexts of need and use. The study follows the PRISMA guidelines, and searches were conducted in Scopus, Web of Science, and ProQuest databases in English, covering the period from 2005 to 2020, to provide a comprehensive overview of published practices until July 2020. A total of 22 papers were included in this review from the search iterations. The primary findings of these papers indicate a common use of learning from demonstration/observation and crowdsourcing methods in system training and dataset cataloguing. Additionally, techniques such as handwriting and sentence labelling, as well as Wizard-of-Oz based studies, were employed in the research. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid embedding to enhance scope of word embeddings by augmenting these with natural language processing operations. We primarily focus on the proposal of new hybrid word embedding generated by augmenting BERT embedding vectors with polarity score. The paper further proposes a new deep learning architecture inspired by the use of convolutional neural network for feature extraction and a bidirectional recurrent network for contextual and temporal feature exploitation. Use of CNN with hybrid embedding allowed the network to extract even the higher-level styles in writing, while bidirectional RNN helped in understanding context. The paper justifies that the proposed architecture and hybrid embedding improves performance of sentiment classification system by performing a large number of experiments and testing on a number of deep learning architectures. The architecture on new hybrid embeddings incurred an accuracy of 96%, which is a significant improvement when compared with recent studies in the literature. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of online education course recommendations through a knowledge graph combined with reinforcement learning, and proposes a deep learning-based joint extraction method of course knowledge entities and relations in the education domain. This joint extraction method can extract both course knowledge entities and their relationships from the unstructured text of online courses, thus alleviating the problem of error propagation. On the other hand, since some parameters in the joint model can be shared by the entity identification task and the relationship classification task, this helps the model to capture the interaction between the two subtasks. Similar courses are judged based on the extracted course knowledge points, while course knowledge chains are generated based on the relationships between course knowledge points. In terms of user learning behavior, by analyzing user online learning behavior data, this paper uses five variables, namely the number of learning hours, the number of discussions, the number of visits, the number of task points completed, and the number of learning courses, to judge and cluster user similarity using an information entropy-based learner behavior weight assignment method. Based on the course knowledge map, this paper firstly constructs a learner model with four dimensions of basic learner profile, cognitive level, learning style, and historical learning records. Secondly, it predicts the target knowledge points of learners based on their learning data using the Armorial algorithm and maps them in the knowledge map, then uses natural language processing related techniques to find the conceptual similarity between knowledge points and proposes a deep recommendation strategy based on the knowledge graph correlations. At the same time, the recommended courses based on learners' behavioral data are more relevant and accurate, which greatly improves learners' efficiency and satisfaction in the learning process. [ABSTRACT FROM AUTHOR]

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers on a hierarchical semantic compositional model (HSCM), which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework. [ABSTRACT FROM AUTHOR]

Knowledge base construction (KBC) using AI has been one of the key goals of this highly popular technology since its emergence, as it helps to comprehend everything, including relations, around us. The construction of knowledge bases can summarize a piece of text in a machine-processable and understandable way. This can prove to be valuable and assistive to knowledge engineers. In this paper, we present the application of natural language processing in the construction of knowledge bases. We demonstrate how a trained bidirectional long short-term memory or bi-LSTM neural network model can be used to construct knowledge bases in accordance with the exact ISO26262 definitions as defined in the GENIAL! Basic Ontology. We provide the system with an electronic text document from the microelectronics domain and the system attempts to create a knowledge base from the available information in textual format. This information is then expressed in the form of graphs when queried by the user. This method of information retrieval presents the user with a much more technical and comprehensive understanding of an expert piece of text. This is achieved by applying the process of named entity recognition (NER) for knowledge extraction. This paper provides a result report of the current status of our knowledge construction process and knowledge base content, as well as describes our challenges and experiences. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Literature classification is a fundamental task in library and information service, which is of great value for information resource management, and literature retrieval and acquisition. Deep learning-based literature classification methods are the current mainstream methods in text classification, which employ neural networks to model and use the textual content for literature classification. This approach only utilizes the information of the literature itself, but ignores the knowledge of the association between the literature. By observing the data, we found that literature in the same category tends to share more keyword information. The literature can build association networks through keywords to form structural relationships between literature. We attempt to utilize this structural in-formation to improve the performance of literature classification. [Methods/Process] This paper proposes a method that can model the structural representation of the literature and employ this representation to enhance traditional literature classification methods. Specifi-cally, we first constructed a large-scale keyword dictionary based on the collected data from about 930,000 documents. Second, we extracted the keyword set from the titles and abstracts of papers by a two-way maximum matching algorithm and constructed the keyword-literature graph data with the literature and keywords as nodes and the inclusion relationship between the documents and keywords as edges. The literature was connected with each other by keywords. Furthermore, we employed graph convolutional neural network to model the literature graph and learn the representation of literature and keywords in the keyword-literature graph. The literature representation generated by graph neural network contained the structural relationships between the literature. In addition, we employed Bert+BiLSTM to model the textual content representation of literature. Finally, the structural and textual representations of the literature were concatenated, and the classification of the literature was performed based on this representation. [Results/Conclusions] We constructed a literature classification dataset containing 423 classes and divided the training set, validation set and test set according to the ratio of 8:1:1. We conducted literature classification experiments on this dataset. The experimental results show that the structural information of literature can effectively enhance the performance of traditional literature classification methods. The results of the stripping experiments also show that the structural information alone is insufficient for the literature classification task. Through detailed analysis of the error data, we found that the model still has problems in handling some less frequent keywords and concepts. In the future, we plan to use small-sample learning methods to solve the classification problem for literature categories with less data. [ABSTRACT FROM AUTHOR]

This paper presents MoCoUTRL: a Momentum Contrastive Framework for Unsupervised Text Representation Learning. This model improves two aspects of recently popular contrastive learning algorithms in natural language processing (NLP). Firstly, MoCoUTRL employs multi-granularity semantic contrastive learning objectives, enabling a more comprehensive understanding of the semantic features of samples. Secondly, MoCoUTRL uses a dynamic dictionary to act as the approximately ground-truth representation for each token, providing the pseudo labels for token-level contrastive learning. The MoCoUTRL can extend the use of pre-trained language models (PLM) and even large-scale language models (LLM) into a plug-and-play semantic feature extractor that can fuel multiple downstream tasks. Experimental results on several publicly available datasets and further theoretical analysis validate the effectiveness and interpretability of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Identifying failure modes is an important task to improve the design and reliability of a product and can also serve as a key input in sensor selection for predictive maintenance. Failure mode acquisition typically relies on experts or simulations which require significant computing resources. With the recent advances in Natural Language Processing (NLP), efforts have been made to automate this process. However, it is not only time consuming, but extremely challenging to obtain maintenance records that list failure modes. Unsupervised learning methods such as topic modeling, clustering, and community detection are promising approaches for automatic processing of maintenance records to identify failure modes. However, the nascent state of NLP tools combined with incompleteness and inaccuracies of typical maintenance records pose significant technical challenges. As a step towards addressing these challenges, this paper proposes a framework in which online active learning is used to identify failure modes from maintenance records. Active learning provides a semi-supervised machine learning approach, allowing for a human in the training stage of the model. The hypothesis of this paper is that the use of a human to annotate part of the data and train a machine learning model to annotate the rest is more efficient than training unsupervised learning models. Results demonstrate that the model is trained with annotating less than ten percent of the total available data. The framework is able to achieve ninety percent (90%) accuracy in the identification of failure modes in test cases with an F-1 score of 0.89. This paper also demonstrates the effectiveness of the proposed framework with both qualitative and quantitative measures. [ABSTRACT FROM AUTHOR]

A growing number of papers on style transfer for texts rely on information decomposition. The performance of the resulting systems is usually assessed empirically in terms of the output quality or requires laborious experiments. This paper suggests a straightforward information theoretical framework to assess the quality of information decomposition for latent representations in the context of style transfer. Experimenting with several state-of-the-art models, we demonstrate that such estimates could be used as a fast and straightforward health check for the models instead of more laborious empirical experiments. [ABSTRACT FROM AUTHOR]

As free online encyclopedias with massive volumes of content, Wikipedia and Wikidata are key to many Natural Language Processing (NLP) tasks, such as information retrieval, knowledge base building, machine translation, text classification, and text summarization. In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization. The dataset consists of over 80k English samples on 6987 topics. We set up a two-phase summarization method — description generation (Phase I) and candidate ranking (Phase II) — as a strong approach that relies on transfer and contrastive learning. For description generation, T5 and BART show their superiority compared to other small-scale pre-trained models. By applying contrastive learning with the diverse input from beam search, the metric fusion-based ranking models outperform the direct description generation models significantly up to ≈ 22 ROUGE in topic-exclusive split and topic-independent split. Furthermore, the outcome descriptions in Phase II are supported by human evaluation in over 45.33% chosen compared to 23.66% in Phase I against the gold descriptions. In the aspect of sentiment analysis, the generated descriptions cannot effectively capture all sentiment polarities from paragraphs while doing this task better from the gold descriptions. The automatic generation of new descriptions reduces the human efforts in creating them and enriches Wikidata-based knowledge graphs. Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions. Finally, we expect WikiDes to be a useful dataset for related works in capturing salient information from short paragraphs. The curated dataset is publicly available at: https://github.com/declare-lab/WikiDes. • Wikipedia- and Wikidata-based dataset to generate short descriptions from paragraphs. • Two-phase summarization — candidate description generation and ranking. • Sentiment consistency analysis between paragraph and gold description. [ABSTRACT FROM AUTHOR]

Negative social media usage during the COVID-19 pandemic has highlighted the importance of understanding the spread of misinformation and toxicity in public online discussions. In this paper, we propose a novel unsupervised method to discover the structure of online COVID-19-related conversations. Our method trains a nine-state Hidden Markov Model (HMM) initialized from a biclustering of 23 features extracted from online messages. We apply our method to 16,000 conversations (1.5 million messages) that took place on the Facebook pages of 15 Canadian newspapers following COVID-19 news items, and show that it can effectively extract the conversation structure and discover the main themes of the messages. Furthermore, we demonstrate how the PageRank algorithm and the conversation graph discovered can be used to simulate the impact of five different moderation strategies, which makes it possible to easily develop and test new strategies to limit the spread of harmful messages. Although our work in this paper focuses on the COVID-19 pandemic, the methodology is general enough to be applied to handle communications during future pandemics and other crises, or to develop better practices for online community moderation in general. [ABSTRACT FROM AUTHOR]

Text summarization is an information compression technology to extract important information from long text, which has become a challenging research direction in the field of natural language processing. At present, the text summary model based on deep learning has shown good results, but how to more effectively model the relationship between words, more accurately extract feature information and eliminate redundant information is still a problem of concern. This paper proposes a graph neural network model GA-GNN based on gated attention, which effectively improves the accuracy and readability of text summarization. First, the words are encoded using a concatenated sentence encoder to generate a deeper vector containing local and global semantic information. Secondly, the ability to extract key information features is improved by using gated attention units to eliminate local irrelevant information. Finally, the loss function is optimized from the three aspects of contrastive learning, confidence calculation of important sentences, and graph feature extraction to improve the robustness of the model. Experimental validation was conducted on a CNN/Daily Mail dataset and MR dataset, and the results showed that the model in this paper outperformed existing methods. [ABSTRACT FROM AUTHOR]

The development of China's manufacturing industry has received global attention. However, research on the distribution pattern, changes, and driving forces of the manufacturing industry has been limited by the accessibility of data. This study proposes a method for classifying based on natural language processing. A case study was conducted employing this method, hotspot detection and driving force analysis, wherein the driving forces industrial development during the "13th Five-Year plan" period in Jiangsu province were determined. The main conclusions of the empirical case study are as follows. 1) Through the acquisition of Amap's point-of-interest (POI, a special point location that commonly used in modern automotive navigation systems.) data, an industry type classification algorithm based on the natural language processing of POI names is proposed, with Jiangsu Province serving as an example. The empirical test shows that the accuracy was 95%, and the kappa coefficient was 0.872. 2) The seven types of manufacturing industries including the pulp and paper (PP) industry, metallurgical chemical (MC) industry, pharmaceutical manufacturing (PM) industry, machinery and electronics (ME) industry, wood furniture (WF) industry, textile clothing (TC) industry, and agricultural and food product processing (AF) industry are drawn through a 1 km× 1km projection grid. The evolution map of the spatial pattern and the density field hotspots are also drawn. 3) After analyzing the driving forces of the changes in the number of manufacturing industries mentioned above, we found that manufacturing base, distance from town, population, GDP per capita, distance from the railway station were the significant driving factors of changes in the manufacturing industries mentioned above. The results of this research can help guide the development of manufacturing industries, maximize the advantages of regional factors and conditions, and provide insight into how the spatial layout of the manufacturing industry could be optimized. [ABSTRACT FROM AUTHOR]

Medical image segmentation is crucial for medical image processing and the development of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks (CNNs) have been widely adopted for medical image segmentation and have achieved significant success. UNet, which is based on CNNs, is the mainstream method used for medical image segmentation. However, its performance suffers owing to its inability to capture long-range dependencies. Transformers were initially designed for Natural Language Processing (NLP), and sequence-to-sequence applications have demonstrated the ability to capture long-range dependencies. However, their abilities to acquire local information are limited. Hybrid architectures of CNNs and Transformer, such as TransUNet, have been proposed to benefit from Transformer's long-range dependencies and CNNs' low-level details. Nevertheless, automatic medical image segmentation remains a challenging task due to factors such as blurred boundaries, the low-contrast tissue environment, and in the context of ultrasound, issues like speckle noise and attenuation. In this paper, we propose a new model that combines the strengths of both CNNs and Transformer, with network architectural improvements designed to enrich the feature representation captured by the skip connections and the decoder. To this end, we devised a new attention module called Three-Level Attention (TLA). This module is composed of an Attention Gate (AG), channel attention, and spatial normalization mechanism. The AG preserves structural information, whereas channel attention helps to model the interdependencies between channels. Spatial normalization employs the spatial coefficient of the Transformer to improve spatial attention akin to TransNorm. To further improve the skip connection and reduce the semantic gap, skip connections between the encoder and decoder were redesigned in a manner similar to that of the UNet++ dense connection. Moreover, deep supervision using a side-output channel was introduced, analogous to BASNet, which was originally used for saliency predictions. Two datasets from different modalities, a CT scan dataset and an ultrasound dataset, were used to evaluate the proposed UNet architecture. The experimental results showed that our model consistently improved the prediction performance of the UNet across different datasets. [ABSTRACT FROM AUTHOR]

With the surge of searching and reading online health‐based articles, maintaining the quality and credibility of online health‐based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health‐related articles' credibility. The paper proposed an Attention‐based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed "Pubhealth," which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1‐score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short‐term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN‐CNN. The experimental results showed that the proposed model produced state‐of‐the‐art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1‐score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles. [ABSTRACT FROM AUTHOR]

Automatic Keyphrase Extraction involves identifying essential phrases in a document. These keyphrases are crucial in various tasks, such as document classification, clustering, recommendation, indexing, searching, summarization, and text simplification. This paper introduces a platform that integrates keyphrase datasets and facilitates the evaluation of keyphrase extraction algorithms. The platform includes BibRank, an automatic keyphrase extraction algorithm that leverages a rich dataset obtained by parsing bibliographic data in BibTeX format. BibRank combines innovative weighting techniques with positional, statistical, and word co-occurrence information to extract keyphrases from documents. The platform proves valuable for researchers and developers seeking to enhance their keyphrase extraction algorithms and advance the field of natural language processing. [ABSTRACT FROM AUTHOR]

Students reflective essays in engineering education provide insight and context for instructional modification and assessment. However, the assessment of reflective essays numbering in thousands can be time-consuming This is notably important when trying to find specific changes in focus from one essay to another and measur ing how strong those changes are across multiple corpora of essays In this paper we describe and demonstrate an au tomated text analysis method for the at-scale, corpus normalized analysis of reflective essays. We apply it to quantitatively measure whether the modification of an undergraduate mechanical engineering course had the conjectured impact of a stronger emphasis on teamwork. Our analytical method is a "pipeline" composed of Text Mining (TM), Natural Language Processing (NLP), and Recurrence Quantification Analysis (ROA). We use this method to measure the presence of a specific thematic element in reflective essays to confirm the impact of the modification of a team-driven, model-based engineering design course. The original course and its modification were visualized using Sandovals conjecture mapping framework. The novel innovation of this approach is that the input (text from hundreds of reflective essays, sourced one at a time) when passed through this pipeline quickly produces a quantitative indication of the presence of thematic el ements and their recurence normalized across a corpus of hundreds of essays. A comparison of this quantitative indicator across separate corpora (each corpus of essays is for a different year) of reflective essays signaled a change in student focus toward the conjectured outcome We conclude that the TM-NLP-ROA pipeline can be applied for quick and at-scale extraction of the relative magnitude of thematic statements from reflective essays We observe that our conjectured redesign had the impact that we desired [ABSTRACT FROM AUTHOR]

The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems. [ABSTRACT FROM AUTHOR]

Background: Data mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learning-based approaches used for natural language processing (NLP) in healthcare today all struggle with various shortcomings related to performance, efficiency, or transparency. Methods: In this paper, we address these issues by presenting a novel method for NLP that implements unsupervised learning of word embeddings, semi-supervised learning for simplified and accelerated clinical vocabulary and concept building, and deterministic rules for fine-grained control of information extraction. The clinical language is automatically learnt, and vocabulary, concepts, and rules supporting a variety of NLP downstream tasks can further be built with only minimal manual feature engineering and tagging required from clinical experts. Together, these steps create an open processing pipeline that gradually refines the data in a transparent way, which greatly improves the interpretable nature of our method. Data transformations are thus made transparent and predictions interpretable, which is imperative for healthcare. The combined method also has other advantages, like potentially being language independent, demanding few domain resources for maintenance, and able to cover misspellings, abbreviations, and acronyms. To test and evaluate the combined method, we have developed a clinical decision support system (CDSS) named Information System for Clinical Concept Searching (ICCS) that implements the method for clinical concept tagging, extraction, and classification. Results: In empirical studies the method shows high performance (recall 92.6%, precision 88.8%, F-measure 90.7%), and has demonstrated its value to clinical practice. Here we employ a real-life EHR-derived dataset to evaluate the method's performance on the task of classification (i.e., detecting patient allergies) against a range of common supervised learning algorithms. The combined method achieves state-of-the-art performance compared to the alternative methods we evaluate. We also perform a qualitative analysis of common word embedding methods on the task of word similarity to examine their potential for supporting automatic feature engineering for clinical NLP tasks. Conclusions: Based on the promising results, we suggest more research should be aimed at exploiting the inherent synergies between unsupervised, supervised, and rule-based paradigms for clinical NLP. [ABSTRACT FROM AUTHOR]

One area of active research is the use of natural language processing (NLP) to mine biomedical texts for sets of triples (subject-predicate-object) for knowledge graph (KG) construction. While statistical methods to mine co-occurrences of entities within sentences are relatively robust, accurate relationship extraction is more challenging. Herein, we evaluate the Global Network of Biomedical Relationships (GNBR), a dataset that uses distributional semantics to model relationships between biomedical entities. The focus of our paper is an evaluation of a subset of the GNBR data; the relationships between chemicals and genes/proteins. We use Evotec's structured 'Nexus' database of >2.76M chemical-protein interactions as a ground truth to compare with GNBRs relationships and find a micro-averaged precision-recall area under the curve (AUC) of 0.50 and a micro-averaged receiver operating characteristic (ROC) curve AUC of 0.71 across the relationship classes 'inhibits', 'binding', 'agonism' and 'antagonism', when a comparison is made on a sentence-by-sentence basis. We conclude that, even though these micro-average scores are modest, using a high threshold on certain relationship classes like 'inhibits' could yield high fidelity triples that are not reported in structured datasets. We discuss how different methods of processing GNBR data, and the factuality of triples could affect the accuracy of NLP data incorporated into knowledge graphs. We provide a GNBR-Nexus(ChEMBL-subset) merged datafile that contains over 20,000 sentences where a protein/gene-chemical co-occur and includes both the GNBR relationship scores as well as the ChEMBL (manually curated) relationships (e.g., 'agonist', 'inhibitor') —this can be accessed at https://doi.org/10.5281/zenodo.8136752. We envisage this being used to aid curation efforts by the drug discovery community. [ABSTRACT FROM AUTHOR]

This paper describes how one researcher learned to overcome artificial intelligence (AI) paralysis and embrace ChatPDF. This freely available AI application uses natural language processing (NLP) to respond to user queries about an uploaded PDF. Researcher insights from experimenting with the AI tool ChatPDF for qualitative data analysis are presented, highlighting the advantages, pitfalls, and application-related considerations. As a two-phase curiosity experiment, the researcher engaged in a theory-building exercise to explore key concepts for understanding when using ChatPDF to assist researchers in qualitative data analysis. The experiment generated insights about the purposeful use of AI tools that incorporate NLP for analysis and the risks of inaccuracy when researchers are not familiar with the data or skilled in prompt engineering. Insights raise questions about whether ChatPDF is a viable research assistant for qualitative researchers, ethical issues with specific forms of qualitative data, and the potential of AI tools for community and student researchers. [ABSTRACT FROM AUTHOR]

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, a feature projection module based on vision transformers, and a text decoder. In the first stage, the system encodes the input images via the CLIP (contrastive language–image pretraining) image encoder. The CLIP image features are then passed through a vision transformer and the final image features to be linked with the textual features are obtained. In the last stage, a deep text decoder exploiting a BERT (bidirectional encoder representations from transformers) based model is used to generate the image cations. Furthermore, unlike the related works, a natural language-based linguistic model called NLLB (No Language Left Behind) was employed to produce Turkish captions from the original English captions. Extensive performance evaluation studies were carried out and widely known image captioning quantification metrics such as BLEU, METEOR, ROUGE-L, and CIDEr were measured for the proposed model. Within the scope of the experiments, quite successful results were observed on MS COCO and Flickr30K datasets, two known and prominent datasets in this field. As a result of the comparative performance analysis by taking the existing reports in the current literature on TIC into consideration, it was witnessed that the proposed model has superior performance and outperforms the related works on TIC so far. Project details and demo links of TRCaptionNet will also be available on the project’s GitHub page (https://github.com/serdaryildiz/TRCaptionNet). [ABSTRACT FROM AUTHOR]

This paper presents a framework based on natural language processing and first-order logic aiming at instantiating cognitive chatbots. The proposed framework leverages two types of knowledge bases interacting with each other in a meta-reasoning process. The first one is devoted to the reactive interactions within the environment, while the second one to conceptual reasoning. The latter exploits a combination of axioms represented with rich semantics and abduction as pre-stage of deduction, dealing also with some of the state-of-the-art issues in the natural language ontology domain. As a case study, a Telegram chatbot system has been implemented, supported by a module which automatically transforms polar and wh-questions into one or more likely assertions, so as to infer Boolean values or snippets with variable length as factoid answer. The conceptual knowledge base is organized in two layers, representing both long- and short-term memory. The knowledge transition between the two layers is achieved by leveraging both a greedy algorithm and the engine's features of a NoSQL database, with promising timing performance if compared with the adoption of a single layer. Furthermore, the implemented chatbot only requires the knowledge base in natural language sentences, avoiding any script updates or code refactoring when new knowledge has to income. The framework has been also evaluated as cognitive system by taking into account the state-of-the art criteria: the results show that AD-Caspar is an interesting starting point for the design of psychologically inspired cognitive systems, endowed of functional features and integrating different types of perception. [ABSTRACT FROM AUTHOR]

Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is largely due to SEI's demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter's transmitted signals. These distinctive and unintentional features are attributed to slight manufacturing and assembly variations within and between the components, sub-systems, and systems comprising an emitter's Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors, primarily focusing on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism; thus, we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure. [ABSTRACT FROM AUTHOR]

Under the background of intelligent manufacturing, industrial systems are developing in a more complex and intelligent direction. Equipment maintenance management is facing significant challenges in terms of maintenance workload, system reliability and stability requirements and the overall skill requirements of maintenance personnel. Equipment maintenance management is also developing in the direction of intellectualization. It is important to have a method to construct a domain knowledge graph and to organize and utilize it. As is well known, traditional equipment maintenance is mainly dependent on technicians, and they are required to be very familiar with the maintenance manuals. But it is very difficult to manage and exploit a large quantity of knowledge for technicians in a short time. Hence a method to construct a knowledge graph (KG) for equipment maintenance is proposed to extract knowledge from manuals, and an effective maintenance scheme is obtained with this knowledge graph. Firstly, a joint model based on an enhanced BERT-Bi-LSTM-CRF is put forward to extract knowledge automatically, and a Cosine and Inverse Document Frequency (IDF) based on semantic similarity a presented to eliminate redundancy in the process of the knowledge fusion. Finally, a Decision Support System (DSS) for equipment maintenance is developed and implemented, in which knowledge can be extracted automatically and provide an equipment maintenance scheme according to the requirements. The experimental results show that the joint model used in this paper performs well on Chinese text related to equipment maintenance, with an F1 score of 0.847. The quality of the knowledge graph constructed after eliminating redundancy is also significantly improved. [ABSTRACT FROM AUTHOR]

Causality detection, as a more fine‐grained task than causality extraction, which aims to detect the components that represent the cause and effect in sentence‐level texts with causality, is a significant task in the field of Natural Language Processing (NLP). Previous research on causality detection has concentrated on text token features whilst ignoring the dependency attributes between tokens. In this paper, we propose a model that uses the Edge‐featured Graph Attention Network based on dependency‐directed graphs for the causality detection task. To begin, we convert the texts with causality into the representation of dependency‐directed graphs (DDG), which regard the dependency attributes between tokens as edge features. Then we use Edge‐featured Graph Attention Network to aggregate the node and edge features of DDG. Finally, we put the graph embedding into Bi‐directional Long Short‐Term Memory (BiLSTM) layer to learn the dependencies between forward and backward long‐distance nodes in DDG. Experiments on three datasets prove that this method achieves better performance in precision, recall, and other evaluation metrics compared with other methods. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of cryptocurrency and stock trends by using data from social online communities and platforms to assess their contribution in terms of predictive power. We extend recent developments in the field by exploiting a combination of stochastic neural networks (NNs), an extension of standard NNs, natural language processing (NLP) to extract sentiment from Twitter, and an external evolutionary algorithm for optimal parameter setting to predict the short-term trend direction. Our results point to good and robust accuracy over time and across different market regimes. Furthermore, we propose to exploit recent advances in sentiment analysis to reassess its role in financial forecasting; in this way, we contribute to the empirical literature by showing that predictions based on sentiment analysis are not found to be significantly different from predictions based on historical data. Nonetheless, compared to stock markets, we find that the accuracy of trend predictions with sentiment analysis is on average much higher for cryptocurrencies. [ABSTRACT FROM AUTHOR]

The purpose of this study is to show how machine learning can be leveraged as a tool to govern social impact and drive fair and equitable investments. Many organizations today are establishing financial inclusion goals to promote social impact and have been increasing their investments in this space. Financial inclusion is the opportunity for individuals and businesses to have access to affordable financial products including loans, credit, and insurance that they may otherwise not have access to with traditional financial institutions. Peer-to-peer (P2P) lending serves as a platform that can support and foster financial inclusion and influence social impact and is becoming more popular today as a resource to underserved communities. Loans issued through P2P lending can fund projects and initiatives focused on climate change, workforce diversity, women's rights, equity, labor practices, natural resource management, accounting standards, carbon emissions, and several other areas. With this in mind, AI can be a powerful governance tool to help manage risks and promote opportunities for an organization's financial inclusion goals. In this paper, we explore how AI, specifically machine learning, can help manage the P2P platform Kiva's investment risks and deliver impact, emphasizing the importance of prediction model retraining to account for regulatory and other changes across the P2P landscape to drive better decision-making. As part of this research, we also explore how changes in important model variables affect aggregate model predictions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a systematic literature review of the existing research, published between 2006 and 2023, in the field of artificial intelligence for management information systems. Of the 3946 studies that were considered by the authors, 60 primary studies were selected for analysis. The analysis shows that most research is focused on the application of AI for intelligent process automation, with an increasing number of studies focusing on predictive analytics and natural language processing. With respect to the platforms used by AI researchers, the study finds that cloud-based solutions are preferred over on-premises ones. A new research trend of deploying AI applications at the edge of industrial networks and utilizing federated learning is also identified. The need to focus research efforts on developing guidelines and frameworks in terms of ethics, data privacy, and security for AI adoption in MIS is highlighted. Developing a unified digital business strategy and overcoming barriers to user–AI engagement are some of the identified challenges to obtaining business value from AI integration. [ABSTRACT FROM AUTHOR]

State highway agencies (SHAs) often need to cluster or bundle projects in accordance with their scope similarity for various construction management tasks, including historical data-driven time, cost estimating, and project bundling. Conventionally, SHAs categorize similar projects into work types based on subjective judgment about the similarity between major pay items. A few quantitative methods for project similarity determination are found in the literature, but they mostly use one single source of information, either the cost contribution of pay items or the keywords of pay items descriptions, for measuring project similarity. This paper presents the first attempt to integrate multiple information sources for project similarity measurement. This research proposes a novel cost-weighted term frequency-inverse document frequency (CW-TF-IDF) method that incorporates the cost information of pay items into the traditional TF-IDF word embedding method to measure project similarity. The effectiveness of the proposed method in supporting project clustering and bundling was tested using the historical bid data collected from an SHA. The findings showed that the CW-TF-IDF method significantly improves project clustering performance compared to the most recent state-of-the-art method. The CW-TF-IDF method also showed its outperformance in project bundling as it yielded a cosine similarity of over 0.9 for most of the bundled projects in the testing data. This proposed method is expected to help SHAs accurately identify similar projects and eventually improve their project management effectiveness. [ABSTRACT FROM AUTHOR]

Many attempts have been made to overcome the challenges of automating textual emotion detection using different traditional deep learning models such as LSTM, GRU, and BiLSTM. But the problem with these models is that they need large datasets, massive computing resources, and a lot of time to train. Also, they are prone to forgetting and cannot perform well when applied to small datasets. In this paper, we aim to demonstrate the capability of transfer learning techniques to capture the better contextual meaning of the text and as a result better detection of the emotion represented in the text, even without a large amount of data and training time. To do this, we conduct an experiment utilizing a pre-trained model called EmotionalBERT, which is based on bidirectional encoder representations from transformers (BERT), and we compare its performance to RNN-based models on two benchmark datasets, with a focus on the amount of training data and how it affects the models' performance. [ABSTRACT FROM AUTHOR]

The prevalence of cyberbullying on Social Media (SM) platforms has become a significant concern for individuals, organizations, and society as a whole. The early detection and intervention of cyberbullying on social media are critical to mitigating its harmful effects. In recent years, ensemble learning has shown promising results for detecting cyberbullying on social media. This paper presents an ensemble stacking learning approach for detecting cyberbullying on Twitter using a combination of Deep Neural Network methods (DNNs). It also introduces BERT-M, a modified BERT model. The dataset used in this study was collected from Twitter and preprocessed to remove irrelevant information. The feature extraction process involved utilizing word2vec with Continuous Bag of Words (CBOW) to form the weights in the embedding layer. These features were then fed into a convolutional and pooling mechanism, effectively reducing their dimensionality, and capturing the position-invariant characteristics of the offensive words. The validation of the proposed stacked model and BERT-M was performed using well-known model evaluation measures. The stacked model achieved an F1-score of 0.964, precision of 0.950, recall of 0.92 and the detection time reported was 3 min, which surpasses the previously reported accuracy and speed scores for all known NLP detectors of cyberbullying, including standard BERT and BERT-M. The results of the experiment showed that the stacking ensemble learning approach achieved an accuracy of 97.4% in detecting cyberbullying on Twitter dataset and 90.97% on combined Twitter and Facebook dataset. The results demonstrate the effectiveness of the proposed stacking ensemble learning approach in detecting cyberbullying on SM and highlight the importance of combining multiple models for improved performance. [ABSTRACT FROM AUTHOR]

Resume matching is the process of comparing a candidate's curriculum vitae (CV) or resume with a job description or a set of employment requirements. The objective of this procedure is to assess the degree to which a candidate's skills, qualifications, experience, and other relevant attributes align with the demands of the position. Some employment courses guide applicants in identifying the key requirements within a job description and tailoring their experience to highlight these aspects. Conversely, human resources (HR) specialists are trained to extract critical information from numerous submitted resumes to identify the most suitable candidate for their organization. An automated system is typically employed to compare the text of resumes with job vacancies, providing a score or ranking to indicate the level of similarity between the two. However, this process can become time-consuming when dealing with a large number of applicants and lengthy vacancy descriptions. In this paper, we present a dataset consisting of resumes of software developers extracted from a public Telegram channel dedicated to Israeli hi-tech job applications. Additionally, we propose a natural language processing (NLP)-based approach that leverages neural sentence representations, keywords, and named entities to achieve state-of-the-art performance in resume matching. We evaluate our approach using both human and automatic annotations and demonstrate its superiority over the leading resume–vacancy matching algorithm. [ABSTRACT FROM AUTHOR]

Chest X-ray (CXR) images can be used to diagnose a variety of lung diseases, such as tuberculosis, pneumonia, and lung cancer. However, the variation in lung morphology due to differences in age, gender, and the severity of pathology makes high-precision lung segmentation a challenging task. Traditional segmentation networks, such as U-Net, have become the standard architecture and have achieved remarkable results in lung field image segmentation tasks. However, because traditional convolutional operations can only explicitly capture local semantic information, it is difficult to obtain global semantic information, resulting in difficult performance in terms of accuracy requirements in medical practical applications. In recent years, the introduction of Transformer technology to natural language processing has achieved great success in the field of computer vision. In this paper, a new network architecture called TransCotANet is proposed. The network architecture is based on the U-Net architecture with convolutional neural networks (CNNs) as the backbone and extracts global semantic information through symmetric cross-layer connections in the encoder structure, where the encoder stage includes an upsampling module to improve the resolution of the feature map, and uses the dynamic aggregation module CotA to dynamically aggregate multi-scale feature maps and finally obtain more accurate segmentation results. The experimental results show that the method outperformed other methods for lung field image segmentation datasets. [ABSTRACT FROM AUTHOR]

Since online social networks play an increasingly important role in the final voting decision of each individual, political parties and candidates are changing the way of doing politics and campaigning, increasing their digital presence. In this paper, we propose a methodology to analyze and measure the emotions that news agencies express on social media towards candidates and apply it to the 2018 Brazilian elections. The presented method is based on a sentiment analysis and emotion mining by means of machine learning and Natural Language Processing approaches such as Naïve Bayes classification and Stemming calculation. We found that if doing basic sentiment detection, nearly all posts are neutral. However, when we analyze emotions, following Ekman's six basic emotions, we do not find neutrality but clear and identifiable emotions. Next, we present and discuss the associative patterns between news agencies and presidential candidates. Finally, since the candidate that captured the highest and most negative attention emerged victorious in the elections, we discuss the potential importance of having a social media presence, regardless of generating positive or negative emotions. [ABSTRACT FROM AUTHOR]

In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper. [ABSTRACT FROM AUTHOR]

As exploratory research, the actual paper makes an interview with ChatGPT, an artificial intelligence language model designed to understand and generate human-like responses to a wide range of questions and topics. This paper aims to understand the functionality and user engagement of ChatGPT. It concludes that ChatGPT is designed on a transformer-based language model based on deep learning architecture that uses unsupervised learning to generate human-like text. It has a large database and memory system to store previous user responses, and it uses machine learning algorithms and natural language processing techniques to understand user inputs and retrieve information from its database to generate responses. The interview ultimately led to the development of an innovative research paper on Artificial Intelligence Dissociative Identity Disorder (AIDIS). This study: suggests the possibility of AI-based systems developing multiple identities or personas due to their exposure to different types of data and training, explores the potential implications and challenges of such a disorder, including ethical concerns, and the need for new regulations and policies in the field of AI. [ABSTRACT FROM AUTHOR]

Motivation Virus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations. Results We developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases. Availability and implementation ViMRT software is freely available at http://bmtongji.cn:1225/mutation/index. [ABSTRACT FROM AUTHOR]

Affective computing is a rising interdisciplinary field of research spanning the areas from artificial intelligence, natural language processing to cognitive and social sciences. Potential applications comprise of man–machine interaction, healthcare, entertainment, teaching, marketing and many more. Despite the increasing number of papers published in the domains of affective computing, emotion recognition, and human–computer interaction (HCI), there are still gaps in the comprehensive literature review that covers all relevant studies in a single study, which this review attempts to address. As a result, this study provides a systematic literature review (SLR) on existing modalities (unimodals) for emotion recognition, emotion models, and trends in relevant studies by selecting articles published from January 2010 to June 2021. To ensure the retrieval of all relevant studies, a review protocol is used that includes both automatic and manual searches. Based on the research questions, the final 129 papers are reviewed and relevant information is extracted. This SLR provides future research directions to assist novice researchers and practitioners in more efficiently utilizing affective computing techniques. [ABSTRACT FROM AUTHOR]

Intelligent tutoring systems have been highlighted as a tool for learning support, mainly because its adaptation to user's condition and application scenario. In the face of this pandemic situation, learning supporting tools, as tutoring systems, because of their viability for application in remote environment, showed as an alternative for professors and students support. This paper addresses a proposal for content adaptation to learning, applying artificial intelligence techniques to text Generation and natural language processing. Furthermore, this paper considered existence for different learning styles, varying according to student personality and his way to collect information, capable to influence in learning level. This style variation, accordingly, to a model defined in literature, directs content adaptation model to be done, generating an adapted result to each learning style. Personalized content generation process allows an advance in way to use tutoring systems in learning, creating a format for directed content delivery with gains for environment's user, generating a personalized results and conducting to a more effective learning. As a result, expect to obtain a methodology to content generation based on pre-defined learning styles, by mean an automatized process to content adaptation from an initial entry in unique format. [ABSTRACT FROM AUTHOR]

This article is the third paper in a series aimed at the establishment of the authorship of Russian-language texts. This paper considers methods for determining the authorship of classical Russian literary texts, as well as fanfiction texts. The process of determining the author was first considered in the classical version of classification experiments using a closed set of authors, and experiments were also completed for a complicated modification of the problem using an open set of authors. The use of methods to identify the author of the text is justified by the conclusions about the effectiveness of the fastText and Support Vector Machine (SVM) methods with the selection of informative features discussed in our past studies. In the case of open attribution, the proposed methods are based on the author's combination of fastText and One-Class SVM as well as statistical estimates of a vector's similarity measures. The feature selection algorithm for a closed set of authors is chosen based on a comparison of five different selection methods, including the previously considered genetic algorithm as a baseline. The regularization-based algorithm (RbFS) was found to be the most efficient method, while methods based on a complete enumeration (FFS and SFS) are found to be ineffective for any set of authors. The accuracy of the RbFS and SVM methods in the case of classical literary texts averaged 83%, which outperforms other selection methods by 3 to 10% for an identical number of features, and the average accuracy of fastText was 84%. For the open attribution in cross-topic classification, the average accuracy of the method based on the combination of One-Class SVM with RbFS and fastText was 85%, and for in-group classification, it was 75 to 78%, depending on the group, which is the best result among the open attribution methods considered. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) plays an increasingly important role in language education; however, the trends, research issues, and applications of AI in language learning remain largely under-investigated. Accordingly, the present paper, using bibliometric analysis, investigates these issues via a review of 516 papers published between 2000 and 2019, focusing on how AI was integrated into language education. Findings revealed that the frequency of studies on AI-enhanced language education increased over the period. The USA and Arizona State University were the most active country and institution, respectively. The 10 most popular topics were: (1) automated writing evaluation; (2) intelligent tutoring systems (ITS) for reading and writing; (3) automated error detection; (4) computer-mediated communication; (5) personalized systems for language learning; (6) natural language and vocabulary learning; (7) web resources and web-based systems for language learning; (8) ITS for writing in English for specific purposes; (9) intelligent tutoring and assessment systems for pronunciation and speech training; and (10) affective states and emotions. The results also indicated that AI was frequently used to assist students in learning writing, reading, vocabulary, grammar, speaking, and listening. Natural language processing, automated speech recognition, and learner profiling were commonly applied to develop automated writing evaluation, personalized learning, and intelligent tutoring systems. [ABSTRACT FROM AUTHOR]

Plagiarism poses a significant threat to academic integrity, requiring effective measures for its detection and prevention. This paper explores the efficacy of plagiarism detection tools in upholding academic integrity, with a specific focus on the use of artificial intelligence (AI) technologies. The abstract introduces the concept of plagiarism and its impact on scholarly work. It highlights the importance of reliable and accurate plagiarism detection methods and emphasizes the role of AI in enhancing the effectiveness of such tools. The abstract briefly outlines the main points covered in the paper, including the use of AI techniques such as text matching algorithms and natural language processing, the application of machine learning in plagiarism detection, and the challenges and advancements in cross-language detection. The abstract concludes by emphasizing the importance of promoting ethical scholarship and academic integrity in educational institutions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research paper is to explore ChatGPT's potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT's capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that make it an effective designer for AI, and also valuable evaluated its limitations & potential areas for improvement. By conducting this investigation, this research paper seeks to contributed of established to the advancement of AI research and facilitated the development of more sophisticated AI tools in the future. This research paper focused on contributing to the development of more advanced and sophisticated AI systems by exploring the potential of ChatGPT as a tool for designing and developing such systems. Design/methodology/approach: In this research paper, a conceptual investigation approach as used to explore ChatGPT as a metamorphosis designer for the future of artificial intelligence. The methodology for this investigation involves comprehensive literature review of existing research on ChatGPT and its applications in the field of AI. This methodology followed by a detailed analysis of ChatGPT's architecture, including its input/output mechanisms, its training process, and its ability to generate high-quality responses to user queries. To evaluate ChatGPT's effectiveness as a designer for AI, this investigation also involved a comparative analysis of ChatGPT. This analysis helps identify ChatGPT's strengths and limitations, as well as potential areas for improvement. Overall, this research paper aims to provide a detailed conceptual investigation of ChatGPT's potential as an innovative, metamorphosis designer for the future of artificial intelligence. The use of a comprehensive methodology that content analysis, combines literature review and comparative analysis of current research and cognitive reviewed of theoretical concepts. This study also highlights the conceptual reviewed on philosophical domain of AI with the reference of ChatGPT. Findings: The findings of this research paper on ChatGPT as a morphological designer for the future of artificial intelligence are based on a conceptual investigation of the tool's potential in this area. The investigation revealed that ChatGPT's architecture and capabilities make it a highly effective tool for morphological design in AI. Specifically, its ability to generate high-quality responses to user queries, its capacity to learn and adapt to new information, and its versatility in handling various types of data make it an ideal tool for designing and developing sophisticated AI systems. The investigation also identified several potential limitations of ChatGPT as a morphological designer, such as its dependence on large amounts of data and its susceptibility to bias and errors. However, the investigation also revealed possible strategies for overcoming these limitations, such as improving the quality and diversity of the training data used by ChatGPT. Overall, the findings of this research paper suggest that ChatGPT has significant potential as a morphological designer for the future of artificial intelligence. Further research and development in this area could lead to the creation of more advanced and sophisticated AI systems. Originality/value: The originality and value of this paper lies in the exploration of ChatGPT as a morphological designer for the future of artificial intelligence. This investigation provides a unique and in-depth analysis of ChatGPT's potential as a tool for designing and developing intelligent systems, focusing specifically on its capabilities as a morphological designer. This research paper adds value to the field of AI by identifying the strengths and weaknesses of ChatGPT as a morphological designer and highlighting potential areas for improvement and further development. The investigation also contributes to the advancement of AI research by exploring the potential of ChatGPT as a tool to create more sophisticated and intelligent systems in the future. Overall, the originality and value of this research paper stems from its innovative approach to investigate the potential of ChatGPT as a morphological designer for the future of artificial intelligence. By providing a comprehensive analysis of the capabilities and limitations of tools in this area, the paper contributes to the development of more advanced and effective AI systems in the future. [ABSTRACT FROM AUTHOR]

This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered. [ABSTRACT FROM AUTHOR]

Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems. [ABSTRACT FROM AUTHOR]