Understanding Of Context Research Articles

A BERT-GRU Model for Measuring the Similarity of Arabic Text

Semantic Textual Similarity (STS) aims to assess the semantic similarity between two pieces of text. As a challenging task in natural language processing, various approaches for STS in high-resource languages, such as English, have been proposed. In this paper, we are concerned with STS in low resource languages such as Arabic. A baseline approach for STS is based on vector embedding of the input text and application of similarity metric on the embedding space. In this contribution, we propose a cross-encoder neural network (Cross-BERT-GRU) to handle semantic similarity of Arabic sentences that benefits from both the strong contextual understanding of BERT and the sequential modeling capabilities of GRU. The architecture begins by inputting the BERT word embeddings for each word into a GRU cell to model long-term dependencies. Then, max pooling and average pooling are applied to the hidden outputs of the GRU cell, serving as the sentence -pair encoder. Finally, a softmax layer is utilized to predict the degree of similarity. The experiment results show a Spearman correlation coefficient of around 0.9 and that Cross-BERT-GRU outperforms the other BERT models in predicting the semantic textual similarity of Arabic sentences. The experimentation results also indicate that the performance improves by integrating data augmentation techniques.

Editorial

Dear Readers, It gives me great pleasure to announce the sixth regular issue of 2024. In this issue, 6 papers cover various topical aspects of computer science by 19 authors from 7 countries: Brazil, Chile, France, India, Saudi Arabia, Tunisia, and Turkey. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. As always, I would like to thank all authors for their sound research and the editorial board and our guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, help to maintain the quality of our journal.  In this regular issue, I am very pleased to introduce the following 6 accepted articles: George Marsicano, Edna Dias Canedo, Glauco V. Pedrosa, Cristiane S. Ramos, and Rejane M. da C. Figueiredo from Brazil look in their study into digital transformation of public services in a startup-based environment by 23 focus groups and 175 participants in total. Mauricio Solar and Pablo Aguirre from Chile discuss their research on 3D chest CT processes applying a ResNet-50 model to which a new dimension of information has been added, namely a simple autoencoder. In a collaborative work between researchers from Tunisia and France, Rakia Saidi, Fethi Jarray, and Didier Schwab propose in their article a cross-encoder neural network (Cross-BERT-GRU) to deal with the semantic similarity of Arabic sentences that benefits from both the strong contextual understanding of BERT and the sequential modeling capabilities of GRU. Also in a collaborative research between institutions from Tunisia and Saudi Arabia, Nozha Jlidi, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika present their research on MediaPipe with GNN for human activity recognition. G.V.Vidya Lakshmi and S. Gopikrishnan from India look into missing values research for the IoT domain and in particular present IMD-MP technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations. Last but not least, F. Didem Alay, Nagehan İlhan, and M. Tahir Güllüoğlu address in their article a comparative study of data mining methods for solar radiation and temperature forecasting models. Enjoy Reading Cordially,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Performance management systems in startups: an analysis of stages of development and catalyst factors

PurposeThis study aims to understand the manifestation and adherence of performance management system (PMS) components in Brazilian startups, concurrently considering the different stages of development and catalyzing factors.Design/methodology/approachWe gathered data through semi-structured interviews with founders and C-level executives from 19 startups. We used an inductive and exploratory method to spot patterns and differences through content analysis, aiming for a contextual understanding of startup PMS manifestation.FindingsWe analyzed the PMS of each startup, highlighting the consistency and differences among PMS components in each stage, considering the value proposition, goals, performance perspectives, monitoring indicators, action plans and performance evaluation processes. We also observe various catalyst factors that played an important role in accelerating the early development of the PMS in each stage.Practical implicationsThis study provides a practical understanding of the meaning of each PMS component maturity that could be applied to startups in different stages and the catalyzing factors that played an important role in accelerating the initial development of PMS, providing practitioners with a holistic and situated context approach.Originality/valueThis is one of the first studies to provide empirical evidence on the manifestation and adherence of different components of the PMS in Brazilian startups, considering concurrently the organizational life cycle and existing catalyzing factors to the reality of these organizations.

Introducing ChatSQC: Enhancing statistical quality control with augmented AI

We introduce ChatSQC, an innovative chatbot system that combines the power of OpenAI’s Large Language Models (LLM) with a specific knowledge base in Statistical Quality Control (SQC). Our research focuses on enhancing LLMs using specific SQC references, shedding light on how data preprocessing parameters and LLM selection impact the quality of generated responses. By illustrating this process, we hope to motivate wider community engagement to refine LLM design and output appraisal techniques. We also highlight potential research opportunities within the SQC domain that can be facilitated by leveraging ChatSQC, thereby broadening the application spectrum of SQC. A primary goal of our work is to provide a template and proof-of-concept on how LLMs can be utilized by our community. To continuously improve ChatSQC, we ask the SQC community to provide feedback, highlight potential issues, request additional features, and/or contribute via pull requests through our public GitHub repository. Additionally, the team will continue to explore adding supplementary reference material that would further improve the contextual understanding of the chatbot. Overall, ChatSQC serves as a testament to the transformative potential of AI within SQC, and we hope it will spur further advancements in the integration of AI in this field.

Implementation of Humanism Learning in Learning Islamic Religious Education in the Modern Era

The purpose of writing this work is to analyze the challenges of the modern era for Islamic religious learning and the implementation of humanism learning. Literature review as an effective approach in collecting, analyzing, and synthesizing information related to the implementation of humanism learning in Islamic religious education in the modern era. With systematic steps, this research allows the identification of relevant humanism principles, strategies, benefits, and challenges. The results of this literature review research provide a deep understanding and an important contribution to the development of Islamic religious education following the demands of the times. This method also offers a strong theoretical foundation for understanding the relationship between humanism and Islamic education. The result of this research is that Islamic learning in the modern era is faced with complex challenges such as the integration of technology with religious values and contextual understanding in a multicultural environment. Solutions that can be applied include developing an online learning platform that pays attention to religious values and prioritizes inclusive interfaith understanding. Strengthening the religious-based character of education and integrating Islamic religious studies with other subjects in an interdisciplinary attitude is also important in answering this challenge. The implementation of humanism learning can form inclusive, empowered, and noble generation learning in Islamic religious education, but challenges such as the integration of humanist values with religious teachings and improving the quality of education need to be overcome to succeed.

The Role of Institutional Logics in Shaping Sustainable Talent Management: A Comparative Study of Two South Korean Conglomerates

This research examines the evolution of human resource management (HRM) practices within Samsung and Lotte, two major South Korean conglomerates. Both companies have been profoundly influenced by the Japanese management paradigm, especially in areas like seniority-based promotion rooted in Confucian values. Drawing from institutional theory, the study elucidates how similar economic trajectories in South Korea and Japan fostered comparable institutional logics and pressures in HRM. However, as organizations navigate institutional shifts, their responses and resulting HRM adaptations can diverge. Utilizing a comparative approach through the lens of the institutional logic theory, key findings unveil as follows: (1) Samsung and Lotte’s HR practices exhibit a strong Japanese influence, highlighting cultural/historical context’s importance. (2) Despite similar pressures, the conglomerates developed distinct HR practices attributed to differing institutional logics. (3) Institutional logics play a pivotal role in shaping HRM and influencing organizational behavior. (4) Organizations adapt HR practices in response to institutional complexities, leading to practice divergence. (5) The study extends institutional theory’s application in understanding organizations’ varied responses to similar pressures. (6) Findings offer HR professionals insights on tailoring strategies based on contextual understanding. The study extends the application of institutional theory in deciphering varied organizational responses. Practically, it provides HR professionals guidance on contextually appropriate HRM strategies. Companies across Asia can leverage these insights to anticipate HR practice shifts and align them with evolving institutional frameworks.

Financial Report Sentiment Analysis Using Loughran-mcdonald Dictionary and BERT

In the ever-changing world of financial markets, understanding investor behavior and making informed decisions relies heavily on sentiment analysis. This study delves into the integration of traditional techniques, such as the Loughran- McDonald dictionary, with advanced natural language processing (NLP) methods utilizing BERT (Bidirectional Encoder Representations from Transformers). The goal is to enhance the accuracy and depth of sentiment analysis in financial reports.To begin, we employ the specialized Loughran-McDonald dictionary designed for financial sentiment analysis. This lexicon includes domainspecific word lists for positive and negative sentiments, forming a solid foundation for sentiment scoring. Expanding on this foundation, we incorporate BERT, an advanced transformerbased NLP model. BERT’s contextual understanding of language and ability to capture intricate semantic relationships within financial texts aim to overcome the limitations of rule-based sentiment analysis. The methodology involves preprocessing financial reports, integrating Loughran-McDonald sentiment scores, and fine-tuning BERT for financial sentiment classification. This hybrid approach leverages both the domain expertise encoded in the dictionary and BERT’s contextual comprehension of financial jargon and nuances. We validate and evaluate our implementation using a diverse dataset comprising quarterly earnings releases, annual reports, and other relevant disclosures. Performance metrics such as precision, recall, and F1 score are analyzed to assess the effectiveness of our hybrid approach compared to individual methods. The findings have significant implications for financial analysts, investors, and policymakers by providing a more nuanced understanding of sentiment in financial reports. Our hybrid approach aims to offer improved accuracy in capturing sentiment polarity while facilitating more informed decision-making in today’s complex and dynamic realm of financial markets.

Generative Ai in Software Development : an Overview and Evaluation of Modern Coding Tools

Generative AI has significantly transformed software development by leveraging advanced machine learning models to automate coding tasks, generate code, and enhance productivity. This paper provides an overview and evaluation of modern AI-powered coding tools, including GitHub Copilot, OpenAI Codex, DeepCode, Amazon CodeGuru, TabNine, Kite, and IntelliCode, which use large language models (LLMs) to offer real-time code suggestions, automated error detection, and intelligent code completions. Despite their benefits, these tools face challenges related to accuracy, contextual understanding, security, privacy, and ethical considerations, necessitating thorough review and testing of AI-generated code by developers. The integration of AI in coding also raises concerns about proprietary information protection and ethical implications such as job displacement. This paper explores the capabilities, applications, and limitations of current generative AI tools, highlighting their impact on software development and discussing future directions. Emphasis is placed on the need for improved model training, enhanced contextual understanding, secure AI training methods, and ethical AI usage. By addressing these challenges, the industry can maximize the potential of generative AI, creating more accurate, reliable, and ethically sound tools that support a collaborative and innovative software development environment.

Perioperative capacity and contextual challenges in teaching hospitals of southern Ethiopia: explanatory sequential mixed-methods research

BackgroundPrevious Ethiopian literature on surgical capacity and challenges has focused on quantitative investigations, lacking contextual understanding. This explanatory sequential mixed-methods research (MMR) aimed to assess perioperative capacity and contextual challenges at three teaching hospitals in southern Ethiopia.MethodsA quantitative survey assessed workforce, infrastructure, service delivery, financing, and information systems. The survey findings were explained by qualitative semi-structured interviews of twenty perioperative providers. Descriptive statistics were integrated with qualitative thematic analysis findings using the narrative waving approach. Key findings from both datasets were linked using a joint display table.ResultsThe survey revealed shortages in the specialist workforce (with a ratio of 0.58 per 100,000 population), surgical volume (at 115 surgeries per 100,000 population), equipment, supplies, financing, and perioperative data tracking. Hospitals’ radiology services and blood products were only available 25–50% of the time, while anesthetic agents and essential laboratory services were often available 51–75% of the time. Perioperative management protocols were used rarely (1–25% of the time). Over 90% of patients lack health insurance coverage. Qualitative data also revealed scarcity of perioperative resources and equipment; unaffordable perioperative costs, lack of health insurance coverage, and unforeseen expenses; poor patient safety culture and communication barriers across the perioperative continuum of care; workforce shortages, job dissatisfaction, and concerns of competence; and weak national governance, and sociopolitical turmoil, and global market volatility exacerbating local challenges. These challenges are linked to risks in quality of care and patient safety, according to clinicians.ConclusionThe study identifies deficiencies in the health system and sociopolitical landscape affecting safe surgery conduct. It highlights the need for comprehensive health system strengthening to expand workforce, upgrade facilities, improve safety culture, resilience, and leadership to ensure timely access to essential surgery. Exploring external factors, such as the impact of national governance and sociopolitical stability on reform efforts is also essential.

The origin and function of external representations

External representations ( ERs) are objects or performances in the world whose proper function is to communicate about other things in the world. Why and how did we make them, and what do they give us? We outline a simple framework for thinking about ERs grounded in modern machine learning. We propose a minimal set of neural mechanisms needed for open-ended ER production. Our constructivist enactive view contrasts with nativist views which propose specialist neural modules requiring symbolic internal representations. We propose a plausible set of (biological and cultural) evolutionary steps to full Gricean symbolic communication via a set of increasingly complex enactive algorithms for ER production. A pragmatic space of games is defined, which includes not only fully cooperative language games but also science, art, and evolved signal manipulation games. This space is defined by the complexity of learning needed by sender and receiver. We propose that one important step towards open-ended ER use was selection for bush reading, which like mind-reading is an inferential process requiring complex contextual and syntactic understanding of cues about events displaced in space and time. Bush reading pre-adapted receivers to be receptive, competent, and perspicacious interpreters of later intentionally produced signals about hidden topics such as felt mental states. This paved the way for minimally Gricean communication, which subsequently could be bootstrapped into explicit theories of mind, folk psychology narratives, and symbolic language in general. Recent findings in cognitive archaeology are integrated within the framework, and new experiments in machine learning suggested.

Efficiency Divide: Comparative Analysis of Human & Neural Network Algorithm Development

The paper delves into a comparative analysis between human and artificial intelligence (AI) capabilities in algorithm development, with a specific focus on the challenges presented in the "Advent of Code." The research thoroughly investigates the performance of Generative Pre-trained Transformers (GPTs), such as ChatGPT and Bard, in solving intricate algorithmic problems and benchmarks these results against those achieved by human participants. A sizeable portion of the study is dedicated to understanding the nuances of prompt engineering in AI and how it affects the problem-solving process, alongside exploring the choice of programming languages used by both AI and humans. The methodology of the research is extensive, involving the participation of both AI models and human subjects, who vary in their levels of programming expertise. This approach allows for a comprehensive evaluation of the correctness and efficiency of solutions, along with the time taken to resolve the given problems. The results from this study reveal intriguing insights. While AI models like GPTs demonstrate an impressive speed in problem resolution, they often fall short in accuracy when compared to human problem-solvers, particularly in tasks demanding deeper contextual understanding and creative reasoning. Furthermore, the study delves into the impact of time constraints on the effectiveness of problem-solving strategies employed by both AI and humans. It finds that under strict time constraints, AI models can quickly generate solutions, but these solutions may lack the depth and accuracy found in those devised by human participants. This aspect of the research highlights the trade-off between speed and precision in AI-driven problem solving. The research extends its implications beyond mere performance comparison. It suggests the potential for a synergistic approach where the computational efficiency and rapid problem-solving abilities of AI can be effectively combined with the nuanced understanding and creative problem-solving skills inherent in humans. This hybrid approach could redefine the future landscape of programming and algorithm development. The study not only provides a critical analysis of the capabilities of AI in the realm of algorithmic problem-solving but also paves the way for future exploration into the collaborative dynamics of human-AI interaction in programming. It highlights the evolving role of AI in programming and underscores the importance of balancing AI’s computational prowess with human creativity and adaptability in solving complex, real-world problems.

The street begging economy: investigating the economic strategies of street beggars in Ghana

PurposeThis study investigates the economic strategies street beggars (SBs) employ in Ghana to gain a competitive advantage within the contested urban spaces they occupy. By conceptualising SBs as economic actors, the research shifts the focus from perceiving them as passive recipients of charity to recognising their intentional decision-making and strategic behaviour to maximise financial benefits.Design/methodology/approachThe study adopts a qualitative case study approach, using non-participant observation and semi-structured interviews with 40 SBs in Kumasi, Ghana. Thematic analysis was used to identify the economic strategies used by SBs to gain a competitive advantage.FindingsThe study reveals three main strategies adopted by SBs: child advertisement, body marketing, and narrative marketing. These strategies emerge from the SBs' contextual understanding of the public’s social protection needs and are used to develop strategies that give them an advantage over other SBs within contested urban streets.Research limitations/implicationsFuture research could explore the effectiveness of different economic strategies employed by street beggars and compare their financial outcomes. Investigating the public’s perceptions and attitudes towards these strategies could provide valuable insights.Practical implicationsPolicymakers and city authorities should acknowledge some street begging as an economic activity and develop regulations, designate begging areas, and establish guidelines for acceptable practices. Creating targeted skill training, entrepreneurship programs, and access to microfinance can facilitate the transition of street beggars from begging to more sustainable forms of livelihood.Social implicationsThis study challenges the perception of street beggars as passive victims, highlighting their agency and strategic decision-making. Recognizing their economic strategies can inform policies that regulate begging as an economic activity, create alternative opportunities, and prevent exploitation of vulnerable groups, ultimately promoting more sustainable and dignified livelihoods for street beggars.Originality/valueThis study seeks to enrich the current literature and bridge the research gap by investigating the phenomenon of street begging and the diverse economic strategies street beggars employ to gain a competitive advantage within urban areas. This research moves beyond computing financial income and regulating begging by conceptualizing SBs as economic actors and begging as an economic activity. It explores how SBs use conscious efforts and rational strategies to maximize financial benefits from passers-by and outperform other SBs, providing novel insights into the complex dynamics of street begging.

Key ambidextrous IT governance mechanisms for successful digital transformation: A case study of Bank Rakyat Indonesia (BRI)

Many organizations tried to accelerate their digital transformation, but some of them failed, possibly due to poor governance. Previously the influence of ambidextrous IT governance mechanisms on digital transformation and organizational performance were studied. However, there is a need to investigate what are the key ambidextrous IT governance mechanisms that lead to a successful digital transformation and how they are implemented. A case study was conducted at BRI to address the problem. BRI is an incumbent bank that recently won numerous awards. The case study was chosen to investigate one instance in detail to discover contextual understanding that might not have become apparent priorly. The data was collected by semi-structured interviews triangulated with internal documents until data saturation. Subsequently, the collected data underwent thematic analysis. The findings revealed seven key ambidextrous IT governance mechanisms, encompassing board and executive, strategy and architecture, talent and culture, data and information, development and operation, internal and external collaborations, as well as risk and audit. The study also examined how BRI digital transformation dimensions (digital transformation strategy, strategic alignment, digital and IT assets, knowledge and capability, and innovation culture) impacted organizational performance. Lastly, BRI's organizational performance achievements were examined using operational excellence, customer experience, industry presence, and financial return dimensions. This study offers valuable research insights by exploring the key ambidextrous IT governance mechanisms that influence digital transformation and organizational performance. The study also provides the practical implications on how to implement key ambidextrous mechanisms toward a successful digital transformation, including its organizational performance achievements.

Contextualizing urban road network hierarchy and its role for sustainable transport futures: A systematic literature review using bibliometric analysis and content analysis tools

AbstractUrban road networks play a crucial role in transport and urban planning and have the potential to contribute to more sustainable futures if their hierarchy is properly understood. However, the concept of the urban road network hierarchy, which refers to street classification and prioritization, is not well defined within the domain of transport engineering management, leaving many questions unanswered. Is it simply a planning tool, or does it extend to defining the essence of cities? Is it a qualitative or quantitative concept? Does it emerge organically or require proactive planning? Given the lack of comprehensive answers to these questions, this research aims to provide a contextual understanding of the urban road network hierarchy through the lens of sustainable transport futures. To this purpose, we conducted a systematic literature review, which is an effective method for consolidating knowledge on a specific topic. A total of 42 articles were analyzed using both quantitative bibliometric analysis and qualitative content analysis. Our work demonstrates that the road network hierarchy consists of 16 sub-concepts. Four main research trends were identified and discussed: a) road morphology and structure, b) advanced algorithms for street classification, c) integrated street classification planning, and d) the social dimension of street classification. Recent literature indicates a shift toward alternative road network hierarchy approaches that prioritize sustainable mobility over car-centric models. In conclusion, our analysis reveals that the urban road network hierarchy is a multifaceted yet under researched “vehicle for change,” which, if utilized effectively, offers opportunities to reimagine urban road environments.

Survey on Systematic Analysis of Deep Learning Models Compare to Machine Learning

This survey provides a comprehensive analysis of the systematic differences and advancements between deep learning (DL) and traditional machine learning (ML) models. By examining a wide array of research papers, the study highlights the unique strengths and applications of both methodologies. Deep learning, with its multi-layered neural networks, excels in handling large, unstructured datasets, making significant strides in image and speech recognition, natural language processing, and complex pattern recognition tasks. Conversely, traditional machine learning models, which rely on feature extraction and simpler algorithms, remain highly effective in structured data scenarios such as classification, regression, and clustering problems. The survey elucidates the criteria for choosing between DL and ML, focusing on factors like data size, computational resources, and specific application requirements. Furthermore, it discusses the evolving landscape of hybrid models that integrate DL and ML techniques to leverage the strengths of both approaches. This analysis provides valuable insights for researchers and practitioners aiming to deploy the most suitable AI models for their specific needs, emphasizing the importance of contextual understanding in the rapidly advancing field of artificial intelligence.

Molecular and genetics inquiry program using Drosophila eyes absent gene.

The aim of this study was to develop molecular genetics inquiry programs using the eyes absent gene of Drosophila melanogaster. The program was composed of various molecular genetics experiments, including mutation observation, cross-breeding, searching for genetic information in web databases, gDNA extraction, and PCR. Each experiment was designed to include a reasoning process, thus aligning the program closely with the structure of authentic scientific research. This program was also developed with a modular design to provide flexibility in its implementation. The program was implemented for middle school students affiliated with a university science education institute for the gifted, and surveys indicated that students had positive experiences with the program. Our findings suggest that the program provides students with a contextual understanding of how authentic research is conducted. Finally, we suggest ways to implement the program effectively.

GENDER DIFFERENCES IN WRITING ANXIETY AMONG EFL UNDERGRADUATE STUDENTS: A SURVEY STUDY AT UNIVERSITAS JAMBI

Writing is a skill that is necessary for success in the academic world. Writing is a necessary activity in education that fosters significant growth in both teaching and learning. However, good writing requires a variety of skills, including a wide vocabulary, the capacity for contextual understanding, analytical strength, the capacity for inference, the capacity for critical thought, and much more. This skill is unquestionably difficult to acquire, especially when writing in English, which naturally puts more pressure and anxiety on students. This study was designed to investigate the types and levels of writing anxiety experienced by EFL students taking an academic writing course at an English department at a university in Jambi, regardless of their gender. The instrument of this study was a close-ended questionnaire that was adopted from Cheng (2004). It consists of 22 statements about different types of anxiety (cognitive, somatic, and avoidance behavior). There were 225 students who participated in the study. Majority of them 82.2% (185 students) were female and only 17.8% (40 students) were male. The results of the study indicated that there are differences in the level of anxiety experienced by male and female students. The highest score of the two genders is in the somatic anxiety type; namely, women have a mean score of 3.36 and men have a mean score of 3.07. Although both have high levels, there is a slight difference in numbers, so women experience higher levels of anxiety than men, and the type of anxiety they have is somatic anxiety.

Border controls and (im)mobilities: experiences from a public transport node

The Öresund Bridge between Sweden and Denmark was opened in 2000, allowing both road traffic and fast commuter trains to travel between Malmö and Copenhagen. Various agreements have long facilitated passport-free travel. However, at the end of 2015, temporary border controls were first introduced. This paper aims to provide a deeper understanding of portrayals in the news media and everyday mobility experiences relating to border controls, with a special focus on Hyllie Station in the city of Malmö, in southern Sweden. The article is based on a compilation of the way Hyllie Station has been depicted in the news media for a deeper contextual understanding combined with a public participation GIS dataset focusing on the safety and security experiences of public transport nodes. Hyllie station, border controls and the presence of security personnel are depicted in different ways and at different levels. The complexities of life are intensified due to the daily cross-border commute and extra time required by border controls. The issue of mobility justice is raised, and the interrelationship between power and mobility becomes more and more tangible. Hyllie is a traffic node wherein the barriers of mobility and immobility become blurred, and power is displayed on different levels.

Hierarchical Inverse Distance Transformer for Enhanced Localization in Dense Crowds

Achieving precise individual localization within densely crowded scenes poses a significant challenge due to the intricate interplay of occlusions and varying density patterns. Traditional methods for crowd localization often rely on convolutional neural networks (CNNs) to generate density maps. However, these approaches are prone to inaccuracies stemming from the extensive overlaps inherent in dense populations. To overcome this challenge, our study introduces the Hierarchical Inverse Distance Transformer (HIDT), a novel framework that harnesses the multi-scale global receptive fields of Pyramid Vision Transformers. By adapting to the multi-scale characteristics of crowds, HIDT significantly enhances the accuracy of individual localization. Incorporating Focal Inverse Distance techniques, HIDT adeptly addresses issues related to scale variation and dense overlaps, prioritizing local small-scale features within the broader contextual understanding of the scene. Rigorous evaluation on standardized benchmarks has unequivocally validated the superiority of our approach. HIDT exhibits outstanding performance across various datasets. Notably, on the JHU-Crowd++ dataset, our method demonstrates significant improvements over the baseline, with MAE and MSE metrics decreasing from 66.6 and 253.6 to 59.1 and 243.5, respectively. Similarly, on the UCF-QNRF dataset, performance metrics increase from 89.0 and 153.5 to 83.6 and 138.7, highlighting the effectiveness and versatility of our approach.

Developing a contextual understanding of empowerment through education: narratives from highly educated women in Pakistan

ABSTRACT Current international discourse foregrounds gender parity in education as a means of empowering women in societies with strong adherence to traditional gender norms, such as Pakistan. This discourse contends that women’s access to higher education and subsequent employment enables them to identify and reject traditional cultural values and norms. However, some scholars criticise the gender parity approach as portraying a simplistic narrative that fails to recognise the social, cultural, and family contexts within which women are embedded. Drawing on an ecological approach, this article examines how education and empowerment are experienced by highly educated women in Pakistan with a particular focus on these contexts. Our findings demonstrate that education and employment are often experienced as disempowering due to their reinforcement of traditional gender roles and values. However, we also found that women move iteratively between acquiescing to traditional gender norms and pushing the boundaries of acceptable practice through this acquiescence. Our findings demonstrate that for the women in our study, empowerment was experienced through their ability to strike a delicate balance between compliance with traditional customs and the pursuit of opportunities that opened because of this compliance.