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There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work, we propose the ChatExtract method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data's correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. ChatExtract can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data, we find precision and recall both close to 90% from the best conversational LLMs, like GPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to ChatExtract, due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using ChatExtract. Efficient data extraction from research papers accelerates science and engineering. Here, the authors develop an automated approach which uses conversational large language models to achieve high precision and recall in extracting materials data. [ABSTRACT FROM AUTHOR]

The text discusses the intersection of natural language understanding (NLU) and reasoning in the context of large language models (LLMs) and traditional logic-based approaches. It highlights the strengths and weaknesses of both approaches and explores the potential for hybrid models that combine symbolic and distributional representations. The text also mentions specific applications of hybrid approaches in natural language inference, question-answering, sentiment analysis, and dialog. The document concludes by introducing a special issue that features selected contributions from the NALOMA workshop series, which focuses on hybrid methods in NLU. [Extracted from the article]

Through call for Papers and the Review Process, the Special Issue has Accepted 22 Articles Covering Transfer Learning Model Designs and Applications. These 22 articles in this special issue provide a general picture of the development and applications of transfer learning method. Transfer Learning is a Machine Learning Paradigm which Reuses Previously Trained Models to help Solve a new Learning task. [Extracted from the article]

For this special issue of I New General Computing i , we have selected two papers that received particularly excellent evaluations from among the oral presentations at the international sessions of the 35th Japanese Society for Artificial Intelligence (JSAI2021) held from June 8 (Tuesday) to June 11 (Friday), 2021. The two papers were reviewed by four NGC Guest Editors and NGC Guest Editorial Members, and it was determined that both papers met the criteria to be recommended to NGC. We asked each International Program Committee member to recommend one paper to the New Generation Computing (NGC) Program Committee if it was particularly outstanding. [Extracted from the article]

The authors discuss common control software maintenance tasks and tool capabilities based on existing research, and demonstrate the implementation of these capabilities in the 4diac IDE. However, there is limited empirical research on the usability and utility of model-based development environments for visual programming languages. The MODELS conference series is the premier venue for model-based software and systems engineering covering all aspects of modeling, from languages and methods to tools and applications. [Extracted from the article]

Large Language Models (LLMs) are a type of machine learning that handles a wide range of Natural Language Processing (NLP) scenarios. Recently, in December 2022, a company called OpenAI released ChatGPT, a tool that, within a few months, became the most representative example of LLMs, automatically generating unique and coherent text on many topics, summarising and rewriting it, or even translating it to other languages. ChatGPT originated some controversy in academia since students can generate unique text for writing assessments being sometimes extremely difficult to distinguish whether it comes from ChatGPT or a person. In research, some journals specifically banned ChatGPT in scientific papers. However, when used correctly, it becomes a powerful tool to rewrite, for instance, scientific papers and, thus, deliver researchers' messages in a better way. In this paper, we conduct an empirical study of the impact of ChatGPT in research. We downloaded the abstract of over 45,000 papers from over 300 journals from Dec 2022 and Feb 2023 belonging to different research editorials. We use four of the most known ChatGPT detection tools and conclude that ChatGPT played a role in around 10% of the papers published in every editorial, showing that authors from different fields have rapidly adopted such a tool in their research. [ABSTRACT FROM AUTHOR]

This is an active community that aims at advancing knowledge in the design, development, and application of novel methods, tools, and techniques for HCI. The special issue on "New Frontiers in Multimedia-based and Multimodal HCI" considers research in the area of multimedia-based and multimodal systems for advanced human-computer interaction (HCI) for exploring and expanding on its frontiers. The authors tested the proposed framework to evaluate an Augmented Reality app for smartphones and assess if it could be useful to stimulate interest and awareness about the specific urban area where it is used. [Extracted from the article]

The process of retrieving suitable papers which are related to an interesting topic while doing research normally takes a lot of time and effort. Citation recommendation is frequently used to solve this problem by automatically suggesting a list of candidate papers that should match with the user's references or topics of interest. Applying the recent research results of deep learning in multiple disciplines, the performance of citation recommendation systems has been significantly improved with the facilitation of powerful deep neural data analysis and representation learning techniques. However, most of the recent Neural Citation Network (NCN) model-based techniques still encounter limitations related to the capability of integrating auxiliary information to assist the citation contextual learning process. Thus, in this paper, we propose a novel context-aware NCN-based model with the extra textual data integration and Bidirectional Encoder Representations from Transformers (BERT) model to improve the performance of the citation recommendation task. To do this, an extensive deep neural auto-encoding mechanism with a self-attention-based mechanism is utilized in our proposed model to flexibly learn both associated textual and citation contextual data in the given dataset. These enriched citation contextual information representations are then utilized to improve the performance of the citation recommendation task as the end-to-end neural learning process. Extensive experiments in the standard arXiv dataset show the effectiveness and good performance of the proposed model. [ABSTRACT FROM AUTHOR]

Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. The comparison result of the proposed algorithm with other algorithms proves that EDOLSCA also has advantages in heat exchanger optimal design. Finally, the experimental results confirm the efficiency of the proposed scheme in real-world urban scenarios; The last paper by Lidong Zhang et al. on "Elite and dynamic opposite learning enhanced sine cosine algorithm for application to plat-fin heat exchangers design problem" proposes a novel variant of sine and cosine algorithm named EDOLSCA enhanced by dynamic opposite learning algorithm and the elite strategy. [Extracted from the article]

Clarivate Analytics’s Web of Science (WoS) is the world’s leading scientific citation search and analytical information platform. It is used as both a research tool supporting a broad array of scientific tasks across diverse knowledge domains as well as a dataset for large-scale data-intensive studies. WoS has been used in thousands of published academic studies over the past 20 years. It is also the most enduring commercial legacy of Eugene Garfield. Despite the central position WoS holds in contemporary research, the quantitative impact of WoS has not been previously examined by rigorous scientific studies. To better understand how this key piece of Eugene Garfield’s heritage has contributed to science, we investigated the ways in which WoS (and associated products and features) is mentioned in a sample of 19,478 English-language research and review papers published between 1997 and 2017, as indexed in WoS databases. We offered descriptive analyses of the distribution of the papers across countries, institutions and knowledge domains. We also used natural language processingtechniques to identify the verbs and nouns in the abstracts of these papers that are grammatically connected to WoS-related phrases. This is the first study to empirically investigate the documentation of the use of the WoS platform in published academic papers in both scientometric and linguistic terms. [ABSTRACT FROM AUTHOR]

Is academic integrity research presented from a positive integrity standpoint? This paper uses Natural Language Processing (NLP) techniques to explore a data set of 8,507 academic integrity papers published between 1904 and 2019. Two main techniques are used to linguistically examine paper titles: (1) bigram (word pair) analysis and (2) sentiment analysis. The analysis sees the three main bigrams used in paper titles as being "academic integrity" (2.38%), "academic dishonesty" (2.06%) and "plagiarism detection" (1.05%). When only highly cited papers are considered, negative integrity bigrams dominate positive integrity bigrams. For example, the 100 most cited academic integrity papers of all time are three times more likely to have "academic dishonesty" included in their titles than "academic integrity". Similarly, sentiment analysis sees negative sentiment outperforming positive sentiment in the most cited papers. The history of academic integrity research is seen to place the field at a disadvantage due to negative portrayals of integrity. Despite this, analysis shows that change towards positive integrity is possible. The titles of papers by the ten most prolific academic integrity researchers are found to use positive terminology in more cases that not. This suggests an approach for emerging academic integrity researchers to model themselves after. [ABSTRACT FROM AUTHOR]

Experimental results on five public gene expression profile datasets verify that the proposed algorithm outperforms other methods in terms of diversity, distribution stability, and quality of generated samples. The fifth paper by I Tingyu Ye i et al. on "Artificial bee colony algorithm with an adaptive search manner and dimension perturbation" proposes a modified artificial bee colony (ABC) algorithm with an adaptive search manner and dimension perturbation (ASDABC). The tenth paper by I Fei Han i et al. on "Gene-CWGAN: A Data Enhancement Method for Gene Expression Profile Based on Improved CWGAN-GP" tackles the problem of high dimension and small sample size of gene expression profile data classification. [Extracted from the article]

Due to the growing number of scholarly publications, finding relevant articles becomes increasingly difficult. Scholarly knowledge graphs can be used to organize the scholarly knowledge presented within those publications and represent them in machine-readable formats. Natural language processing (NLP) provides scalable methods to automatically extract knowledge from articles and populate scholarly knowledge graphs. However, NLP extraction is generally not sufficiently accurate and, thus, fails to generate high granularity quality data. In this work, we present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. TinyGenius is employed to populate a paper-centric knowledge graph, using five distinct NLP methods. We extend our previous work of the TinyGenius methodology in various ways. Specifically, we discuss the NLP tasks in more detail and include an explanation of the data model. Moreover, we present a user evaluation where participants validate the generated NLP statements. The results indicate that employing microtasks for statement validation is a promising approach despite the varying participant agreement for different microtasks. [ABSTRACT FROM AUTHOR]

This paper introduces a diagnostic model that effectively diagnoses in fourteen different stages, by fusing functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) information. Georgios Theodoridis and Athanasios Tsadiras from the Aristotle University of Thessaloniki, Greece, have authored the seventh paper which is entitled "Applying machine learning techniques to predict and explain subscriber churn of an online drug information platform." Machine learning (ML) and more specifically deep learning (DL) algorithms are considered among the most paramount technologies of both artificial intelligence (AI) and 4 SP th sp industrial revolution. This paper provides an in-depth comparison of various machine learning (ML) techniques and advanced preprocessing methods, in an effort to successfully perform online subscriber churn prediction. [Extracted from the article]

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people's sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people's needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs. [ABSTRACT FROM AUTHOR]

The fast growth of social robotics (SR) has not been unidirectional, but rather towards a multidisciplinary scenario, creating a need for collaboration between different fields. This divergent expansion calls for a clear analysis of the field aimed at better orienting the research, thus paving the future of social robotics. This paper aims at understanding how the SR research field evolved in the last two decades by analyzing academic publications in SR and human–robot interaction using natural language processing (NLP) techniques. The analysis spotted an overlap between SR and human–robot interaction research fields that have been disambiguated using a data-driven approach that leads to the identification of a new group of papers we clustered under the concept of "soft HRI." This research topic has been analyzed by extracting trends and insights. Finally, another topic modelling step has been applied to identify seven sub-topics that have been discussed and analyzed picturing the current state of the art of SR. The paper reports a complete overview of the SR research field identifying various topics and sub-topics helping researchers in understanding the evolution of this field, thus supporting the strategic placing and evolution of their research activities. [ABSTRACT FROM AUTHOR]

As the processing power of mobile terminals increases, wireless network applications such as voice assistants can put more context-sensitive tasks on the mobile terminals, thus reducing the wireless network bandwidth needed and the cost of data storage in the cloud. Co-reference annotation, identifying the same semantics in context, is one of the critical techniques in these tasks. However, there are some problems with the existing co-reference annotation standards. First, the annotation is incomplete. Second, the types of annotated mentions are inconsistent. Third, there are currently no metrics for the above characteristics. Analyzing the above-mentioned issues, this paper proposes a new co-reference annotation standard. The new standard can annotate more semantics and co-reference relations and only adopts two types of mentions for annotation. Meanwhile, this paper presents a performance evaluation corpus and designs three performance metrics for evaluating the new standard according to the completeness of semantic annotation, the completeness of co-reference annotation, and the consistency of mention. The experiment shows that the new standard outperforms all the baseline methods and achieves 0.95 in the completeness of semantic annotation, 0.68 in the completeness of co-reference annotation, and 0.57 in the consistency of types of mentions. [ABSTRACT FROM AUTHOR]

As social media and web-based forums have grown in popularity, the fast-spreading trend of fake news has become a major threat to the government and other agencies. With the rise of social media and internet platforms, misinformation may quickly spread over borders and language boundaries. Detecting and neutralizing fake news in several languages can help to protect the integrity of global elections, political discourse, and public opinion. The lack of a robust multilingual database for training the classification models makes detecting fake news a difficult task. This paper looks at it by describing several forms of fake news (like serious fabrications, large-scale hoaxes, stance news, deceptive news, satire news, clickbait, misinformation, rumour). This review paper includes different steps, features, tools for mitigating the scourge of information pollution, and different available datasets. This study presented a taxonomy for detecting fake news, which gives a comprehensive overview and analysis of existing DL-based algorithms focusing on diverse techniques. This paper also includes the monolingual and multilingual fake news detection models. Finally, this paper ends with the technical challenges. [ABSTRACT FROM AUTHOR]

This paper examines Russia's propaganda discourse on Twitter during the 2022 invasion of Ukraine. The study employs network analysis, natural language processing (NLP) techniques, and qualitative analysis to identify key communities and narratives associated with the prevalent and damaging narrative of "fascism/Nazism" in discussions related to the invasion. The paper implements a methodological pipeline to identify the main topics, and influential actors, as well as to examine the most impactful messages in spreading this disinformation narrative. Overall, this research contributes to the understanding of propaganda dissemination on social media platforms and provides insights into the narratives and communities involved in spreading disinformation during the invasion. [ABSTRACT FROM AUTHOR]

Emission data collection has always been a significant burden and challenge for Chinese counties to develop a CO2 emission inventory. This paper proposed a fuel consumption-based method to develop a local-specific CO2 emission rate database for Chinese counties using only open-source big data. Localized vehicle fuel consumption data is obtained through natural language processing (NLP) algorithm and large language model (LLM). The emission rates derived by our proposed method are consistent with field test results in literature. Besides, the CO2 emission estimation results using local-specific traffic activity data indicate that our method could effectively improve the accuracy of vehicle emission assessment. Compared with conventional method, the novel approach proposed in this paper can provide a pathway for convenient, universal, and cost-saving assessment for local scale CO2 emission rates. With this method, it is possible to formulate a local-specific CO2 emission database in various Chinese counties using only open-access big data. [ABSTRACT FROM AUTHOR]

In a society where computers have become ubiquitous, it is necessary to develop a broader understanding of the nature of computing and programming, not just from a technical viewpoint but also from a historical and philosophical perspective. Computers and computer programs do not exist in a vacuum. Instead, they are a part of a rich socio-technological context that provides ways for understanding computers and reasoning about programs. This includes not only formal logic, mathematics, sciences, and technology but also cognitive sciences and sociology. The focus of this special issue is on questions that arise when we consider computing and programming in a wider context. In particular, the papers in this special issue explore the interplay between computing or programming and mathematics, formal logic, sciences, technology, and society. [ABSTRACT FROM AUTHOR]

A privacy policy describes the operations an organization carries out on its users' personal data and how it applies data protection principles. The automated analysis of privacy policies is a multidisciplinary research topic producing a growing but scattered body of knowledge. We address this gap by conducting a systematic mapping study which provides an overview of the field, identifies research opportunities, and suggests future research lines. Our study analyzed 39 papers from the 1097 publications found on the topic, to find what information can be automatically extracted from policies presented as textual documents, what this information is applied to, and what analysis techniques are being used. We observe that the techniques found can identify individual pieces of information from the policies with good results. However, further advances are needed to put them in context and provide valuable insight to end-users, organizations dealing with data protection laws and data protection authorities. [ABSTRACT FROM AUTHOR]

Identification of important works and assessment of importance of publications in vast scientific corpora are challenging yet common tasks subjected by many research projects. While the influence of citations in finding seminal papers has been analysed thoroughly, citation-based approaches come with several problems. Their impracticality when confronted with new publications which did not yet receive any citations, area-dependent citation practices and different reasons for citing are only a few drawbacks of them. Methods relying on more than citations, for example semantic features such as words or topics contained in publications of citation networks, are regarded with less vigour while providing promising preliminary results. In this work we tackle the issue of classifying publications with their respective referenced and citing papers as either seminal, survey or uninfluential by utilising semantometrics. We use distance measures over words, semantics, topics and publication years of papers in their citation network to engineer features on which we predict the class of a publication. We present the SUSdblp dataset consisting of 1980 labelled entries to provide a means of evaluating this approach. A classification accuracy of up to.9247 was achieved when combining multiple types of features using semantometrics. This is +.1232 compared to the current state of the art (SOTA) which uses binary classification to identify papers from classes seminal and survey. The utilisation of one-vector representations for the ternary classification task resulted in an accuracy of.949 which is +.1475 compared to the binary SOTA. Classification based on information available at publication time derived with semantometrics resulted in an accuracy of.8152 while an accuracy of.9323 could be achieved when using one-vector representations. [ABSTRACT FROM AUTHOR]

Building information networks using semantic based techniques to avoid tedious work and to achieve high efficiency has been a long-term goal in the information management world. A great volume of research has focused on developing large scale information networks for general domains to pursue the comprehensiveness and integrity of the information. However, constructing customised information networks containing subject-specific knowledge has been neglected. Such research can potentially return high value in terms of both theoretical and practical contribution. In this paper, a new type of network, solution-oriented information network, is coined that includes research problems and proposed techniques as nodes, and the relationship between them. A lightweight Semantic-based Knowledge Fusion Model (SKFM) is proposed leveraging the power of Natural Language Processing (NLP) and Crowdsourcing to construct the proposed information networks using academic papers (knowledge) from Scopus. SKFM relies on NLP in terms of automatic components while Crowdsourcing is initiated when uncertain cases arise. Applying the NLP technique assists to develop a semi-automatic knowledge fusion method for saving effort and time in extracting information from academic papers. Leveraging human power in uncertain cases is to make sure the essential concepts for developing the information networks are extracted reliably and connected correctly. SKFM shows a theoretical contribution in terms of lightweight knowledge extraction and reconstruction framework, as well as practical value by providing solutions proposed in academic papers to address corresponding research issues in subject-specific areas. Experiments have been implemented which have shown promising results. In the research field of intrusion detection, the information of attack types and proposed solutions has been extracted and integrated in a graphic manner with high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The aim of this experiment is to present an easy way to compare fragments of texts in order to detect (supposed) results of copy and paste operations between articles in the domain of Natural Language Processing (NLP), including Speech Processing. The search space of the comparisons is a corpus labeled as NLP4NLP, which includes 34 different conferences and journals and gathers a large part of the NLP activity over the past 50 years. This study considers the similarity between the papers of each individual event and the complete set of papers in the whole corpus, according to four different types of relationship (self-reuse, self-plagiarism, reuse and plagiarism) and in both directions: a paper borrowing a fragment of text from another paper of the corpus (that we will call the source paper), or in the reverse direction, fragments of text from the source paper being borrowed and inserted in another paper of the corpus. The results show that self-reuse is rather a common practice, but that plagiarism seems to be very unusual, and that both stay within legal and ethical limits. [ABSTRACT FROM AUTHOR]

People's mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis. [ABSTRACT FROM AUTHOR]

Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining technique to extract human-readable semantic “topics” (i.e., word clusters) from a corpus of textual documents. In software engineering, topic modeling has been used to analyze textual data in empirical studies (e.g., to find out what developers talk about online), but also to build new techniques to support software engineering tasks (e.g., to support source code comprehension). Topic modeling needs to be applied carefully (e.g., depending on the type of textual data analyzed and modeling parameters). Our study aims at describing how topic modeling has been applied in software engineering research with a focus on four aspects: (1) which topic models and modeling techniques have been applied, (2) which textual inputs have been used for topic modeling, (3) how textual data was “prepared” (i.e., pre-processed) for topic modeling, and (4) how generated topics (i.e., word clusters) were named to give them a human-understandable meaning. We analyzed topic modeling as applied in 111 papers from ten highly-ranked software engineering venues (five journals and five conferences) published between 2009 and 2020. We found that (1) LDA and LDA-based techniques are the most frequent topic modeling techniques, (2) developer communication and bug reports have been modelled most, (3) data pre-processing and modeling parameters vary quite a bit and are often vaguely reported, and (4) manual topic naming (such as deducting names based on frequent words in a topic) is common. [ABSTRACT FROM AUTHOR]

Natural language inference (NLI) is a fundamental task of natural language processing (NLP). Most recent NLI research has focused on explaining the model's decisions in generating causal explanations (i.e., why did a premise/hypothesis pair as input lead to their inference relation as output?). As layer-based language models can learn language structure information, this paper conducts a sample-by-sample analysis of the linguistic feature relation between premise and hypothesis that is expected to guide NLI modeling and interpretation better. Our empirical study verifies that the linguistic feature relation of premise/hypothesis pairs can be seen in NLI inference models, which can be used to interpret inference samples. Meanwhile, experimental results show that these linguistic features relation interpretation can help the NLI model achieve comparable inference accuracy compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was proposed, many pre-trained models such as the XLNet model, the RoBERTa model, and the ALBERT model were also proposed in the research community. These models perform very well in various natural language processing tasks. In this paper, we describe and compare these well-known models. In addition, we also apply several types of existing and well-known models which are the BERT model, the XLNet model, the RoBERTa model, the GPT2 model, and the ALBERT model to different existing and well-known natural language processing tasks, and analyze each model based on their performance. There are a few papers that comprehensively compare various transformer models. In our paper, we use six types of well-known tasks, such as sentiment analysis, question answering, text generation, text summarization, name entity recognition, and topic modeling tasks to compare the performance of various transformer models. In addition, using the existing models, we also propose ensemble learning models for the different natural language processing tasks. The results show that our ensemble learning models perform better than a single classifier on specific tasks. [ABSTRACT FROM AUTHOR]

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]

This cannot be achieved only through conventional Machine Learning (ML) frameworks and technologies. The technological advancement in recent years has seen a great shift leading to a greater magnitude of data storage, communication and subsequent processing with reduced size and cost of the systems. The thirteen paper "A Novel Emergency Situation Awareness Machine Learning Approach to Assess Flood Disaster Risk based on Chinese Weibo" is authored by Bai Hua, Yu Hualong, Yu Guang and Huang Xing. [Extracted from the article]

Recently, a new form of structured summary on scientific papers is explored by grouping cited text spans from the reference paper. Its primary goal is to generate summaries based on the cited paper itself. Previously, traditional scientific summarization focused on citation-based methods by aggregating all citances that cite one unique paper without doing content-based citation analysis, while sometimes citations might differ between researchers or time slots. By investigating original text spans where scholars cited, the new method can reflect exact contributions of reference papers more. Therefore, how to identify cited text spans accurately becomes the first important problem to solve. Generally, it can be converted into finding the sentences in reference paper that is more similar with citation sentences. Taking it as a classification task, we investigate the potential of four actions to improve identification performance. Firstly, feature selections are conducted carefully according to multi-classifiers. Secondly, we apply sampling-based algorithms to preprocess class-imbalanced datasets. Since we integrated results via a weighted voting system, the third action is tuning parameters like, voting weights for multi-classifiers integration or running settings to see if we can improve performance further. Evaluation results show effectiveness of each action and demonstrate that researchers can take these actions for more accurate cited text spans identification when doing scientific summarization. [ABSTRACT FROM AUTHOR]

The case element is a brief description of the case-related events. Extracting the case elements in the news text has great significance for downstream case field natural language processing tasks. In view of the case field relevance and intrinsic relevance of the case elements, this paper proposes a joint case element extraction method based on case domain correlation and graph convolutional network: modeling sentence contextual information by bi-directional long short-term memory networks, then using it to predict the case field correlation for guarantying the elements' relevance of cases by joint learning; and modeling the dependency relationship of candidate elements by graph convolutional network to capture its intrinsic relevance. The experiments show that the method proposed in this paper improves accuracy rate by 6. 6% in extracting case elements. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries. [ABSTRACT FROM AUTHOR]

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work. [ABSTRACT FROM AUTHOR]

Prolonged and over-excessive interaction with cyberspace poses a threat to people's health and leads to the occurrence of Cyber-Syndrome, which covers not only physiological but also psychological disorders. This paper aims to create a tree-shaped gold-standard corpus that annotates the Cyber-Syndrome, clinical manifestations, and acupoints that can alleviate their symptoms or signs, designating this corpus as CS-A. In the CS-A corpus, this paper defines six entities and relations subject to annotation. There are 448 texts to annotate in total manually. After three rounds of updating the annotation guidelines, the inter-annotator agreement (IAA) improved significantly, resulting in a higher IAA score of 86.05%. The purpose of constructing CS-A corpus is to increase the popularity of Cyber-Syndrome and draw attention to its subtle impact on people's health. Meanwhile, annotated corpus promotes the development of natural language processing technology. Some model experiments can be implemented based on this corpus, such as optimizing and improving models for discontinuous entity recognition, nested entity recognition, etc. The CS-A corpus has been uploaded to figshare. [ABSTRACT FROM AUTHOR]

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Textual Semantic Similarity is a crucial part of text matching tasks, and it has a very wide range of applications in natural language processing (NLP) tasks such as search engines, question-answering systems, information retrieval, natural language inference. Although there are a variety of approaches about textual semantic similarity, many do not succeed in achieving the semantic representation of a sentence or text that represents it well, and ignore that different words serve different roles in expressing the meaning of the whole sentence in different degrees. Therefore, our paper proposes a Siamese Bert network model to obtain textual semantic similarity. Firstly, we utilize the Bert network model to obtain the semantic features of each word in the sentence as input and utilize the merit of the Siamese network, reducing the training parameters, sharing the same encoder and feature weight information with each other. Then we use the attention mechanism to obtain more advanced semantic features. Furthermore, the similarity between two sentences can be derived by the methods of calculating the distance or concatenating their high-level semantic representations. In this paper, we apply the network structure to three related semantic similarity datasets, which perform better than other approaches. [ABSTRACT FROM AUTHOR]

The present paper aims to propose a new method for neutralizing contradictions in neural networks. Neural networks exhibit numerous contradictions in the form of contrasts, differences, and errors, making it extremely challenging to find a compromise between them. In this context, neutralization is introduced not to resolve these contradictions, but to weaken them by transforming them into more manageable and concrete forms. In this paper, contradictions are neutralized or weakened through four neutralization methods: comprehensive, nullified, compressive, and collective. Comprehensive neutralization involves increasing the neutrality of all components in a neural network. Nullified neutralization is employed to weaken contradictions among different computational and optimization procedures. Compressive neutralization aims to simplify multi-layered neural networks while preserving the original internal information as much as possible. Collective neutralization is achieved by considering as many final networks as possible under different conditions, inputs, learning steps, and so on. The proposed method was applied to two data sets, one of which consisted of irregular forms resulting from natural language processing. The experimental results demonstrate that comprehensive neutralization could enhance the neutrality of all components and represent features across a broader range of components, thereby improving generalization. Nullified neutralization enabled a compromise between neutrality maximization and error minimization. Through compressive and collective neutralization of a large number of compressed weights, it became possible to interpret compressed and collective weights. In particular, inputs that were considered relatively unimportant by conventional methods emerged as highly significant. Finally, these results were compared with those obtained in the field of the human-centered approach to provide a clearer understanding of the significance of contradiction resolution, applied to neural networks. [ABSTRACT FROM AUTHOR]

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometric, information retrieval (IR), text mining, and natural language processing techniques can assist to address this challenge, but have yet to be widely used in digital libraries (DL). This special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL) was compiled after the first joint BIRNDL workshop that was held at the joint conference on digital libraries (JCDL 2016) in Newark, New Jersey, USA. It brought together IR and DL researchers and professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric, and recommendation techniques that can advance the state of the art in scholarly document understanding, analysis, and retrieval at scale. This special issue includes 14 papers: four extended papers originating from the first BIRNDL workshop 2016 and the BIR workshop at ECIR 2016, four extended system reports of the CL-SciSumm Shared Task 2016 and six original research papers submitted via the open call for papers. [ABSTRACT FROM AUTHOR]

Citations play a fundamental role in supporting authors' contribution claims throughout a scientific paper. Labelling citation instances with different function labels is indispensable for understanding a scientific text. A single citation is the linkage between two scientific papers in the citation network. These citations encompass rich native information, including context of the citation, citation location, citing and cited paper titles, DOI, and the website's URL. Nevertheless, previous studies have ignored such rich native information during the process of datasets' accumulation, thereby resulting in a lack of comprehensive yet significantly valuable features for the citation function classification task. In this paper, we argue that such important information should not be ignored, and accordingly, we extract and integrate all of the native information features into different neural text representation models via trainable embeddings and free text. We first construct a new dataset entitled, NI-Cite, comprising a large number of labelled citations with five key native features (Citation Context, Section Name, Title, DOI, Web URL) against each dataset instance. In addition, we propose to exploit the recently developed text representation models integrated with such information to evaluate the performance of citation function classification task. The experimental results demonstrate that the native information features suggested in this paper enhance the overall classification performance. [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 authors summarize preference construction related research and also discuss aspects of counteracting cognitive biases. In his paper I Music emotion recognition using recurrent neural networks and pretrained models i the author I Jacek Grekow i presents experiments based on using recurrent neural networks for emotion detection in musical segments. This special issue entitled I Methodologies for Intelligent Systems i is a collection of 10 invited papers that represent extended and improved versions of outstanding work presented at the 25th International Symposium on Methods for Intelligent Systems (ISMIS), an international forum for the exchange of scientific, research and technological achievements in building intelligent systems. [Extracted from the article]

People worldwide have suffered tremendously in terms of their mental health due to years of exposure to stress, anxiety, and the pressures of today's fast-paced lifestyles. The digitization of the data made possible by advances in health-care technology worldwide has allowed for a more precise mapping of the many variations of human biology than was possible before. People's methods of interaction with one another are evolving due to the rapid development of technology. Twitter, Facebook, Telegram, and Instagram have all risen to prominence as platforms where users can openly discuss their innermost thoughts, psyche, and feelings with one another. Texts are put through a psychological analysis process to pull out relevant details, characteristics, and user feedback. Psychological analysts rely on social media for the early identification of depressive symptoms and patterns of behavior. Machine learning has been recognized as a powerful method for sifting through the vast quantities of data in the health-care industry. Predicting the likelihood of mental diseases and executing likely treatment outcomes is a common application of ML techniques in mental health. This paper compiles a list of different mental health disorders along with the methods used in detecting and diagnosing mental health-related issues using online social media. [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]

While the link between financial market movement and economic policy uncertainty indices is well-established in literature, uncertainty in the form of 'foreboding' emanating from catastrophic events has not been explored in literature. This paper explores "foreboding", which reflects uncertainty at its extreme, following the Covid-19 pandemic. Using Natural Language Processing on minute-by-minute news data, I construct two Foreboding Indices, representing 'foreboding' or 'fearful apprehension', for 28,622 Covid-related news for the period July 2020–August 2021. The impact of foreboding on financial market volatility is explored using a logistic regression model. Both the indices show a marked increase in June–July, 2020, in January 2021, April, 2021, and July–August, 2021 and have a positive impact on volatility for hourly S&P 500 Index. Understanding of foreboding sentiment is crucial for central banks looking to monitor financial market volatility. Appropriate signaling in accordance to sentiment can help central banks handle detrimental impacts of market volatility. Moreover, FI can be used for market practitioners to gauge the sentiment and take effective trading decisions. [ABSTRACT FROM AUTHOR]

In this editorial, we explore the urgent challenges created by the rise of infodemics —a term used to describe the epidemic spread of fake news, misinformation, and disinformation through social networks initially associated with the COVID-19 pandemic. This issue has drawn significant attention from various academic fields, including computer science, artificial intelligence, mathematics, physics, biology, sociology, and psychology, among others. This special issue is dedicated to advancing infodemics research across various academic domains. The selected papers include relevant contributions advancing the state of the art in the area, ranging from network analysis for identifying influential nodes and communities in networks to language processing for text classification and filtering relevant messages within extensive corpora. [ABSTRACT FROM AUTHOR]

The usage of social media and the internet has grown rapidly throughout the world and has assimilated into daily life. Through the internet and social media, people can communicate their feelings, ideas, and thoughts, yet, with the growth of social networking sites, bullying is also growing. Cyberbullying is the term for bullying that involves the use of technology, and the internet may be a source of offensive, damaging, and destructive to other people's content. Bullying can long-term affect young people's capacity to socialize and build enduring friendships. Social media sites offer a great opportunity for bullying and harassment, and young people who use these sites are in danger. This paper's framework was presented for detecting cyberbullying and divided into two main sections. NLP (natural language processing) is the first component, and ML is the second component (machine learning). In the first phase, data are collected from online news sources and Twitter, posts and messages on Facebook, Instagram, WhatsApp, and YouTube comments. Additionally, we collected data from the application that we built to receive complaints and threats that reach students and young people, regardless of the type of threat, whether bullying, intimidation, or even abuse in both English and Arabic. Before using machine learning algorithms on our data, we must clean it up and prepare it for the discovery phase because it contains many extraneous characters. To train the model and determine which model has the highest accuracy, we combine NLP with various machine learning techniques, including support vector machine, Naive Bayes, logistic regression, random forest, extreme gradient boosting algorithm, and convolutional neural networks (CNN). This paper developed an effective technology to detect and avoid bullying in social networks. Many workbooks are used to train and learn about bullying behavior. Evaluation of the proposed model for cyberbullying data shows that CNN performs better than other algorithms used in the study. [ABSTRACT FROM AUTHOR]

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