Context-aware Research Articles

Unraveling the DNA methylation landscape in dog blood across breeds.

DNA methylation is a covalent bond modification that is observed mainly at cytosine bases in the context of CG pairs. DNA methylation patterns reflect the status of individual tissues, such as cell composition, age, and the local environment, in mammals. Genetic factors also impact DNA methylation, and the genetic diversity among various dog breeds provides a valuable platform for exploring this topic. Compared to those in the human genome, studies on the profiling of methylation in the dog genome have been less comprehensive. Our study provides extensive profiling of DNA methylation in the whole blood of three dog breeds using whole-genome bisulfite sequencing. The difference in DNA methylation between breeds was moderate after removing CpGs overlapping with potential genetic variation. However, variance in methylation between individuals was common and often occurred in promoters and CpG islands (CGIs). Moreover, we adopted contextual awareness methodology to characterize DNA primary sequences using natural language processing (NLP). This method could be used to effectively separate unmethylated CGIs from highly methylated CGIs in the sequences that are identified by the conventional criteria. This study presents a comprehensive DNA methylation landscape in the dog blood. Our observations reveal the similar methylation patterns across dog breeds, while CGI regions showed high variations in DNA methylation level between individuals. Our study also highlights the potential of NLP approach for analyzing low-complexity DNA sequences, such as CGIs.

Nomophobia Among Adolescents: The Role of Hadith in Overcoming Nomophobia in Adolescents Through Character Education

Nomophobia, or fear and anxiety about not having access to a cell phone, has become a common phenomenon among teenagers. Teenagers' dependence on technology, especially smartphones, has led to the emergence of nomophobia as a result of losing control of accurate information. The negative impacts of nomophobia include high stress, loss of focus, and anti-social tendencies. This research aims to examine the role of hadith in overcoming nomophobia in adolescents through character education. As a source of inspiration and guidance, Hadith can help teenagers develop better character and overcome homophobic tendencies. The method used in this research is a literature study, which collects data and reviews it from relevant reference sources, which are then connected to research to describe the problem formulation. The results of this research explain how a clearer picture of hadith can help teenagers overcome nomophobia and increase awareness of culture and social context.

A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection

In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. General object detection frameworks encounter difficulties in adequately tackling these challenges, leading to substantial information loss during network downsampling, inadequate feature fusion, and being unable to meet real-time requirements. In this paper, we propose a Real-Time Small Object Detection YOLO (RTSOD-YOLO) model to tackle the various challenges faced in UAVs detection. We further enhance the adaptive nature of the Adown module by incorporating an adaptive spatial attention mechanism. This mechanism processes the downsampled feature maps, enabling the model to better focus on key regions. Secondly, to address the issue of insufficient feature fusion, we employ combined serial and parallel triple feature encoding (TFE). This approach fuses scale-sequence features from both shallow features and twice-encoded features, resulting in a new small-scale object detection layer. While enhancing the global context awareness of the existing detection layers, this also enriches the small-scale object detection layer with detailed information. Since rich redundant features often ensure a comprehensive understanding of the input, which is a key characteristic of deep neural networks, we propose a more efficient redundant feature generation module. This module generates more feature maps with fewer parameters. Additionally, we introduce reparameterization techniques to compensate for potential feature loss while further improving the model’s inference speed. Experimental results demonstrate that our proposed RTSOD-YOLO achieves superior detection performance, with mAP50/mAP50:95 reaching 97.3%/51.7%, which represents improvement of 3%/3.5% over YOLOv8, and 2.6%/0.1% higher than YOLOv10. Additionally, it has the lowest parameter count and FLOPs, making it highly efficient in terms of computational resources.

Enhancing Automated Peer Code Reviews in Software Engineering Education with Context-Aware Generative AI

This study investigates the enhancement of peer code reviews in software engineering education through the integration of Generative Artificial Intelligence (GenAI) with contextual awareness. Previous implementations of GenAI, such as ChatGPT, lacked detailed context about the educational content and assessment goals, limiting their effectiveness. Our work-in-progress research addresses this gap by providing GenAI with additional static and dynamic contextual information, including project overviews, pull-request descriptions, and comments. In a controlled study involving 26 students from a 12-week software engineering course, we compared the efficacy of the original GenAI system with a context-enhanced version. Results demonstrated that the context-aware GenAI provided more accurate and useful feedback, as perceived by the students. These findings suggest that incorporating contextual information improves the quality of automated peer reviews, offering a promising tool for educators to enhance student learning and engagement in code review activities.

Integrating computer vision and AI for interactive augmented reality experiences in new media

Abstract. Augmented reality (AR) is a groundbreaking technology that fully immerse the user in a mixed 'reality' where the real and virtual coexist in a unique manner. Integrating more artificial intelligence (AI) and computer vision into AR devices can greatly improve user input and provide a whole novel interface. Through AI technology, such as gesture recognition, object tracking, and face recognition, AR systems can offer more intuitive and engaging interactions. An AR system featuring such improved AI technologies can process real-time data while having the contextual awareness to respond to users' input and the surrounding environment on the fly. It can also provide narrative integration, character development, and dynamic environments to users, thereby enabling them to have a more personalised and meaningful experience. This paper examines the evolution of new media by discussing the possibilities of using AI and computer vision in AR devices to create personalised experiences, all the while critically looking at technical challenges and the opportunities they present to the field of future research and development. It also looks at case studies across different sectors, such as education, training, tourism, gaming, retail, and aviation to justify the potential of future development of AI-enhanced AR.

Smooth Talking: Generative Versus Scripted Coaching Chatbot Adoption and Efficacy Comparison

Workplace coaching is an essential individual and organizational development intervention, but high costs limit its application. AI coaching chatbots could scale coaching, however, traditional scripted chatbots lacks mainstream adoption due to limited context awareness and robotic responses. Recent advances in large language models and generative AI offer an alternative, however, generative AI is linked to risks and controversies. Should coaching chatbots use generative AI? We compared a scripted, rules-based coaching chatbot (ScriptBot, n = 126) with a generative AI chatbot (GenBot, n = 116) in a randomized controlled trail experiment. We measured technology adoption (UTAUT2), self-determination (autonomy, competence, relatedness) and goal attainment. For all technology adoption and self-determination variables, GenBot outperformed ScriptBot. However, for goal attainment there was no significant difference. Using generative AI in coaching chatbots improves adoption and coaching outcomes, but for highly structured interactions such as goal attainment, scripted conversations (with lower risk) seem to work just as well.

Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy

Colonoscopy screening is the gold standard procedure for assessing abnormalities in the colon and rectum, such as ulcers and cancerous polyps. Measuring the abnormal mucosal area and its 3D reconstruction can help quantify the surveyed area and objectively evaluate disease burden. However, due to the complex topology of these organs and variable physical conditions, for example, lighting, large homogeneous texture, and image modality estimating distance from the camera (aka depth) is highly challenging. Moreover, most colonoscopic video acquisition is monocular, making the depth estimation a non-trivial problem. While methods in computer vision for depth estimation have been proposed and advanced on natural scene datasets, the efficacy of these techniques has not been widely quantified on colonoscopy datasets. As the colonic mucosa has several low-texture regions that are not well pronounced, learning representations from an auxiliary task can improve salient feature extraction, allowing estimation of accurate camera depths. In this work, we propose to develop a novel multi-task learning (MTL) approach with a shared encoder and two decoders, namely a surface normal decoder and a depth estimator decoder. Our depth estimator incorporates attention mechanisms to enhance global context awareness. We leverage the surface normal prediction to improve geometric feature extraction. Also, we apply a cross-task consistency loss among the two geometrically related tasks, surface normal and camera depth. We demonstrate an improvement of 15.75% on relative error and 10.7% improvement on δ1.25 accuracy over the most accurate baseline state-of-the-art Big-to-Small (BTS) approach. All experiments are conducted on a recently released C3VD dataset, and thus, we provide a first benchmark of state-of-the-art methods on this dataset.

Green Edge: Harnessing situational awareness for sustainable systems

Green computing and Building Management Systems (BMS)are closely related through their shared goal of improving energy efficiency and reducing environmental impact. Field controllers that connect with the sensors and actuators at the field level act as an interface between field devices and enterprise applications. Data transfer, protocol conversion, data processing, computing, and control actuators based on predefined logic are the functions of the field controllers. Fast response time is one of the crucial parameters of these field controllers hence reducing the load on these field controllers by enabling Edge computing functionality is very much required. Maharudrayya Nandimath and others [16] in their “Edge Computing in IBMS (Integrated Building Management Systems). (2024 International Conference on Computer Communication and Informatics (iccci. in)) the paper suggested many of the measures to reduce the load on these field controllers. Building Management Systems are essential for optimizing the operation of modern buildings, ensuring comfort, safety, and energy efficiency. By integrating advanced technologies such as Situation Awareness (SA) and Artificial Intelligence (AI), BMS can be significantly enhanced to provide real-time monitoring, predictive maintenance, and intelligent decision-making. This paper explores the potential of combining SA and AI with BMS, leveraging insights from recent research on industrial processes, IoT cloud-edge continuum, and smart cities. The proposed system architecture includes feedback loops and contextual awareness, aiming to improve building operations and reduce energy consumption, thereby supporting global sustainability goals.

Exploring the Challenges of Context in Accessing Mental Health Support in Rural New Zealand: A Case Study Approach.

This paper explores the complexities that impact access to mental health services in rural New Zealand. Historical, cultural, social and political factors will be examined against the philosophical positioning of Foucault and Fairclough. This research is a single-embedded case study design exploring participants' discourses in the context of a rural, bounded geographical area of New Zealand. The results show that mental health support that addresses people's actual needs rather than the needs that governments map against ever-changing policy is required and that an awareness of context within case study research is important. The process of case study design is described, including building upon a rationale for selecting the case, collecting data and conducting case analysis and interpretation. This study examines factors influencing the real-life rural context of accessing mental health support. This article demonstrates that case-study research can be valuable for navigating context complexity and developing nuanced understandings of complex phenomena. The paper highlights how the multifaceted case study context is more than mapping discourses against a rural backdrop. It is necessary to consider the power dynamics that shape experiences and their impact on service creation and its consequent delivery. Rather than services being created that are complex and not meeting people's needs, there is a need to listen to the people who have experienced mental health distress and provide services and support in locations other than clinical settings.

Capturing multiple emotions from conversational data using fine tuned transformers

Emotion detection is one of the crucial topics of Natural Language Processing (NLP) in recent years, and now one of the biggest motivating factors in correct identification and interpretation of a wide range of emotional expressions in textual data. This research examines the use of Bidirectional Encoder Representations from Transformers (BERT), a trained transformer model for emotion detection in textual data. This analysis evaluates how good BERT is at identifying emotions such as surprise, anger, fear, happiness and sadness compared with ordinary machine learning as well as deep learning techniques. The addition of weighted emotions approach enhances the model performance and gives a deeper emotional context awareness making it more effective to deal with complicated emotional utterances. The purpose of the research is to develop a fine-tuned BERT model with a weighted emotion framework to enhance the accuracy of emotion classification in conversational text. The context is improving emotion recognition in scenarios where multiple emotions co-exist, addressing limitations in traditional models by capturing subtle and overlapping emotional expressions. In terms of training the methods multiple datasets are considered and also the research examines various models performance. The article further discusses possible application areas of BERT modified in light of NLP.

FDT‐Net: Frequency‐Aware Dual‐Branch Transformer‐Based Optic Cup and Optic Disk Segmentation With Parallel Contour Information Mining and Uncertainty‐Guided Refinement

ABSTRACTAccurate segmentation of the optic cup and disc in fundus images is crucial for the prevention and diagnosis of glaucoma. However, challenges arise due to factors such as blood vessels, and mainstream networks often demonstrate limited capacity in extracting contour information. In this paper, we propose a segmentation framework named FDT‐Net, which is based on a frequency‐aware dual‐branch Transformer (FDBT) architecture with parallel contour information mining and uncertainty‐guided refinement. Specifically, we design a FDBT that operates in the frequency domain. This module leverages the inherent contextual awareness of Transformers and utilizes Discrete Cosine Transform (DCT) transformation to mitigate the impact of certain interference factors on segmentation. The FDBT comprises global and local branches that independently extract global and local information, thereby enhancing segmentation results. Moreover, to further mine additional contour information, this study develops the parallel contour information mining (PCIM) module to operate in parallel. These modules effectively capture more details from the edges of the optic cup and disc while avoiding mutual interference, thus optimizing segmentation performance in contour regions. Furthermore, we propose an uncertainty‐guided refinement (UGR) module, which generates and quantifies uncertainty mass and leverages it to enhance model performance based on subjective logic theory. The experimental results on two publicly available datasets demonstrate the superior performance and competitive advantages of our proposed FDT‐Net. The code for this project is available at https://github.com/Rookie49144/FDT‐Net.

Mapping the reliability multiverse of contextual cuing.

Cronbach (1957) famously noted the divergence between the experimental and psychometric traditions in psychology and called for a unification, but many domains of cognitive experimental psychology continue to pay minimal heed to basic psychometric principles. The present article considers the lack of attention devoted to the reliability of measures extracted in a popular visual search task for studying putatively unconscious mental processes, contextual cuing, and the inferential fallacies that this neglect can cause. Two experiments (total N = 200) demonstrated that the reliability of contextual cuing and awareness measures can be increased by three manipulations designed to increase between-participant variability in search performance. At the same time, the data were subjected to a multiverse analysis, which found that specific data preprocessing pipelines result in more reliable estimates. Nevertheless, the reliability estimates remained too low for drawing firm conclusions from standard statistical techniques. Interpreting results from analyses based on individual differences, such as the typical low correlations between implicit and explicit measures, will be challenging so long as the underlying measures have poor reliability. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Case studies on the impact of ex-post legislative evaluations in Dutch healthcare: a within and cross-case analysis

ABSTRACT Globally, ex-post legislative evaluations are becoming increasingly important for understanding how laws function in practice and identifying their limitations and their effects upon stakeholders. This study delves into the impact of ex-post legislative evaluations within the Dutch healthcare system. Building upon insights from previous literature, we aim to refine existing ideas within the field through empirical data. Utilising the realist evaluation method, we examine three distinct case studies followed by a cross-case analysis. Our research underscores that the impact of these evaluations extends beyond policy and politics to the broader societal arena. Our findings also point towards opportunities for strengthening the impact of ex-post legislative evaluations within this broader societal arena. Specifically, we identify strategic phases within the evaluation process through which we aim to maximise impact. Finally, the study emphasises the importance of context awareness, the strategic utilisation of research quality as well as interactional factors for enhancing impact.

Infrared target detection algorithm based on multipath coordinate attention mechanism

Abstract The current generation of infrared target detection algorithms frequently exhibits a high degree of dependency on parameter configurations within complex operational environments. This often results in a reduction in detection accuracy, an increase in the number of model parameters, and a slowing of the detection process. To address these limitations, a new algorithm, CGhostNet-Attention-YOLO (CAY), is proposed in this paper. Firstly, we designed a lightweight backbone network, CGhostNet, with the objective of improving feature extraction efficiency, thereby enabling accurate and real-time feature extraction. Furthermore, we proposed a Multipath Coordinate Attention (MPCA) mechanism, which incorporates both channel and positional information, thereby facilitating enhanced context awareness and the comprehension of relationships between different positions. This effectively enhances the model's ability to comprehend the overall meaning and addresses the issue of missed detections in infrared targets, significantly improving detection accuracy. Moreover, we employed the Inner-SIoU loss function to accelerate model convergence, reduce loss, and enhance the robustness of the model. Finally, comparative experiments were conducted on our dataset (IFD) as well as publicly available datasets, including FLIR, Pascal VOC, and NEU-DET. The results demonstrate that the CAY algorithm achieved a mean Average Precision (mAP@0.5) of 81.3% on the IFD dataset, 86.1% on the FLIR dataset, 79.2% on the Pascal VOC dataset, and 79.9% on the NEU-DET dataset, with a 27% reduction in the number of parameters. These findings validate the feasibility of the proposed algorithm.

Estimating Repetitions in Reserve During the Bench Press Exercise: Should We Consider Sex and the Exercise Equipment?

Estimating repetitions in reserve (RIR) has been established as a valid and practical method for monitoring set configuration concerning proximity to failure in resistance training. However, factors such as sex and exercise equipment could interfere with the estimation process. Therefore, this study aimed to determine the influence of sex (female vs male) and exercise equipment (free-weight vs Smith machine) on the RIR estimation at different relative loads during the bench press exercise. Men would be more accurate than women in the RIR estimation, and participants would better perceive their actual effort at the Smith machine compared with the free-weight bench press exercise. Repeated measures design. Level 3. In a counterbalanced order, recreationally trained participants (12 female, 14 male) performed 2 identical sessions with the free-weight bench press exercise in 1 week and 2 identical sessions with the Smith machine bench press exercise in another week (first session for familiarization and second session for validity analysis). In each session, participants performed single sets of repetitions to failure against 3 relative loads (65%, 75%, and 85% of the 1-repetition maximum [1RM]). Results revealed a significant interaction between RIR and sex at 65% 1RM (P < 0.01; women: -1.2 [-1.7 to -0.6] repetitions; men: -0.1 [-0.6 to 0.3] repetitions), and between RIR and exercise equipment at 65% 1RM (P < 0.01; free-weight: -1.1 [-1.5 to -0.6] repetitions; Smith machine: -0.2 [-0.6 to 0.2] repetitions), but not at 75% and 85% 1RM. RIR estimation is a valid method for monitoring set configuration. However, this method tends to underestimate RIR at 65%1RM with female athletes and in the free-weight exercise for both sexes. Strength and conditioning coaches should help their athletes understand effort awareness in the aforementioned context.

GPT-4 and Beyond : Advancements in AI Language Models

This comprehensive article explores the groundbreaking advancements and implications of GPT-4, the latest iteration in the Generative Pre-trained Transformer series. It delves into GPT-4's enhanced comprehension and contextual awareness, multilingual proficiency, expanded knowledge base, multimodal capabilities, and improved safety and ethical considerations. The article provides quantifiable improvements and practical implications across various domains, including natural language processing, machine translation, research assistance, education, and content creation. It also addresses the challenges and limitations of the model, such as biases and computational requirements, while discussing future directions in AI development. The piece concludes with projections for future AI capabilities, emphasizing more human-like reasoning, handling of complex tasks, and seamless integration with other systems, while also highlighting the ethical considerations and challenges that accompany these advancements.

TS‐Mixer: A lightweight text representation model based on context awareness

AbstractLarge pre‐trained models (PTMs) have shown their powerful ability in multiple natural language processing tasks. However, using them in practical application remains a challenge due to the significant computational cost and memory requirements. In order to achieve the balance of computational cost and accuracy, MLP architecture can be used as an alternative to the self‐attention module, such as pNLP‐Mixer and Hyper‐Mixer. Experiments indicate that, MLP‐based models can attain competitive performance with low cost. They maintain the balance of computation cost and accuracy successfully, yet, this is at the expense of not being able to capture short‐range dependencies. In this paper, a novel MLP‐based model, termed TS‐Mixer, is proposed which can capture local dependencies by shifting operation. Compared with other MLP‐based models, the parameters of TS‐Mixer are decoupled from the sequence length, hence it has a smaller model size in long sequence tasks. In addition, TS‐Mixer has linear computational complexity, therefore it can be used as a lightweight alternative to the self‐attention model. Experiments show that the TS‐Mixer outperforms other MLP‐based models, which achieves higher accuracy with fewer parameters in multiple downstream tasks. Notably, compared with pre‐trained models, TS‐Mixer can reach more than 90% of their accuracy with 1% or even one thousandth of the parameters (0.174 ~ 1.2 M). These results demonstrate that TS‐Mixer can achieve a better balance between the computing resources and accuracy. Code is available at: https://github.com/wyl-privacy-project/TS-Mixer.

Automatic closed captions and subtitles in academic video presentations: possibilities and shortcomings

In light of the increasing number of academic events being recorded or held online since the onset of the COVID-19 pandemic, the present work combines automation processes in audiovisual translation and academic texts–more specifically, video presentations. The research questions are whether the automatic generation of captions is functional to ensure accessibility in academic events and how much post-editing effort would such content require in case a machine translation of the subtitles is to be applied. The research method comprises several phases. First, in a corpus of video presentations of specialised content in English, captions were generated automatically using YouTube Studio to ascertain the general quality and the type of errors generated in the automatically generated closed captions according to Multidimensional Quality Metrics (MQM) framework. These auto-generated captions were corrected and annotated by considering the following parameters: a) pre-editing time, b) type of error according to MQM framework, and c) severity of the error. Second, the auto-generated captions and corrected were machine translated into Spanish. Furthermore, errors detected in the machine translation of the subtitles (English-Spanish) were post-edited and errors were analysed following the MQM. Reception by a potential audience was also studied, as evaluated by academics from the same field of expertise. The main conclusion is that most errors in machine-translated subtitles stem from incorrect caption segmentation and lack of context awareness, making it essential to correct the closed captions before translation. This thesis is supported by the reception study in which the level of comprehension was higher when the transcription was pre-edited, as most of the problems arise from the closed captions rather than from the translation itself.

A comprehensive Analysis of Approaches and Difficulties for Cybersecurity Threats- Article Review

This study explores a broad range of cybersecurity research projects, including emerging fields like IoT and linked auto security as well as more established threats like malware and Distributed Denial of Service (DDoS) assaults. Scholars utilize a range of approaches, such as deep learning and machine learning, while paying close attention to clear dataset descriptions and the consequences of false positives and negatives. Accuracy and contextual awareness are critical, especially for Internet of Things security. Rapid threat identification relies on automation and effectiveness, and a commitment to progress is demonstrated by the incorporation of cutting-edge methods like Genetic, Wolf Optimization and new models. Finding a balance between feature selection, precision, and execution time presents a significant challenge. Similar examinations are made simpler by the accessibility of shared benchmark datasets. The final goal represented by reinforce bunch guards and advance computerized trust. This review gives data and courses to explore the continuously changing network protection scene.

A comprehensive Analysis of Approaches and Difficulties for Cybersecurity Threats- Article Review

This study explores a broad range of cybersecurity research projects, including emerging fields like IoT and linked auto security as well as more established threats like malware and Distributed Denial of Service (DDoS) assaults. Scholars utilize a range of approaches, such as deep learning and machine learning, while paying close attention to clear dataset descriptions and the consequences of false positives and negatives. Accuracy and contextual awareness are critical, especially for Internet of Things security. Rapid threat identification relies on automation and effectiveness, and a commitment to progress is demonstrated by the incorporation of cutting-edge methods like Genetic, Wolf Optimization and new models. Finding a balance between feature selection, precision, and execution time presents a significant challenge. Similar examinations are made simpler by the accessibility of shared benchmark datasets. The final goal represented by reinforce bunch guards and advance computerized trust. This review gives data and courses to explore the continuously changing network protection scene.