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In contrast with the accelerating trend of boundary-spanning (horizontal) technological innovation, the current Cooperative Patent Classification (CPC) scheme applies a hierarchical (vertical) structure to innovation output in terms of patents. For this reason, we argue that the CPC can be complemented with dynamic technological innovation system (TIS) discovery through machine learning that accounts for horizontal relationships across seemingly disparate technologies. Using a design science approach, we propose a framework to discover boundary-spanning TISs by leveraging the textual information from millions of patents. We validate our framework in terms of the ability of discovered relationships to predict future innovation quantity and quality in different technology classes. Our novel TIS-based innovation metrics that leverage patenting activity in related technology classes are significantly associated with future innovation intensity in focal technologies. We conduct experiments with machine learning models to further tease out the predictive utility of our TIS discovery framework. [ABSTRACT FROM AUTHOR]

The diagnostic of sexual desire disorders is based on not only physiological and psychological symptoms but also on the extent of distress about these symptoms. Using data from a 2022 national survey of 3,000 respondents in Austria, this paper explores the range of relational distress individuals experience about their level of sexual desire. Participants were asked, ‘Have you ever been ostracised or ridiculed in your life for feeling too much (not enough) sexual desire? If yes, in what way?’ Gender and sexual minorities were more likely than cisgender and heterosexual participants to report negative reactions to their level of desire. The paper presents the results of a thematic analysis of the 341 written answers to the latter question. The analysis identified three themes: a) harassment, such as mocking, bullying, or public humiliation; b) the pathologization of desire as a sexual disorder; and c) negative social classification with respect to gender, age, or sexual orientation. By investigating different types of negative social reactions that individuals experience with respect to their level of sexual desire, the paper critically questions an individualised notion of sexual distress that has often been the basis for biomedical and psychological interventions by highlighting social factors that produce distress in the first place. [ABSTRACT FROM AUTHOR]

Animal sociality, the extent and manner in which conspecifics associate with each other, ultimately affects an individual's survival and reproductive success. It is shaped by the spatiotemporal configuration and composition of the social units (e.g. individual, pair, group) in a population. Here, we assessed the formation and structure of social networks of a presumed nonsocial species with individual-based movement data of 153 GPS-marked brown bears, Ursus arctos. We explored changes in the frequency of dyadic associations in relation to distinct seasonal patterns (i.e. mating, hyperphagia and hunting seasons) that affect bear behaviour. We found seasonally distinctive frequencies in brown bear associations throughout their active period and that reproduction was the main driver for associations in the population, that is, the highest frequency of associations occurred during the mating season and male–female dyads during the mating season included the majority (73%) of observed associations. We also observed dyadic associations during the hyperphagia and hunting seasons, but found no significant changes in frequency during these seasons. In addition, we found that social structures during both the mating and nonmating periods were nonrandom, that is, dyadic associations occurred more often than expected. Animal sociality is commonly viewed as a classification of social versus nonsocial, but our results suggest that it is rather a dynamic continuum primarily influenced by variation in a species' spatiotemporal configuration (i.e. seasonal movements, social unit structure) and demographic composition (i.e. age, sex). Our results also support the contention that studies focusing on animal sociality should include a sociospatial perspective, as both components are tightly linked. Since sociality can affect individual fitness, and vice versa, advancing the knowledge on assumed 'solitary' species is paramount for the conservation and sustainable management of their populations. • Animal sociality is a dynamic continuum instead of a binary classification. • The sociospatial environment of a population is under constant change. • We performed a network analysis using a GPS location database from a solitary species. • In brown bears, a 'solitary species', sociality is driven by reproduction. • Brown bear social interactions are nonrandom outside the mating season. [ABSTRACT FROM AUTHOR]

Humpback whale (Megaptera novaeangliae) nonsong vocalizations, or social calls, are much more poorly understood than humpback song. We examined humpback whale social calls from a foraging ground in southern British Columbia (BC) and developed a catalog for humpback social calls in BC. We tagged four humpback whales on the eastern edge of Swiftsure Bank, BC, in early September 2020, with a passive acoustic and movement tag. We manually classified 32 call types in our data set based on comparisons with published classifications of humpback social calls. Many of the calls identified in our data set had similar characteristics to calls from other locations. We also used two statistical classification methods, a cluster analysis and a random forest. The cluster analysis grouped 20 of these call types into four categories, and the random forest classifier was able to accurately classify all 20 call types 87.6% of the time. This study fills a geographical gap of humpback whale social calls on foraging grounds and is a first step towards categorizing the social calls of humpback whales in BC. [ABSTRACT FROM AUTHOR]

Background: Emotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are various emotion classification schemes. Which one is suitable for textual emotion analysis? Methods: We proposed a framework for evaluating emotion classification schemes based on manual annotation experiments. Considering both the quality and efficiency of emotion analysis, we identified five criteria, which are solidity, coverage, agreement, compactness, and distinction. Qualitative and quantitative factors were synthesized using the AHP, where quantitative metrics were derived from annotation experiments. Applying this framework, 2848 Sina Weibo posts related to public events were used to evaluate the five emotion schemes: SemEval's four emotions, Ekman's six basic emotions, ancient China's Seven Emotions, Plutchik's eight primary emotions, and GoEmotions' 27 emotions. Results: The AHP evaluation result shows that Ekman's scheme had the highest score. The multi-dimensional scaling (MDS) analysis shows that Ekman, Plutchik, and the Seven Emotions are relatively similar. We analyzed Ekman's six basic emotions in relation to the emotion categories of the other schemes. The correspondence analysis shows that the Seven Emotions' joy aligns with Ekman's happiness, love demonstrates a significant correlation with happiness, but desire is not significantly correlated with any emotion. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation. Trust is somewhat associated with happiness, but anticipation is weakly associated with happiness. Each emotion of Ekman's corresponds to several similar emotions in GoEmotions. However, some emotions in GoEmotions are not clearly related to Ekman's, such as approval, love, pride, amusement, etc. Conclusion: Ekman's scheme performs best under the evaluation framework. However, it lacks sufficient positive emotion categories for the corpus. [ABSTRACT FROM AUTHOR]

A mathematical description of the economic behavior of a representative household in an imperfect market of consumer loans and deposits is studied. The model is formalized as a finite-horizon optimal control problem. A classification of the behavior of social layers depending on market condition parameters is obtained. An infinite-horizon optimal control synthesis is constructed. [ABSTRACT FROM AUTHOR]

Our study, conducted between April 2022 and January 2024, was aimed at clarifying components of the rest-activity rhythm (RAR) involved in improvement of social frailty state before or after a 3-month multi-component exercise intervention in the elderly. Participants were recruited from the general population in Akita prefecture, Japan. We administered a four-item social frailty screening questionnaire to classify the severity of social frailty in each participant before and after the 3-month intervention. The RAR parameters were measured on an Actiwatch Spectrum Plus device worn by the subjects for 7 continuous days. As the final sample, 65 participants classified into the improved/maintained group (n = 36) or the deteriorated group (n = 29) according to the change in the social frailty classification after the intervention were included in the analysis. The results of a binomial logistic regression analysis showed significantly higher values of interdaily stability (IS) and usual walking speed at the post-test after the intervention. Based on our findings, we propose that stability of the rest-activity rhythm related to synchronization with external stimuli (such as social effect and physical activity) might have clinical impact on improvement of social frailty state in elderly community-dwellers. [ABSTRACT FROM AUTHOR]

In the computer-aided diagnosis of lung cancer, the automatic segmentation of pulmonary nodules and the classification of benign and malignant tumors are two fundamental tasks. However, deep learning models often overlook the potential benefits of task correlations in improving their respective performances, as they are typically designed for a single task only. Therefore, we propose a multi-task network (MT-Net) that integrates shared backbone architecture and a prediction distillation structure for the simultaneous segmentation and classification of pulmonary nodules. The model comprises a coarse segmentation subnetwork (Coarse Seg-net), a cooperative classification subnetwork (Class-net), and a cooperative segmentation subnetwork (Fine Seg-net). Coarse Seg-net and Fine Seg-net share identical structure, where Coarse Seg-net provides prior location information for the subsequent Fine Seg-net and Class-net, thereby boosting pulmonary nodule segmentation and classification performance. We quantitatively and qualitatively analyzed the performance of the model by using the public dataset LIDC-IDRI. Our results show that the model achieves a Dice similarity coefficient (DI) index of 83.2% for pulmonary nodule segmentation, as well as an accuracy (ACC) of 91.9% for benign and malignant pulmonary nodule classification, which is competitive with other state-of-the-art methods. The experimental results demonstrate that the performance of pulmonary nodule segmentation and classification can be improved by a unified model that leverages the potential correlation between tasks. [ABSTRACT FROM AUTHOR]

Efficiency in the selection of prospective employees can have a significant impact on increasing costs and extending the time required in the recruitment process of HR Information Systems in boarding schools. However, the hiring process is often still done manually and traditionally, resulting in high costs and time and lack of efficiency. Social media has become an important part of modern society, including in the employee recruitment and selection process. Social media can be used to obtain more comprehensive information about prospective employees, such as educational background, skills, work experience, and personality. This research investigates using Linear Regression Algorithm on social media classification and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method as ranking criteria in classifying prospective employees. One of the proposed solutions is to utilize information from social media to evaluate the potential and personality of prospective employees. This research specifically reviews the application of Linear Regression Algorithm and TOPSIS method in categorizing prospective employees based on information obtained from social media, focusing on HR Information System in a particular boarding school. The results showed that the application of social media classification system with linear regression algorithm with Mean absolute error 10.50, Residual sum of squares (MSE): 147.16, R-squared: 1.0 and TOPSIS method with an accuracy rate of 0.51238 for the first rank can improve the efficiency of the recruitment process. [ABSTRACT FROM AUTHOR]

Social factors and psychological characteristics can influence selection and development in talent pathways. However, the interaction between these two factors is relatively unknown. The aim of this study was to investigate the implications of socioeconomic status and psychological characteristics in English academy soccer players (n = 58; aged 11 to 16 years). To assess socioeconomic status, participants' home postcodes were coded according to each individual's social classification and credit rating, applying the UK General Registrar Classification system and CameoTM geodemographic database, respectively. Participants also completed the six factor Psychological Characteristics for Developing Excellence Questionnaire (PCDEQ). A classification of 'higher-potentials' (n = 19) and 'lower-potentials' (n = 19) were applied through coach potential rankings. Data were standardised using z-scores to eliminate age bias and data were analysed using independent sample t-tests. Results showed that higher-potentials derived from families with significantly lower social classifications (p = 0.014) and reported higher levels for PCDEQ Factor 3 (coping with performance and developmental pressures) (p = 0.007) compared to lower-potentials. This study can be used to support the impetus for researchers and practitioners to consider the role of social factors and psychological characteristics when selecting and developing sporting talent. For example, facilitating player-centred development within an academy and, where necessary, providing individuals with additional support. [ABSTRACT FROM AUTHOR]

Aims Design Data Sources Methods Results Conclusion Implications for the Profession and/or Patient Care Impact Reporting Method Patient or Public Contribution The aim of this study is to summarize the characteristics of social participation classification and examine the association between activities and health outcomes among older adults.Scoping review.Eight databases (Cumulative Index to Nursing and Allied Health Literature, The Cochrane Library, Embase, ProQuest, Psychological Information Database, PubMed, Scopus and Web of Science) were searched. Reference lists of relevant social participation reviews were also considered.This study applied a five‐stage methodological framework. A narrative synthesis summarized social participation classification and activities and their associations with health outcomes among older adults (≥65 years) living at home, in the community or in nursing residences.Forty‐two articles published between 1975 and 2022 were selected. Four classification criteria of social participation were extracted and summarized from these studies. Based on the depth and breadth of social interactions, this review proposed a four‐level classification schema. A lower risk of mortality and less visual impairment were associated with participation in level‐one, level‐three or level‐four activities, whereas less depression, less pain and better cognitive function were linked to participation in level‐three or level‐four activities.Future studies should provide a clear definition, establish classification criteria for participation and properly select activity forms while considering both subjective and objective dimensions.The results could provide data for designing targeted social participation interventions to improve specific health outcomes among older adults.This review could help researchers examine the role of social participation activities in specific health outcomes. Moreover, a proposed classification of social participation activities would benefit researchers and community nurses in discerning the similarities and differences among activities.This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses—Extension for Scoping Reviews guideline.No patient or public contribution. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the research was a comparative analysis of the level of social development in the European Union countries, taking into account the income inequality index. Design/methodology/approach: Research in this area is conducted using multidimensional analysis, because this phenomenon can be described based on many partial criteria. The article uses QGIS software to classify European Union countries according to the selected indicators such as: the Human Development Index and the Ginni Index describing the level of income inequality. Findings: W artykule zidentyfikowano aspekty wpływające najistotniej na poziom rozwoju społecznego oraz określono kryteria wpływające na wzrost zadowolenia społecznego w krajach wysokorozwiniętych. Practical implications: The article shows several trends on the example of European Union countries, that the level of development varies within the EU itself, but on a global scale on a very large scale. The presented HDI and inequality index can be a very important measurement providing synthetic information. The trend of these parameters over time is also an important element of the assessment. It has also been shown that countries with the lowest level of human development are characterized by the highest level of income inequality. Originality/value: Research has shown that European Union countries are among the countries with the highest level of human development in the world. However, EU countries vary greatly in terms of income inequality. [ABSTRACT FROM AUTHOR]

Social media platforms are increasingly used during disasters. In the United States, users often consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our approach uses transfer learning and classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification but also that real-time classification of social media images using a small training set is possible. [ABSTRACT FROM AUTHOR]

Copyright of Direito e Práxis is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper comparatively investigates the relation between controlled vocabularies assigned by the experts in Library of Congress and tags assigned by users in Library Thing database in three subjects, Economics, History and Sociology under Social Science domain. Based on Term matching (S= 14.80 %, E= 12.77 % and H= 8.06 %) and Jaccard similarity coefficient (E= 0.15, S= 0.15 and H= 0.11), we found little matching between both vocabularies. We also found that experts mostly use double-word and multi-word specific topical terms (S= 73.14 %, E= 72.89 % and H= 61.05 %), whereas social taggers mostly use single-word general non-topical terms (E= 54.88 %, H= 54.21 % and S= 48.55%) and little topical and few personal terms. While comparison with LCSH subfield, we found that experts prefer topical terms for all subjects, whereas, taggers only prefer it for Economics and geographic subdivision terms for History and Sociology, but they don’t prefer chronological terms for tagging. Even, experts prefer little title-based terms (H= 196 terms, S= 195 terms and E= 175 terms) but taggers mostly prefer title-based terms (H= 673 terms, S= 564 terms and E= 444 terms) in three subjects. However, the study concludes that both vocabularies are different, but libraries can exploit those uncontrolled vocabularies and can introduce ‘hybrid metadata ecology’ which combines controlled vocabularies, classification and folksonomies for better subject access and retrieval of social science documents. [ABSTRACT FROM AUTHOR]

The popularity of social networks as primary mediums for sharing visual content has made it crucial for forensic experts to identify the original platform of multimedia content. Various methods address this challenge, but the constant emergence of new platforms and updates to existing ones often render forensic tools ineffective shortly after release. This necessitates the regular updating of methods and models, which can be particularly cumbersome for techniques based on neural networks which cannot quickly adapt to new classes without sacrificing performance on previously learned ones – a phenomenon known as catastrophic forgetting. Recently, researchers aimed at mitigating this problem via a family of techniques known as continual learning. In this paper we study the applicability of continual learning techniques to the social network identification task by evaluating two relevant forensic scenarios: Incremental Social Platform Classification , for handling newly introduced social media platforms, and Incremental Social Version Classification , for addressing updated versions of a set of existing social networks. We perform an extensive experimental evaluation of a variety of continual learning approaches applied to these two scenarios. Experimental results demonstrate that, although Continual Social Network Identification remains a difficult problem, catastrophic forgetting can be significantly mitigated in both scenarios by retaining only a fraction of the image patches from past task training samples or by employing previous tasks prototypes. • We investigate continual learning methods for social network identification. • We exploit a state-of-the-art dual-branch neural network designed for this task. • We define two realistic experimental scenarios on multiple datasets. • Exemplar-based methods yield good performance with limited memory requirements. • Prototype-based methods are a viable solution when storing exemplars is not feasible. [ABSTRACT FROM AUTHOR]

Online labor markets and the humans that power them serve a critical role in the advancement of artificial intelligence and supervised machine learning via the creation of useful training datasets. The use of human effort in online labor markets is not enough, however, as a key factor is understanding the possible interventions that market operators can leverage to incentivize human effort among their labor force. We propose that platforms could implement mechanisms such as rewards or punishments at individual or group levels to incentivize real-effort and output. We apply our interventions using a collaborative image tagging experiment--a folksonomy--and the results provide interesting insights and nonobvious consequences. On average, interventions applied at the group level outperformed interventions applied at the individual level. Punishing the group provided the most controversial incentive strategy and provided a nonobvious significant improvement in effort. Rewarding or sanctioning an individual had similar effects on average, with both treatments leading to significant increases in effort post-intervention. In contrast to predictions, sanctioning appears to have significantly motivated those that were punished. Overall, the interventions applied in our real-effort collaborative image tagging experiment had a significant impact on behavior, which provides guidance for online labor market operators and the use of incentives in the creation of labeled machine learning training datasets. [ABSTRACT FROM AUTHOR]

Purpose: The present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale. Design/methodology/approach: The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface. Findings: The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool. Originality/value: Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections. [ABSTRACT FROM AUTHOR]

More than 80% of people who commit suicide disclose their intention to do so on social media. The main information we can use in social media is user-generated posts, since personal information is not always available. Identifying all possible emotions in a single textual post is crucial to detecting the user's mental state; however, human emotions are very complex, and a single text instance likely expresses multiple emotions. This paper proposes a new multi-label emotion graph representation for social media post-based mental health classification. We first construct a word–document graph tensor to describe emotion-based contextual representation using emotion lexicons. Then, it is trained by multi-label emotions and conducts a graph propagation for harmonising heterogeneous emotional information, and is applied to a textual graph mental health classification. We perform extensive experiments on three publicly available social media mental health classification datasets, and the results show clear improvements. [ABSTRACT FROM AUTHOR]

In this article, we analyse overarching patterns of social stratification in, and quality differences between, Swedish preschools. Drawing on a Bourdieusian social classification scheme, we evidence in what type of preschools different social groups enrol their children in, and to what extent Swedish preschool children encounter different levels of quality at municipal, for-profit and non-profit providers in the preschool market. The analysis employs individual register data from Statistics Sweden covering all families in Swedish preschools for the year 2019. This comprises information on approximately 510 000 children and 122 000 unique families. We use descriptive statistics, looking at over- and under-representation in enrolment patterns of 38 social groups along with quality indicators of different providers in terms of preschool size, teacher and staff ratio and the level of teachers with Swedish background. Results from this total population analysis of Swedish preschool enrolment reveal indicators of particular class practices, where upper- and middle-class families are overrepresented in both for-profit and non-profit preschools in contrast to working-class families. Simultaneously, private providers may not always deliver high standards of structural quality, which clouds the image of how quality is viewed by parents in general, and by upper- and middle-class families in particular. [ABSTRACT FROM AUTHOR]

This method is able to synthesize fine-detailed images by the use of a global attention that gives more attention to the words in the textual descriptions. Also we have the deep attention multimodal similarity model (DAMSM) that calculates the matching loss in the generator. Though this work produced images of high quality, there was some loss while training the system and it takes enough time for training. Although there has been little study on applying character-level Dense Net algorithms for text classification tasks; the Dense Net structures we suggested in this paper have shown outstanding performance in image classification tasks. Extensive testing has revealed that they perform better when it comes to their ability to withstand interruption and that they can influence exerted many organizations implementing information usage and language information on the specifications of user privacy protection, framework implies, and regulatory requirements. [ABSTRACT FROM AUTHOR]

Social media platforms generate vast amounts of data, necessitating efficient content classification and community detection methods. This study addresses this challenge through the utilization of VGGNet analytics, a powerful deep learning architecture. We employed a twostep approach, beginning with VGGNet-based content classification to categorize social media posts. Subsequently, a community detection algorithm was applied to identify distinct user groups based on their interactions and content preferences. This research contributes an novel framework that seamlessly integrates VGGNet for content analysis and community detection, enhancing the understanding of user behavior in social media platforms. The proposed method aims to provide more accurate and insightful results compared to traditional approaches. Our experiments on diverse social media datasets demonstrate the effectiveness of the VGGNet-based approach. The content classification accurately assigns posts to relevant categories, while the community detection algorithm identifies cohesive user groups. The results highlight the potential for improved content recommendation systems and targeted marketing strategies. [ABSTRACT FROM AUTHOR]

Sentiment analysis is the computational study of the emotions, attitudes and opinions of humans through the extraction of meaningful information. Social media platforms that allow consumers to share and publish content, are enriched with opinionating information that many analytical researches are currently, however, limited to a specific domain. This research presents an architecture to analyze a limited resource language, Persian language, and focuses on the analysis of social media, consisting of informal comments across different domains. The proposed model applies a transformer-based model, ParsBERT, to classify the sentiments of social media comments. Since social media comments have different domains, it is necessary for the proposed model to classify sentiments of comments in different domains. ParsBERT has been fine-tuned on a Persian corpus that has been generated for the purpose of this study. The generated corpus has been gathered from 28,710 Instagram comments in different topic domains and have been labeled as either negative or positive comments. The proposed model has been evaluated based on different test data belonging to different time-periods and topic domains and results have been compared with recent methods for the task of sentiment analysis for three different scenarios. Results show that when the training and test data are from different domains an accuracy of 68% is achieved, which is higher than other shallow methodologies and deep learning methods for determining the sentiments of social media comments in different domains. [ABSTRACT FROM AUTHOR]

This research aims to identify some patterns of author (as user) generated tags to the papers of library and information science field in Academia.edu. The research method is typically based on text analysis and word frequency distribution. The population contains over 6000 papers tagged in Academia.edu, and their abstracts were extracted from 159 English journals of the library and information science (LIS) field in the Scopus database. The growth of different types of tags in terms of the number of their words (one-word, two-word, three-word, and four-word and more), as well as the total number of tags over time, appeared as a logistic curve. It was also found that two-word tags had the most matching (54.92%) and four-word tags or more the least matching (1.76%) with different sections of papers (title, abstract, and authors' keywords). The total tags matched 7.5% with the title, 76.61% with the abstract, and 15.89% with the authors' keywords. Regarding the reuse of tags, it was revealed that on the one hand, 38.8% of the tags had been reused; on the other hand, 16% of the tags were reused in the first year, and more than 50% of the tags were reused in the first three years. Finally, it can be said that the users' consensus on specific terms can identify the new patterns of users' tagging at least partially compatible with professional indexing concepts, and by focusing on the most widely used tags and their sustainable distribution, the weighting of indexing terms and even classification schemes may be achieved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Sciences Institute is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article represents one of the first attempts at building a direct measure of occupational exposure to robotic labour-saving technologies. After identifying robotic and labour-saving robotic patents, the underlying 4-digit CPC (Cooperative Patent Classification) code definitions, together with O*NET (Occupational Information Network) task descriptions, are employed to detect functions and operations which are more directed to substituting the labour input and their exposure to labour-saving automation. This measure allows us to obtain fine-grained information on tasks and occupations according to their text similarity ranking. Occupational exposure by wage and employment dynamics in the United States is then studied, and complemented by investigating industry and geographical penetration rates. [ABSTRACT FROM AUTHOR]

Copyright of HiSTOReLo: Revista de Historia Regional y Local is the property of Universidad Nacional de Colombia, Centro Editorial Facultad de Ciencias Humanas y Economicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This essay examines the combination of hands and clothing (or lack thereof) on animals in Sir John Tenniel's illustrations of Alice's Adventures in Wonderland. The hierarchical distinctions of characters in the narrative echo, distort, and question Victorian social classifications and mark Alice—a child—as distinct. [ABSTRACT FROM AUTHOR]

The work process of specialists in protection from information consists of many time-consuming tasks, including data collection, datasets formation, and data manual labelling. In this paper, we attempted to help such specialists with a two-model approach based on the iterative online training of binary classifiers. This approach is used for inappropriate information detection and applied on text posts from the VKontakte social network. The first model is used to detect text posts that are corresponding to the selected topic and is trained on the data that is labelled positively and negatively by experts as well as random text data. The second model is used to improve the accuracy of the first model and is trained only on the data that is labelled by the experts. The novelty of the approach lies in the constantly growing dataset, while the classifiers training process takes place during the operator's work. The approach works with texts of any size and content and applicable for Russian social networks. The research contribution lies in the original approach for inappropriate information detection. The practical significance of the approach lies in the automation of routine tasks to reduce the burden on specialists in the area of protection from information. Experimental evaluation of the approach is focused on its iterative retraining part. For the experiment, text posts of different topics from the VKontakte social network were collected and labelled. Those topics include: Aggression, Dangerous conspiracy theories, Radicalism, Gambling, Prostitution, and Sects. After that, we evaluated precision, recall, F-measure and ROC-AUC metrics for classifiers trained on random subsamples of different sizes and different topics. Those metrics were evaluated for both one-model and two-model implementations of the approach, while the following classifiers were used: linear support vector machine, passive–aggressive classifier, multilayer perceptron. Moreover, the advantages and disadvantages of the approach, as well as future work directions, were indicated. [ABSTRACT FROM AUTHOR]

With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigating the performance of these techniques on the Arabic language. This paper presents a comparative study of quantum computing and machine learning for two datasets of Arabic language document classification. In the first dataset of 213,465 Arabic tweets, both classic machine learning (ML) and quantum computing approaches achieve high accuracy in sentiment analysis, with quantum computing slightly outperforming classic ML. Quantum computing completes the task in approximately 59 min, slightly faster than classic ML, which takes around 1 h. The precision, recall, and F1 score metrics indicate the effectiveness of both approaches in predicting sentiment in Arabic tweets. Classic ML achieves precision, recall, and F1 score values of 0.8215, 0.8175, and 0.8121, respectively, while quantum computing achieves values of 0.8239, 0.8199, and 0.8147, respectively. In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. Classic ML completes the analysis in approximately 2 min, while quantum computing takes approximately 1 min and 53 s. The accuracy of classic ML is higher at 0.9241 compared to 0.9205 for quantum computing. However, both approaches achieve high precision, recall, and F1 scores, indicating their effectiveness in accurately predicting sentiment in the dataset. Classic ML achieves precision, recall, and F1 score values of 0.9286, 0.9241, and 0.9249, respectively, while quantum computing achieves values of 0.92456, 0.9205, and 0.9214, respectively. The analysis of the metrics indicates that quantum computing approaches are effective in identifying positive instances and capturing relevant sentiment information in large datasets. On the other hand, traditional machine learning techniques exhibit faster processing times when dealing with smaller dataset sizes. This study provides valuable insights into the strengths and limitations of quantum computing and machine learning for Arabic document classification, emphasizing the potential of quantum computing in achieving high accuracy, particularly in scenarios where traditional machine learning techniques may encounter difficulties. These findings contribute to the development of more accurate and efficient document classification systems for Arabic data. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) for social image classification are prone to performance reduction and overfitting when trained on datasets plagued by noisy or imbalanced labels. Weight loss methods tend to ignore the influence of noisy or frequent category examples during the training, resulting in a reduction of final accuracy and, in the presence of extreme noise, even a failure of the learning process. A new advisor network is introduced to address both imbalance and noise problems, and is able to pilot learning of amain network by adjusting the visual features and the gradient with a meta-learning strategy. In a curriculum learning fashion, the impact of redundant data is reduced while recognizable noisy label images are downplayed or redirected. Meta Feature Re-Weighting (MFRW) and Meta Equalization Softmax (MES) methods are introduced to let the main network focus only on the information in an image deemed relevant by the advisor network and to adjust the training gradient to reduce the adverse effects of frequent or noisy categories. The proposed method is first tested on synthetic versions of CIFAR10 and CIFAR100, and then on the more realistic ImageNet-LT, Places-LT, and Clothing1M datasets, reporting state-of-the-art results. [ABSTRACT FROM AUTHOR]

This article presents a framework for classifying and analyzing tweets on social media platforms using Federated Learning (FL). The framework incorporates feature mapping and feature indexing techniques to determine the threshold computation value for categorizing tweets as either "positive" or "negative." It is platform-agnostic and has been validated using a diverse dataset from platforms like X (formerly known as Twitter), Koo, and Instagram. The framework achieved an accuracy rate of 93.54% in classifying trending topic posts. This research highlights the significance of social media and its role in data analysis, particularly through the lens of FL. [Extracted from the article]

Sociodemographic information, such as a speaker's regional origin, is intimately related to the judgments and social evaluations that listeners assign to that speaker. This association between linguistic form and social information can also lead to linguistic profiling, a harmful form of discrimination. The present study examines the geographic classifications and social attitudes attributed to ten phonetic variants used within regional varieties of Iberian (i.e., European) Spanish. We are specifically interested in understanding listeners' geographical classifications and language attitudes held toward Andalusian Spanish, which is a less privileged regional variety spoken in Spain's southern region, as compared to north-central Peninsular Spanish (NCPS). The results of an online survey show that 165 listeners were fairly consistent when geographically classifying Andalusian-sounding stimuli as originating from the south of Spain. Importantly, the respondents also attributed less favorable social meaning to the Andalusian-sounding stimuli in comparison to the NCPS-sounding stimuli. We link the findings to broader themes in sociolinguistics, such as language-based discrimination, linguistic insecurity, and the social motivations of language change. [ABSTRACT FROM AUTHOR]

The primary methods of communication in the modern world are social networks, which are rife with harmful messages that can injure both psychologically and financially. Most websites do not offer services that automatically delete or send malicious communications back to the sender for correction, or notify the sender of inaccuracies in the content of the messages. The deployment of such systems could make use of techniques for identifying and categorizing harmful messages. This paper suggests a novel multilayered preprocessing approach for the recognition and classification of malicious social network messages to limit negative impact, resulting in fewer toxic messages, scams, and aggressive comments in social media messages and commenting areas. As a result, less technical knowledge would be required to investigate the effects of harmful messages. The dataset was created using the regional Lithuanian language with four classes: aggressive, insulting, toxic, and malicious. Three machine learning algorithms were examined, five use cases of a multilayered preprocessing approach were suggested, and experiments were conducted to identify and classify harmful messages in the Lithuanian language. [ABSTRACT FROM AUTHOR]

Osteoarthritis is an agonising disease that affects millions of people every year and the occurrence rate is also increasing promptly. The prompting leads to irrelevant lifestyle among the individuals and early detection and diagnosis help to reduce the influence of osteoarthritis to a greater extent. In this research, knee osteoarthritis is identified using the optimised deep convolutional neural network (deep CNN) classifier from the necessary features extracted. The significant steps in this research are feature extraction using a hybrid pretrained model and classification using optimised deep CNN. The hybrid pretrained model is developed using the Visual Geometry Group (VGG), Resnet 101, and Alexnet architectures and the composed characteristics provide effortless training with reduced error and loss function. The deep CNN classifier is optimised using the social wolf swarm-based optimisation that effectively tunes the classifier by its fitness function and finally, the knee osteoarthritis is determined. The proposed method achieved an efficient accuracy rate of 98%, f1 measure of 94.03%, precision of 98%, and recall of 97.173%, which is more efficient. [ABSTRACT FROM AUTHOR]

In the phase of digital communication, social media sites have developed into a major centre for individuals to express their ideas, emotions, and views. Amidst this influx of user-generated content, mental health conditions, notably depression, have garnered increasing attention due to their pervasive impact on individuals and societies. Early detection and intervention are crucial in managing and preventing its adverse effects. As a consequence an innovative machine learning (ML) textual data classification framework is designed to detect depression through social media streams, employing a Firefly-Optimized Support Vector Machine (FFO-SVM) and Artificial Bee Colony (ABC) Optimized SVM classifiers. Initially, data collection and preprocessing are performed, followed by feature extraction using Time Frequency-Inverse Document Frequency (TF-IDF). After extraction of features classification is performed using FFO-SVM and ABC-SVM classifiers. To tune the parameters of SVM to work efficiently, FFO and ABC are employed. The proposed framework combines the power of ML with the optimization capabilities of the ABC and FFO Algorithm to enhance classification accuracy. Through extensive experimentation and analysis, the framework's performance is evaluated using relevant metrics. Results indicated that proposed classification techniques outperformed conventional methods, showcasing its effectiveness in handling the complexity of depression detection from social media data. [ABSTRACT FROM AUTHOR]

In order to ensure the healthy development of social networks and the harmony and stability of the society, as well as to facilitate effective supervision by regulatory authorities, a classification method of social bots is proposed based on the identification of social bots in the early stage. First of all, the topic-related introduction is used to expand the topic, and on this basis, the SBERT (Sentence-BERT) model is applied to make relevance judgments between the micro-blog text and the expanded topics to identify polluters. Then, an opinion sentence recognition method that combines social bots opinion sentence generation rules with a deep learning model TextCNN is proposed to further distinguish commenters and spreaders. Finally, in order to improve the classification effect of the model, the transfer learning method is used to train the model with the help of a large number of micro-blogs of ordinary Weibo accounts, so as to better improve the classification effect of social bots. The comparative experimental results show that the topic expansion method can effectively improve the classification results of the SBERT model for the relevance of micro-blog text topics. When the parameter k of the expanded topic model is set at 20, the content of the expanded topic sequence is more consistent with the core content of most Weibo text sequences, and the obtained model has the best performance. By analyzing the opinion-based micro-blog text generation rules of social bots, focusing on the keywords that express opinions, the problem of difficulty in recognizing opinion sentences produced by the low quality of opinion sentences of social bots is well resolved, and the recognition effect of opinion sentences has been improved by more than 10%. Through the introduction of transfer learning, the problem of insufficient social bots data is effectively alleviated, and the classification effect of social bots is greatly improved, with an increase of more than 10%. [ABSTRACT FROM AUTHOR]

This article examines how 'trauma' has been conceptualised in the unresolved state of mind classification in the Adult Attachment Interview, introduced by Main and Hesse in 1990. The unresolved state of mind construct has been influential for three decades of research in developmental psychology. However, not much is known about how this measure of unresolved trauma was developed, and how it relates to other conceptualisations of trauma. We draw on previously unavailable manuscripts from Main and Hesse's personal archive, including various editions of unpublished coding manuals, and on Main–Bowlby correspondence from the John Bowlby Archive at the Wellcome Trust in London. This article traces the emergence of the unresolved state of mind classification, and examines the assumptions about trauma embedded in the construct. These assumptions are situated both in the immediate context of the work of Main and Hesse and in terms of wider discourses about trauma in the period. Our analysis considers how a particular form of trauma discourse entered into attachment research, and in doing so partly lost contact with wider disciplinary study of trauma. [ABSTRACT FROM AUTHOR]

What motivates states' choice of social classification? Existing explanations highlight scientific beliefs of modern states or social engineering by ideological regimes. Focusing on the initial state-building period of two Communist regimes, China and North Korea, this article complements the existing literature and suggests that social classification reflects three missions of political leaders: regime distinction, governance, and power consolidation. Population categories are created to distinguish the new government from the old, to selectively provide welfare, and to attack political opponents. The varying weight of the missions and their manifestation in social classification depend on new ruling elites' cohesion and past experiences. This comparative historical analysis sheds light on the rise of political chaos in China and the personalistic dictatorship in North Korea in the 1970s. [ABSTRACT FROM AUTHOR]

As a geocultural and temporally defined form of production, science fiction is fully within the scope of anthropological analysis. In this article, I aim to analyze the representation of the notion of humanity in Latin American science fiction. Specifically, I examine four narratives by Peruvian authors that offer a critical political lens on contemporary Peruvian society. As the notion of humanity is a complex and multifaceted concept explored by different disciplines, I seek to contribute to the discussion by providing a case study from social anthropology. Science fiction is a particularly relevant medium for social and political critique, as it allows for exploring real and contemporary situations by constructing possible worlds. Methodologically, I approach these four narratives ethnographically, that is, by contextualizing them within the Peruvian socio-political space. In terms of analysis, this article is framed within the anthropological exploration of colonial relations, the construction of social classifications, the boundaries and borders between the human and the non-human, and human impacts on the planet. I argue that science fiction can offer innovative perspectives that enrich a critical anthropological perspective. [ABSTRACT FROM AUTHOR]

After decades of research on the topic of reciprocity, there is still no consensus about the meaning of the term. Instead, there has been a proliferation of reciprocity terms with varied definitions, some of which overlap in ways that lead to confusion for scholars studying cooperation. In this paper, we provide a summary of 34 reciprocity terms and their definitions from across a variety of disciplines. We then report the results of a survey of cooperation experts spanning biology, anthropology, economics, sociology, and psychology (N = 85) about the extent to which they consider 30 of these definitions of reciprocity to be truly reciprocity. Experts also rated the extent to which they considered seventeen hypothetical scenarios to be examples of reciprocity. We used exploratory factor analysis and found that responses clustered around four dimensions of transfers: Balanced (e.g., Balanced reciprocity), Reputation-based (e.g., Generalized reciprocity), Debt-based (e.g., Calculated reciprocity), and Unconditional (e.g., Negative reciprocity). Although researchers agreed that the term reciprocity was useful and necessary, there was low agreement among scholars about what should be considered reciprocity. However, there was high agreement that unconditional transfers, which are characterized by a lack of expectations of repayment, should not be considered reciprocity. We propose that scholars of cooperation consider using these four dimensions when referring to cooperative transfers rather than using reciprocity terms in order to facilitate communication across disciplines, resolve issues related to ambiguous definitions of reciprocity, and provide a solution to the lack of consensus about what constitutes reciprocity. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to explore existing theories and approaches underlying corporate social and environmental responsibility. It also explored methods adopted by corporates in identifying and classifying stakeholders. Methodology: Documentary research approach, which consists of reviewing, analysing and examining information was adopted. The sources included journal articles, books, magazines, websites, frameworks and guidelines. Findings: The results of the study indicated that social and environmental stakeholders are now a force to reckon with. Old literature classifies social and environmental stakeholders as the negligent type but recent developments have realised that neglecting social and environmental stakeholders can be costly. Originality/Value: Guidelines and frameworks need to revise the classification of social and environmental stakeholders. [ABSTRACT FROM AUTHOR]

Cultured meat (CM) has emerged as a breakthrough technology, which has the potential to minimise future negative impacts associated with the rapidly growing world population; such as serious environmental, sustainability, global public health, and animal welfare concerns. Recently numerous researchers in academia and companies have been putting significant efforts into scientific and translational development in this field. Since various pillars of CM manufacturing hold substantial translational potential, there has been a steady interest in filing patent applications globally in the past decade. For the first time, we have performed a patentometric analysis for cultured meat by highlighting the current patents landscape, determining prolific organisations and geographical locations active in the area, and identifying the overall outlook for the field. In total, > 190 patents published in the English language were collected from 1997 to 2020 for the analysis. Relevant data were extracted from the patents, including technology focus, country of origin, assignees, priority date, cooperative patent classification (CPC) code, to analyse the current patent landscape. [ABSTRACT FROM AUTHOR]

The social internet of things (SIoT) facilitates numerous networking services and novel applications for the IoT, making it more productive and powerful. The state-of-the-art machine learning architectures often face different challenges when processing big data such as increased memory volume, increased training time, and higher computational costs. To tackle these challenges, this paper presents a novel framework for big data classification. Initially, an adaptive filter removes unwanted data and noises. The dimensionality of the filter data is then reduced via the Hadoop map reducer. After that, the battle royale optimization (BRO) algorithm is applied for feature selection. The optimal feature selection process performed by the BRO algorithm improves the accuracy and performance of the proposed fuzzy optimal deep convolutional neural network (FO-DCNN). The FO-DCNN model is formed by integrating the fuzzy-based remora optimization (F-RO) algorithm and deep CNN architecture for robust SIoT data classification. When evaluated using the Twitter, Rotten tomato, skin disease, diabetes, and hepatitis datasets, the proposed model offers improvements in terms of classification accuracy, memory consumption, and computational time. [ABSTRACT FROM AUTHOR]

Objective: Coffee is one of the most consumed beverages worldwide, and its production and consumption generate large amounts of by‐products annually. Coffee by‐products and coffee beans are rich in bioactive compounds of great commercial value, including potential applications as active ingredients in skin care products and cosmetic formulations. In addition, there has been growing interest in the use of natural ingredients for cosmetic purposes. Considering the importance of coffee in the world economy, its chemical constituents with potential for cosmetic and dermatological application, and the importance of patents for innovation and technological development, the present study aimed to review recent patents involving coffee and coffee by‐product use in cosmetics. Methods: This review was carried out using Espacenet. The following inclusion criteria were established: patents that included the terms "coffee" and "skin" in the title, abstract and claims and belonged to the classification A61Q, which is related to the "specific use of cosmetics or similar toilet preparations" considering the International Patent Classification (IPC) or Cooperative Patent Classification (CPC). Results: Considering the 52 patents analysed, the bean was the main way to obtain extracts (39), followed by green beans (7), silverskin (3), peel and pulp (1), pulp (1) and beans and leaves (1). The formulations are mainly intended for use in nonspecific areas of skin (29), eye areas (12), scalp hair (9) and lip skin (2) with claims of anti‐ageing, moisturizers, sun protection, hair growth, anti‐dandruff, etc. Conclusion: Coffee and its residues have high amounts of phenolic compounds, caffeine, fatty acids and other substances known to have important biological properties for the skin. Coffee and its by‐products are promising ingredients to be incorporated into topical formulations, ensuring skin health benefits and reducing the environmental impact. [ABSTRACT FROM AUTHOR]

The effects of global concerns such as climate change and environmental pollution must be considered also when dealing with the design or revamping of products or services nowadays. Existing practices, such as the 3R recovery approach and the Circular Economy approach, which aim to reduce waste and increase recycling, regeneration, and reusability, need to be applied during this process. This paper is embedded in this context, and it presents a study that was conducted within a manufacturing company on the possibility of reusing end-oflife mosquito nets, which are difficult to recycle, with the aim of reducing their environmental impact and creating new business opportunities. To achieve this goal, several methods for identifying new product applications have been evaluated from the literature and the most suitable one has been selected. The method is based on the identification of the product functions; then, with a series of patent searches, the Cooperative Patent Classifications (CPCs) of the resulting patents are extracted to be used as external stimuli during the design process. The results obtained in terms of ideas generated are then shown at the end of the paper, suggesting the actual effectiveness of the method applied. [ABSTRACT FROM AUTHOR]

In this rejoinder we argue, based on the papers of this commentary series, that 'race' is such a tricky notion because it can be used in (at least) two very different and contradictory ways—as a concept to disentangle racism and racialisation (what M'charek calls the 'work race actually does') and as a way of categorization and social classification, in which case it might create the very essentialised hierarchies 'race' as a critical concept tries to disentangle. We wonder if it is indeed possible to use 'race' as a concept without evoking 'race' as a social classification. At first, we give a short summary of the four discussion papers and then delve into two aspects the papers share, namely the need for spatial and temporal contextualization and comparison, as well as their choice not to take up our invitation to compare race as a category with gender. In a second step we will discuss two points arising from the papers, (1) 'race' as category, not as lens, often resulting in essentialism and (2) 'race' as potentially obscuring racism and racialisation. We conclude by proposing to give more space to complexity. [ABSTRACT FROM AUTHOR]