Augmented Technology Research Articles

Running Guidance for Visually Impaired People Using Sensory Augmentation Technology Based Robotic System

Running Guidance for Visually Impaired People Using Sensory Augmentation Technology Based Robotic System

A Chinese Grammatical Error Correction Model Based On Grammatical Generalization And Parameter Sharing

Abstract Chinese grammatical error correction (CGEC) is a significant challenge in Chinese natural language processing. Deep-learning-based models tend to have tens of millions or even hundreds of millions of parameters since they model the target task as a sequence-to-sequence problem. This may require a vast quantity of annotated corpora for training and parameter tuning. However, there are currently few open-source annotated corpora for the CGEC task; the existing researches mainly concentrate on using data augmentation technology to alleviate the data-hungry problem. In this paper, rather than expanding training data, we propose a competitive CGEC model from a new insight for reducing model parameters. The model contains three main components: a sequence learning module, a grammatical generalization module and a parameter sharing module. Experimental results on two Chinese benchmarks demonstrate that the proposed model could achieve competitive performance over several baselines. Even if the parameter number of our model is reduced by 1/3, it could reach a comparable $F_{0.5}$ value of 30.75%. Furthermore, we utilize English datasets to evaluate the generalization and scalability of the proposed model. This could provide a new feasible research direction for CGEC research.

ScAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.

Exploring the integration of art and technology

The integration of art and technology has emerged as a formidable force in the creative realm, presenting a wide array of opportunities and challenges. With the advent of the internet, collaborative artistic practices have flourished, while financial burdens on artists have been alleviated through democratized production software, reducing their reliance on corporate entities. Technology has empowered artists with increased creative freedom and direct engagement with their fans through social media platforms. Nevertheless, concerns have been raised regarding the authenticity of creative practices, sparking debates about the appropriate extent of technological intervention. The mainstream acceptance of artificial intelligence (AI) and augmentation technologies has further transformed the creative landscape. AI tools now cater to both professionals and amateurs, enabling diverse creative endeavours, ranging from the generation of visual art to assisting in music composition. This Special Issue delves into the profound changes that technology has brought to artistic and creative expression. The articles explore topics such as AI-generated communication, the role of AI in advertising campaigns, AI tools in music composition, technological innovations in art, light augmentation in urban spaces, augmented reality (AR) in literature and the integration of AR in design magazines. The overarching aim is to stimulate critical discussions regarding the future of art, creativity and the implications of technological advancements.

UPLC-QDA and machine learning for distinguishing different commodity specifications of Fritillariae Cirrhosae Bulbus and application of data augmentation technology

This study aimed to establish a method based on machine learning technology for accurately predicting the commodity specifications of Fritillariae Cirrhosae Bulbus and explore the application of data augmentation technology in the field of drug analysis. The correlation optimized warping(COW) algorithm was used to perform peak calibration on the UPLC-QDA multi-channel superimposed data of 30 batches of samples, and the data were normalized. Through unsupervised learning methods such as clustering analysis, principal component analysis(PCA), and correlation analysis, the general characteristics of the data were understood. Then, the logistic regression algorithm was used for supervised learning on the data, and the condition tabular generative adversarial networks(CTGAN) was used to generate a large amount of data. Logistic regression classification models were trained separately using the real data and the data generated by CTGAN, and these models were evaluated. The logistic regression model trained with real data achieved cross-validation and test set accuracies of 0.95 and 1.00, respectively, while the logistic regression model trained with both real and CTGAN-generated data achieved cross-validation and test set accuracies of 0.99 and 1.00, respectively. The results indicate that machine learning can accurately predict the classification of Songbei, Qingbei, and Lubeibased on UPLC-QDA detection data. CTGAN-generated data can partially compensate for the lack of data in drug analysis, improving the accuracy and predictive ability of machine learning models.

Understanding future human cybernetic integration: A framework to map enhancement technologies

Technology advancements have enabled humans to go beyond restauration leading to a new reality where human enhancement is no longer fiction. Besides the major societal impacts they will have, these evolutions of technology are also raising complex philosophical and ethical questions. Notwithstanding the widespread interest for augmentation technologies, there are few frameworks available to categorise and compare the varied variety of existing and prospective technologies in this field. A structured framework is needed to harness the full potential of sensory technologies while facilitating the discussion and management of potential risks and challenges. This article presents a framework that aims to bridge the gap between technologies designed for compensating disabilities, enhancing human perception beyond the abilities of individuals without disabilities, and those intended for entertainment purposes. Furthermore, in the past, research on sensory perception tended to isolate sensory modalities based on the stimuli to which they are most sensitive. This new proposed framework allows for the analysis of technologies on a continuum ranging from unimodality to transmodality and to take in account the degree of integration with biological system. In addition, this framework answers the need to classify these technologies based on the crucial aspect of social acceptability, specifically by taking into consideration their varying degrees of invasiveness. All of these elements included into the suggested framework will enable researchers to map and analyse not just existing technologies, but also those that have not even been invented yet.

Study on High-resolution Remote Sensing Image Scene Classification Using Transfer Learning

Remote sensing image classification (RSIC) has been increasingly concerned and becoming a challenging task. Recently, deep convolutional neural networks (DCNN) offer the effective classification method include the capacity to handle high-dimensional data and to distinguish classes with very complex characteristics on the remote sensing community. However, the focus of these methods is on publicly available data sets in the field of remote sensing, there are few studies on RSIC composed of different benchmark datasets, which the complexity, diversity, and similarity of data greatly increase the difficulty of classification. In this paper, we reconstructed and selected one new dataset from two standard benchmark remote sensing datasets: UC Merged Land-Use and NWPU-RESISC45. We utilize three transfer learning frameworks to extract the high-level feature map and feed feature information into the proposed model for partial and full fine-tuning. Data augmentation technology is used to increase the number of training samples and dropout strategies to prevent overfitting. The experimental results demonstrate that the proposed methodology achieved remarkable performance in scene classification of overall accuracy: 90.1%,91.0%,93.3 with VggNet, DesNet, InceptionNet, respectively.

Deep Regularized Waveform Learning for Beam Prediction With Limited Samples in Non-Cooperative mmWave Systems

Millimeter wave (mmWave) systems need beam management to establish and maintain reliable links. This complex and time-consuming process seriously affects communication efficiency. Benefiting from data-driven technology in deep learning, the beam can be predicted from the waveform without coordination between transceivers. By passively listening enough waveforms that are transmitted from the base station (BS) to other receivers, the BS can predict which beam is suitable for transmitting in the downlink. However, training such a waveform learning neural network usually requires a large number of labeled training samples. This is a huge challenge, because it is difficult for the receiver to get the precise signal parameters from the transmitter in advance in the non-cooperative mmWave system. As a result, the limited samples may cause overfitting and seriously restrict the performance. Although the data augmentation technology can improve the performance under limited samples, existing data augmentation methods are mostly based on strong prior knowledge which cannot further exploit the potential characteristics of data in the real environment. This paper proposes a mixed regularization training method for training the beam prediction neural network under limited training samples. Specifically, data augmentation is implemented in the data pre-processing procedure with prior knowledge and then the signal splicing strategy is proposed in the training procedure. In order to mine the time correlation characteristics of signals, the cyclic time shift (CTS) based data augmentation method is proposed in the data augmentation step. The simulation results show that our proposed deep regularized waveform learning method needs less training samples under the same performance. Moreover, the proposed method can achieve best performance compared with existing data augmentation methods.

Simulation and Analysis of Image Augmentation Technology

Simulation and Analysis of Image Augmentation Technology

Accelerated dynamic MR imaging with joint balanced low-rank tensor and sparsity constraints.

Dynamic magnetic resonance imaging (DMRI) is an essential medical imaging technique, but the slow data acquisition process limits its furtherdevelopment. By exploiting the inherent spatio-temporal correlation of MR images, low-rank tensor based methods have been developed to accelerate imaging. However, the tensor rank used by these methods is defined by an unbalanced matricization scheme, which cannot capture the global correlation of DMR data efficiently during the reconstructionprocess. In this paper, an effective reconstruction model is proposed to achieve accurate reconstruction by using the tensor train (TT) rank defined by a well-balanced matricization scheme to exploit the hidden correlation of DMR data and combining sparsity. Meanwhile, the ket augmentation (KA) technology is introduced to preprocess the DMR data into a higher-order tensor through block structure addressing, which further improves the ability of TT rank to explore the local information of the image. In order to solve the proposed model, the alternating direction method of multipliers (ADMM) is used to decompose the optimization problem into several unconstrainedsubproblems. The performance of the proposed method was validated on the 3D DMR image dataset by using different sampling trajectories and sampling rates. Extensive numerical experiments demonstrate that the reconstruction quality of the proposed method is superior to several state-of-the-art reconstructionmethods. The proposed method successfully utilizes the TT rank to explore the global correlation of DMR images, enabling more detailed information of the image to be captured. Besides, with the sparse priors, the proposed method can further improve the overall reconstruction quality for highly undersampled MRimages.

Utility and Usability of Two Forms of Supplemental Vibrotactile Kinesthetic Feedback for Enhancing Movement Accuracy and Efficiency in Goal-Directed Reaching.

Recent advances in wearable sensors and computing have made possible the development of novel sensory augmentation technologies that promise to enhance human motor performance and quality of life in a wide range of applications. We compared the objective utility and subjective user experience for two biologically inspired ways to encode movement-related information into supplemental feedback for the real-time control of goal-directed reaching in healthy, neurologically intact adults. One encoding scheme mimicked visual feedback encoding by converting real-time hand position in a Cartesian frame of reference into supplemental kinesthetic feedback provided by a vibrotactile display attached to the non-moving arm and hand. The other approach mimicked proprioceptive encoding by providing real-time arm joint angle information via the vibrotactile display. We found that both encoding schemes had objective utility in that after a brief training period, both forms of supplemental feedback promoted improved reach accuracy in the absence of concurrent visual feedback over performance levels achieved using proprioception alone. Cartesian encoding promoted greater reductions in target capture errors in the absence of visual feedback (Cartesian: 59% improvement; Joint Angle: 21% improvement). Accuracy gains promoted by both encoding schemes came at a cost in terms of temporal efficiency; target capture times were considerably longer (1.5 s longer) when reaching with supplemental kinesthetic feedback than without. Furthermore, neither encoding scheme yielded movements that were particularly smooth, although movements made with joint angle encoding were smoother than movements with Cartesian encoding. Participant responses on user experience surveys indicate that both encoding schemes were motivating and that both yielded passable user satisfaction scores. However, only Cartesian endpoint encoding was found to have passable usability; participants felt more competent using Cartesian encoding than joint angle encoding. These results are expected to inform future efforts to develop wearable technology to enhance the accuracy and efficiency of goal-directed actions using continuous supplemental kinesthetic feedback.

Development of Instructional Media of Thai Classical Masked Dance (Khon) Conform to Chant by Using Augmented Technology (AR) for Grade 6^th Students

The purposes of this research were (1) to develop Instructional Media of Thai Classical Masked Dance (Khon) Conform to Chant by Using Augmented Technology (AR) for Grade 6^th Students to 80/80 criterion efficiency (2) to compare learning achievement between pre-test and post-test after using Instructional Media of Thai Classical Masked Dance (Khon) Conform to Chant by Using Augmented Technology (AR) for Grade 6^th Students, and (3) to study the students satisfaction toward Instructional Media of Thai Classical Masked Dance (Khon) Conform to Chant by Using Augmented Technology (AR) for Grade 6^th Students. The samples consist of 40 Grade 6^th Students enrolled in dramatic subject in the second semester of 2020 academic year of Kasetsart University Laboratory School, Center for Educational Research and Development, by using purposive sampling technique. The instruments used in this research included Instructional Media, tests, and students’ satisfaction assessment. Using percentage, mean, standard deviation, and t-test for dependent samples for data analysis. The results of the research revealed that (1) the efficiency of Instructional Media of Thai Classical Masked Dance (Khon) Conform to Chant by Using Augmented Technology (AR) for Grade 6^th Students was 82.31/82.88 (2) learning achievement of post-test (¯x = 33.15 S.D. = 1.25) was higher than pre-test (¯x = 26.03 S.D. = 3.08) at .05 significantly, and (3) the students’ satisfaction toward Instructional Media was at highest level. (¯x = 4.53 S.D. = 0.60)

Variable Augmented Network for Invertible Modality Synthesis and Fusion.

As an effective way to integrate the information contained in multiple medical images under different modalities, medical image synthesis and fusion have emerged in various clinical applications such as disease diagnosis and treatment planning. In this paper, an invertible and variable augmented network (iVAN) is proposed for medical image synthesis and fusion. In iVAN, the channel number of the network input and output is the same through variable augmentation technology, and data relevance is enhanced, which is conducive to the generation of characterization information. Meanwhile, the invertible network is used to achieve the bidirectional inference processes. Empowered by the invertible and variable augmentation schemes, iVAN not only be applied to the mappings of multi-input to one-output and multi-input to multi-output, but also to the case of one-input to multi-output. Experimental results demonstrated superior performance and potential task flexibility of the proposed method, compared with existing synthesis and fusion methods.

Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

The curse of dimensionality refers to the problem of increased sparsity and computational complexity when dealing with high-dimensional data. In recent years, the types and variables of industrial data have increased significantly, making data-driven models more challenging to develop. To address this problem, data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensional industrial data. This paper systematically explores and discusses the necessity, feasibility, and effectiveness of augmented industrial data-driven modeling in the context of the curse of dimensionality and virtual big data. Then, the process of data augmentation modeling is analyzed, and the concept of data boosting augmentation is proposed. The data boosting augmentation involves designing the reliability weight and actual-virtual weight functions, and developing a double weighted partial least squares model to optimize the three stages of data generation, data fusion, and modeling. This approach significantly improves the inter-pretability, effectiveness, and practicality of data augmentation in the industrial modeling. Finally, the proposed method is verified using practical examples of fault diagnosis systems and virtual measurement systems in the industry. The results demonstrate the effectiveness of the proposed approach in improving the accuracy and robustness of data-driven models, making them more suitable for real-world industrial applications.

Relevance of hazards in exoskeleton applications: a survey-based enquiry

Exoskeletons are becoming the reference technology for assistance and augmentation of human motor functions in a wide range of application domains. Unfortunately, the exponential growth of this sector has not been accompanied by a rigorous risk assessment (RA) process, which is necessary to identify the major aspects concerning the safety and impact of this new technology on humans. This situation may seriously hamper the market uptake of new products. This paper presents the results of a survey that was circulated to understand how hazards are considered by exoskeleton users, from research and industry perspectives. Our analysis aimed to identify the perceived occurrence and the impact of a sample of generic hazards, as well as to collect suggestions and general opinions from the respondents that can serve as a reference for more targeted RA. Our results identified a list of relevant hazards for exoskeletons. Among them, misalignments and unintended device motion were perceived as key aspects for exoskeletons’ safety. This survey aims to represent a first attempt in recording overall feedback from the community and contribute to future RAs and the identification of better mitigation strategies in the field.

Pairwise Cross-Document Event Coreference Resolution with Contrastive Learning

Cross-Document (CD) Event Coreference Resolution (ECR) is a fundamental task in Natural Language processing (NLP) and Knowledge Base Population (KBP). Resolving the event coreference relationship is a challenging task, which necessitates a thorough semantic comprehension of the events. Existing methods have spontaneously formulated this problem as a binary classification task based on sentence segments. However, the information distributed in the longer context is ignored by most prior works. Recent event coreference works focus on adding auxiliary information to improve the performance. The information such as involved entity information, structural information or database queried information is extracted from related documents as long context. Whereas extraction error, matching error and other types of error are introduced in the process of obtaining related information when the context is incomplete. In this work, we present a Contrastive Learning based Pairwise Event Coreference (CLPEC) framework to complete and optimize the CD-ECR task utilizing contextual information with contrastive learning technology. We also adopt augmentation technology to preprocess event representations and improve our performance. Experiment results show that we achieve competitive results on a number of key metrics on the ECB+ corpus.

The placebo effect of human augmentation: Anticipating cognitive augmentation increases risk-taking behavior

Human Augmentation Technologies improve human capabilities using technology. In this study, we investigate the placebo effect of Augmentation Technologies. Thirty naïve participants were told to be augmented with a cognitive augmentation technology or no augmentation system while conducting a Columbia Card Task. In this risk-taking measure, participants flip win and loss cards. The sham augmentation system consisted of a brain–computer interface allegedly coordinated to play non-audible sounds that increase cognitive functions. However, no sounds were played throughout all conditions. We show a placebo effect in human augmentation, where a sustained belief of improvement remains after using the sham system and an increase in risk-taking conditional on heightened expectancy using Bayesian statistical modeling. Furthermore, we identify differences in event-related potentials in the electroencephalogram that occur during the sham condition when flipping loss cards. Finally, we integrate our findings into theories of human augmentation and discuss implications for the future assessment of augmentation technologies.

Where is Community Involvement in Open Science? A Commentary on "(Why) Are Open Research Practices the Future for the Study of Language Learning?"

Where is Community Involvement in Open Science? A Commentary on "(Why) Are Open Research Practices the Future for the Study of Language Learning?"

Adults with learning disabilities’ experiences of disclosing abuse, who access community learning disability teams, in Southeast England, UK

BackgroundThere is a high prevalence of abuse experienced by the learning disability (LD) population, with a clear impact of abuse on individuals. Several theoretical frameworks support the importance of disclosure in the recovery process, with regards to shifting self-blame and guilt and creating new narratives for these experiences. The key facilitators and barriers to disclosure and responses received have been studied for individuals without LDs, however there is a paucity of research in this topic area for the LD population.Rapid reviewA rapid review of the experiences health and social care professionals working with adults with LDs who have been abused highlighted variability in the care provided to this cohort. This was due to differences in the implementation of policy and procedures, the influence of relationships between professionals, service users and their families, perceptions of LDs, and clinicians’ own knowledge and thresholds, and confidence in their clinical expertise.Aim and methodThe aim of the empirical study was therefore to explore adults with LDs ‘ experiences of disclosing abuse. Semi structured interviews were conducted remotely, due to Covid-19, with five participants with LDs who had experienced historical abuse, and were open to Community Learning Disability Teams (CLDTs) in Southeast England, UK. A reflexive thematic analysis was used to analyse the data.Results and conclusionsFour themes were generated ‘Abuse is discovered not disclosed’, ‘Exposure of the abuse triggers a set response’, ‘Carrying the burden of what happened’ and ‘Finding a way out’, under the overarching theme of ‘The Journey of Disclosure’. The themes suggested disclosure occurred on a non-liner timeline, not at one timepoint, with abuse not being disclosed but identified by others, a lack of power and control over the process for participants and the importance of accessing the right support. Facilitators and barriers were identified, some unique to those with LDs, such as the influence of clinicians’ knowledge, or lack of, not only about abuse but also LD on the identification of abuse e.g. the role of diagnostic overshadowing, and the potential additional complexity of relationships for adults with LDs. Other facilitators and barriers were in line with previous research for those without LDs, such as trust, being listened to and believed. Several areas for future research are highlighted, such as the experiences of adults with LDs who may have different communication abilities e.g. use of augmented technology, adults with LDs from Black and Asian racialised communities who experiences of abuse and disclosure may be different, and further exploration of the experiences of disclosing specific types of abuse. The clinical and policy implications are discussed, such as reinforcing the need for professionals to receive training in the area of supporting individuals with LDs to disclose abuse and responding appropriately.

Biphasic Interpositional Allograft for Rotator Cuff Repair Augmentation Is Safe in an Ovine Model

This study is a preclinical histological assessment of a biphasic acellular interpositional cancellous allograft in an ovine model of rotator cuff repair (RCR) designed to better understand its safety profile and effects on tendon healing after rotator cuff repair. Thirty skeletally mature sheep with clinically normal shoulders with an artificially created degenerative infraspinatus tendon (IST) tear were randomized to control and treatment groups. Animals were euthanized at 3-weeks, 6-weeks, and 12-weeks. After gross dissection, rotator cuff specimens were fixed with formalin and polymerized for sectioning and staining. Blinded histological scores evaluated inflammatory cell infiltrates, signs of degradation, particulate debris, collagen arrangement, neo-vascularization, and enthesis qualitative measures. There were no treatment specimens that exhibited histological signs of a significant infection, inflammatory infiltrate, or foreign body reaction such as granuloma or fibrous capsule formation. Histological scores in all categories were not significantly different at all time points, including the primary endpoint mean cumulative inflammatory score (control: 3.66 ± 1.21 versus treated: 4.33 ± 1.51, p = 0.42), when comparing the treatment and control RCR groups. In general, the degree of tendon healing and host tissue response was essentially equivalent between the two groups with observation of low overall levels of inflammation and progressive improvements in collagen organization, reduced tenocyte activity, and fibrocartilaginous enthesis reformation. This histological study demonstrated the use of a biphasic interpositional allograft for rotator cuff repair augmentation in an ovine model does not generate an inflammatory response or foreign body reaction. Use of the biphasic interpositional allograft resulted in a histological profile that was essentially equivalent to that of a standard RCR at 3-, 6-, and 12-week post-operative timepoints. These findings suggest that a biphasic interpositional allograft is safe for further clinical investigation in humans before broader clinical application. Patch augmentation of RCR is a popular technique that has shown clinical success in improving the likelihood of a successful repair in patients at elevated risk for retear. Newer augmentation technologies are being developed to address the biology at the interface between the bone and soft tissue where failure typically occurs.