Augmented Technology Research Articles

An automatic detection method for cervical liquid‐based cells based on improved Yolov5s network

AbstractTo address the significant challenges of high false positive and false negative rates in existing algorithms for detecting cervical fluid‐based cells, an enhanced Yolov5s network is introduced. This paper details a novel approach that dynamically adjusts the weights of channels and the spatial attention in modules, substantially improving feature extraction from small objects and boosting the detection capabilities of the network. Furthermore, Mixup data augmentation technology is incorporated to counter the issue of imbalanced data categories in the custom dataset. The Complete Intersection over Union loss function is also employed to refine coordinate localization accuracy during training. Tested on the proprietary cervical cytology dataset, the modified Yolov5s achieves a mean Average Precision of 92.1%, surpassing the previous state‐of‐the‐art by 5.6%. This enhancement substantiates the efficacy of the proposed model. Code and models are accessible at https://github.com/youyi888/yolov5_CPCA.

Cloud based adaptive learning system: virtual reality and augmented reality assisted educational pedagogy development on clinical simulation

An adaptive learning system provides support for student’s academic progress by varying learning routes and pedagogical contents to satisfy various students’ unique academic needs. The primary goal of this research work is to enhance and expand occupational efficiency through the use of technological pedagogical content knowledge (TPCK) frameworks in nursing education. The authors developed an adaptive federated cloud computing learning system in order to better prepare students for the demands of real-world professional practice. The article examined the use of virtual reality(VR) and augmented (AR) technologies in nursing education and developed an instrument that skillfully combines knowledge from the reputable literature on clinical competencies with the peculiarities and ingenuousness particular to virtual simulations. Designated for the remote sharing and utilization of combined educational resources, cloud computing is a distributed information communication technology (ICT) infrastructure built on the Internet of Things (IoT) framework. To increase the occupational efficiency among the nurses and midwives, an educational simulation applied in a way that gamifies the curriculum component for interactive educational pedagogy will be necessary. As a completely integrated part of the curriculum, simulation has proven to be beneficial for nursing students at all levels of instruction. Nursing educators have a plethora of options at their disposal to introduce simulation-based learning approaches into their curricula in the modern era. A three-dimensional (3D) interactive environment that provides users with a sense of spatial presence is created through computer technology, which is an immersive digital ecosystem of VR and AR utilized to improve teaching and learning in nursing education. In this study, the authors offered a cost- effective clinical simulation laboratory for clinical students, nurses, midwives, and practicing healthcare professionals in a manner that ensures constant learning and occupational efficiency by adopting the TPCK model in its central discussion and implementation. The TPCK model has demonstrated how to recognize the boundaries between technological components and pedagogical structures as well as how they are connected when creating facts in nursing education.

Transhuman Communication: Human Augmentation Technologies through Co-Speculation Workshops

The advancement of human-computer integration as a research field promises to introduce transhumanistic ways of communicating through the enhanced abilities of augmented humans. Our work seeks to illuminate this experiential landscape, exploring a diverse set of human augmentation technologies (HATs) for communication purposes. We investigated this topic through four co-speculation workshops focusing on physical, cognitive, sensory and emotional augmentations with 35 participants. Through a reflexive thematic analysis of the workshop data, we outlined eight HAT speculations for transhuman communication, grouped into four overarching augmentation clusters: (1) Bodily Changes in/for Communication, (2) Communication with Transferrable and Collective Beings, (3) Communication through Emotion and Memory, and (4) Communication with Augmented/Altered Perception. By serving as a foundation for discussions on transhumanism and communication in CSCW and beyond, these speculations contribute to a design space highlighting design opportunities and challenges to developing and researching near-future communication technologies.

Human-robot collaborative decision method of hexapod robot based on prior knowledge and negotiation strategy

Against the unstructured terrain environment, human drivers often rely on remote operating patterns to accomplish tasks with robots. With the gradual improvement in robot intelligence, a key problem that urgently requires attention is how to bridge cognitive and perceptual differences between human and robot in collaborative decision-making processes. Particularly effective resolution of differences in decision outcomes is crucial. Therefore, this paper utilizes the decision logic process and prior knowledge model of human drivers to form a robot's decision framework, narrowing the cognitive differences between human and robot. Besides, embedding the human-robot-environment features that affect the decision-making process as prior knowledge into the robot's decision-making system can effectively reduce perceptual gaps. Under the same decision framework, a negotiation strategy is proposed for forming a unified decision outcome between human and robot by finding an optimal concession rate. This study utilizes a hexapod robot simulated driving platform to gather experimental data and develop prior knowledge for robots. It also leverages virtual reality equipment and visual augmentation technologies to establish a remote human-robot collaborative decision-making experimental system. The experimental results conducted in complex obstacle terrain demonstrate the effectiveness of adopting the negotiation strategy for human-robot collaborative decision-making. Compared with the individual human or robot decision, the human-robot collaborative decision method significantly enhances robot stability, reduces energy consumption, and minimizes collisions.

Internet of Things and the Evolution of Human 2.0: An Analytical Study on Enhancing Human Capabilities

The Internet of Things (IoT) technology has introduced new tools for enhancing human capabilities, promising improved quality of life and professional efficiency. The concept of "Augmented Human" (AH) refers to using technology to enhance and improve natural human abilities, whether physical or cognitive, through devices and technologies that boost sensory perception, physical strength, and cognitive performance. However, the integration of IoT with human augmentation technology has expanded the scope of IoT-related challenges beyond current boundaries. So far, these potential challenges associated with IoT-enabled human augmentation have not investigated. Therefore, this article aims to introduce readers to the concept of human augmentation (AH) and to summarize the key research challenges posed by such systems, thereby encouraging readers' interest in this topic. The article explores emerging IoT applications in human augmentation, along with devices and design principles, connectivity requirements, and security aspects. Keywords: Internet of Things, human capabilities, flexible hybrid electronics.

Detection Method of Stator Coating Quality of Flat Wire Motor Based on Improved YOLOv8s.

The stator of a flat wire motor is the core component of new energy vehicles. However, detecting quality defects in the coating process in real-time is a challenge. Moreover, the number of defects is large, and the pixels of a single defect are very few, which make it difficult to distinguish the defect features and make accurate detection more difficult. To solve this problem, this article proposes the YOLOv8s-DFJA network. The network is based on YOLOv8s, which uses DSFI-HEAD to replace the original detection head, realizing task alignment. It enhances joint features between the classification task and localization task and improves the ability of network detection. The LEFG module replaces the C2f module in the backbone of the YOLOv8s network that reduces the redundant parameters brought by the traditional BottleNeck structure. It also enhances the feature extraction and gradient flow ability to achieve the lightweight of the network. For this research, we produced our own dataset of stator coating quality regarding flat wire motors. Data augmentation technology (Gaussian noise, adjusting brightness, etc.) enriches the dataset, to a certain extent, which improves the robustness and generalization ability of YOLOv8s-DFJA. The experimental results show that in the performance of YOLOv8s-DFJA compared with YOLOv8s, the mAP@.5 index increased by 6.4%, the precision index increased by 1.1%, the recall index increased by 8.1%, the FPS index increased by 9.8FPS/s, and the parameters decreased by 3 Mb. Therefore, YOLOv8s-DFJA can be better realize the fast and accurate detection of the stator coating quality of flat wire motors.

The Human-Technology Continuum.

Managers in health care today face an array of digital technologies that assist or augment certain human tasks. But these technologies are often fraught and present challenges to managers, whose competencies must evolve to keep pace with technological advancements. Drawing on theory about technology, work, and organizations, we present a human-technology continuum to facilitate this discussion for managers. Furthermore, we illustrate how managerial competencies are linked to the entire human-technology continuum, rather than to specific technologies, using diabetes management examples. The human-technology continuum indicates that augmentative technologies are layered onto assistive ones in health care settings. This suggests that technological advancements not only enhance but alter managerial competencies. Digital technology stretches the boundaries of managers' day-to-day work in health care. Therefore, we make the following suggestions so the managers can be responsive to ongoing digital transformations: restructuring work, training the workforce, neutralizing threats, establishing ethical boundaries, and building partnerships.

DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.

Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes. We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis. The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model. The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.

Negotiating Networked Learning Relationships with Augmentation Technologies

The hosts of this round table discussion, members of the Building Digital Literacy (BDL) research cluster of the Digital Life Institute (www.digitallife.org), adopt a critical disposition (NLEC, 2021a, 2021b) toward emerging augmentation technologies that sit at the core of networked learning. Augmentation technologies, such as wearable devices that extend human senses, augment creative abilities, or overcome physical limitations (Pederson & Hill, 2021), represent the engine that drives the next generation of networked learning. As emerging augmentation technologies, use of data analytics, and “smart” technologies proliferate, we see the critical need for research, presentation, and discussion of the implications for networked learning. This round table invites conversation about the role of artificial intelligence, big data, and learning analytics in networked learning.

The AR Cloud: Navigating Metaverse Augmentation Technologies for Enhanced Co-Creation of Value Within Services

In the rapidly evolving landscape of metaverse technologies, the potential for enhancing service interactions is immense. However, many of these technologies fall short in offering context-driven and customizable experiences. This paper proposes that AR Cloud (ARC), a novel external metaverse technology can bridge this gap. ARC stands out by enabling real-time interactions, fostering contextual awareness, ensuring high content flexibility, promoting social engagement, and delivering personalized experiences. We present a conceptual model that juxtaposes ARC with established metaverse augmentation technologies, such as lifelogging and augmented reality. Our discussion focuses on the transformative impact of ARC on the value co-creation process, emphasizing the enhanced well-being outcomes—both hedonic and eudemonic—that arise from ARC-powered service interactions. We conclude by charting a course for future research on ARC, touching upon its attributes, potential moderating variables, its role in sustained value co-creation, and the potential service outcomes it could support.

Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive Learning

Graph contrastive learning has demonstrated significant superiority for collaborative filtering. These methods typically use augmentation technology to generate contrastive views, and then train graph neural networks with contrastive learning as an auxiliary task. Although these methods are very effective, they do not consider using contrastive learning from the perspective of user–item interaction. As a result, they do not fully leverage the potential of contrastive learning. It is well known that contrastive learning can maximize the consistency of positive pairs and minimize the agreement of negative pairs. Collaborative filtering expects high consistency between users and the items they like and low consistency between users and the items they dislike. If we treat the items that users like as positive examples and the items they dislike as negative examples, contrastive learning can work very well with the goal of collaborative filtering. Based on the above understanding, we propose a new objective function called DCL loss, which improves graph collaborative filtering from the perspective of user–item interaction when Directly using Contrastive Learning. Extensive experiments have shown that when a model adopts DCL loss as its objective function, both its recommendation performance and training efficiency exhibit significant improvements.

Exploring the Potentials of Wearable Technologies in Managing Vestibular Hypofunction.

The vestibular system is dedicated to gaze stabilization, postural balance, and spatial orientation; this makes vestibular function crucial for our ability to interact effectively with our environment. Vestibular hypofunction (VH) progresses over time, and it presents differently in its early and advanced stages. In the initial stages of VH, the effects of VH are mitigated using vestibular rehabilitation therapy (VRT), which can be facilitated with the aid of technology. At more advanced stages of VH, novel techniques that use wearable technologies for sensory augmentation and sensory substitution have been applied to manage VH. Despite this, the potential of assistive technologies for VH management remains underexplored over the past decades. Hence, in this review article, we present the state-of-the-art technologies for facilitating early-stage VRT and for managing advanced-stage VH. Also, challenges and strategies on how these technologies can be improved to enable long-term ambulatory and home use are presented.

Impermanent identifiers: Enhanced source code comprehension and refactoring

In response to the prevailing challenges in contemporary software development, this article introduces an innovative approach to code augmentation centered around Impermanent Identifiers. The primary goal is to enhance the software development experience by introducing dynamic identifiers that adapt to changing contexts, facilitating more efficient interactions between developers and source code, ultimately advancing comprehension, maintenance, and collaboration in software development. Additionally, this study rigorously evaluates the adoption and acceptance of Impermanent Identifiers within the software development landscape. Through a comprehensive empirical examination, we investigate how developers perceive and integrate this approach into their daily programming practices, exploring perceived benefits, potential barriers, and factors influencing its adoption. In summary, this article charts a new course for code augmentation, proposing Impermanent Identifiers as its cornerstone while assessing their feasibility and acceptance among developers. This interdisciplinary research seeks to contribute to the continuous improvement of software development practices and the progress of code augmentation technology.

Detection research of insulating gloves wearing status based on improved YOLOv8s algorithm

The safety hazards may be caused by power grid operators not wearing insulating gloves according to regulations for live electrical working. Additionally, existing methods for detecting the wearing status of insulating gloves suffer from low recognition accuracy, slow detection speed, and large memory occupation by weight files. To address these issues, a Mixup-CA-Small-YOLOv8s (MCS-YOLOv8s) algorithm is proposed for detecting the wearing status of insulating gloves. Firstly, the mixup data augmentation technology using image mixing is introduced, increasing the data’s diversity and improving the model’s generalization ability. Secondly, the coordinate attention (CA) module is added to the original backbone network to strengthen the channel and positional information, suppressing the secondary feature information. Finally, a small target detection structure is designed by removing the last bottom feature detection layer in the original neck network and adding a shallow feature. The ability of small targets’ feature extraction is enhanced without increasing too much computation. The experimental results indicate that the mean average precision of the MCS-YOLOv8s algorithm on the test set is 0.912, the detection speed is 87 FPS, and the model’s weight memory occupies 15.7 MB. It is verified that the model has the advantages of high detection accuracy, fast speed, and small weight memory, which has great significance in ensuring the safe and stable operation of the power grid.

Virtual House using Augmented Reality and Cost Estimation

Augmented and virtual technologies both play vital roles in the construction industry. Augmented technologies, however, have a higher benefit when compared to virtual. The two technology types are discussed; with their similarities and differences explained. The past, present, and future is described. Some benefits to using augmented technology are discussed. The drawbacks are mentioned, with the way to correct them detailed.

Fault Diagnosis Method for Railway Signal Equipment Based on Data Enhancement and an Improved Attention Mechanism

Railway signals’ fault text data contain a substantial amount of expert maintenance experience. Extracting valuable information from these fault text data can enhance the efficiency of fault diagnosis for signal equipment, thereby contributing to the advancement of intelligent railway operations and maintenance technology. Considering that the characteristics of different signal equipment in actual operation can easily lead to a lack of fault data, a fault diagnosis method for railway signal equipment based on data augmentation and an improved attention mechanism (DEIAM) is proposed in this paper. Firstly, the original fault dataset is preprocessed based on data augmentation technology and retained noun and verb operations. Then, the neural network is constructed by integrating a bidirectional long short-term memory (BiLSTM) model with an attention mechanism and a convolutional neural network (CNN) model enhanced with a channel attention mechanism. The DEIAM method can more effectively capture the important text features and sequence features in fault text data, thereby facilitating the diagnosis and classification of such data. Consequently, it enhances onsite fault maintenance experience by providing more precise insights. An empirical study was conducted on a 10-year fault dataset of signal equipment produced by a railway bureau. The experimental results demonstrate that in comparison with the benchmark model, the DEIAM model exhibits enhanced performance in terms of accuracy, precision, recall, and F1.

Chemical space deconstruction-based dynamic model ensemble architecture for molecular property prediction

Green solvents, catalysts, functional materials, drugs, and other chemical products now have a quick engine for design thanks to machine learning (ML) based prediction of molecular properties. However, the accuracy and stability of ML-based models can be impeded by poor data quality, which is rarely studied in chemical product discovery and design. Inspired by the dynamic ensemble selection (DES), an improved DES based on chemical space deconstruction is proposed in this work to accommodate the prediction task of molecular properties. We innovatively developed a chemical space representation and deconstruction model based on a self-organizing mapping (SOM) neural network, facilitating the rapid implementation of the improved DES on molecular samples. Consequently, a novel dynamic model ensemble architecture (SOM-DES) is proposed as a model enhancement technology to build a more accurate and stable ensemble model, aiming to improve the predictive performance on the chemical subspace within poor-quality data. To achieve the architecture, a supervised dimensionality reduction algorithm has been improved to enhance the deep mining of molecular feature information for DES optimization. Additionally, a novel resampling strategy based on the combination of the geometric synthetic minority oversampling technique (G-SMOTE) algorithm and chemical space deconstruction, as a data augmentation technology, has been proposed for mitigating the disadvantage of unbalanced data during DES training. The prediction task for enthalpy of formation of the ideal gas is applied as a case study to demonstrate the superiority of the proposed SOM-DES. The results indicate that the proposed SOM-DES (R2 = 0.9731, RMSE = 55.4639) outperforms the traditional static ensemble strategy (SES, R2 = 0.9552, RMSE = 71.5045) in terms of global chemical spatial precision. More importantly, for chemical subspaces that are difficult to predict due to low data quality, SOM-DES shows a significant reduction in prediction errors compared to SES.

Exploring the potential of data augmentation in poetry generation with small-scale corpora

Poetry generation is a complex task in the field of natural language processing, especially when working with small datasets. Data augmentation techniques have been shown to be an effective way to improve the performance of deep learning models in various tasks, including image classification and speech recognition. Therefore, this study focuses on exploring the impact of four different data augmentation methods - Synonym Replacement, Random Insertion, Random Swap, and Random Deletion - on the performance of poetry generation with a small poetry dataset. The results of the study reveal that Random Insertion performed well in terms of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), and manual evaluation when compared to other data augmentation techniques. Synonym Replacement performed poorly in all three evaluations. This study confirms the potential value of data augmentation technology in poetry generation tasks and provides innovative perspectives and directions for future research in this area. Data augmentation can be employed to help address the problem of limited data in poetry generation tasks and enhance the efficiency of deep learning models. Future research could focus on exploring more advanced data augmentation techniques and their impact on poetry generation tasks.

MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning.

Accurately predicting molecular metabolic stability is of great significance to drug research and development, ensuring drug safety and effectiveness. Existing deep learning methods, especially graph neural networks, can reveal the molecular structure of drugs and thus efficiently predict the metabolic stability of molecules. However, most of these methods focus on the message passing between adjacent atoms in the molecular graph, ignoring the relationship between bonds. This makes it difficult for these methods to estimate accurate molecular representations, thereby being limited in molecular metabolic stability prediction tasks. We propose the MS-BACL model based on bond graph augmentation technology and contrastive learning strategy, which can efficiently and reliably predict the metabolic stability of molecules. To our knowledge, this is the first time that bond-to-bond relationships in molecular graph structures have been considered in the task of metabolic stability prediction. We build a bond graph based on 'atom-bond-atom', and the model can simultaneously capture the information of atoms and bonds during the message propagation process. This enhances the model's ability to reveal the internal structure of the molecule, thereby improving the structural representation of the molecule. Furthermore, we perform contrastive learning training based on the molecular graph and its bond graph to learn the final molecular representation. Multiple sets of experimental results on public datasets show that the proposed MS-BACL model outperforms the state-of-the-art model. The code and data are publicly available at https://github.com/taowang11/MS.

Self-healing control of abnormal conditions for fused magnesium furnace based on data augmentation and improved JITL

Abnormal conditions that arise during the fused magnesia smelting process (FMSP) can negatively impact product quality and increase energy consumption. However, due to the process's nonlinear and time-varying nature and the limited availability of abnormal condition data, researching effective self-healing control measures has been severely constrained. Therefore, this paper proposes a fused magnesium furnace (FMF) abnormal condition self-healing control method based on data augmentation and improved just-in-time learning (JITL). Firstly, data augmentation technology is used to generate virtual abnormal condition samples to expand the historical dataset. Then, an improved JITL model is constructed to solve the self-healing measures corresponding to abnormal conditions. Experimental results demonstrate that the proposed method can effectively restore the FMF to normal conditions and outperforms existing methods.