Contextual Integration Research Articles

MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications

With advancements in autonomous driving, LiDAR has become central to 3D object detection due to its precision and interference resistance. However, challenges such as point cloud sparsity and unstructured data persist. This study introduces MS3D (Multi-Scale Feature Fusion 3D Object Detection Method), a novel approach to 3D object detection that leverages the architecture of a 2D Convolutional Neural Network (CNN) as its core framework. It integrates a Second Feature Pyramid Network to enhance multi-scale feature representation and contextual integration. The Adam optimizer is employed for efficient adaptive parameter tuning, significantly improving detection performance. On the KITTI dataset, MS3D achieves average precisions of 93.58%, 90.91%, and 88.46% in easy, moderate, and hard scenarios, respectively, surpassing state-of-the-art models like VoxelNet, SECOND, and PointPillars.

An Approach of Integration of Contextual Data in E-Service System for Management of Multimodal Cargo Transportation

Our research area concerns the development of an intelligent e-service system to help manage multimodal transportation processes. To better respond to the requirements of sustainable development, we encourage the development of multimodal cargo transportation. Therefore, it is important to ensure that the dissemination and management of information in multimodal transportation requires more accurate information transmission and implementation for better coordination of these processes with the interaction of all process participants. Also, contextual data integration into the e-service provision processes is important for more adequate real cargo transportation management. The transition to multimodal freight transport and the increase in its activity directly impact the sustainable development of this sector as transport flows are removed from ground roads and distributed more evenly to load more railways and sea vessels. This research aims to develop an approach to developing the infrastructure of an e-service system with the ability to integrate contextual data and influence the management of multimodal transportation. The methodological approach is based on methods of conceptual representation of information and methods for recognizing the flow of needful information during multimodal freight transportation according to adaptable management processes. The e-service provision system creates benefits for cargo drivers and delivery managers with more accurate information implementation and more adequate coordination of processes under real conditions by helping them make the right decisions.

A Multi-Scale Liver Tumor Segmentation Method Based on Residual and Hybrid Attention Enhanced Network with Contextual Integration.

Liver cancer is one of the malignancies with high mortality rates worldwide, and its timely detection and accurate diagnosis are crucial for improving patient prognosis. To address the limitations of traditional image segmentation techniques and the U-Net network in capturing fine image features, this study proposes an improved model based on the U-Net architecture, named RHEU-Net. By replacing traditional convolution modules in the encoder and decoder with improved residual modules, the network's feature extraction capabilities and gradient stability are enhanced. A Hybrid Gated Attention (HGA) module is integrated before the skip connections, enabling the parallel processing of channel and spatial attentions, optimizing the feature fusion strategy, and effectively replenishing image details. A Multi-Scale Feature Enhancement (MSFE) layer is introduced at the bottleneck, utilizing multi-scale feature extraction technology to further enhance the expression of receptive fields and contextual information, improving the overall feature representation effect. Testing on the LiTS2017 dataset demonstrated that RHEU-Net achieved Dice scores of 95.72% for liver segmentation and 70.19% for tumor segmentation. These results validate the effectiveness of RHEU-Net and underscore its potential for clinical application.

Understanding Contextual Awareness, Data Handling, and Integration Challenges in AI Agents

Understanding Contextual Awareness, Data Handling, and Integration Challenges in AI Agents

Integrating advanced human-computer interaction and machine learning models for optimizing VR systems in educational and business applications

This paper presents the development of an advanced Human-Computer Interaction (HCI) model and algorithm integrating Natural Language Processing (NLP) to optimize Virtual Reality (VR) systems for educational and business applications. The proposed model enhances user experience and operational efficiency by addressing interface design, user engagement, real-time data processing, and accessibility. Continuous learning and contextual data integration ensure adaptive and personalized interactions, improving the functionality and applicability of VR environments. Fig.: 2. Refs.: 11 titles.

Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception.

Diabetic Retinopathy (DR) is a significant cause of vision loss in diabetic patients, making early detection and accurate severity classification essential for effective management and prevention. This study aims to develop an enhanced DR severity classification approach using advanced model architectures and transfer learning to improve diagnostic accuracy and support better patient care. We propose a novel model, Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet), designed to classify DR severity using fundus images from the Messidor 1 and Messidor 2 datasets. The XceSE_SparseLwMLA-UNet integrates several advanced mechanisms: the Squeeze-and-Excitation (SE) mechanism for adaptive feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for effective contextual information integration, and transfer learning from the Xception architecture to enhance feature extraction capabilities. The SE mechanism refines channel-wise feature responses, while SparseLwMLA enhances the model's ability to identify complex DR patterns. Transfer learning utilizes pre-trained weights from Xception to improve generalization across DR severity levels. The proposed XceSE_SparseLwMLA-UNet model demonstrates superior performance in DR severity classification, achieving higher accuracy and improved multi-class F1 scores compared to existing models. The model's color-coded segmentation outputs offer interpretable visual representations, aiding medical professionals in assessing DR severity levels. The XceSE_SparseLwMLA-UNet model shows promise for advancing early DR diagnosis and management by enhancing classification accuracy and providing valuable visual insights. Its integration of advanced architectural features and transfer learning contributes to better patient care and improved visual health outcomes.

The Chromatic Awareness of Black and White in Andalusian and Omani Poetry: Ibn Khafaja and Sulaiman al-Nabhani as a Model

This study delves into the nuanced perception of black and white colors within the realm of Andalusian and Omani poetry, focusing on the works of Ibn Khafaja and Sulaiman bin Muzaffar al-Nabhani. It aims to uncover the depth of these poets' engagement with color, examining how they utilize black and white to articulate their personal dilemmas and psychological intricacies that surface through their poetic expressions. This exploration is anchored in the belief that color serves as a versatile poetic motif, with the poetic rendition of color experiences offering a reconstitution of reality tailored by the poets' psychological states, thereby rendering color projection as a means of catharsis. Employing Sigmund Freud's psychoanalytic theory as its foundational methodology, this research probes into the unconscious elements of poetic creation, viewing it as a vehicle for expressing the suppressed aspects of the human psyche. The selection of black and white, predominant in the poets' anthologies, facilitates an exploration of diverse psychological and existential experiences, necessitating an analysis of color within poetry through its contextual integration to unveil its expressive and functional potency.

Feature ensemble network for medical image segmentation with multi‐scale atrous transformer

AbstractRecent years have witnessed notable advancements in medical image segmentation through deep convolutional neural networks. However, a notable limitation lies in the local operation of convolution, which hinders the ability to fully exploit global semantic information. To overcome the challenges prevalent in medical image segmentation, the feature ensemble network with multi‐scale atrous transformer is proposed. At the core of the approach lies the multi‐scale contextual integration module, which is based on the multi‐scale atrous transformer and facilitates contextual integration of multi‐level features. To extract discriminative fine‐grained features of the target region, a hybrid attention mechanism that synergistically combines spatial and channel attention, thereby sharpening the model's focus on crucial target information within high‐level features, is incorporated. Additionally, the channel‐aware feature reconstruction module is introduced as an innovative component engineered to tackle feature similarity issues across different categories. This module performs feature reconstruction based on channel perception, effectively widening the feature gap between categories and enhancing the segmentation capability. It is worth mentioning that our approach surpasses the state‐of‐the‐art method using three benchmark datasets in medical image segmentation.

Pocket Parks in Urban Design: Enhancing Urban Environment and Community Well-being

As urban environments become increasingly dense, pocket parks have emerged as essential elements in enhancing the urban environment and improving the quality of life in densely populated areas. These compact, strategically designed spaces offer a range of benefits, extending from ecological enrichment to fostering social connections. This paper explores the concept and significance of pocket parks in urban design, examining their contributions to both ecological diversity and community well-being. Central to their success is a commitment to innovative design principles that emphasize efficient space utilization, contextual integration, and ecological sensitivity. These principles ensure that pocket parks are not only practical and visually appealing but also resonate with local community needs and the surrounding environment. This study aims to highlight the transformative potential of pocket parks in creating more livable, sustainable, and inclusive urban environments, underlining their role as dynamic elements in contemporary urban development.

A multi-attention residual integrated network with enhanced fireworks algorithm for remote sensing image classification

This research examines a multi-attention residual integrated network with an enhanced fireworks algorithm for remote sensing image classification. Remote sensing (RS) picture classification is important for land cover mapping, environmental monitoring, and urban planning. Remote sensing image classification is important in earth observation since the military and commercial sectors have focused on it. Due to RS data's high complexity and limited labelled examples, classifying RS pictures is difficult. Deep Learning (DL) techniques have made great strides in RS image categorization, expanding this field's potential. This research introduces Multi-Attention Residual Integrated Network with Enhanced Fireworks Algorithm (MAR-EFA) to improve hyper spectral image identification. MARIN-EFA improves feature fusion and removes unneeded features to overcome technique constraints. The suggested method weights features using different attention models. These characteristics are then carefully extracted and integrated using a residual network. Final contextual semantic integration on deeply fused features is done with a Bi-LSTM network. Our population-based Enhanced Fireworks Algorithm (EFA) is inspired by fireworks' explosive performance and optimises MARIN parameters. Attention techniques and an improved optimisation algorithm improve performance over current systems. Numerous Eurosat dataset studies were assessed using various performance indicators. The simulation results show that MARIN-EFA outperforms current methods. The suggested technique shows promise for improving RS picture classification and allowing more accurate and reliable data categorization.

The Application of Contextual Integration in English Language Learning

In the process of social development, language plays a crucial role. It serves not only as a carrier of thought and culture but also as a fundamental tool for communication and exchange among individuals. In the journey of learning English, the integration of context can effectively aid students in understanding and mastering the language’s meaning, facilitating accurate translation of relevant sentences. From the perspective of contemporary English language learning, context integration can better elucidate the relationships within language contexts, encompassing factors such as time, space, and objects. Therefore, context integration holds significant practical importance in English language acquisition. It not only enhances learning efficiency but also enables students to accurately comprehend the meaning conveyed in English texts, thus averting translation errors and communication barriers. With this in mind, this paper offers a brief analysis of the application of context integration in English language learning, aiming to provide readers with valuable insights.

The Impact of Façade Design on Visual Pollution Case study: Peshawa-Qazi Street (100 m) in Erbil

Visual pollution refers to the negative impact of various environmental elements on the visual experience of individuals and the quality of the surroundings. This includes unsightly buildings and other man-made structures that disrupt natural beauty. The design of building facades plays a significant role in determining visual pollution. This study aimed to assess the impact of facade design on visual pollution by testing which facade design considerations most contribute to visual pollution in Peshawa-Qazi Street (100 m) in Erbil City. An online survey was conducted with 283 participants in six architectural departments within engineering colleges and other online engineering platforms in Erbil, Duhok, and Suleimani. Respondents included architectural students from the 3rd to 5th stage, academic staff, and professional architects. They rated the impact of individual facade elements, contextual integration, and other factors on visual pollution. A one-sample T-test was used to compare mean scores to a test value of (2.5). Results showed that all three categories of façade design considerations significantly increase visual pollution compared to the test value (p < 0.05). Considerations regarding the overall context of a facade had the most significant impact (mean of 1.93 higher than the test value), followed by other factors (mean of 1.79 higher) and individual elements (mean of 0.71 higher). To decrease visual pollution, it is recommended to the policymakers and municipalities to develop regulations, façade design guidelines and for architects to follow the principles of architectural form and composition regarding the integration of building facades with their surroundings, façade practical considerations, and refined composition of façade elements.

Chat GPT: From Natural Language Processing to Responsible AI - Implications, Challenges, and Future Developments

This research paper provides a comprehensive overview of Chat GPT, a cutting- edge natural language processing technology that has rapidly gained popularity recently. With the ability to generate human-like responses and a growing capacity to understand complex language and contextual nuances, Chat GPT has the potential to revolutionize the way we interact with machines and greatly enhance communication and productivity across a wide range of industries and fields. The paper covers the background and current state of Chat GPT, including its architecture, training process, and applications. It highlights the advancements made in the development of the technology, particularly the introduction of the latest version, GPT-4, which has over 100 trillion parameters compared to just 175 billion of GPT- 3.5 (500 times). It can even generate output (text, art, etc.) that is nearly indistinguishable from that made by a human. In addition to its potential benefits, the paper also examines the ethical, social, economic, and technical implications of Chat GPT. It also identifies concerns around privacy and data security, the potential for the technology to exacerbate existing biases and inequalities, and the risk of misuse or unintended consequences. It is crucial that these challenges are addressed to ensure that Chat GPT is developed and used in a responsible and beneficial manner. Furthermore, the paper discusses the regulations and policies that must be implemented to ensure the responsible development and use of Chat GPT. It emphasizes the importance of transparency and accountability, as well as the need to protect users' rights and promote fair access to the technology. The paper also explores the future developments of Chat GPT, including improvements in multilingual capabilities, emotional intelligence, and personalization. It also highlights the potential for Chat GPT to continue to evolve and improve, particularly with regards to contextual understanding and integration with human assistance. Keywords: Chat GPT, natural language processing, GPT - 4, AI, ethics, regulations, future developments, challenges, limitations, applications, multilingual capabilities, emotional intelligence, personalization.

LSTM-NAS-Net: Enhanced Brain Tumor Segmentation in MRI and CT Images using LSTM-Autoencoder-based Neural Architecture Search

Brain Tumour (BT) a mass or a lump or a growth which occurs due to abnormal cell division or unusual growth of cells in the brain tissue. Initially, the two major types of BT are Primary BT and Secondary BT, the tumour that originate from brain is known as Primary BT and it may be cancerous or non-cancerous. The tumour the initiates from other part of the body and spreads to the brain is stated as secondary BT. Diagnosing BT generally involves a multiple investigation method, such as MRI, CT, PET, SPECT as well as the neurological examinations and blood investigations, whereas some of the patients may need biopsies to evaluate the tumour size and stage. Here we use MRI and CT images for BT segmentation whereas these modalities play a major role in diagnosing, treating, planning and monitoring the BT patients. Moreover, the multimodal data can provide a quantitative information’s about the tumour size, shape, volume and texture. While segmenting the BT the lack of segmentation methods and the interpretability of the segmented regions are limited. To overcome this, we propose a novel LSTM autoencoder bas NAS method which is used for the extracting the BT features and these features can be fused using Contextual Integration Module (CIM) and segmented using the Segmentation Guided Regulizer (SGR) which helps to overcome the stated issues. Finally, the performance metrices are calculated by comparing with the state-of -the -art methods and our method achieves a best segmenting metrices.

Theory-based implementation of an enhanced recovery protocol for cranial surgery.

There is growing evidence for the use of enhanced recovery protocols (ERPs) in cranial surgery. As they become widespread, successful implementation of these complex interventions will become a challenge for neurosurgical teams owing to the need for multidisciplinary engagement. Here, the authors describe the novel use of an implementation framework (normalization process theory [NPT]) to promote the incorporation of a cranial surgery ERP into routine neuro-oncology practice. A baseline audit was conducted to determine the degree of implementation of the ERP into practice. The Normalization MeAsure Development (NoMAD) questionnaire was circulated among 6 groups of stakeholders (neurosurgeons, anesthetists, intensivists, recovery nurses, preoperative assessment nurses, and neurosurgery ward staff) to examine barriers to implementation. Based on these findings, a theory-guided implementation intervention was delivered. A repeat audit and NoMAD questionnaire were conducted to assess the impact of the intervention on the uptake of the ERP. The baseline audit (n = 24) demonstrated limited delivery of the ERP elements. The NoMAD questionnaire (n = 32) identified 4 subconstructs of the NPT as barriers to implementation: communal specification, contextual integration, skill set workability, and relational integration. These guided an implementation intervention that included the following: 1) teamwork-focused training; 2) ERP promotion; and 3) procedure simplification. The reaudit (n = 21) demonstrated significant increases in the delivery of 5 protocol elements: scalp block (12.5% of patients before intervention vs 76.2% of patients after intervention, p < 0.00001), recommended analgesia (25.0% vs 100.0%, p < 0.00001) and antiemetics (12.5% vs 100.0%, p < 0.00001), trial without catheter (13.6% vs 88.9%, p < 0.00001), and mobilization on the 1st postoperative day (45.5% vs 94.4%, p < 0.00001). There was a significant reduction in the mean hospital length of stay from 6.3 ± 3.4 to 4.2 ± 1.7 days (p = 0.022). Two months after implementation, a repeat NoMAD survey demonstrated significant improvement in communal specification. Here, the authors have demonstrated the successful implementation of a cranial surgery ERP by using a systematic theory-based approach.

Expert goalkeepers' and coaches' views on anticipation and cue utilisation facing backcourt throws in handball goalkeeping.

Handball goalkeepers have to act under severe spatio-temporal pressure in both standardised (e.g., 7 m penalty) and non-standardised situations (e.g., backcourt throws) which require them to predict action outcome before ball flight is visible. So far, research on goalkeepers' cue utilisation for anticipation of an opponent's action has mainly focused on 7 m throw situations whereas little attention has been paid to the latter, more complex and far more frequently occurring backcourt throw situations. To address this gap, we conducted semi-structured interviews with N = 6 expert handball goalkeepers and goalkeeper coaches [all of whom were (former) expert handball goalkeepers] on anticipation and cue utilisation when facing backcourt throws. The interviews were subsequently transcribed, coded and results were inductively as well as deductively categorised by means of a thematic analysis. Results reveal a variety of kinematic and contextual cues relevant for action anticipation that become available before the game and before or during the throw. Participants reported to use information from the offence (e.g., thrower's jump; opposing team's task distribution) and the defence (e.g., defensive players' strategies, block position) for anticipation in backcourt throw situations. Additionally, we identified several factors that influence cue availability and utilisation. Our findings provide a thorough basis to (a) guide future research that yields questions on kinematic and contextual cue integration and in-situ cue usage as well as (b) inform the development of training programs to foster goalkeepers' anticipatory skill.

Contextual Integration through Computation: Algorithmic Approaches for Incorporating Lattice Patterns into Facade Designs

Context and technology have significantly shaped architectural design throughout history, with contextuality playing a vital role in assigning meaning to designs. In light of computational tools and algorithms that revolutionize architectural design, enabling intricate and complex building geometries within shorter timeframes and heightened efficiency, this study aims to underscore the potential of algorithmic façade design. It focuses on contextual integration, utilizing the Newari lattice window as a foundational element for design exploration. The study employed computational modeling with parametric tools, scripting, and generative algorithms to create digital models that reflect the Newari lattice window pattern and fractal geometry. It utilized Text-Based Language (TBLs) like Python and Visual-Based Language (VBLs) like Grasshopper for visual experimentation and precision. Demonstrated through the re-design of the façade of the existing commercial building, Civil Mall, a significant building in Kathmandu Valley’s urban landscape, the research showcases algorithmic prowess in seamlessly integrating traditional Newari wisdom into intricate geometrical patterns. By transcending manual methods through recursion, iteration, randomness, and embracing unforeseen possibilities, the study establishes a foundational reference, enriching Nepal's architectural discourse beyond the traditional versus modern binary. This might be helpful in fostering a distinctive modern architectural language unique to Nepal. The study holds potential for further research on the prevailing traditional architectural forms and geometry in the country.

Contextual sensory integration training vs. traditional vestibular rehabilitation: a pilot randomized controlled trial

BackgroundWe created a clinical virtual reality application for vestibular rehabilitation. Our app targets contextual sensory integration (C.S.I.) where patients are immersed in safe, increasingly challenging environments while practicing various tasks (e.g., turning, walking). The purpose of this pilot study was to establish the feasibility of a randomized controlled trial comparing C.S.I. training to traditional vestibular rehabilitation.MethodsThirty patients with vestibular dysfunction completed the Dizziness Handicap Inventory (DHI), Activities-Specific Balance Confidence Scale (ABC), Visual Vertigo Analog Scale (VVAS), Functional Gait Assessment (FGA), Timed-Up-and-Go (TUG), and Four-Square Step Test (FSST). Following initial assessment, the patients were randomized into 8 weeks (once per week in clinic + home exercise program) of traditional vestibular rehabilitation or C.S.I. training. Six patients had to stop participation due to the covid-19 pandemic, 6 dropped out for other reasons (3 from each group). Ten patients in the traditional group and 8 in the C.S.I group completed the study. We applied an intention to treat analysis.ResultsFollowing intervention, we observed a significant main effect of time with no main effect of group or group by time interaction for the DHI (mean difference − 18.703, 95% CI [-28.235, -9.172], p = 0.0002), ABC (8.556, [0.938, 16.174], p = 0.028), VVAS, (-13.603, [-25.634, -1.573], p = 0.027) and the FGA (6.405, [4.474, 8.335], p < 0.0001). No changes were observed for TUG and FSST.ConclusionPatients’ symptoms and function improved following either vestibular rehabilitation method. C.S.I training appeared comparable but not superior to traditional rehabilitation.Trial registrationThis study (NCT04268745) was registered on clincaltrials.gov and can be found at https://clinicaltrials.gov/ct2/show/NCT04268745.

A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding

Sentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%.

Expectation-based gist facilitation: Rapid scene understanding and the role of top-down information.

Scene meaning is processed rapidly, with "gist" extracted even when presentation duration spans a few dozen milliseconds. This has led some to suggest a primacy of bottom-up information. However, gist research has typically relied on showing successions of unrelated scene images, contrary to our everyday experience in which the world unfolds around us in a predictable manner. Thus, we investigated whether top-down information-in the form of observers' predictions of an upcoming scene-facilitates gist processing. Within each trial, participants (N = 370) experienced a series of images, organized to represent an approach to a destination (e.g., walking down a sidewalk), followed by a target scene either congruous or incongruous with the expected destination (e.g., a store interior or a bedroom). A series of behavioral experiments revealed that appropriate expectations facilitated gist processing; inappropriate expectations interfered with gist processing; sequentially-arranged scene images benefitted gist processing when semantically related to the target scene; expectation-based facilitation was most apparent when presentation duration was most curtailed; and findings were not simply the result of response bias. We then investigated the neural correlates of predictability on scene processing using event-related potentials (ERPs) (N = 24). Congruency-related differences were found in a putative scene-selective ERP component, related to integrating visual properties (P2), and in later components related to contextual integration including semantic and syntactic coherence (N400 and P600, respectively). Together, results suggest that in real-world situations, top-down predictions of an upcoming scene influence even the earliest stages of its processing, affecting both the integration of visual properties and meaning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).