10,513 research outputs found

    The Impact of Intersectional Racial and Gender Biases on Minority Female Leadership Over Two Centuries

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    This study scrutinizes the enduring effects of racial and gender biases that contribute to the consistent underrepresentation of minority women in leadership roles within American private, public, and third sector organizations. We adopt a cultural situatedness approach, merging psychological schema theory with sociological intersectionality theory, to evaluate the enduring implications of these biases on female leadership development. Our examination is concentrated on Black female leaders, employing an extensive analysis of leadership rhetoric data spanning 200 years across the aforementioned sectors. We shed light on the continued scarcity of minority female representation in leadership roles, highlighting the role of intersectionality dynamics. Despite Black female leaders frequently embracing higher risks to counter intersectional invisibility compared to their White counterparts, their aspirations are not realized and problems not solved generation after generation, forcing Black female leaders to concentrate on the same issues for dozens and, sometimes, hundreds of years. Our findings suggest that the compound influence of racial and gender biases hinders the advancement of minority female leadership by perpetuating stereotypical behavioral schemas, leading to persistent discriminatory outcomes. We argue for the necessity of organizations to initiate a cultural transformation that fosters positive experiences for future generations of female leaders, recommending a shift in focus from improving outcomes for specific groups to creating an inclusive leadership culture

    A visual analytics platform for competitive intelligence

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    Silva, D., & Bação, F. (2023). MapIntel: A visual analytics platform for competitive intelligence. Expert Systems, [e13445]. https://doi.org/https://www.authorea.com/doi/full/10.22541/au.166785335.50477185, https://doi.org/10.1111/exsy.13445 --- Funding Information: This work was supported by the (research grant under the DSAIPA/DS/0116/2019 project). Fundação para a Ciência e Tecnologia of Ministério da Ciência e Tecnologia e Ensino SuperiorCompetitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbours to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modelling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups data set, using the semantic document labels provided, and demonstrate the superiority of Transformer-based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.preprintauthorsversionepub_ahead_of_prin

    A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy

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    The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the efficiency of this process. Social media has become the main ways for the public to obtain information and express opinions and feelings. This motivated us to mine the perceptions and behavioral responses towards plastic usage using social media data. In this paper, we proposed a framework for data collection and analysis based on mainstream media in the UK to obtain public opinions on plastics. An unsupervised machine learning model based on Latent Dirichlet Allocation (LDA) has been employed to analyse and cluster the topics to deal with the lack of annotation of the data contents. An additional dictionary method was then proposed to evaluate the sentiment of the comments. The framework also provides tools to visualise the model and results to stimulate insightful understandings. We validated the framework's effectiveness by applying it to analyse three mainstream social media, where 6 first-level topic categories and 13 second-level topic categories from the comment texts related to plastics have been identified. The results show that public sentiment towards plastic products is generally stable. The spatiotemporal distribution of each topic's sentiment is highly correlated with the number of occurrences

    Modelling Factors Influencing Bank Customers’ Readiness for Artificial Intelligent Banking Products

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    In the era of globalisation and technological development, artificial intelligence (AI) plays a significant role in financial activities and services. AI in financial technology has a clear potential to accelerate the financial industry's transformation by offering excellent value to customers by providing tailor-made products and services, thus improving customer experience. The paper aims to model the factors influencing bank customers' readiness for artificially intelligent banking products within the South African banking sector. Data were collected from 346 banking customers within South Africa. The study results revealed that demographic and socio-cultural variables influence the readiness for artificially intelligent banking products. Behavioural finance biases also influence bank customers' readiness for artificially intelligent banking products. Furthermore, the study also found that customers' readiness for artificial intelligent banking products is faced with the limitation of the inaccessibility to technological tools in rural areas. Consequently, policies that can improve infrastructure and enable rural citizens to cope with advanced technology can improve bank customers' readiness for artificially intelligent banking products in South Africa

    Analysing behavioural factors that impact financial stock returns. The case of COVID-19 pandemic in the financial markets.

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    This thesis represents a pivotal advancement in the realm of behavioural finance, seamlessly integrating both classical and state-of-the-art models. It navigates the performance and applicability of the Irrational Fractional Brownian Motion (IFBM) model, while also delving into the propagation of investor sentiment, emphasizing the indispensable role of hands-on experiences in understanding, applying, and refining complex financial models. Financial markets, characterized by ’fat tails’ in price change distributions, often challenge traditional models such as the Geometric Brownian Motion (GBM). Addressing this, the research pivots towards the Irrational Fractional Brownian Motion Model (IFBM), a groundbreaking model initially proposed by (Dhesi and Ausloos, 2016) and further enriched in (Dhesi et al., 2019). This model, tailored to encapsulate the ’fat tail’ behaviour in asset returns, serves as the linchpin for the first chapter of this thesis. Under the insightful guidance of Gurjeet Dhesi, a co-author of the IFBM model, we delved into its intricacies and practical applications. The first chapter aspires to evaluate the IFBM’s performance in real-world scenarios, enhancing its methodological robustness. To achieve this, a tailored algorithm was crafted for its rigorous testing, alongside the application of a modified Chi-square test for stability assessment. Furthermore, the deployment of Shannon’s entropy, from an information theory perspective, offers a nuanced understanding of the model. The S&P500 data is wielded as an empirical testing bed, reflecting real-world financial market dynamics. Upon confirming the model’s robustness, the IFBM is then applied to FTSE data during the tumultuous COVID-19 phase. This period, marked by extraordinary market oscillations, serves as an ideal backdrop to assess the IFBM’s capability in tracking extreme market shifts. Transitioning to the second chapter, the focus shifts to the potentially influential realm of investor sentiment, seen as one of the many factors contributing to fat tails’ presence in return distributions. Building on insights from (Baker and Wurgler, 2007), we examine the potential impact of political speeches and daily briefings from 10 Downing Street during the COVID-19 crisis on market sentiment. Recognizing the profound market impact of such communications, the chapter seeks correlations between these briefings and market fluctuations. Employing advanced Natural Language Processing (NLP) techniques, this chapter harnesses the power of the Bidirectional Encoder Representations from Transformers (BERT) algorithm (Devlin et al., 2018) to extract sentiment from governmental communications. By comparing the derived sentiment scores with stock market indices’ performance metrics, potential relationships between public communications and market trajectories are unveiled. This approach represents a melding of traditional finance theory with state-of-the-art machine learning techniques, offering a fresh lens through which the dynamics of market behaviour can be understood in the context of external communications. In conclusion, this thesis provides an intricate examination of the IFBM model’s performance and the influence of investor sentiment, especially under crisis conditions. This exploration not only advances the discourse in behavioural finance but also underscores the pivotal role of sophisticated models in understanding and predicting market trajectories

    Optimized Dictionaries: A Semi-Automated Workflow of Concept Identification in Text-Data

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    Identifying social science concepts and measuring their prevalence and framing in text data has been a key task of scientists ever since. Whereas debates about text classifications typically contrast different approaches with each other, we propose a workflow that generates optimized dictionaries that are based on the complementary use of expert dictionaries, machine learning, and topic modeling. We demonstrate our case by identifying the concept of "territorial politics" in leading newspapers vis-à-vis parliamentary speeches in Spain (1976-2018) and the UK (1900-2018). We show that our optimized dictionaries outperform singular text-identification techniques with F1-scores around 0.9 for unseen data, even if the unseen data comes from a different political domain (media vs. parliaments). Optimized dictionaries have increasing returns and should be developed as a common good for researchers overcoming costly particularism

    Faster inference from state space models via GPU computing

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    Funding: C.F.-J. is funded via a doctoral scholarship from the University of St Andrews, School of Mathematics and Statistics.Inexpensive Graphics Processing Units (GPUs) offer the potential to greatly speed up computation by employing their massively parallel architecture to perform arithmetic operations more efficiently. Population dynamics models are important tools in ecology and conservation. Modern Bayesian approaches allow biologically realistic models to be constructed and fitted to multiple data sources in an integrated modelling framework based on a class of statistical models called state space models. However, model fitting is often slow, requiring hours to weeks of computation. We demonstrate the benefits of GPU computing using a model for the population dynamics of British grey seals, fitted with a particle Markov chain Monte Carlo algorithm. Speed-ups of two orders of magnitude were obtained for estimations of the log-likelihood, compared to a traditional ‘CPU-only’ implementation, allowing for an accurate method of inference to be used where this was previously too computationally expensive to be viable. GPU computing has enormous potential, but one barrier to further adoption is a steep learning curve, due to GPUs' unique hardware architecture. We provide a detailed description of hardware and software setup, and our case study provides a template for other similar applications. We also provide a detailed tutorial-style description of GPU hardware architectures, and examples of important GPU-specific programming practices.Publisher PDFPeer reviewe

    The research landscape of direct, sensory human–nature interactions

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    This is the final version. Available on open access from Wiley via the DOI in this record. Data availability statement: Data used for this article are all available in the public domain. Datasets used for analyses are available at Figshare: https://doi.org/10.6084/m9.figshare.24125334Gaining a comprehensive understanding of the human–nature interactions research landscape can benefit researchers by providing insights into the most relevant topics, popular research areas and the distribution of topics across different disciplines, journals and regions. The research literature on direct human–nature interactions is constituted from a rich and diverse spectrum of disciplines. This multidisciplinary structure poses challenges in keeping up with developments and trends. We conducted a multidisciplinary text-analysis review of research on direct, sensory human–nature interactions to understand the main topics of research, the types of interactions, the disciplines within which they manifest in the literature, their growth through time and their global localities and contexts. Our analysis of 2773 articles showed that there has been recent growth in research interest in positive human–nature interactions that is biased towards high-income countries. There is a substantial body of research on negative human–nature interactions, mostly from the medical fields, which is distinct from research on positive human–nature interactions in other fields such as ecology, psychology, social science, environmental management and tourism. Of particular note is the very large amount of medical research on the causes and consequences of snake bites, particularly in Asia. Understanding the relationship between these two contrasting types of interactions is of significant practical importance. More recent attention towards positive human–nature interactions in high-income societies biases views of the relationship between people and nature. Research into human–nature interactions needs to take the next step towards a unified and holistic understanding of the benefits and costs of direct experiences with nature. This step is crucial to achieve a more sustainable future that benefits both biodiversity and human society, during great environmental and climatic change. Read the free Plain Language Summary for this article on the Journal blog.Japan Society for the Promotion of ScienceToyota Foundatio

    A novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition in remote sensing images

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    The massive yearly population growth is causing hazards to spread swiftly around the world and have a detrimental impact on both human life and the world economy. By ensuring early prediction accuracy, remote sensing enters the scene to safeguard the globe against weather-related threats and natural disasters. Convolutional neural networks, which are a reflection of deep learning, have been used more recently to reliably identify land use in remote sensing images. This work proposes a novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition from remote sensing images. First, we proposed using the fast neural approach to generate cloud-effect satellite images. In neural style, we proposed a 5-layered residual block CNN to estimate the loss of neural-style images. After that, we proposed two novel architectures, named 3-layered bottleneck CNN architecture and 3-layered bottleneck self-attention CNN architecture, for the classification of land use images. Training has been conducted on both proposed and original neural-style generated datasets for both architectures. Subsequently, features are extracted from the deep layers and merged employing an innovative serial approach based on weighted entropy. By removing redundant and superfluous data, a novel Chimp Optimization technique is applied to the fused features in order to further refine them. In conclusion, selected features are classified using the help of neural network classifiers. The experimental procedure yielded respective accuracy rates of 99.0% and 99.4% when applied to both datasets. When evaluated in comparison to state-of-the-art (SOTA) methods, the outcomes generated by the proposed framework demonstrated enhanced precision and accuracy

    Identificación de las temáticas de investigación del Chocó en la literatura indizada en Scopus

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    El objetivo de esta artículo radica en extraer las temáticas de investigación de los resúmenes y datos bibliográficos de los artículos indexados en la base de datos Scopus y que tienen como objeto de estudio al departamento del Chocó (Colombia). De esta manera, se buscaron las palabras clave Chocó AND Colombia en la base de datos Scopus, se exportaron las referencias bibliográficas a EndNote y se extrajeron los datos de autor(es), título, publicación periódica, volumen, número, año y resumen, se convirtieron en un archivo de texto, se eliminaron referencias y símbolos. La manipulación del archivo en pdf se realizó con la ejecución de preparación del texto, tokenización, lematización y obtención de lista de bigrams que se efectuaron en el entorno de desarrollo integrado (EDI) de RStudio. Así, se encontraron 668 registros bibliográficos de documentos indexados en Scopus. Las palabras con el mayor número de frecuencia de aparición: «species», «Colombia», «Chocó», «forest», «pacific», «tropical», etcétera. Se encontraron 89 841 bigrams, entre los que destacan «new species», «pacific coast», «colombian pacific», entre otros. Las colocaciones de palabras muestran que «gold» combina con «mining», «mercury», «platinum», y así sucesivamente. «Chocó» combina con «Colombia», «biogeographical», «rain», «tropical», y demás. «Biodiversity» combina con «conservation», «tropical», «agricultural», etcétera. «Climate» combina con «change», «variability», «basin», y más. Se concluye que las palabras más frecuentes evidencian que hay una preocupación por el estudio de la minería, la biodiversidad, el cambio climático, el bosque tropical, el océano pacífico, entre otros
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