Real-world Scenarios Research Articles

Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction

Wheeled lifting robots have found widespread applications in various industrial and logistical environments. However, traditional robots are far from adequate in terms of visual perception capabilities. Additionally, their remote control methods suffer from inefficiencies, which tend to bring safety concerns. To address these issues, this work proposes an autonomous multi-sensor-enabled wheeled lifting robot system, i.e., AMSeWL-R, to facilitate remote autonomous operations. Specifically, AMSeWL-R integrates real-time simultaneous localization and mapping with object detection on a wheeled lifting robot. Additionally, a novel mobile-ROS interaction method is proposed to achieve real-time communication and control between a mobile device and a ROS host. Furthermore, a lightweight object detection algorithm based on YOLOv8, i.e., YOLOv8-R, is proposed to achieve faster detection. Experimental results validate the effectiveness of the AMSeWL-R system for accurately detecting objects and mapping its surroundings. Furthermore, TensorRT acceleration is employed during practical testing on a Jetson Nano to achieve real-time detection using the proposed YOLOv8-R, demonstrating its efficacy in real-world scenarios.

Experimental validation of an analytical transient model for saturated boosting gain in DC–DC converters with variable duty cycle

This paper presents an innovative approach to characterize the boosting gain saturation phenomenon in DC–DC converters with variable duty cycles, a crucial component in energy management applications such as renewable energy systems and IoT devices. While existing literature predominantly relies on steady-state models to explain boosting gain behaviour, this study reveals discrepancies between theoretical predictions and experimental observations, particularly at high duty cycles. The research introduces an analytical model to accurately capture the boosting gain dynamics of a boost chopper converter, addressing the limitations of traditional steady-state analyses. Through experimental validation and numerical simulations using CAD tools, a significant boosting gain saturation of approximately 2.2 is empirically observed, highlighting the necessity for a transient model approach. By providing a comprehensive understanding of the boosting ratio constraints in practical boost converters, this work contributes to advancing the analytical modelling techniques in the field. The mathematical model’s validation through rigorous experimental measurements and simulation analyses underscores its reliability and applicability in real-world scenarios. The structured organization of the paper elucidates the development and verification process, culminating in insightful conclusions that underscore the significance of transient modelling in enhancing the performance and efficiency of DC–DC converters.

Demystifying Machine Learning

This paper delves into the rapidly evolving domain of Artificial Intelligence (AI), with a particular focus on Machine Learning (ML), a dynamic and influential subset of AI. It explores how ML empowers computers to learn from data, identify patterns, and make decisions with minimal human intervention. The manuscript examines the broad utility of ML across various real-world scenarios, emphasizing its critical role in enabling organizations to evolve and maintain a competitive edge in the fast-paced technological landscape. It discusses the necessity for organizations to adopt new ways of working and embrace the opportunities presented by AI to remain viable in the global, online marketplace. The paper reviews the evolution of ML, evaluates its advantages and disadvantages, and contemplates the future directions ML could lead organizations willing to integrate this powerful technology. The overarching theme is the transformative potential of ML in reshaping organizational strategies and operations for a more interconnected and intelligent future.

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

Fourier analysis in single photon imaging

Single photon imaging has become an established sensing approach. Compared to intensity imaging, versatile advantages have been demonstrated, such as imaging with high sensitivity, at a high frame rate, and with a high dynamic range. In this paper, we investigate the Fourier analysis of single photon counting at a frame rate of approximately 100 kHz and a high spatial resolution of 512 px × 512 px. We observed signal modulation in raw data as well as in data converted to photon flux, but with the data processing, the signal’s frequency response is affected by significant damping. Thus, analysis sensible to signal frequency should work on the raw single photon counting signal. Furthermore, imaging of magnitude and phase in the Fourier domain can visualize areas of certain signal modulation, and the gradient of the phase angle can reveal the direction of movements. Finally, we have applied our method to real world scenarios by analyzing unmanned aerial vehicle’s motion in outdoor experiments.

The paradigm shift in science education: Namibian science teachers’ perceptions and experiences with inquiry-based instruction

This study explored Namibian science teachers’ perceptions of and approaches towards integrating inquiry-based instruction in science education. A survey questionnaire with 133 participants determined their opinions, attitudes, and beliefs revealing a strong preference for inquiry-based methods, with an average rating of 4.36 on a five-point Likert scale. Teachers emphasised its effectiveness in engaging learners, fostering critical thinking, and connecting scientific principles to real-world scenarios. The study examines key factors that impact the enactment of inquiry-based instruction, including teachers’ prior experiences, understanding of inquiry, and philosophical viewpoints on the nature of science. The findings from this study showed that the allocation of time in the curriculum, the behaviour of learners, and the availability of resources were recognised as important factors impeding the successful implementation of inquiry-based instruction. The study suggests tailored professional development to address challenges and enhance teachers’ ability to embrace inquiry-based practices effectively. These insights contribute to understanding determinants influencing innovative pedagogical approaches in Namibian science classrooms, informing educational policies and initiatives for teacher training in the country.

The paradigm shift in science education: Namibian science teachers’ perceptions and experiences with inquiry-based instruction

This study explored Namibian science teachers’ perceptions of and approaches towards integrating inquiry-based instruction in science education. A survey questionnaire with 133 participants determined their opinions, attitudes, and beliefs revealing a strong preference for inquiry-based methods, with an average rating of 4.36 on a five-point Likert scale. Teachers emphasised its effectiveness in engaging learners, fostering critical thinking, and connecting scientific principles to real-world scenarios. The study examines key factors that impact the enactment of inquiry-based instruction, including teachers’ prior experiences, understanding of inquiry, and philosophical viewpoints on the nature of science. The findings from this study showed that the allocation of time in the curriculum, the behaviour of learners, and the availability of resources were recognised as important factors impeding the successful implementation of inquiry-based instruction. The study suggests tailored professional development to address challenges and enhance teachers’ ability to embrace inquiry-based practices effectively. These insights contribute to understanding determinants influencing innovative pedagogical approaches in Namibian science classrooms, informing educational policies and initiatives for teacher training in the country.

Comprehensive Review of Robotized Freight Packing

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

Vocational and University Paramedicine Education

This paper highlights key research areas within paramedicine education in Australia. Firstly, it discusses the need for a comparative analysis of accreditation standards, focusing on the recent shift towards a pre-employment model and its impact on program content, particularly the balance between theoretical and practical components. Secondly, it addresses the importance of exploring the influence of ethics education on paramedic practice, investigating how a robust ethical foundation acquired during education translates into ethical practices in real-world scenarios. Additionally, it suggests conducting a comparative study of international models to understand different education approaches, emphasising the importance of identifying best practices to enhance paramedic education in Australia. Lastly, it emphasises the significance of assessing the impact of accreditation changes in 2018, specifically the shift in accreditation responsibilities from the Council of Ambulance Authorities to the Paramedicine Board of Australia, aiming to understand its implications on the quality and consistency of paramedicine education across various institutions. Overall, this paper highlight the critical research gaps and areas of investigation essential for advancing paramedicine education and improving the quality of paramedic training in Australia.

Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification

By utilizing hybrid quantum–classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs’ training process. Understanding the role of quanvolutional operations and how they interact with classical neural networks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite Images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neural networks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, this study provides evidence of the potential advantages of HNNs in certain scenarios. Overall, the findings of this research contribute to advancing sustainable applications of machine learning by proposing novel techniques, optimizing model architectures, and demonstrating the feasibility of adopting HNNs for real-world image classification problems. These insights can inform the development of more efficient and environmentally friendly machine learning solutions.

Comprehensive Review of Robotized Freight Packing

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

Hand Gesture Recognition System using Deep Learning

This paper presents a comprehensive exploration of a hand gesture recognition system centered on the YOLOv5 object detection algorithm. The system is meticulously designed to fulfill specific objectives, including real-time gesture detection, high accuracy, scalability, robustness to environmental variability, and seamless integration with other system components. Drawing upon the strengths of YOLOv5, the proposed system aims to provide a reliable and efficient solution for hand gesture recognition, thereby enhancing human- computer interaction and enabling various applications across different domains. The methodology encompasses several stages, including data acquisition, preprocessing, object detection using YOLOv5, integration with other system modules, evaluation, and validation. Through a series of experiments and extensive testing in real-world scenarios, the effectiveness and usability of the system are thoroughly assessed, demonstrating its potential for practical deployment and widespread adoption. Additionally, this paper discusses the implications of the findings, identifies areas for further research and development, and offers insights into the future directions of hand gesture recognition systems utilizing YOLOv5. Overall, this study contributes to advancing the field of human-computer interaction and gesture recognition technology, paving the way for innovative applications and enhanced user experiences in various domains. Keywords— Hand Gesture Recognition, YOLOv5, Real-time Detection, Human-Computer Interaction, Object Detection, Machine Learning

AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain Adaptation

Trip purpose is a meaningful aspect of travel behaviour for the understanding of urban mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip purposes are naturally diverse and complicated, but the available predictive data sources are limited in real-world scenarios. On the other hand, since trip purpose labelling is costly and the development levels of cities are unbalanced, it is infeasible to access large-scale labeled data in less developed cities to train advanced prediction models. To narrow the gaps, this paper presents A new G raph E mbedding N etwork and active D omain A daptation based framework (AGENDA) that only requires open data sources and is capable of predicting in both label-rich cities and label-scarce cities. Specifically, in label-rich source cities, we first use the vehicle’s GPS trajectory and open POI check-ins to augment trip contexts. Then we establish a supervised graph embedding network with two attention mechanisms to extract the passenger’s latent activity semantics and a classifier to predict trip purpose. To enable the prediction in label-scarce target cities, we further devise an active domain adaptation framework, in which adversarial domain adaptation is used to transfer the source-learned knowledge, and active learning is used to integrate human intelligence in the model training. A group of experiments are conducted with real-world datasets in Beijing and Shanghai. Evaluation results demonstrate that the proposed framework significantly outperforms existing trip purpose prediction algorithms, and could make accurate trip purpose prediction in label-scarce cities with much fewer labelling efforts.

Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification

By utilizing hybrid quantum–classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs’ training process. Understanding the role of quanvolutional operations and how they interact with classical neural networks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite Images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neural networks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, this study provides evidence of the potential advantages of HNNs in certain scenarios. Overall, the findings of this research contribute to advancing sustainable applications of machine learning by proposing novel techniques, optimizing model architectures, and demonstrating the feasibility of adopting HNNs for real-world image classification problems. These insights can inform the development of more efficient and environmentally friendly machine learning solutions.

AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain Adaptation

Trip purpose is a meaningful aspect of travel behaviour for the understanding of urban mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip purposes are naturally diverse and complicated, but the available predictive data sources are limited in real-world scenarios. On the other hand, since trip purpose labelling is costly and the development levels of cities are unbalanced, it is infeasible to access large-scale labeled data in less developed cities to train advanced prediction models. To narrow the gaps, this paper presents A new G raph E mbedding N etwork and active D omain A daptation based framework (AGENDA) that only requires open data sources and is capable of predicting in both label-rich cities and label-scarce cities. Specifically, in label-rich source cities, we first use the vehicle’s GPS trajectory and open POI check-ins to augment trip contexts. Then we establish a supervised graph embedding network with two attention mechanisms to extract the passenger’s latent activity semantics and a classifier to predict trip purpose. To enable the prediction in label-scarce target cities, we further devise an active domain adaptation framework, in which adversarial domain adaptation is used to transfer the source-learned knowledge, and active learning is used to integrate human intelligence in the model training. A group of experiments are conducted with real-world datasets in Beijing and Shanghai. Evaluation results demonstrate that the proposed framework significantly outperforms existing trip purpose prediction algorithms, and could make accurate trip purpose prediction in label-scarce cities with much fewer labelling efforts.

Dual First and Second Surface Solar Mirrors of Polished WS2 and Silver by Dynamical Chemical Plating Technique on Polycarbonate.

This work proposes for the first time protecting-reflecting on both sides of plated mirrors and a solution to polycarbonate surface vulnerability to weathering and scratching using tungsten disulfide (WS2) by mechanical polishing. The ability of the dynamic chemical plating (DCP) technique to deposit Ag films at the nanometer scale on a polycarbonate (PC) substrate and its characteristics to be metallized is also shown. These deposits hold significant promise for concentrated solar power (CSP) applications. Complementarily, the application of WS2 as a reflective film for CSP by mechanical polishing on smooth polycarbonate surfaces is both novel and practical. This technique is innovative and scalable without needing reactants or electrical potential, making it highly applicable in real-world scenarios, including, potentially, on-site maintenance. The effects of surface morphology and adhesion, and the reflectivity parameters of the silver metallic surfaces were investigated. Wettability was investigated because it is important for polymeric surfaces in the activation and metal deposition immediately after redox reactions. The flame technique improved wettability by modifying the surface with carbonyl and carboxyl functional groups, with PC among the few industrial polymers that resisted such a part of the process. The change in the chemical composition, roughness, and wettability of the surfaces effectively improved the adhesion between the Ag film and the PC substrate. However, it did not significantly affect the adhesion between PC and WS2 and showed its possible implementation as a first surface mirror. Overall, this work provides a scalable, innovative method for improving the durability and reflectivity of polycarbonate-based mirrors, with significant implications for CSP applications.

Applications of Augmented Reality in Neuro-Oncology: A Case Series

AbstractAugmented reality (AR) is a technological tool that superimposes two-dimensional virtual images onto three-dimensional real-world scenarios through the integration of neuronavigation and a surgical microscope. The aim of this study was to demonstrate our initial experience with AR and to assess its application in oncological neurosurgery. This is a case series with 31 patients who underwent surgery at Santa Casa BH for the treatment of intracranial tumors in the period from March 4, 2022, to July 14, 2023. The application of AR was evaluated in each case through three parameters: whether the virtual images auxiliated in the incision and craniotomy and whether the virtual images aided in intraoperative microsurgery decisions. Of the 31 patients, 5 patients developed new neurological deficits postoperatively. One patient died, with a mortality rate of 3.0%. Complete tumor resection was achieved in 22 patients, and partial resection was achieved in 6 patients. In all patients, AR was used to guide the incision and craniotomy in each case, leading to improved and precise surgical approaches. As intraoperative microsurgery guidance, it proved to be useful in 29 cases. The application of AR seems to enhance surgical safety for both the patient and the surgeon. It allows a more refined immediate operative planning, from head positioning to skin incision and craniotomy. Additionally, it helps decision-making in the intraoperative microsurgery phase with a potentially positive impact on surgical outcomes.

An Adaptive Noisy Label-Correction Method Based on Selective Loss for Hyperspectral Image-Classification Problem

Due to the intricate terrain and restricted resources, hyperspectral image (HSI) datasets captured in real-world scenarios typically contain noisy labels, which may seriously affect the classification results. To address this issue, we work on a universal method that rectifies the labels first and then trains the classifier with corrected labels. In this study, we relax the common assumption that all training data are potentially corrupted and instead posit the presence of a small set of reliable data points within the training set. Under this framework, we propose a novel label-correction method named adaptive selective loss propagation algorithm (ASLPA). Firstly, the spectral–spatial information is extracted from the hyperspectral image and used to construct the inter-pixel transition probability matrix. Secondly, we construct the trusted set with the known clean data and estimate the proportion of accurate labels within the untrusted set. Then, we enlarge the trusted set according to the estimated proportion and identify an adaptive number of samples with lower loss values from the untrusted set to supplement the trusted set. Finally, we conduct label propagation based on the enlarged trusted set. This approach takes full advantage of label information from the trusted and untrusted sets, and moreover the exploitation on the untrusted set can adjust adaptively according to the estimated noise level. Experimental results on three widely used HSI datasets show that our proposed ASLPA method performs better than the state-of-the-art label-cleaning methods.

Open-world structured sequence learning via dense target encoding

Structured sequences are popularly used to describe graph data with time-evolving node features and edges. A typical real-world scenario of structured sequences is that unknown class labels continuously arrive and thus the training and testing often across different class spaces. This scenario is also referred to as the open-world learning problem on structured sequences. In this paper, we present a new Dense Open-world Structured Sequence Learning model (DOSSL for short) to learn graph streams in the open-world learning setting. To capture both structural and temporal information, DOSSL uses a GNN-based stochastic recurrent neural network for learning node representation in graph streams, then a truncated Laplacian distribution to describe the latent distribution of graph nodes, and a sampling function is used to generate node representations. Further, DOSSL learns dense target embeddings for the known classes to improve the compactness of known class distribution and reserve enough space for open-world unknown classes. The ultimate open-world classifier is optimized to detect the samples from unknown classes under the constraints of DVAE loss, label loss, class uncertainty loss, and dense target loss. Through empirical analysis conducted on real-world datasets, it has been demonstrated that the advanced technique known as DOSSL exhibits the ability to acquire precise node classifiers by harnessing the power of graph streams.