Reviewerly/exHarmony huggingface dataset:
DiVRsify: Break the Cycle and Develop VR for Everyone

Virtual reality technology is biased.It excludes approximately 95% of the world's population by being primarily designed for male, western, educated, industrial, rich, and democratic populations.This bias may be due to the lack of diversity in virtual reality researchers, research participants, developers, and end users, fueling a noninclusive research, development, and usability cycle.The objective of this article is to highlight the minimal virtual reality research involving understudied populations with respect to dimensions of diversity, such as gender, race, culture, ethnicity, age, disability, and neurodivergence.Specifically, we highlight numerous differences in virtual reality usability between underrepresented groups compared to commonly studied populations.These differences illustrate the lack of generalizability of prior virtual reality research.Lastly, we present a call to action with the aim that, over time, will break the cycle and enable virtual reality for everyone.V irtual reality (VR) researchers argue that the diverse array of VR applications has the potential to change the world.A subset of these applications includes driving and flight simulators, surgical training, exposure therapy, physical therapy, empathy exercises, and perspective taking.Although these applications are intended to be useful for everyone, regardless of their age, gender, race, culture, ethnicity, class, ability, neurodiversity, etc., the majority of VR development is focused on a minority of the population.This narrow focus limits what we are calling the VR research, development, and usability cycle.This cycle excludes the majority of the population from being involved in the use, research, and development of VR applications and hardware.The VR research, development, and usability cycle (Figure 1) begins with the majority of researchers being Male, White, Educated, Industrialized, Rich, and Democratic (M-WEIRD). 9The WEIRD population is less than 6% of the world's population.Assuming that men are roughly half of this population, the M-WEIRD population is representative of less than 3% of the world's population.At their inception, VR research questions are primarily created by M-WEIRD researchers and evaluated on M-WEIRD participants. 19Subsequently, the knowledge gained from this limited subsection of the population is broadly applied 1) in industry by M-WEIRD developers and then 2) influences future research by M-WEIRD researchers.The hardware and software developed by industry professionals, based on knowledge gained and investigated by M-WEIRD researchers and participants is then used by M-WEIRD users.This cycle often excludes other VR users who do not identify as M-WEIRD and may limit the broad effectiveness of VR research and applications.Research and design decisions may disproportionately dissuade non-M-WEIRD people from effectively
A brief introduction to supervised, unsupervised, and reinforcement learning

Inducing models is a critical aspect of biosignal information processing. Models can be used to learn a mapping from a characterization of signals to categories associated with the problem at hand. Models can also be used to group biosignal information into coherent clusters for further analysis. Models can be used to characterize sequential decision processes to decide the best action to make at a given state, to model certain human behavior. There is a bulk of machine learning techniques that can be used for inducing different models from data. This chapter provides a general overview of the most relevant machine learning techniques that can be used for modeling different aspects of biosignals. The aim of this chapter is thus to serve as a reference of machine learning techniques for biosignal processing students and practitioners.
An Evolutionary Forest for Regression

Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years. This article proposes an evolutionary approach to generate an oblique RF for regression problems. More specifically, our method induces an oblique RF by transforming the original feature space to a new feature space through the evolutionary feature construction method. To speed up the searching process, the proposed method evaluates each set of features based on a decision tree (DT) rather than an RF. In order to obtain an RF, we archive top-performing features and corresponding trees during the search. In this way, both the features and the forest can be constructed simultaneously in a single run. The proposed evolutionary forest is applied to 117 benchmark problems with different characteristics and compared with some state-of-the-art regression methods, including several variants of the RF and gradient boosted DTs (GBDTs). The experimental results suggest that the proposed method outperforms the existing RF and GBDT methods.
Image-Based Incipient Fault Classification of Electrical Substation Equipment by Transfer Learning of Deep Convolutional Neural Network

A transfer learning-based deep convolutional neural network (DCNN) is employed in this article to identify several typical electrical substation equipment incipient faults. Image dataset contains 11 000 captured electrical substation equipment images, and each image inside the dataset was labeled as normal condition or typical incipient faults (insulation oil leakage, insulator contamination, rusting, and paint off). High-dimension features of electrical substation equipment images were first extracted by classical pre-trained DCNN architectures through the transfer learning method, and different incipient faults were then classified by the fully-connected (FC) neural network using the SoftMax activation function. Remarkable fault classification accuracy was obtained on the validation image, which verifies the effectiveness of the purposed method. Performances of various pre-trained classic DCNN architectures were explored and evaluated by t-distributed stochastic neighbor embedding (t-SNE) feature cluster maps, learning curves, and confusion matrixes. Results show that a model consists of MobileNetV2 DCNN architecture and two FC neural network layers could finalize fault classification tasks on 1000 images in 25 s with an accuracy of 98% which achieves better performance than traditional image classify methods. The proposed method could be useful for designing an electrical substation image monitoring system thus providing early prediction of incipient equipment faults.
Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.
Application of Artificial Intelligence Software based on Semantic Web Technology in English Learning and Teaching

&lt;p&gt;English teaching and learning in the information age need to be carried out with the help of intelligent system software to change the disadvantages of traditional English teaching as well as learning. According to the actual circumstances of teaching besides learning, this paper combines semantic Web technology and artificial intelligence technology to construct an English learning and teaching system. Moreover, this paper divides the entire speech data into frames in the speech processing, performs subsequent analysis in units of frames, and uses the autocorrelation function method in the time domain to extract the pitch of each frame of data corresponding to the English sentence. In addition, this paper combines the actual needs of English learning and teaching to construct system function modules, and designs experiments to analyze system performance and make statistics on user satisfaction. From the research results, it can be seen that the system constructed in this paper basically meets the actual needs of English autonomous learning and English teaching.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Semi-Supervised Action Quality Assessment With Self-Supervised Segment Feature Recovery

Action Quality Assessment aims to evaluate how well an action performs. Existing methods have achieved remarkable progress on fully-supervised action assessment. However, in real-world applications, with expert’s experience, it is not always feasible to manually label all samples. Therefore, it is important to study the problem of semi-supervised action assessment with only a small amount of samples annotated. A major challenge for semi-supervised action assessment is how to exploit the temporal pattern from unlabeled videos. Inspired by the temporal dependencies of the action execution, we propose a self-supervised learning on the unlabeled videos by recovering the feature of a masked segment of an unlabeled video. Furthermore, we leverage adversarial learning to align the representation distribution of the labeled and the unlabeled samples to close their gap in the sample space since unlabeled samples always come from unseen actions. Finally, we propose an adversarial self-supervised framework for semi-supervised action quality assessment. The extensive experimental results on the MTL-AQA and the Rhythmic Gymnastics datasets will demonstrate the effectiveness of our framework, achieving the state-of-the-art performances of semi-supervised action quality assessment.
Wheeler languages

The recently introduced class of Wheeler graphs, inspired by the Burrows-Wheeler Transform (BWT) of a given string, admits an efficient index data structure for searching for subpaths with a given path label, and lifts the applicability of the Burrows-Wheeler Transform from a single string to an entire language. In this paper we study the regular languages accepted by automata having a Wheeler graph as transition function and prove results on determinization, Myhill-Nerode characterization, decidability, and closure properties for this class of languages.
Is data-driven decision-making driven only by data? When cognition meets data

This paper aims in the context of data-driven decision making (DDDM) at investigating how biases related to the intuitive and rational types of human reasoning interact and how the trust in data changes applying the parallel-competitive theory. Using ethnographic research based on participatory observations, we explore the case of a traditional transportation firm in the northern UK and its earliest use of data to inform strategic decisions. We found that biases are grouped into what we called cognition trap, data trap, and trap recognition zones. We further observed three facets of trust in data as decision makers were falling into the three trap zones. These findings contribute to the parallel-competitive theory by unveiling the intriguing synergy of the intuitive and rational types of reasoning in DDDM and providing fine-grained insights related to biases and trust changes. The study also enlarges our understanding of the inception and nature of cognitive and data biases in the DDDM context. Managerial implications are also highlighted and further discussed.
Dominant color component and adaptive whale optimization algorithm for multilevel thresholding of color images

Optimal multilevel thresholding for image segmentation got much importance in recent years. Several entropic and non-entropic objective functions with evolutionary computing algorithms have been successfully implemented to get the optimal multilevel thresholds for gray scale images. The problem of multilevel thresholding becomes complex for color images. Because, the basic color components (red, blue, green) of the color image are extracted and the multiple optimum threshold values are calculated for each of the components separately. This makes the methods computationally intensive and inaccurate. Further, the required color information is not retained in the thresholded output. To solve these problems, an efficient technique is proposed in this paper, extracting only the dominant color component (DCC) of an image, for optimal thresholding. A novel segmentation score is introduced to justify the methodology. The optimum threshold values are obtained using a newly suggested evolutionary computing technique named adaptive whale optimization algorithm (AWOA). The main contributions are – (i) a novel DCC approach is introduced, (ii) an efficient optimizer AWOA is proposed, (iii) a new segmentation score is introduced, (iv) experimental results on standard test color images are explored. The outcomes are compared with all existing method's approaches (using all the RGB components) on color image thresholding. Its performance analysis using standard metrics is deliberated in detail. Statistical analysis is also performed. From the outcomes, it is perceived that the suggested DCC-AWOA concept yields high quality segmented images. The work may encourage further research to explore its high dimensional applications.
Combining Non-sampling and Self-attention for Sequential Recommendation

With the rapid development of social media and big data technology, user's sequence behavior information can be well recorded and preserved on different media platforms. It is crucial to model the user preference through mining their sequential behaviors. The goal of sequential recommendation is to predict what a user may interact with in the next moment based on the user's historical record of interactive sequence. However, existing sequential recommendation methods generally adopt a negative sampling mechanism (e.g. random and uniform sampling) for the pairwise learning, which brings the defect of insufficient training to the model, and decrease the evaluation performance of the entire model. Therefore, we propose a Non-sampling Self-attentive Sequential Recommendation (NSSR) model that combines non-sampling mechanism and self-attention mechanism. Under the premise of ensuring the efficient training of the model, NSSR model takes all pairs in the training set as training samples, so as to achieve the goal of fully training the model. Specifically, we take the interactive sequence as the current user representation, and propose a new loss function to implement the non-sampling training mechanism. Finally, the state-of-the-art result is achieved on three public datasets, Movielens-1M, Amazon Beauty and Foursquare_TKY, and the recommendation performance increase by about 29.3%, 25.7% and 42.1% respectively.
PoT-GAN: Pose Transform GAN for Person Image Synthesis

Pose-based person image synthesis aims to generate a new image containing a person with a target pose conditioned on a source image containing a person with a specified pose. It is challenging as the target pose is arbitrary and often significantly differs from the specified source pose, which leads to large appearance discrepancy between the source and the target images. This paper presents the Pose Transform Generative Adversarial Network (PoT-GAN) for person image synthesis where the generator explicitly learns the transform between the two poses by manipulating the corresponding multi-scale feature maps. By incorporating the learned pose transform information into the multi-scale feature maps of the source image in a GAN architecture, our method reliably transfers the appearance of the person in the source image to the target pose with no need for any hard-coded spatial information depicting the change of pose. According to both qualitative and quantitative results, the proposed PoT-GAN demonstrates a state-of-the-art performance on three publicly available datasets for person image synthesis.
Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

In order to achieve a highly accurate estimation of solar energy resource potential, a novel hybrid ensemble-learning approach, hybridizing Advanced Squirrel-Search Optimization Algorithm (ASSOA) and support vector regression, is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria. Long-term measured meteorological data, including mean-air temperature, relative humidity, wind speed, alongside global horizontal irradiation and extra-terrestrial horizontal irradiance, were obtained for the two cities of Tamanrasset-and-Adrar for two years. Five computational algorithms were considered and analyzed for the suitability of estimation. Further two new algorithms, namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization approach. The accuracy of the developed models was analyzed in terms of five statistical error metrics, as well as the Wilcoxon rank-sum and ANOVA test. Among the previously selected algorithms, K Neighbors Regressor and support vector regression exhibited good performances. However, the newly proposed ensemble algorithms exhibited even better performance. The proposed model showed relative root mean square errors lower than 1.448% and correlation coefficients higher than 0.999. This was further verified by benchmarking the new ensemble against several popular swarm intelligence algorithms. It is concluded that the proposed algorithms are far superior to the commonly adopted ones.
RobustFL: Robust Federated Learning Against Poisoning Attacks in Industrial IoT Systems

Industrial Internet of Things (IIoT) systems are key enabling infrastructures that sustain the functioning of production and manufacturing. To satisfy the intelligence demands, federated learning has been envisioned as a promising technique for IIoT applications with privacy training requirements. However, research works have shown that, by training the local model on crafted poisoning samples malicious participants can jeopardize the functionalities of the global model. In this article, we propose a robust federated learning method, named RobustFL, in IIoT systems to defend against poisoning attacks. The main idea is that we conduct an adversarial training framework, in which an extra logits-based predictive model is built at the server-side to predict which participant a given logit belongs to. Meanwhile, the federated model is adversarially trained to prevent this predictive behavior, thus mitigating the poisoning attack influences. We evaluate the poisoning attack and our defense method on three benchmark datasets. Experimental results demonstrate the superiority of our proposed method in terms of high accuracy and efficiency in defending against poisoning attacks.
Ecologies of subversion

research-article Share on Ecologies of subversion: troubling interaction design for climate care Author: Ann Light University of Sussex and Malmö University University of Sussex and Malmö UniversityView Profile Authors Info & Claims InteractionsVolume 29Issue 1January - February 2022 pp 34–38https://doi.org/10.1145/3501301Online:06 January 2022Publication History 2citation879DownloadsMetricsTotal Citations2Total Downloads879Last 12 Months879Last 6 weeks71 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Tracking, tracing, trust: contemplating mitigating the impact of <scp>COVID</scp> ‐19 through technological interventions

Medical Journal of AustraliaVolume 213, Issue 2 p. 94-94.e1 Letters Tracking, tracing, trust: contemplating mitigating the impact of COVID-19 through technological interventions Christopher Culnane, Christopher Culnane University of Melbourne, Melbourne, VICSearch for more papers by this authorKobi Leins, Kobi Leins University of Melbourne, Melbourne, VICSearch for more papers by this authorBenjamin IP Rubinstein, Corresponding Author Benjamin IP Rubinstein [email protected] University of Melbourne, Melbourne, VIC[email protected]Search for more papers by this author Christopher Culnane, Christopher Culnane University of Melbourne, Melbourne, VICSearch for more papers by this authorKobi Leins, Kobi Leins University of Melbourne, Melbourne, VICSearch for more papers by this authorBenjamin IP Rubinstein, Corresponding Author Benjamin IP Rubinstein [email protected] University of Melbourne, Melbourne, VIC[email protected]Search for more papers by this author First published: 24 June 2020 https://doi.org/10.5694/mja2.50683Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References 1Leins K, Culnane C, Rubinstein BI. Tracking, tracing, trust: contemplating mitigating the impact of COVID-19 through technological interventions. Med J Aust 2020; 213: 6–8. 10.5694/mja2.50669 PubMedWeb of Science®Google Scholar 2Madden M, Gilman M, Levy K, Marwick A. Privacy, poverty, and Big Data: a matrix of vulnerabilities for poor Americans. Washington University Law Rev 2017; 95: 53–125. Google Scholar Citing Literature Volume213, Issue2July 2020Pages 94-94.e1 ReferencesRelatedInformation
The Potential Value of Mixed Reality in Neurosurgery

Mixed reality (MR) merges virtual information into the real world through computer technology, in which the real environment and virtual objects can get spliced in the same image or space at real time so that it can effectively express and integrate the virtual and real worlds and allow high feedback interaction. This technology combines the many advantages of virtual realityand augmented reality, and has a promising future in the medical field. At present, MR technology is just at the beginning stage in the medical field in the world, whose application in neurosurgery is also rarely reported. Given this, the authors described the research progress of MR in neurosurgery including preoperative planning and intraoperative guidance, doctor-patient communication, teaching rounds, physician training, and so on.