Understanding Of Context Research Articles

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

The seamless integration of indoor and outdoor positioning has gained considerable attention due to its practical implications in various fields. This paper presents an innovative approach aimed at detecting and delineating outdoor, indoor, and transition areas using a time series analysis of Global Navigation Satellite System (GNSS) error statistics. By leveraging this contextual understanding, the decision-making process between GNSS-based and Visual-Inertial Odometry (VIO) for trajectory estimation is refined, enabling a more robust and accurate positioning. The methodology involves three key steps: proposing the division of our context environment into a set of areas (indoor, outdoor, and transition), exploring two methodologies for the classification of space based on a time series of GNSS error statistics, and refining the trajectory estimation strategy based on contextual knowledge. Real data across diverse scenarios validate the approach, yielding trajectory estimations with accuracy consistently below 10 m.

Alignment of Work Skills for Management Professionals in Southern Peru

Objective: Informal employment in Latin America, especially in Peru, poses challenges to job stability, as workers' skills are not aligned with market needs, leading to increased unemployment and underemployment. It is crucial for universities that offer management programs in Peru to understand the labor market, identify the necessary competencies for their graduates, and update their curricula accordingly. This study analyzes the competencies required for management professionals in southern Peru, identifying gaps and proposing solutions to improve education-employment alignment. Method: A survey with 33 multiple-choice questions was conducted among 41 private companies and 20 public institutions. Exploratory factor analysis was used to categorize competencies and the Wilcoxon test was used to determine the gap between the supply and demand of skills. Result and Conclusion: The results reveal three categories of competencies: specific, interpersonal and technological, highlighting skills such as teamwork, adaptability to change, ethical behavior, commitment to quality, and decision making. A gap is observed where demand exceeds supply, indicating an unmet need for competencies, except in social responsibility and civic engagement. Research implications: These findings are valuable for universities offering management programs in Peru, as they allow curriculum improvement and, consequently, optimize the teaching-learning process, as well as rationalize efforts and resources to meet labor market demands. Originality/value: It addresses a real and relevant issue that affects both graduates and companies in the specific context of southern Peru, providing a more detailed and contextualized understanding of the challenges and opportunities in this region.

Leveraging Large Language Models for Clinical Abbreviation Disambiguation.

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods.

Exploring Reading Motivation of Undergraduate Level Students: A Narrative Inquiry

Exploring Reading Motivation of Undergraduate Level Students: A Narrative Inquiry

Computational and Problem-Solving Skills in Mathematics of AIMS Students

Life is an arena of problems. A human child has to meet and solve problems as he grows; problems on his physical surroundings, his intellectual associations, and in his social contacts. These dilemmas grow in number and complexity as we age. His success in life is, in large measure, determined by the capacity and competence to solve them. Mathematics is a subject of problems and studying Mathematics is different from studying other subjects because it is learned by doing and solving boundless problems. For this reason, the efficiency and ability in solving problems are guarantees for success in learning this kind of subject. The researchers, being Mathematics instructors, observed that the students find difficulty in solving Mathematics problems. The researchers generally identified the salient reasons for this kind of conflict namely: poor background in algebra, poor verbal and contextual understanding, and poor logical ability. Conclusively, a direct relationship can be inferred between the problem-solving skills of students and the salient factors affecting the specified ability. From this observation, the researchers want to conduct an immediate action in order to quench this growing phenomenon. Seventy-five (75) or 63.0% of the respondents are graduates of private schools while 44 or 37.0% are graduates of public schools. On computational skills, the respondents can perform the four basic operations in real numbers, apply the properties of equalities of real numbers as well and derive formulas from a given equation. On problem solving skills, the result indicates that respondents are not good in problem solving which involves plane and solid figure. Recommendations were arrived at to enhance the computational and problem-solving skills of the students.

DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection

This paper introduces an innovative end-to-end trainable framework named Dense Attention-aware Spatio-Temporal Network (DAST-Net) for video anomaly detection. The framework adeptly leverages both spatial and temporal data in an unsupervised manner, eliminating the need for manually crafted features. To enhance spatial feature representation, DAST-Net incorporates visual attention-aware residual connections within the denser residual network (DenserResNet), deviating from traditional identity skip connections. The rationale behind this connection choice is to augment the contextual understanding of features across various scales. For capturing temporal patterns, the framework employs a Convolutional LSTM Autoencoder (ConvLSTM-AE) module, enabling effective learning and representation of temporal dependencies in video data. Consequently, discriminating features from attention modules are combined with the features extracted by the ConvLSTM-AE module, enhancing visual recognition capabilities for both spatial and temporal aspects. Our proposed architecture outperforms state-of-the-art methods on four benchmark datasets, showcasing AUC scores of 85.4% on Ped1, 97.9% on Ped2, 89.8% on Avenue, and 73.7% on the ShanghaiTech dataset. The results demonstrate the performance of our method in identifying unusual events in video data.

Building knowledge from the epistemology of the South: the importance of training researchers in initial teacher training

This study aims to reflect on the importance of considering epistemologies of the South and research training as benchmarks in the initial teacher of educators in the Latin American context. In recent years, there has been a growing recognition of the need to diversify and decolonize knowledge production in education, especially in the Global South. The epistemology of the South provides a conceptual framework that challenges traditional Eurocentric perspectives and promotes alternative ways of knowing and understanding education. To achieve the research objective, a literature review was conducted focusing on key concepts such as Southern epistemology, researcher training, and initial teacher education. The review encompassed academic articles, books and relevant theoretical frameworks. Content analysis techniques were employed to critically examine the literature, identifying themes, theoretical perspectives, and implications for research training in Initial Teacher Education (ITE). The findings of this study suggest that the incorporation of the epistemology of the South into researcher training can have a transformative impact on the educational sector. By adopting ways of knowing and understanding, future researchers can develop a more nuanced and contextualized understanding of educational phenomena. This approach promotes critical thinking skills and encourages researchers to question dominant narratives and power structures in education. Additionally, the study highlights the importance of integrating research training into initial teacher education. By fostering the development and consolidation of research skills and a critical mindset in future teachers, it promotes their transformation into change agents within the Latin American educational system in which they operate. From this perspective, research-oriented teacher training programs empower educators to engage in evidence-based practices, contribute to educational research, and advocate for pedagogical approaches that respond to local contexts and social realities in which they act. This study underscores the need to integrate Southern epistemologies and research training within into initial teacher education. By doing so, we can nurture a new generation of educators with critical thinking skills capable of effecting significant societal changes. This research contributes to the ongoing dialogue on the decolonization of education and promotes the development of inclusive and contextually relevant educational practices.

Repositioning the Theory of Dialectical Translation of Religious Texts in Qur'anic Perspective

Dialectics focuses on understanding the context of the al-Qur'an and is integrated as a method of dialogue between the original text and the translation. By developing a new framework that combines al-Qur’an dialectics and religious values through textual analysis and literature review. This study provides a critical review of translation theory, investigating the relationship between language and religious meaning in the al-Qur'an text. The combination of dialectical aspects and al-Qur'an perspectives is expected to make a significant contribution to the development of theories on the translation of religious texts, especially in the Islamic context. The practical implications include the development of more contextualized and detailed translation guidelines as well as a more comprehensive understanding of the religious meaning embedded in the al-Qur'an text. The methodology used is library research using a descriptive qualitative approach and book research involving textual analysis of religious texts in the al-Qur'an, followed by a literature review of existing translation theories. The results of this analysis form the basis for the development of a dialectical model in the translation of religious texts, in which cultural, historical and linguistic contexts become key considerations. By utilizing the al-Qur'an perspective, this research directs the translation of religious texts towards a deeper and more contextual understanding.

Sonovisual: Transforming Image Description into Dynamic Narratives

Sonovisual is an innovative framework aimed at bridging the gap between static image descriptions and immersive, dynamic narratives. Traditional image descriptions are often limited to static textual representations, lacking the ability to fully convey the visual essence and emotional depth embedded within an image. This project introduces a novel approach that combines cutting-edge image recognition technologies with advanced natural language processing techniques, facilitating the seamless transformation of image features into engaging and vivid narratives. The core of the Sonovisual framework lies in its integration of sophisticated deep learning algorithms, enabling the extraction of intricate visual details and contextual information from images. Leveraging state-of-the-art machine learning models, the system can recognize objects, scenes, and intricate visual patterns, and subsequently generate descriptive narratives that not only encapsulate the visual content but also evoke a multisensory experience for the audience. Through the integration of adaptive storytelling techniques, Sonovisual dynamically adapts its narrative style and tone to cater to different audiences and contexts. By incorporating sentiment analysis and contextual understanding, the framework ensures that the generated narratives resonate with the emotional nuances and cultural sensitivities associated with the depicted imagery. The Sonovisual project holds promise for various applications, including enriching accessibility features for the visually impaired, enhancing educational materials with immersive visual narratives, and augmenting multimedia content with engaging storytelling elements. Additionally, its potential extends to fostering new avenues for creative expression and facilitating enhanced human-computer interaction through visually enriched storytelling experiences.

Semantic segmentation model for concrete cracks based on parallel Swin-CNNs framework

In recent years, crack detection has been the focus of relevant research since concrete fractures are the most dangerous damage to structures. Computer vision-based approaches are frequently employed for their distinct benefits. However, the crack segmentation model based only on convolutional neural networks (CNNs) is still inadequate in generalization because of its inherent bias produced by its low contextual understanding capacity. This research provides a framework PSC (Parallel Swin-CNNs) that employs a multi-scale feature fusion pyramid decoder to partition concrete cracks semantically using Swin Transformer and CNNs. This research evaluates classic CNN models U-Net, U-Net++, DeepLabV3, PSPNet, Feature Pyramid Network (FPN), and DeepCrack on two datasets. The proposed PSC model achieves the best crack segmentation results, with a maximum improvement of 36.57% in F1-score and 62.38% in Intersection over Union value on both datasets, a reduction in parameters of 2.95%–40.89% except for PSPNet. The proposed PSC model demonstrates versatile applicability across various scenarios, effectively overcoming interferences such as light shadows, oil stains, potholes, and textured surfaces while maintaining high computational efficiency.

Dilated convolution for enhanced extractive summarization: A GAN-based approach with BERT word embedding

Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance.

Evaluating Complex Entity Knowledge Propagation for Knowledge Editing in LLMs

In today’s world, where information keeps growing rapidly and changing constantly, language models play a crucial role in making our lives easier across different fields. However, it is tough to keep these models updated with all the new data while making sure they stay accurate and relevant. To tackle this challenge, our study proposes an innovative approach to facilitate the propagation of complex entity knowledge within language models through extensive triplet representation. Using a specially curated dataset (CTR-KE) derived from reliable sources like Wikipedia and Wikidata, the research assesses the efficacy of editing methods in handling intricate relationships between entities across multiple tiers of information. By employing a comprehensive triplet representation strategy, the study aims to enrich contextual understanding while mitigating the risks associated with distorting or forgetting critical information. The study evaluates its proposed methodology using various evaluation metrics and four distinct editing methods across three diverse language models (GPT2-XL, GPT-J, and Llama-2-7b). The results indicate the superiority of mass-editing memory in a transformer (MEMIT) and in-context learning for knowledge editing (IKE) in efficiently executing multiple updates within the triplet representation framework. This research signifies a promising pathway for deeper exploration of data representation for knowledge editing within large language models, and improved understanding of contexts to facilitate continual learning.

Identifying influential architectural design variables for early-stage building sustainability optimization

Architectural design variables (ADVs) highly influence a building's sustainability performance. Thus, identifying which ADVs are most influential in a building's early stages is of great significance, especially when using computational building design optimization tools. Currently, sensitivity analysis based on computer simulations is the most commonly used means to identify which ADVs are the most influential in the early stages. However, we suggest that a stakeholder perspective should also be considered as stakeholders possess domain-specific knowledge and expertise as well as a contextual understanding that can greatly enhance the development and deployment of building design optimization tools. To explore the above, we combined a literature review with survey data from 24 architects and sustainability consultants in the Nordics. Surprisingly, we found that the influential ADVs in the literature do not always align with those of our surveyed stakeholders. For example, we found that the literature considers building plan, window-to-wall-ratio (WWR), and wall material as the most influential ADVs, which contrasts with storey number, storey height, WWR, roof material and wall material considered by stakeholders to be the most influential. We also found that the most influential ADVs differ across different sustainability optimization objectives, and that these also differ from the literature. Despite our limited survey sample, our study provides insights into influential ADVs and as such has implications for the development, use, and performance of computational building design optimization tools.

Unraveling the Potential of ChatGPT: Investigating the Efficacy of Reading Text Adaptation

The purpose of this study is to investigate the effectiveness of ChatGPT in reading text adaptation. Its primary focus is on examining ChatGPT's ability to adapt texts with particular attention to its contextual understanding and limitations. Employing a mixed-methods approach, the research combines quantitative analysis and qualitative evaluation. Quantitative measures assess the consistency of ChatGPT's responses across various text adaptation scenarios, whereas qualitative evaluations involve the assessment of language teachers' attitudes and perceptions towards the use of ChatGPT for text adaptation in their teaching practices. Ethical considerations pertaining to potential biases and misinformation in the model's output are also discussed. The outcomes of this investigation contribute to understanding ChatGPT's strengths and limitations in reading text adaptation. The research has practical implications for domains such as language education, content creation, and information retrieval, where accurate and adaptable text comprehension is crucial.

Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach.

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%-40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)-based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.

ChatGPT and ELT: Exploring Teachers’ Voices

This study investigates the perceptions of English language teachers regarding the use of ChatGPT in English Language Teaching (ELT). The study aims to fill the research gap by exploring teachers' perspectives on the integration of ChatGPT as an instructional tool and its implications for ELT practices. Using a mixed methods approach, the study employed focus group discussions and an online survey to gather insights from a sample of 115 English language teachers in Metro Manila, Philippines. Findings revealed English language teachers’ perspectives on the use of ChatGPT in ELT including potential use of ChatGPT in ELT (e.g., integration for vocabulary expansion and writing practice and enhancing language fluency and communication), potential benefits of ChatGPT to English languge learners (e.g., translation and language comprehension support, conversational practice and language development, access to information and cultural understanding), and potential limitations of using ChatGPT in ELT (e.g., cheating and plagiarism, lack contextual understanding and nuance, reliability and accuracy of information). These findings shed light on teachers' perceptions of ChatGPT as a language teaching tool including ChatGPT’s integration into ELT, use of ChatGPT in writing activities, use of ChatGPT in improving language skills, and other benefits of using ChatGPT, including teachers' concerns regarding the authenticity of AI-generated content and the potential impact on language learning outcomes. The findings of this research contribute to the existing literature on the integration of AI technology in ELT and offer practical recommendations for educators seeking to leverage the potential of ChatGPT in language teaching. By giving voice to teachers' perspectives, this study underscores the importance of considering teachers' perceptions and experiences when incorporating emerging technologies like ChatGPT into the ELT classroom. Ultimately, this research aims to foster a deeper understanding of the complex dynamics between AI technology and language teaching, paving the way for informed decision-making and effective implementation in the field of ELT.

Strengths, Weaknesses, Opportunities, and Threats of Using ChatGPT in Scientific Research

The versatility of ChatGPT extends across diverse domains, including scientific research. This study delves into the transformative prospects of integrating ChatGPT into scientific research, achieved through a SWOT analysis. The analysis explores the model's strengths, which encompass a vast knowledge base, language proficiency, information retrieval, and the capacity for continuous learning. Conversely, it exposes its weaknesses, including a lack of contextual understanding, potential overreliance on training data, limitations in verifying information, and constrained critical thinking abilities. Amidst these factors, opportunities arise, such as facilitating literature reviews, fostering collaborative brainstorming, enabling seamless language translation and interpretation, and amplifying knowledge dissemination. Nonetheless, a spectrum of threats looms, encompassing concerns related to plagiarism, ethical quandaries, the propagation of misinformation, and even the potential erosion of higher-order cognitive thinking. These multifaceted aspects necessitate comprehensive consideration. Recommendations for researchers embarking on ChatGPT integration include a balanced approach that harmonizes AI and human ingenuity, thereby upholding research integrity. The potential of ChatGPT to reshape scientific inquiry can only be realized through conscientious use and ongoing oversight.

Architectural Semiotics: Reading Symbolic Meanings in Modern Building Design

Semiotics, as the study of signs and meaning, is a theoretical basis for analyzing the symbolic meanings hidden in architectural elements. This research aims to explore the meaning of symbols contained in modern building design through an architectural semiotic approach. In this research, a qualitative approach was used with descriptive methods. The results of research in architectural semiotics reveal that modern building design can be understood as a symbolic language that carries deep meaning. Through analysis of design elements, such as geometric shapes, innovative materials, and use of space, this research identifies symbols that communicate certain values, aesthetics, and concepts. Involvement of the user's cultural, social context, and subject experience helps refine symbolic interpretation, creating a more contextual and relevant understanding. Overall, architectural semiotics research contributes to constructing a systematic and in-depth picture of symbolic language in modern building design, opening a window for a broader understanding of the complex role of design elements in imbuing the meaning of contemporary architecture.

Employing the BERT model for sentiment analysis of online commentary

The objective of this research is to carry out a tone and semantic sentiment analysis of network comments on new media platforms by leveraging the BERT model. With the burgeoning popularity of social media, network comments, rich in emotional and tonal features, have emerged as a significant part of the online culture. Accurate interpretation and analysis of these comments' sentiment and semantic meanings are paramount to grasping online public opinion and user psychology. In this study, the BERT model, lauded for its bidirectional encoding and contextual understanding capabilities, is selected to scrutinize the sentiment and tone of network comments on new media platforms. Through a process of pre-training and fine-tuning, the sentiment attitudes and polarity of comments are accurately identified along with their conveyed tonal features, such as joy, anger, and sarcasm. Conducting an accurate tone and semantic sentiment analysis of network comments on new media platforms facilitates a profound understanding of user preferences and trends in public opinion. This can assist in optimizing content recommendations, enhancing user experiences, and increasing the operational effectiveness of new media platforms. The outcomes of this research will bear significant implications for studies and applications in online culture, offering invaluable references and guidance in related domains.

A Mediating Effect of Job Attitudes on the Relationship between Corporate Social Responsibility and Service Innovation among Hotel Industry in Beijing

This paper investigates the mediating role of job attitudes in the relationship between corporate social responsibility (CSR) and service innovation within the hotel industry in Beijing, China. Drawing upon Social Exchange Theory and previous research on CSR, job attitudes, and innovation, the study aims to explore how CSR initiatives influence employees' job attitudes, such as job satisfaction, organizational commitment, and perceived organizational support, and subsequently impact service innovation outcomes. The research employs a mixed-method approach, combining quantitative surveys and qualitative interviews with hotel employees and managers to gather comprehensive insights into the underlying mechanisms driving the relationship between CSR, job attitudes, and service innovation. Quantitative data analysis will involve structural equation modeling to examine the direct and indirect effects of CSR on service innovation through job attitudes. Qualitative analysis will provide deeper contextual understanding and identify specific mechanisms and factors influencing the mediation process. The findings of this study are expected to contribute to both theoretical understanding and practical implications for hotel managers and policymakers seeking to leverage CSR initiatives to foster a culture of innovation and enhance competitiveness in the dynamic hospitality industry in Beijing, China.