Semantic Patterns Research Articles

Personality adjectives in Serbian Tweets: An opening

There has been a great interest in investigating relations between personality and language use on the web or social media. Most of the recent studies are based on mining the users’ information available online and then using machine learning algorithms to predict their personality characteristics. On the other hand, a few studies have relied on the traditional lexical hypothesis when exploring personality under the assumption that personality-related attributes could be obtained from dictionaries. However, little is known about personality structure from Twitter - do data strictly reflect personality structure as represented by personality models, or as unique personality semantic patterns. The aim of the study was to assess and interpret the personality adjective-based structure contained in tweets. The data were collected from an open-access „Tweet-sr“ Serbian Twitter linguistic corpus (Ljubešić & Klubička, 2014). Latent Dirichlet Allocation, a topic modeling technique, was conducted to extract topics and cosine similarity was used as a measure to determine topic similarities, as well as topic-personality dimensions’s similarities. The results showed that the optimal solution comprised four non-overlapping topics reflecting specific semantic structures. Topics did not replicate trait constructs but were modestly related to them. The largest similarities were found with Extraversion and Agreeableness, pointing out the conceptual importance of these traits when describing interpersonal behavior. Also, no inter-topic differences in category distributions were found, with the evaluation terms being the second most frequent in three topics. Although tweets are short-form text messages, they have the potential to communicate socially relevant information through personality descriptors.

The role of user-generated content in tourism decision-making: an exemplary study of Andalusia, Spain

PurposeThis research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.Design/methodology/approachThis research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.FindingsThis study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.Originality/valueThis research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

An Empirical Study of User Engagement in Influencer Marketing on Weibo and WeChat

Understanding social media users’ engagement is one of the most crucial steps in a successful deployment of an influencer marketing campaign. On mainstream social media platforms, opinion leaders are the major channels to spread opinions and media content to a large population of consumers. Therefore, analyzing how users respond to the blogs of opinion leaders lies at the core of understanding user engagement in social media. In this article, to study the factors that have great impacts on user engagement, we first collect a cross-platform opinion leaders’ blogs dataset, which includes 344 643 blogs published by 93 opinion leaders who have accounts on both Weibo and WeChat, the two most popular social media platforms in China. Based on this dataset, we conduct both characteristics study and semantic study to investigate the impact factors of user engagement with respect to blogs of opinion leaders. To find out the associations between user engagement and practically accessible attributes of opinion leaders and their blogs, we adopt state-of-the-art (SOTA) sentiment analysis models to process the blog data, develop a normalization technique to alleviate the issue caused by the heterogeneity of fall-out intervals of blogs, and devise a saliency method to compute the integrated gradients of sentences in blogs. Utilizing these computational tools, we reveal that user engagement on the two platforms agrees on some common factors, such as the number of tokens of blogs. Meanwhile, the two platforms differ in some aspects. For example, the semantic patterns that can improve the level of user engagement on the two platforms are very different. Our analysis can provide advertisers with valuable insights on how to plan an influencer marketing campaign on the two platforms.

Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.

Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.

A diachronic study determining syntactic and semantic features of Urdu-English neural machine translation

Machine translation produces marginal accuracy rates for low-resource languages, but its deep learning model expects to yield improved accuracy with time. This longitudinal study investigates how Google Translate's Urdu-to-English translated output has evolved between 2018 and 2021. Accuracy and acceptability of the translations have been determined by, a) an interlinear gloss that identifies core semantic units and grammatical functions to be translated and, b) a descriptive comparison of the translated text's syntactic and semantic properties with those of the source text. Overall, despite a 50 % error rate that persists over the three-year interval, the research reports significant improvement in the overall intelligibility of the translations, in contrast to initial results from 2018, which exhibited rampant non-localized errors. Working backwards from instances of errors to morphosyntactic and semantic patterns underlying them, the study concludes that the pro-drop feature of Urdu, Urdu's case-marking system, identification of clause boundaries, polysemous terms, and orthographically similar words pose the greatest difficulty in neural machine translation. These results point to the need for incorporating syntactic information in training data.

Passion-Net: a robust precise and explainable predictor for hate speech detection in Roman Urdu text

With an aim to eliminate or reduce the spread of hate content across social media platforms, the development of artificial intelligence supported computational predictors is an active area of research. However, diversity of languages hinders development of generic predictors that can precisely identify hate content. Several language-specific hate speech detection predictors have been developed for most common languages including English, Chinese and German. Specifically, for Urdu language a few predictors have been developed and these predictors lack in predictive performance. The paper in hand presents a precise and explainable deep learning predictor which makes use of advanced language modelling strategies for the extraction of semantic and discriminative patterns. Extracted patterns are utilized to train an attention-based novel classifier that is competent in precisely identifying hate content. Over coarse-grained benchmark dataset, the proposed predictor significantly outperforms state-of-the-art predictor by 8.7% in terms of accuracy, precision and F1-score. Similarly, over fine-grained dataset, in comparison with state-of-the-art predictor, it achieves performance gain of 10.6%, 17.6%, 18.6% and 17.6% in terms of accuracy, precision, recall and F1-score.

Semantic pattern recognition of special English words based on data corpus technology

Abstract This paper first researches special English vocabulary teaching defines special English vocabulary definitions and characteristics based on special English usage, and proposes strategies for learning special English vocabulary. The study involves studying the semantic recognition of special English using data corpus technology. Based on data corpus technology, the semantic structure of English vocabulary is analyzed according to Chinese analysis and word vectors, and to facilitate the extraction of English semantic features, IOB2 labeled training corpus is needed. Then, the CRFs network algorithm in the data corpus technology is used to construct the special English semantic recognition model, the lexical annotation set is refined on the preprocessing, and at the same time, the semantic features are added to the recognition model of CRFs mainly used to recognize the noun phrases of gerund class and the experimental analysis is carried out on the recognition of the semantic patterns of the special English vocabulary. The results show that among high school students with high English semantic recognition ability, the correlation coefficient between their aggregates and reading level is 0.753, the correlation coefficient between collocated phrases and reading level is 0.772, and there is a high correlation between the ability to recognize aggregates, collocated phrases, and sentence framing phrases and reading level. This study is designed to improve the efficiency of English reading and communication and has a facilitatory effect on recognizing English lexical semantic patterns.

FCPN: Pruning redundant part-whole relations for more streamlined pattern parsing

Most cropping-and-segmenting pattern parsers typically establish a single metric/scheme to reason diverse inner correlations, resulting in over-general and redundant representations. To make pattern parsing more streamlined and efficient, a fragile correlation pruner network (FCPN) with correlation-steered attention shifters (CSASs) and graph attention expectation-maximum routing agreement (GAEMRA) is proposed. CSASs prune fragile (weak and substitutable) part-to-whole correlations. They stipulate that only those primary entities (representing components) fulfilling the criteria of inter-part diversity and intra-object cohesiveness can update senior entities (representing the whole/intermediate composites). To further boost effects, GAEMRA is defined to shield the redundant voting signals of conventional routing agreement. With CSASs and GAEMRA, FCPN gradually parses objective semantic patterns by clustering highly associated secondary entities in a bottom-up "part backtracking" manner. Quantitative and ablation experiments surrounding face and human parsing demonstrate the superiority of FCPN over the state-of-the-arts, especially for the definition of fine-grained semantic boundaries.

Engineering thinking: Vectors of development in the context of the transformation of the scientific picture of the world

Introduction. Considerable attention is paid to the training of highly qualified engineering personnel today. But the ongoing processes of convergence, the emergence of end-to-end technologies radically change not only the technological platform of production, but lead to changes in the scientific picture of the world, which directly affects the nature of engineering thinking, expanding its range far beyond the technical sphere. Without the understanding of the features of modern engineering thinking, it is impossible to design the content and forms of education for future engineers. Actively ongoing processes of technological transformation in the education system give rise to a situation of uncertainty, and the desire to respond to emerging challenges, most often, is expressed in disparate, local educational solutions, if they are not based on scientifically sound conceptual models of personnel training.Aim. The present research aims to form a systematic understanding of the structure of engineering thinking and identify the trends of its transformation in the context of changes in the scientific picture of the world.Methodology and research methods. Theoretical and methodological analysis was based on the philosophical methodology of science, which made it possible to single out the main general scientific approaches in understanding the essence of engineering thinking and to consider the influence of the modern transforming scientific picture of the world on the structures of thinking in engineering activities. On the basis of the case study method, a reconstruction and analysis of typical situations encountered in the practice of training engineers and developing educational programmes was carried out to demonstrate the significance of the theoretical and methodological results obtained. The case study was used as a method of scientific research, which has its advantages over other empirical methods in the area under study.Results. The main methodological approaches in understanding engineering thinking are identified and analysed: practice-oriented, phenomenological, conceptual, and contextual. A systemic model of engineering thinking is substantiated, including three levels: technological (mental activity, technologies of thinking); regulatory-target (scientific picture of the world, paradigms, style of thinking, etc.); value-semantic (value attitudes, positions, ideals, norms, strategic priorities, semantic patterns). Typical cases that are encountered in the practice of training engineering personnel are analysed. It is shown how the changes in the scientific picture of the world and methodological reflexive analysis allow the strategies for designing educational programmes to be adjusted. The influence of the modern convergent environment on the formation of value-semantic and regulatory-target structures of engineering thinking and on the forms of the engineer’s mental activity is revealed. Innovative models for the training of future engineers are presented (on the example of the Russian State Vocational Pedagogical University), focused on the methodological approaches presented in the article to understanding engineering thinking.Scientific novelty. The novelty of the proposed research is determined by the expansion of the theoretical and methodological framework for considering engineering thinking and the vectors of its development. The issues of the development of engineering thinking are most often discussed within the framework of technological development or in the logic of intra-professional transformations; the authors of the current study rely on a systematic vision of scientific and engineering activities. This made it possible to introduce into the field of scientific consideration of engineering thinking and engineering activities such regulatory epistemological structures as the scientific picture of the world, paradigm, style of thinking, etc., as well as value-semantic structures of thinking and activity (value attitudes, ideals, standards activities, norms, strategic priorities, semantic patterns, etc.). In theoretical terms, the study is important for overcoming narrow technological and technocratic approaches, for a reasonable etermination of the prospects for the development of engineering thinking in the conditions of convergent processes.Practical significance. The practical significance of the study is the determination of the methodological foundations for the practice of decision-making in the field of engineering training.

Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia.

Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease-specific semantic memory patterns. Here, we leveraged automated word-property analysis to capture neurocognitive markers of AD vis-à-vis behavioral variant frontotemporal dementia (bvFTD). Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group-level discrimination, patient-level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns. Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group- and subject-level discrimination only in AD, also predicting executive outcomes. Disease-specific cortical thickness patterns were predicted by frequency in both disorders. Default-mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD. Word-property analysis of fluency can boost AD characterization and diagnosis. We report novel word-property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word-property analysis of fluency can boost AD characterization and diagnosis.

Comparative Analysis Between Chinese Words “突然”、“忽然”and Indonesian Words “Tiba-tiba”, “Mendadak”

Parasynonym is one of the main challenges in second language acquisition process. Chinese and Indonesian language also had many words which has similar meaning. In this paper, we focused on the comparative analysis between Indonesian words "Tiba-tiba" and "Mendadak", and chinese words "突然" and "忽然" in the semantic, pragmatic and translation pattern. The study results shows that "突然" and "忽然" have the meaning of "unexpected", while "tiba-tiba","mendadak" means "unthinkable"; all four words can also mean "rapidly happens", but "突然" can also to emphasize "controllability and purpose", "transition" and "to a certain degree" ; "忽然" can only mean "variability" and "methodology"; "Tiba-tiba" can mean "transition", "degree", "methodality"; "mendadak" can mean "degree" and "method" and "Unexpected event". The aforementioned four words basically have a lot in common in terms of pragmatics and structure, but their difference is that "忽然" cannot be used with adverbs of degree, while "突然", "tiba-tiba" and "mendadak" can. Thus its may concluded that when facing parasynonym in second language acquisition process also need special attention in order to achieve better language mastery.

Binary Semantic Pattern Rules for Chinese-English Machine Translation Based on Machine Learning Algorithms

With the increase of internationalization and the exponential growth of intercultural communication, the importance of interlingual translation has become increasingly prominent. Machine translation has been a booming area of research as technology has advanced. Due to the complexity of language ability and limited understanding of language laws, there are challenges for machine translation. This paper focused on how to construct and apply binary semantic pattern rules through machine learning to improve the translation effect in Chinese-English machine translation. The research results of this paper would contribute to the further development and improvement of Chinese-English machine translation technology. In order to produce high-quality translation results, research in machine translation has recognized the need to analyze and understand the semantics of natural language. To address the important issue of lexical and syntactic ambiguity, representations of binary semantic pattern rules have been developed to formally describe these rules. Based on this, this paper designed and implemented a corpus-based binary semantic rule extraction and optimization algorithm, which used machine learning algorithms to automatically detect the semantic rules of two or more than two phrases in the Chinese corpus, and then automatically optimized and converted them according to the statistical results, and realized the design of Chinese-English machine translation system. The article evaluated the quality of machine translation to test the effectiveness of machine translation binary semantic pattern rules based on machine learning algorithms. The study found that compared with the rule set A, the rule sets B and C obtained automatically by the rule mining algorithm had significantly improved accuracy, both reaching more than 90%. This showed that the binary semantic pattern rule mining algorithm and optimization algorithm proposed in this paper were reasonable.

The semantic pattern in the work of the artist Sherine Neshat

The research which is entitled (the semantic pattern in the works of the artist Sherine Neshat) came in four chapters, the first of which contained the research problem, its importance, its aim, its limits, and the definition of terms in it. It contained a number of results, including: 1. The semantic pattern was a clear representation in the works of the artist Sherine Neshat, which made it a dominant aesthetic theme in the artistic achievement, as the artist used clear semantic patterns in order to convey the message of love and beauty at the same time to the recipient. As well as a set of conclusions, the most important of which are: 2. The works of the artist Sherine Neshat are based on the continuous transformations of the semantic patterns derived from the artistic vision, the nature of the material, and the formal output of the artistic achievement, then the recommendations and finally the references.

한·중 냉각 형용사 대조 연구

Objectives The purpose of this study was to illuminate the semantic extension patterns of cooling adjectives in Korean and Chinese through a comparative study focusing on the Korean words ‘차다’, ‘차갑다’, ‘시리다’, ‘쌀쌀하다’, ‘서늘하다’, ‘싸늘하다’, and the Chinese word ‘涼’.&#x0D; Methods In this study, the definitions and characteristics of the original meanings, extended meanings, metaphor, and metonymy were first summarized. Then, a comparative analysis of the dictionary meanings of cooling adjectives in Korean and Chinese was conducted. Through this process, distinctions were made between the original meanings and extended meanings of these adjectives. Subsequently, corpus data were used to analyze the patterns of semantic extension of cooling adjectives in Korean and Chinese.&#x0D; Results The extended meanings of the Chinese word ‘涼’ and the Korean words ‘차다’, ‘차갑다’, ‘시리다’, ‘쌀쌀하다’, ‘싸늘하다’, and ‘서늘하다’ were found to encompass both similar and completely distinct meanings. However, the most important observation was that even when there are similar meanings, there are significant differences in the extension paths. The extension paths of the character 涼 mainly focus on abstract semantic extensions, and the verb ‘차다’ reveals not only abstract semantic extensions but also sensory extensions. Furthermore, when comparing the extended meanings of 涼 and ‘차갑다’, ‘쌀쌀하다’, ‘서늘하다’, and ‘싸늘하다’, it can be observed that while they share some similar extended meanings, the extension paths of ‘차갑다’, ‘쌀쌀하다’, ‘서늘하다’, and ‘싸늘하다’ are relatively diverse. Lastly, it can be confirmed that there is no similar meaning in the extended meanings of 涼 and ‘시리다’, and from the perspective of extension paths, both words share the commonality of extensions related to ‘state’ and ‘abstract entity’. Additionally, the Chinese character 涼 has additional extension paths related to ‘body parts’ and ‘area (space)’.&#x0D; Conclusions This study is meaningful in that when making sentences using adjectives for cold , the vocabulary is accurately selected and used by referring to the expansion path of the extended meaning of Korean and Chinese adjectives for cold.

Possession in Patani

AbstractThis paper provides the first description of the possessive system in Patani, a South Halmahera-West New Guinea language (Austronesian). Possession in Patani involves the interplay of several parameters. Syntactically, there are two broad possessive constructions: direct, and indirect. Morphologically, both constructions make use of pronominal proclitics and possessive suffixes which express the person and number of the possessor. In the direct construction, this possessive marking attaches directly to the noun, whereas the indirect construction makes use of a possessive particle (a relational classifier) on which the possessive marking occurs. Semantically, all nouns which are directly possessed are inalienable nouns expressing kinship, part-of-whole relations, and close association. In the indirect construction, a semantic distinction is made between items related to eating and drinking on the one hand, and general possession on the other. This distinction is signaled through the choice of one of two possessive classifiers. At the same time, there are some idiosyncrasies which are not explained by the main semantic pattern. When both direct and indirect constructions are used together in a sentence, each part maps a different semantic relationship. Typologically, the Patani order is possessor-possessed, as is common in the region. Finally, both the direct and the indirect construction may be used phrasally (‘my house’), or predicatively (‘I have a house’).

Rphenoscate: An R package for semantics‐aware evolutionary analyses of anatomical traits

Abstract Organismal anatomy is a hierarchical system of anatomical entities often imposing dependencies among multiple morphological characters. Ontologies provide a formal and computable framework for incorporating prior biological knowledge about anatomical dependencies in models of trait evolution. They also offer new opportunities for working with semantic representations of morphological data. In this work, we present a new R package—rphenoscate—that enables incorporating ontological knowledge in evolutionary analyses and exploring semantic patterns of morphological data. In conjunction with rphenoscape, it allows for assembling synthetic phylogenetic character matrices from semantic phenotypes of morphological data. We showcase the package functionality with data sets from bees and fishes. We demonstrate that ontologies can be employed to automatically set up evolutionary models accounting for trait dependencies in stochastic character mapping. We also demonstrate how ontology annotations can be explored to interrogate patterns of morphological evolution. Finally, we demonstrate that synthetic character matrices assembled from semantic phenotypes retain most of the phylogenetic information from their original data sets. Ontologies will become important tools for integrating anatomical knowledge into phylogenetic methods and making morphological data FAIR compliant—a critical step of the ongoing ‘phenomics’ revolution. Our new package offers key advancements towards this goal.

Deep Reinforcement Learning for intrusion detection in Internet of Things: Best practices, lessons learnt, and open challenges

The Internet of Things (IoT) scenario places important challenges even for deep learning-based intrusion detection systems. IoTs are highly heterogeneous networks in which multiple types of nodes and connections between them proliferate at a fast pace. From a deep learning perspective, such complexity translates into dynamic feature spaces where the extraction of semantic patterns and correlations among features may require sophisticated inductive biases to be learnt by gradient-based techniques. The research community has recently suggested using Deep Reinforcement Learning (DRL) as a potent approach to effectively identify cyber-threat attempts in IoTs.DRL consists of a Markov Decision Process-based meta-model that permits solving high-dimensional combinatorial optimization problems where differentiable supervisory signals may be absent. For this reason, multiple intelligent intrusion detection systems have been proposed for the IoT environment where high-level requirements are been pursued alongside the detection accuracy. These goals are related to optimizing the computational overhead, reducing power consumption at the edge, and preserving the privacy of sensitive information, among others.This survey offers a clear bird’s eye view of the most recent design choices for DRL-based intrusion detection systems with a focus on the specific context of IoT. Our aim is not only to offer an exhaustive taxonomy of design alternatives made by DRL practitioners in the field of Intrusion detection, but also to discuss the advantages and the effective deployment of each setting concerning real IoT environments. We hope this work would guide the researchers interested in Intrusion Detection for IoTs to establish solid criteria for the most effective usage of Deep Reinforcement Learning in their future work.

Measuring Latent Trust Patterns in Large Language Models in the Context of Human-AI Teaming

Qualitative self-report methods such as think-aloud procedures and open-ended response questions can provide valuable data to human factors research. These measures come with analytic weaknesses, such as researcher bias, intra- and inter-rater reliability concerns, and time-consuming coding protocols. A possible solution exists in the latent semantic patterns that exist in machine learning large language models. These semantic patterns could be used to analyze qualitative responses. This exploratory research compared the statistical quality of automated sentence coding using large language models to the benchmarks of self-report and behavioral measures within the context of trust in automation research. The results indicated that three large language models show promise as tools for analyzing qualitative responses. The study also provides insight on minimum sample sizes for model creation and offers recommendations for further validating the robustness of large language models as research tools.

DeepIndoorCrowd: Predicting crowd flow in indoor shopping malls with an interpretable transformer network

AbstractAccurate and interpretable prediction of crowd flow would benefit business management and public security. The existing studies are challenged to adapt to the indoor environment due to its complex and dynamic spatial interaction patterns. In this study, we propose a crowd flow predicting method for indoor shopping malls, which simultaneously features temporal variables and semantic factors to suit the shopping mall environment. A deep learning model named DeepIndoorCrowd is presented. The model aims at capturing temporal dependencies and the semantic pattern in crowd flow to generate an accurate multi‐horizon prediction. With a multi‐term temporal dependency capturing structure, the model is effective in learning both daily and weekly patterns of the indoor crowd flow in a shopping mall and is able to provide the temporal interpretation of the prediction result. Moreover, a semantic‐temporal fusion module is introduced to utilize the semantic information of stores in prediction, which has proved to be effective in enhancing the model's ability to learn temporal patterns. Experiments were conducted on a real‐world dataset to verify the proposed approach. The ablation study demonstrates that the DeepIndoorCrowd can effectively improve the efficiency and accuracy of the prediction up to 18.7%. In addition, some interesting indoor crowd flow patterns were discovered by analyzing the model's interpretation of the prediction result. The proposed prediction method provides an intuitive way of modeling indoor crowd flow, and the experiment's outcome can help indoor managers better understand stores' flow traffic.

Look before you leap: Detecting phishing web pages by exploiting raw URL and HTML characteristics

Phishing websites distribute unsolicited content and are frequently used to commit email and internet fraud. Detecting them before any user information is submitted is critical. Several efforts have been made to detect these phishing websites in recent years. Most existing approaches use hand-crafted lexical and statistical features from a website’s textual content to train classification models to detect phishing web pages. However, these phishing detection approaches have limitations, including (1) the tediousness of extracting hand-crafted features, which require specialized domain knowledge to determine which features are useful for a particular platform; and (2) the difficulties encountered by models built on hand-crafted features to capture the semantic patterns in words and characters in URL and HTML content. To address these challenges, this paper proposes WebPhish, an end-to-end deep neural network trained using embedded raw URLs and HTML content to detect website phishing attacks. First, the proposed model automatically employs an embedding technique to extract the corresponding characters into homologous dense vectors. Then, the concatenation layer merges the URL and HTML embedding matrices. Following that, Convolutional layers are used to model its semantic dependencies. Extensive experiments were conducted with real-world phishing data, which yielded an accuracy of 98.1%, showing that WebPhish outperforms baseline detection approaches in identifying phishing pages.