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This paper delves into the intricate relationship between Artificial Intelligence (AI) and the art ecosystem, emphasizing the need for a decolonizing approach in the face of AI's growing influence. It argues that the development of AI is not just a technological leap but also a significant cultural and societal moment, akin to the advent of moving images that Walter Benjamin famously analyzed. The paper examines how AI, particularly in its current oligarchical and corporate-driven form, perpetuates and magnifies the existing social inequalities, thereby necessitating a critical and radical rethinking of its role in society and the arts. At the heart of the discussion is the concept of AI as a broad term encompassing various forms of machine intelligence, from natural language processing to computer vision. The paper criticizes the dominant anthropocentric view of intelligence and creativity, proposing a more inclusive approach that considers the diverse forms of intelligence present in other species and potentially in AI itself. It underscores the role of AI in shaping the art ecosystem, not just in the creative process but also in gatekeeping and decision-making. The paper proposes a framework for decolonizing AI in the art ecosystem, focusing on four key tasks: recognizing access as a form of power, understanding and addressing biases inherent in AI, assessing the impact of AI on marginalized communities, and challenging dominant narratives and epistemologies to create space for alternative voices and perspectives. It emphasizes the need for artists and the art community to engage actively with AI, shaping its development towards more equitable and just outcomes. In conclusion, the paper calls for a radical reimagination of AI's role in society and the arts, advocating for a future where AI is not just about technological advancement but also about fostering a more inclusive, equitable, and creatively diverse world. It invites artists, thinkers, and innovators to join in this journey of reimagining and reshaping the future of AI and the art ecosystem. [ABSTRACT FROM AUTHOR]

In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

Document similarity recognition is one of the most important problems in natural language processing. This paper proposes a plagiarism comparison mechanism called JCF. Initially, the TF–IDF scheme is applied to build a bag of words as the representation of the common features of all documents. Then, the plagiarism comparison is carried out in a coarse-grained manner, which speeds up the similarity comparison. Finally, the most similar documents can then be compared in detail based on a fine-grained approach. In addition, the JCF detects plagiarism at both syntax level and semantic-like level. To prevent the distortion of similarity comparison, this paper further develops a similarity restoration approach such that the proposed JCF can obtain both advantages of quickness and accuracy. Performance studies confirm that the proposed JCF outperforms existing studies in terms of precision, recall and F1 score. [ABSTRACT FROM AUTHOR]

In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of topics. Businesses can leverage this data to extract valuable insights, improve their services, and effectively reach a broader audience based on users' expressed opinions on social media platforms. To harness the potential of this extensive and unstructured data, a deep understanding of Natural Language Processing (NLP) is crucial. Existing approaches for sentiment analysis (SA) often rely on word co-occurrence frequencies, which prove inefficient in practical scenarios. Identifying this research gap, this paper presents a framework for concept-level sentiment analysis, aiming to enhance the accuracy of sentiment analysis (SA). A comprehensive Urdu language dataset was constructed by collecting data from YouTube, consisting of various talks and reviews on topics such as movies, politics, and commercial products. The dataset was further enriched by incorporating language rules and Deep Neural Networks (DNN) to optimize polarity detection. For sentiment analysis, the proposed framework employs predefined rules to trigger sentiment flow from words to concepts, leveraging the dependency relations among different words in a sentence based on Urdu language grammatical rules. In cases where predefined patterns are not triggered, the framework seamlessly switches to its sub-symbolic counterpart, passing the data to the DNN for sentence classification. Experimental results demonstrate that the proposed framework surpasses state-of-the-art approaches, including LSTM, CNN, SVM, LR, and MLP, achieving an improvement of 6–7% on Urdu dataset. In conclusion, this research paper introduces a novel framework for concept-level sentiment analysis of Urdu language data sourced from social media platforms. By combining language rules and DNN, the proposed framework demonstrates superior performance compared to existing methodologies, showcasing its effectiveness in accurately analyzing sentiment in Urdu text data. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

Microelectronics production failure analysis is an important step in improving product quality and development. In fact, the understanding of the failure mechanisms and therefore the implementation of corrective actions on the cause of the failure depend on the results of this analysis. These analyses are saved under textual features format. Then such data need first to be preprocessed and vectorized (converted to numeric). Second, to overcome the curse of dimensionality caused by the vectorisation process, a dimension reduction is applied. A two-stage variable selection and feature extraction is used to reduce the high dimensionality of a feature space. We are first interested in studying the potential of using an unsupervised variable selection technique, the genetic algorithm, to identify the variables that best demonstrate discrimination in the separation and compactness of groups of textual data. The genetic algorithm uses a combination of the K-means or Gaussian Mixture Model clustering and validity indices as a fitness function for optimization. Such a function improves both compactness and class separation. The second work looks into the feasibility of applying a feature extraction technique. The adopted methodology is a Deep learning algorithm based on variational autoencoder (VAE) for latent space disentanglement and Gaussian Mixture Model for clustering of the latent space for cluster identification. The last objective of this paper is to propose a new methodology based on the combination between variational autoencoder (VAE) for the latent space disentanglement, and genetic algorithm (GA) to find, in an unsupervised way, the latent variables allowing the best discrimination of clusters of failure analysis data. This methodology is called VAE-GA. Experiments on textual datasets of failure analysis demonstrate the effectiveness of the VAE-GA proposed method which allows better discrimination of textual classes compared to the use of GA or VAE separately or the combination of PCA with GA (PCA-GA) or a simple Auto-encoders with GA (AE-GA). [ABSTRACT FROM AUTHOR]

Business process compliance is an essential part of business process management, which saves organizations from penalties caused by non-compliant processes. However, current researches on business process compliance mainly focus on checking using general constraint rules that have been formalized without in-depth analysis of related regulatory documents and mostly involve extensive human efforts. In this paper, we aim to propose an automatic and interpretable compliance checking approach for design-time business processes. By combining deep learning and a natural language processing approach based on rule templates, we extract semantic information from regulatory documents at different granularities for subsequent compliance checking. In addition, we match appropriate rules to the process model and detect the degree of violation of the business process from three control-flow perspectives. The effectiveness of this method is validated on two real-world datasets. [ABSTRACT FROM AUTHOR]

Semantic relatedness is one of the most significant aspects of natural language processing. It has been identified as a critical technology for developing intelligent systems like Siri, Microsoft Ice, Cortana, and Xiaoai. In 2014, SemEval ranked SR as the top task. While many existing studies have focused on analyzing the entailment of single phrases, advancements in deep learning have made it possible to analyze complete sentences or texts. While the natural parallelism of membrane computing has shown promise for data processing, harnessing this potential to advance semantic relatedness remains an open problem yet to be tackled. This paper proposes a novel Siamese Manhattan LSTM-SNP approach (SiMaLSTM-SNP) for the SR problem. The approach uses a collaborative Word2vec and 10-Layer Attention strategy to represent and extract sentence pairs and a Siamese LSTM-SNP structure to calculate the hidden states of sentences. The multi-head self-attention layer identifies text associations and redistributes hidden state weights. The last hidden state is extracted, and the relatedness score is calculated using the Manhattan distance. The experiments demonstrate that SiMaLSTM-SNP outperforms 17 classical SR baselines and 7 novel approaches on the standard datasets SICK and STS in terms of mean square error performance. This indicates that SiMaLSTM-SNP can accurately capture the semantic distinction between two sentences and effectively preserve their semantic information. [ABSTRACT FROM AUTHOR]

Span-level method achieves competitive results in Aspect Sentiment Triplet Extraction (ASTE) by enumerating all possible spans. However, previous span-level methods fail to exploit syntactic information to identify the correspondence between aspect terms and opinion terms, which makes the extracted triplets inaccurate. In this paper, we propose a syntactic and semantic dual-enhanced bidirectional network (SSBN) for ASTE task. By constructing word dependencies as a graph and embedding them into features to capture syntactic information more effectively in bidirectional network. Furthermore, we design a pruning strategy that uses part-of-speech information to alleviate the problem of identifying potential aspects and opinions from a large number of spans. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate the effectiveness of the SSBN model. [ABSTRACT FROM AUTHOR]

Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people's hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people's sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731–5780, 2022. https://doi.org/10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people's sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people's reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people's feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers. [ABSTRACT FROM AUTHOR]

There is a growing interest in using social media content for Natural Language Processing applications. However, it is not easy to computationally identify the most relevant set of tweets related to any specific event. Challenging semantics coupled with different ways for using natural language in social media make it difficult for retrieving the most relevant set of data from any social media outlet. This paper seeks to demonstrate a way to present the changing semantics of Twitter within the context of a crisis event, specifically tweets during Hurricane Irma. These methods can be used to identify the most relevant corpus of text for analysis in relevance to a specific incident such as a hurricane. Using an implementation of the Word2Vec method of Neural Network training mechanisms to create Word Embeddings, this paper will: discuss how the relative meaning of words changes as events unfold; present a mechanism for scoring tweets based upon dynamic, relative context relatedness; and show that similarity between words is not necessarily static. We present different methods for training the vector model in Word2Vec for identification of the most relevant tweets for any search query. The impact of tuning parameters such as Word Window Size, Minimum Word Frequency, Hidden Layer Dimensionality, and Negative Sampling on model performance was explored. The window containing the local maximum for AU_ROC for each parameter serves as a guide for other studies using the methods presented here for social media data analysis. [ABSTRACT FROM AUTHOR]

Whether it is an NLP (natural language processing) task or an NLU (natural language understanding) task, many methods are model oriented, ignoring the importance of data features. Such models did not perform well for many tasks based on feature loose, unbalanced tricky data including text classification tasks. In this regard, this paper proposes a classification method called LSTM-SN (long-short term memory RNN fusion social network) based on extremely complex datasets. The approach condenses the characteristics of the dataset. LSTM combines with social network methods derived from specific datasets to complete the classification task, and then use complex network structure evolution methods to discover dynamic social attributes. The experimental results show that this method can overcome the shortcomings of traditional methods and achieve better classification results. Finally, a method to calculate the accuracy of fusion model is proposed. The research ideas of this paper have far-reaching significance in the domain of social data analysis and relation extraction. [ABSTRACT FROM AUTHOR]

The effectiveness of sequence-to-sequence (seq2seq) models in natural language processing has been well-established over time, and recent studies have extended their utility by treating mathematical computing tasks as instances of machine translation and achieving remarkable results. However, our exploratory experiments have revealed that the seq2seq model, when employing a generic sorting strategy, is incapable of inferring on matrices of unseen rank, resulting in suboptimal performance. This paper aims to address this limitation by focusing on the matrix-to-sequence process and proposing a novel diagonal-based sorting. The method constructs a stable ordering structure of elements for the shared leading principal submatrix sections in matrices with varying ranks. We conduct experiments involving maximal independent sets and Sudoku laws, comparing seq2seq models utilizing different sorting methods. Our findings demonstrate the advantages of the proposed diagonal-based sorting in inference, particularly when dealing with matrices of unseen ranks. By introducing and advocating for this method, we enhance the suitability of seq2seq models for investigating the laws of matrix inclusion and exploring their potential in solving matrix-related tasks. [ABSTRACT FROM AUTHOR]

In recent years, along with the rapid development in the domain of artificial intelligence and aerospace, aerospace combined with artificial intelligence is the future trend. As an important basic tool for Natural Language Processing, Named Entity Recognition technology can help obtain key relevant knowledge from a large number of aerospace data. In this paper, we produced an aerospace domain entity recognition dataset containing 30 k sentences in Chinese and developed a named entity recognition model that is Multi-Feature Fusion Transformer (MFT), which combines features such as words and radicals to enhance the semantic information of the sentences. In our model, the double Feed-forward Neural Network is exploited as well to ensure MFT better performance. We use our aerospace dataset to train MFT. The experimental results show that MFT has great entity recognition performance, and the F1 score on aerospace dataset is 86.10%. [ABSTRACT FROM AUTHOR]

The integration of 3D geometric models with semantic information significantly improves the applicability and comprehensibility of cultural heritage. The semantic 3D modeling of Chinese grottoes poses challenges for individuals without expertise in cultural heritage due to gaps in domain knowledge and discrepancies in understanding. However, the existing domain ontology and knowledge graph provide an insufficient representation of the knowledge of Chinese grottoes. To overcome these obstacles, we propose a knowledge graph representation method to provide explicit knowledge for participants at different stages of semantic 3D modeling of Chinese grottoes, which includes schema layer construction and data layer construction. On the schema layer, we design a domain ontology named ChgOnto (Chinese Grottoes Ontology) that consists of four high-level concept classes: spatial object, informational object, digital device, and temporal object. Among the classes in the ChgOnto, the components (e.g., cliff wall, cave roof, cliff wall footing), elements (e.g., primary Buddha statue, pedestal, decoration), the properties (e.g., length, width, depth) of caves and niches in Chinese grottoes as well as the spatial relationships between them are all precisely defined. ChgOnto also reuse the classes from the renowned CIDOC CRM ontology in the cultural heritage field and GeoSPARQL in the geospatial domain, facilitating integration between the two subjects. Considering the schema layer as the conceptual data model, the data layer extracts knowledge from unstructured text through natural language processing tools to instantiate the abstract classes and fill the properties of the schema layer. Finally, the knowledge required for semantic 3D modeling of Chinese grottoes is expressed in the data layer by a knowledge graph in a fixed expression form. Dazu Rock Carvings, a World Heritage site in China, is selected as a case study to validate the practicality and effectiveness of the proposed method. The results reveal that our method offers a robust knowledge-sharing platform for the semantic 3D modeling of Chinese grottoes and demonstrates excellent scalability. The method proposed in this paper can also serve as an informative reference for other types of cultural heritage. [ABSTRACT FROM AUTHOR]

Machines can be equipped with the capability of identifying human emotions through conversation, thus enabling them to empathize with natural persons when they speak to them. The emergence of chatbots and intelligent assistants has led to a heightened focus on emotion recognition tasks. Most existing methodologies primarily focus on the isolated analysis of the speaker's attitudes and behaviors, thereby disregarding the essential interplay between the potential attitudes of interlocutors engaged in a specific conversation and their subsequent immediate dialogue behavior. As a result, the comprehension of the fundamental reasons behind the speaker's emotional fluctuations presents a noteworthy challenge throughout the course of the dialogue. This paper utilizes the attitude behavior theory from social psychology to develop a neural network model, namely the interaction neural network behind attitudes and behaviors of interlocutors for dialogue emotion recognition (DialogueINAB), that emulates the interactive process of the interlocutor's attitude and speech behavior in conversation. Our model presents new insights for recognizing emotions in conversations from a social psychology standpoint. DialogueINAB includes three modules: perception, information interaction, and emotion classifier. Initially, DialogueINAB identifies the features of potential attitudes and speech behaviors of the conversation interlocutors from the dialogue text. Second, by utilizing a crossmodal transformer architecture, the model can simulate the interaction between interlocutors' potential attitude and speech behavior and produce emotional features. Finally, the emotion classifier is supplied with the generated emotion features from the model for emotion recognition in conversation. We demonstrate the superiority of the proposed method through extensive experiments on three standard datasets (IEMOCAP, MELD, and AVEC). Compared with the six public baseline methods, our model improves the Weight-F1 metric by 3.56% and 1.04% on the IEMOCAP and MELD datasets and reduces the MAE metric by an average of 3.3% on the AVEC dataset. [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]

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]

System logs are an important data source for performance monitoring and anomaly detection. Analyzing logs for anomaly detection can improve service quality. At present, although machine learning algorithms for anomaly detection can achieve high accuracy, they lack robustness. The detection model cannot dynamically adapt to changes of logs when system logs contain noises owing to the casualness of the operators or log templates update. In face of this challenge, the paper proposes a robust log anomaly detection method based on contrastive learning and multi-scale Masked Sequence to Sequence (MASS). First, a log feature extraction model integrating the BERT model with contrastive learning is proposed. It can extract effective features by pulling two related normal logs together and pushing apart normal and abnormal logs to ensure that the semantic similarity between normal and abnormal log templates is lower than that between normal log templates, effectively remove abnormal log templates and distinguish log categories to which normal log templates and normal noise log templates belong rather than rudely treating the noise log templates as anomalies, which enhances the robustness of anomaly detection. Then, a multi-scale Masked Sequence to Sequence (MSMASS) model is proposed, the Attention mechanism of the MASS model is replaced with multi-scale Attention to fully learn the context information of different scales of the log sequence, which improves the accuracy of anomaly detection. Contrast experiments are conducted with four baseline methods on common datasets, and the results show that the method proposed in this paper is superior to most existing log-based anomaly detection methods in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Nowadays, the internet and social media ease the process of reviewing products. Consumers expose their thoughts, opinions, and experiences about products and services on various forums, websites, and mobile apps. In effect, internet reviews become a decision-maker for many people before getting their desired goods. Actually, text sentiment analysis consists of extracting insights and sentiments from social texts and consumers' reviews. Hence, various organizations conduct this analysis in order to better understand the attitude as well as the feedback of their customers toward their products. Besides, many scientific researchers are also interested in the analysis of customers' reviews by labeling them into a set of sentiments using some text classification algorithms. The following paper provides a convolutional neural network (CNN) model to classify text reviews' sentiments as negative or positive. Also, we make a comparative analysis using our proposed CNN model and several models' representations of word embedding to get the most efficient model. The experiments are implemented on the Amazon reviews dataset, and the diverse model designs have achieved appropriate performances. Reached results discern the importance of including stop-word in sentiment analysis tasks, in fact, the stop words elimination can provoke an inaccurate prediction of sentiments. Practically, using stop words with the CNN model has improved the accuracy result by 2% opposing the CNN model that has ignored them. Furthermore, we procure that the employment of a random initialization approach provides better performance than supervised and embedding model vectors on large-scale datasets. Effectively, training the representation of word embedding allows the model to learn better features in less computation time. Moreover, our CNN model showed better performance than the baseline machine learning and deep learning methods and improved the accuracy of the CNN to 90% on the Amazon reviews dataset. [ABSTRACT FROM AUTHOR]

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation. [ABSTRACT FROM AUTHOR]

Heart disease remains the major cause of death, despite recent improvements in prediction and prevention. Risk factor identification is the main step in diagnosing and preventing heart disease. Automatically detecting risk factors for heart disease in clinical notes can help with disease progression modeling and clinical decision-making. Many studies have attempted to detect risk factors for heart disease, but none have identified all risk factors. These studies have proposed hybrid systems that combine knowledge-driven and data-driven techniques, based on dictionaries, rules, and machine learning methods that require significant human effort. The National Center for Informatics for Integrating Biology and Beyond (i2b2) proposed a clinical natural language processing (NLP) challenge in 2014, with a track (track2) focused on detecting risk factors for heart disease risk factors in clinical notes over time. Clinical narratives provide a wealth of information that can be extracted using NLP and Deep Learning techniques. The objective of this paper is to improve on previous work in this area as part of the 2014 i2b2 challenge by identifying tags and attributes relevant to disease diagnosis, risk factors, and medications by providing advanced techniques of using stacked word embeddings. The i2b2 heart disease risk factors challenge dataset has shown significant improvement by using the approach of stacking embeddings, which combines various embeddings. Our model achieved an F1 score of 93.66% by using BERT and character embeddings (CHARACTER-BERT Embedding) stacking. The proposed model has significant results compared to all other models and systems that we developed for the 2014 i2b2 challenge. [ABSTRACT FROM AUTHOR]

Many military and business documents such as memorandums, invoices, medical records and bills among others are transmitted over the network in the form of images. These scanned document images may contain some private information that must not be accessed by third party or unauthorized users. Most of the encryption techniques encrypt the complete image but only parts of it require protection, which increases the computational and overhead communication cost. In this paper, we introduce a novel technique for selective multi-modal text recognition and encryption in scanned document images to overcome the issue of security and efficiency. Maximally stable extremal regions (MSER) are used in combination with two filtering techniques to automatically detect regions of interest (ROI) that contain text. Proposed technique combines optical character recognition (OCR) and natural language processing (NLP) in text recognition thus taking advantage of different modalities of data. Multi-modal recognized text is then encrypted using advanced encryption standard in cipher-block chaining mode (AES-CBC) and hybrid chaotic map. Simulation results illustrate the reliability and efficiency of proposed encryption scheme based on ROI. Moreover, security analysis exhibits the robustness of proposed encryption technique against linear and differential cryptographic attacks. [ABSTRACT FROM AUTHOR]

Mental health monitoring of seafarers is an important part of achieving normal development of the ocean shipping industry. In this paper, a dual subjective–objective testing scheme is proposed to achieve a more effective and intelligent assessment of seafarers' mental health status. Firstly, a new seafarers' mental health test scale (SMHT) is revised based on fuzzy factor analysis and the test data of 283 marine practitioners are analyzed using SPSS v24 software; secondly, this paper proposes an intelligent framework module for immersive subjective emotion extraction based on natural language processing, namely semantic summary extraction (SSE), speech emotion extraction (SEE), using hybrid scoring mechanism to obtain semantic and emotion matching values and assist the seafarer mental health scale to obtain the final correction score. The results showed that the assessment results of the SMHT scale exhibited good reliability (Cronbach's alpha of 0.852 ∈ (0.80 - 0.90) and retest reliability R of 0.873 ∈ (0.85 - 0.90) ) and scale association validity (for SCL-90, ( r = 0.468 - 0.841) > 0.45 ). In addition, the calibration rate of the subject-object dual test method was improved by approximately 12.05% compared to the traditional mental health scale. Finally, the advantages and disadvantages of this solution were compared with mental health testing techniques such as CAT, machine learning, SCL-90, and fMRI, and the method demonstrated more accurate psychological testing results, providing a simple and intelligent solution for standardized psychological testing of seafarers. [ABSTRACT FROM AUTHOR]

The article discusses the recent advancements in cheminformatics, particularly in the representation of chemical information for drug design. However, there is a perceived lack of transformative ideas in science compared to previous decades, with a potential explanation being the "low hanging fruit" problem where easier problems have already been solved. Another possible reason for declining research productivity is a shift towards more defensive research and regulatory requirements. Despite this, there is an increase in the number of research papers being published, leading to information overload for researchers. The Journal of Cheminformatics aims to address this by introducing a Scientific Contribution Statement, allowing authors to highlight their contributions and differentiate their work from prior research. The statement is intended to be a helpful tool for both editors and readers in assessing the value of a research paper. [Extracted from the article]

Rain impairs the performance of outdoor vision systems, such as automated driving systems and outdoor surveillance systems. Therefore, as an image preprocessing technique, image deraining has great potential for application. Defects of convolutional neural networks (small receptive field and non-adaptive to input content) limit the further improvement of deraining model performance. Recently, a novel neural network, transformer, has demonstrated impressive performance on natural language processing and vision tasks. However, using transformer for image deraining still has some issues: Although transformers have powerful long-range computing capabilities, it lacks the ability to model local features, which is critical for image deraining. In addition, transformer uses fixed-size patches to process images, which leads to pixels at the edges of the patches that cannot use the local features of neighboring pixels to restore rain-free images. In this paper, we propose a novel pyramid transformer for image deraining. To address the first issue, we design a residual-Dconv feed-forward network (RDFN), where depth-wise convolution improves the capability of modeling local features. To address the second issue, we introduce multi-resolution features into the transformer, which allows the transformer to obtain patches with different scales, thus enabling the boundary pixels to utilize local features. Furthermore, we propose a novel multi-scale fusion bridge (MSFB) to effectively integrate the extracted multi-scale features and capture the correlation between different scales. Extensive experiments on synthetic and real-world images demonstrate that the proposed deraining model achieves superior performance, especially the PSNR value achieves 47.55 dB on the SPA-Data dataset. We also further validate the effectiveness of the proposed model on subsequent high-level computer vision tasks. [ABSTRACT FROM AUTHOR]

The ubiquity of social media and their deep integration in the contemporary society has granted new ways to interact, exchange information, form groups, or earn money—all on a scale never seen before. Those possibilities paired with the widespread popularity contribute to the level of impact that social media display. Unfortunately, the benefits brought by them come at a cost. Social Media can be employed by various entities to spread disinformation—so called 'Fake News', either to make a profit or influence the behaviour of the society. To reduce the impact and spread of Fake News, a diverse array of countermeasures were devised. These include linguistic-based approaches, which often utilise Natural Language Processing (NLP) and Deep Learning (DL). However, as the latest advancements in the Artificial Intelligence (AI) domain show, the model's high performance is no longer enough. The explainability of the system's decision is equally crucial in real-life scenarios. Therefore, the objective of this paper is to present a novel explainability approach in BERT-based fake news detectors. This approach does not require extensive changes to the system and can be attached as an extension for operating detectors. For this purposes, two Explainable Artificial Intelligence (xAI) techniques, Local Interpretable Model-Agnostic Explanations (LIME) and Anchors, will be used and evaluated on fake news data, i.e., short pieces of text forming tweets or headlines. This focus of this paper is on the explainability approach for fake news detectors, as the detectors themselves were part of previous works of the authors. [ABSTRACT FROM AUTHOR]

The relationships between molecular structures and their properties are subtle and complex, and the properties of odor are no exception. Molecules with similar structures, such as a molecule and its optical isomer, may have completely different odors, whereas molecules with completely distinct structures may have similar odors. Many works have attempted to explain the molecular structure-odor relationship from chemical and data-driven perspectives. The Transformer model is widely used in natural language processing and computer vision, and the attention mechanism included in the Transformer model can identify relationships between inputs and outputs. In this paper, we describe the construction of a Transformer model for predicting molecular properties and interpreting the prediction results. The SMILES data of 100,000 molecules are collected and used to predict the existence of molecular substructures, and our proposed model achieves an F1 value of 0.98. The attention matrix is visualized to investigate the substructure annotation performance of the attention mechanism, and we find that certain atoms in the target substructures are accurately annotated. Finally, we collect 4462 molecules and their odor descriptors and use the proposed model to infer 98 odor descriptors, obtaining an average F1 value of 0.33. For the 19 odor descriptors that achieved F1 values greater than 0.45, we also attempt to summarize the relationship between the molecular substructures and odor quality through the attention matrix. [ABSTRACT FROM AUTHOR]

Biomedical Event Extraction (BEE) is a demanding and prominent technology that attracts the researchers and scientists in the field of natural language processing (NLP). The conventional method relies mostly on external NLP packages and manual designed features, where the features engineering is complex and large. In addition, the conventional methods on BEE uses a pipeline process that splits a task into many sub-tasks, however, the relationship between these sub-tasks is not defined. In this paper, such limitations are avoided using the combination technique that relies on Capsule Network (CapsNet) to perform a task. The CapsNet is used for the extraction of feature representation from the input corpora and then the combination technique reconstructs the events from RNN output. This method extracts the tasks from a BEE over several annotated corpora that extract the events from the molecular level in case of multi-level events. The proposed model is compared with state-of-the-art models over various text corpora datasets. The results show an improved rate of accuracy of CapsNet classification over cancer biomedical events than the existing methods. [ABSTRACT FROM AUTHOR]

Modern natural language processing models such as transformers operate multimodal data. In the present paper, multimodal data is explored using multimodal topic modeling on transactional data of bank corporate clients. A definition of the importance of modality for the model is proposed on the basis of which improvements are considered for two modeling scenarios: preserving the maximum amount of information by balancing modalities and automatic selection of modality weights to optimize auxiliary criteria based on topic representations of documents. A model is proposed for adding numerical data to topic models in the form of modalities: each topic is assigned a normal distribution with learning parameters. Significant improvements are demonstrated in comparison with standard topic models on the problem of modeling bank corporate clients. Based on the topic representations of the bank's customers, a 90-day delay on the loan is predicted. [ABSTRACT FROM AUTHOR]

The question answer (QA) system for a reading comprehension task tries to answer the question by retrieving the needed phrase from the given content. Precise answering is the key role of a QA system. An ambiguity is developed when we need to answer a negative question with a positive reply. The negation words change the polarity of the sentence, and hence, the scope of negation words is notable. This has paved the way for studying the role of 'negation' in the natural language processing (NLP) task. The handling of these words is considered a major part of our proposed methodology. In this paper, we propose an algorithm to retrieve and replace the negation words present in the content and query. A comparative study is done for performing word embedding over these words using various state-of-the-art methods. In earlier works when handling the negation the semantics of the sentences are changed. Hence, in this paper we try to maintain the semantics through our proposed methodology. The updated content is embedded into the bi-directional long short-term memory (Bi-LSTM) and thus makes the retrieving of an answer for a question answer system easier. The proposed work has been carried over the Stanford Negation, and the SQuAD dataset with a higher precision value of 96.2% has been achieved in retrieving the answers that are given in the dataset. [ABSTRACT FROM AUTHOR]

An image caption generator produces syntactically and semantically correct sentences to narrate the scene of a natural image. A neural image caption (NIC) generator is a popular deep learning model for automatically generating image captions in plain English. The NIC generator combines a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder. This paper investigates the performance of different CNN encoders and recurrent neural network decoders for finding the best NIC generator model for image captioning. Besides, we test the image caption generators with four image inject models and with decoding strategies such as greedy search and beam search. We conducted experiments on the Flickr8k dataset and analyzed the results qualitatively and quantitatively. Our results validate the automated image caption generator with ResNet-101 encoder, and the LSTM/gated recurrent units decoder outperforms the popular neural image caption NIC generator in the presence of par-inject concatenate conditioning and beam search. For quantitative assessment, we used R O U G E L , C I D E r D , and B L E U n scores to compare the different models. [ABSTRACT FROM AUTHOR]

Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conventional Artificial Intelligence and Machine Learning problems. A binary input image which might seem trivial for convolutional analysis was not easy to classify, since the provided sample was not representative of the given molecule: to describe the same formula, a variety of graphical representations which do not resemble each other can be used. Considering the variety of molecules, the problem shifted from classification to that of formula generation, which makes Natural Language Processing (NLP) a good candidate for an effective solution. This paper describes the evolution of approaches from rule-based structure analyses to complex statistical models, and compares the efficiency of models and methodologies used in the recent years. Although the latest achievements deliver ideal results on particular datasets, the authors mention possible problems for various scenarios and provide suggestions for further development. [ABSTRACT FROM AUTHOR]

Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conventional Artificial Intelligence and Machine Learning problems. A binary input image which might seem trivial for convolutional analysis was not easy to classify, since the provided sample was not representative of the given molecule: to describe the same formula, a variety of graphical representations which do not resemble each other can be used. Considering the variety of molecules, the problem shifted from classification to that of formula generation, which makes Natural Language Processing (NLP) a good candidate for an effective solution. This paper describes the evolution of approaches from rule-based structure analyses to complex statistical models, and compares the efficiency of models and methodologies used in the recent years. Although the latest achievements deliver ideal results on particular datasets, the authors mention possible problems for various scenarios and provide suggestions for further development. [ABSTRACT FROM AUTHOR]

An increasing number of studies have been devoted to electroencephalogram (EEG) identity recognition since EEG signals are not easily stolen. Most of the existing studies on EEG person identification have only addressed brain signals in a single state, depending upon specific and repetitive sensory stimuli. However, in reality, human states are diverse and rapidly changing, which limits their practicality in realistic settings. Among many potential solutions, transformer is widely used and achieves an excellent performance in natural language processing, which demonstrates the outstanding ability of the attention mechanism to model temporal signals. In this paper, we propose a transformer-based approach for the EEG person identification task that extracts features in the temporal and spatial domains using a self-attention mechanism. We conduct an extensive study to evaluate the generalization ability of the proposed method among different states. Our method is compared with the most advanced EEG biometrics techniques and the results show that our method reaches state-of-the-art results. Notably, we do not need to extract any features manually. [ABSTRACT FROM AUTHOR]

With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score. [ABSTRACT FROM AUTHOR]

This research work proposes a novel protocol for rehearsal-based incremental learning models for the classification of business document streams using deep learning and, in particular, transformer-based natural language processing techniques. When implementing a rehearsal-based incremental classification model, the questions raised most often for parameterizing the model relate to the number of instances from “old” classes (learned in previous training iterations) which need to be kept in memory and the optimal number of new classes to be learned at each iteration. In this paper, we propose an incremental learning protocol that involves training incremental models using a weight-sharing strategy between transformer model layers across incremental training iterations. We provide a thorough experimental study that enables us to determine optimal ranges for various parameters in the context of incremental classification of business document streams. We also study the effect of the order in which the classes are presented to the model for learning and the effects of class imbalance on the model’s performances. Our results reveal no significant difference in the performances of our incrementally trained model and its statically trained counterpart after all training iterations (especially when, in the presence of class imbalance, the most represented classes are learned first). In addition, our proposed approach shows an improvement of 1.55% and 3.66% over a baseline model on two business documents dataset. Based on this experimental study, we provide a list of recommendations for researchers and developers for training rehearsal-based incremental classification models for business document streams. Our protocol can be further re-used for other final applications. [ABSTRACT FROM AUTHOR]

The present paper intends to draw the conception of language implied in the technique of word embeddings that supported the recent development of deep neural network models in computational linguistics. After a preliminary presentation of the basic functioning of elementary artificial neural networks, we introduce the motivations and capabilities of word embeddings through one of its pioneering models, word2vec. To assess the remarkable results of the latter, we inspect the nature of its underlying mechanisms, which have been characterized as the implicit factorization of a word-context matrix. We then discuss the ordinary association of the "distributional hypothesis" with a "use theory of meaning," often justifying the theoretical basis of word embeddings, and contrast them to the theory of meaning stemming from those mechanisms through the lens of matrix models (such as vector space models and distributional semantic models). Finally, we trace back the principles of their possible consistency through Harris's original distributionalism up to the structuralist conception of language of Saussure and Hjelmslev. Other than giving access to the technical literature and state of the art in the field of natural language processing to non-specialist readers, the paper seeks to reveal the conceptual and philosophical stakes involved in the recent application of new neural network techniques to the computational treatment of language. [ABSTRACT FROM AUTHOR]

There has been a steep increase in the use of machine learning for various healthcare applications like the diagnosis of diseases, drug discovery, medical image analysis, etc. Machine learning solutions are proven to be more efficient and less time-consuming than conventional approaches. In this paper, we leverage the advantages of machine learning models to enable a humanoid robot to assist mental health patients. Facial expression and human voice are some of the most demonstrative ways to analyze human emotions, especially for the mentally challenged. We carry out this assistance by constantly monitoring the patient's voice (or audio) and facial expressions to predict human emotions. To implement the model of audio monitoring, we train three different machine learning and deep learning models to compare and choose the better model. Similarly, for facial recognition, we train a deep learning model using a specific dataset to predict facial expressions from the video captured in real time. We then integrate the better-performing machine learning models into a web interface for demonstration. [ABSTRACT FROM AUTHOR]

Everybody claims to be ethical. However, there is a huge difference between declaring ethical behavior and living up to high ethical standards. In this paper, we demonstrate that "hidden honest signals" in the language and the use of "small words" can show true moral values and behavior of individuals and organizations and that this ethical behavior is correlated to real-world success; however not always in the direction we might expect. Leveraging the latest advances of AI in natural language processing (NLP), we construct three different "tribes" of ethical, moral, and non-ethical people, based on Twitter feeds of people of known high and low ethics and morals: fair and modest collaborators codified as ethical "bees"; hard-working competitive workers as moral "ants"; and selfish, arrogant people as non-ethical "leeches". Results from three studies involving a total of 49 workgroups and 281 individuals within three different industries (healthcare, business consulting, and higher education) confirm the validity of our model. Associating membership in ethical or unethical tribes with performance, we find that being ethical correlates positively or negatively with success depending on the context. [ABSTRACT FROM AUTHOR]

Precise customer requirements acquisition is the primary stage of product conceptual design, which plays a decisive role in product quality and innovation. However, existing customer requirements mining approaches pay attention to the offline or online customer comment feedback and there has been little quantitative analysis of customer requirements in the analogical reasoning environment. Latent and innovative customer requirements can be expressed by analogical inspiration distinctly. In response, this paper proposes a semantic analysis-driven customer requirements mining method for product conceptual design based on deep transfer learning and improved latent Dirichlet allocation (ILDA). Initially, an analogy-inspired verbal protocol analysis experiment is implemented to obtain detailed customer requirements descriptions of elevator. Then, full connection layers and a softmax layer are added to the output-end of Chinese bidirectional encoder representations from Transformers (BERT) pre-training language model. The above deep transfer model is utilized to realize the customer requirements classification among functional domain, behavioral domain and structural domain in the customer requirement descriptions of elevator by fine-tuning training. Moreover, the ILDA is adopted to mine the functional customer requirements that can represent customer intention maximally. Finally, an effective accuracy of customer requirements classification is acquired by using the BERT deep transfer model. Meanwhile, five kinds of customer requirements of elevator and corresponding keywords as well as their weight coefficients in the topic-word distribution are extracted. This work can provide a novel research perspective on customer requirements mining for product conceptual design through natural language processing. [ABSTRACT FROM AUTHOR]

Due to the rapid growth of textual information on the web, analyzing users' opinions about particular products, events or services is now considered a crucial and challenging task that has changed sentiment analysis from an academic endeavor to an essential analytic tool in cognitive science and natural language understanding. Despite the remarkable success of deep learning models for textual sentiment classification, they are still confronted with some limitations. Convolutional neural network is one of the deep learning models that has been excelled at sentiment classification but tends to need a large amount of training data while it considers that all words in a sentence have equal contribution in the polarity of a sentence and its performance is highly dependent on its accompanying hyper-parameters. To overcome these issues, an Attention-Based Convolutional Neural Network with Transfer Learning (ACNN-TL) is proposed in this paper that not only tries to take advantage of both attention mechanism and transfer learning to boost the performance of sentiment classification but also language models, namely Word2Vec and BERT, are used as its the backbone to better express sentence semantics as word vector. We conducted our experiment on widely-studied sentiment classification datasets and according to the empirical results, not only the proposed ACNN-TL achieved comparable or even better classification results but also employing contextual representation and transfer learning yielded remarkable improvement in the classification accuracy. [ABSTRACT FROM AUTHOR]

Target-dependent sentiment classification is a fine-grained task of natural language processing to analyze the sentiment polarity of the targets. In order to address the difficulty of locating important sentiment information of targeted sentiment classification, recent research mostly applies attention mechanisms to capture the information of important context words, while the attention mechanism is subject to many drawbacks, e.g., dependent on network architecture and expensive. Recent studies show the significant effect of the local context focus (LCF) mechanism in capturing the relatedness between a target's sentiment and its local context. However, the LCF simply applies the fusion of global and local context features to classify sentiment, neglecting to empower the network to be aware of deep information of local context. In this paper, we propose a novel local context-aware network (LCA-Net) based on the local context embedding (LCE). Moreover, accompanied by the sentiment classification loss, the local context prediction (LCP) loss is proposed to enhance the LCE. The experimental results on three commonly used datasets, i.e., the Laptop and Restaurant datasets from SemEval-2014 and a Twitter social dataset, show that all the LCA-Net variants achieve promising performance improvement compared to existing approaches in extracting local context features. Besides, we implement the LCA-Net with different neural networks, validating the transferability of LCA architecture. [ABSTRACT FROM AUTHOR]

With the increasing popularity of open-source platforms, software data is easily available from various open-source tools like GitHub, CVS, SVN, etc. More than 80 percent of the data present in them is unstructured. Mining data from these repositories helps project managers, developers and businesses, in getting interesting insights. Most of the software artefacts present in these repositories are in the natural language form, which makes natural language processing (NLP) an important part of mining to get the useful results. The paper reviews the application of NLP techniques in the field of Mining Software Repositories (MSR). The paper mainly focuses on sentiment analysis, summarization, traceability, norms mining and mobile analytics. The paper presents the major NLP works performed in this area by surveying the research papers from 2000 to 2018. The paper firstly describes the major artefacts present in the software repositories where the NLP techniques have been applied. Next, the paper presents some popular open-source NLP tools that have been used to perform NLP tasks. Later the paper discusses, in brief, the research state of NLP in MSR field. The paper also lists down the various challenges along with the pointers for future work in this field of research and finally the conclusion. [ABSTRACT FROM AUTHOR]

Discourse parsing of scholarly documents is the premise and basis for standardizing the writing of scholarly documents, understanding their content, and quickly locating and extracting specific information from them. With the continuous emergence of a large number of scholarly documents, how to automatically analyze scholarly documents quickly and effectively has become a research hotspot. In this paper, we propose a hybrid model, which considers both section headers and body texts, to recognize generic sections in scholarly documents automatically. We conduct a comprehensive analysis of the semantic difference between short phrases and long narrative text chunks on the SectLabel dataset. The experimental results show that our model achieves 91.67% F 1 -value in the generic section recognization, which is better than the baseline. [ABSTRACT FROM AUTHOR]

Conflict resolution practitioners consistently struggle with access to structured armed conflict data, a dataset already rife with uncertainty, inconsistency, and politicization. Due to the lack of a standardized approach to collating conflict data, publicly available armed conflict datasets often require manipulation depending upon the needs of end users. Transformation of armed conflict data tends to be a manual, time-consuming task that nonprofits with limited budgets struggle to keep up with. In this paper, we explore the use of a deep natural language processing (NLP) model to aid the transformation of armed conflict data for conflict analysis. Our model drastically reduces the time spent on manual data transformations and improves armed conflict event classification by identifying multiple incidence types. This minimizes the human supervision cost and allows nonprofits to access a broader range of conflict data sources to reduce reporting bias. Thus, our model contributes to the incorporation of technology in the peace building and conflict resolution sector. [ABSTRACT FROM AUTHOR]

Online social network is one of the most prominent media that holds information about society's epidemic problem. Due to privacy reasons, most of the users will not disclose their location. Detecting the location of the tweet users is required to track the geographic location of the spreading diseases. This work aims to detect the spreading location of the COVID-19 disease from the Twitter users and content discussed in the tweet. COVID-19 is a disease caused by the "novel coronavirus." About 80% of confirmed cases recover from the disease. However, one out of every six people who get COVID-19 can become seriously ill, stated by the World health organization. Inferring the user location for identifying the spreading location for the disease is a very challenging task. This paper proposes a new technique based on a hypergraph model to detect the Twitter user's locations based on the spreading disease. This model uses hypergraph with weighting factor technique to infer the spreading disease's spatial location. The accuracy of prediction can be improved when a massive volume of streaming data is analyzed. The Helly property of the hypergraph was applied to discard less potential words from the text analysis, which claims this work of unique nature. A weighting factor was introduced to calculate the score of each location for a particular user. The location of each user is predicted based on the one that possesses the highest weighting factor. The proposed framework has been evaluated and tested for various measures like precision, recall and F-measure. The promising results obtained have substantiated the claim for this work compared to the state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning (DL), given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to "remember" previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios. [ABSTRACT FROM AUTHOR]

Machine learning is a branch of the field of artificial intelligence. Deep learning is a complex machine learning algorithm that has unique advantages in image recognition, speech recognition, natural language processing, and industrial process control. Deep learning has It is widely used in the field of wireless communication. Prediction of geological disasters (such as landslides) is currently a difficult problem. Because landslides are difficult to detect in the early stage, this paper proposes a GPS-based wireless communication continuous detection system and applies it to landslide deformation monitoring to achieve early treatment and prevention. This article introduces the GPS multi-antenna detection system based on deep learning wireless communication, and introduces the time series analysis method and its application. The test results show that the GPS multi-antenna detection system of the wireless communication network has great advantages in response time, with high accuracy and small error. The horizontal accuracy is controlled at 0–2 mm and the vertical accuracy is about 1 mm. The analysis method is simple and efficient, and can obtain good results for short-term deformation prediction. [ABSTRACT FROM AUTHOR]

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare. [ABSTRACT FROM AUTHOR]