Reviewerly/exHarmony huggingface dataset:
The effects of augmented reality‐supported instruction in tertiary‐level medical education

Abstract A significant body of the literature has documented the potential of Augmented Reality (AR) in education, but little is known about the effects of AR‐supported instruction in tertiary‐level Medical Education (ME). This quasi‐experimental study compares a traditional instructional approach with supplementary online lecture materials using digital handout notes with a control group ( n = 30) and an educational AR application with an experimental group ( n = 30) to investigate any possible added‐value and gauge the impact of each approach on students' academic performance and training satisfaction. This study's findings indicate considerable differences in both academic performance and training satisfaction between the two groups. The participants in the experimental group performed significantly better than their counterparts, an outcome which is also reflected in their level of training satisfaction through interacting and viewing 3D multimedia content. This study contributes by providing guidelines on how an AR‐supported intervention can be integrated into ME and provides empirical evidence on the benefits that such an approach can have on students' academic performance and knowledge acquisition. Practitioner notes What is already known about this topic Several studies have applied various Augmented Reality (AR) applications across different learning disciplines. The effects of AR on students' perceptions and achievements in higher education contexts is well‐documented. Despite the increasing use of AR‐instruction in Medical Education (ME), there has been no explicit focus on AR's effects on students' academic performance and satisfaction. What this paper adds This quasi‐experimental study compares the academic performance and training satisfaction of students in an experimental group (AR) and a control group (handout notes). This study provides instructional insights into, and recommendations that may help students achieve better academic performance in AR‐supported ME courses. The experimental group reported greater training satisfaction than their counterparts. Implications for practice and policy Students who followed the AR‐supported instruction achieved better academic performance that those in the control group. AR‐supported interventions encourage active learning and lead to significant performance improvement. The experimental group outperformed the control group in academic performance and training satisfaction measurements, despite the lower experimental group's lower pre‐test performance scores.
Visual Transformers: Where Do Transformers Really Belong in Vision Models?

A recent trend in computer vision is to replace convolutions with transformers. However, the performance gain of transformers is attained at a steep cost, requiring GPU years and hundreds of millions of samples for training. This excessive resource usage compensates for a misuse of transformers: Transformers densely model relationships between its inputs - ideal for late stages of a neural network, when concepts are sparse and spatially-distant, but extremely inefficient for early stages of a network, when patterns are redundant and localized. To address these issues, we leverage the respective strengths of both operations, building convolution-transformer hybrids. Critically, in sharp contrast to pixel-space transformers, our Visual Transformer (VT) operates in a semantic token space, judiciously attending to different image parts based on context. Our VTs significantly outperforms baselines: On ImageNet, our VT-ResNets outperform convolution-only ResNet by 4.6 to 7 points and transformer-only ViT-B by 2.6 points with 2.5× fewer FLOPs, 2.1× fewer parameters. For semantic segmentation on LIP and COCO-stuff, VT-based feature pyramid networks (FPN) achieve 0.35 points higher mIoU while reducing the FPN module’s FLOPs by 6.5x.
Anomaly Detection: How to Artificially Increase Your F1-Score with a Biased Evaluation Protocol

Anomaly detection is a widely explored domain in machine learning. Many models are proposed in the literature, and compared through different metrics measured on various datasets. The most popular metrics used to compare performances are F1-score, AUC and AVPR. In this paper, we show that F1-score and AVPR are highly sensitive to the contamination rate. One consequence is that it is possible to artificially increase their values by modifying the train-test split procedure. This leads to misleading comparisons between algorithms in the literature, especially when the evaluation protocol is not well detailed. Moreover, we show that the F1-score and the AVPR cannot be used to compare performances on different datasets as they do not reflect the intrinsic difficulty of modeling such data. Based on these observations, we claim that F1-score and AVPR should not be used as metrics for anomaly detection. We recommend a generic evaluation procedure for unsupervised anomaly detection, including the use of other metrics such as the AUC, which are more robust to arbitrary choices in the evaluation protocol.
The role of cloud computing technology: A savior to fight the lockdown in COVID 19 crisis, the benefits, characteristics and applications

The contagion of the Coronavirus (COVID-19) led to a global lockdown that put governments in emergency mode. With the total number of positive cases worldwide exceeding the 97.46 million mark, social distancing appears to be the only effective strategy to contain the virus at the moment. As a result, companies face obstacles and find it difficult to respond to this current challenge of remote working. The impact of the novel COVID-19 has created many new challenges, and many of us have had to adopt new ways of working. With the need for accessing to critical applications and the scalability of the infrastructure, cloud computing is emerging as an underlying technology. The cloud technology had a major role in fighting the epidemic; it becomes a salvation for governments and organizations in numerous fields of life, education, health, industry, communication, remote surveillance, and more information. Therefore, this study presents the benefits, characteristics and applications of cloud computing and explains how the cloud contributes to improving life in all regions of the world during COVID-19. It shows that the cloud computing helps countries in combating COVID 19, in education and health sectors, also in the economic and commercial aspects. It investigates the current state by distributing an online questionnaire to various people of academic and non-academic backgrounds in different places over the world in the ICT and education sectors. The results showed that there is an effective role for cloud computing during the epidemic.
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving

We introduce MGNet, a multi-task framework for monocular geometric scene understanding. We define monocular geometric scene understanding as the combination of two known tasks: Panoptic segmentation and self-supervised monocular depth estimation. Panoptic segmentation captures the full scene not only semantically, but also on an instance basis. Self-supervised monocular depth estimation uses geometric constraints derived from the camera measurement model in order to measure depth from monocular video sequences only. To the best of our knowledge, we are the first to propose the combination of these two tasks in one single model. Our model is designed with focus on low latency to provide fast inference in real-time on a single consumer-grade GPU. During deployment, our model produces dense 3D point clouds with instance aware semantic labels from single high-resolution camera images. We evaluate our model on two popular autonomous driving benchmarks, i.e., Cityscapes and KITTI, and show competitive performance among other real-time capable methods. Source code is available at https://github.com/markusschoen/MGNet.
Deepfake Detection Using SVM

In recent years the Deep generative networks have made it easy to create real face swaps in images and videos with less traces of manipulation, significantly improving the quality of these deepfakes. This improvement in fake media have gained more concern as for their use in fake terrorism, blackmail etc. This has drawn attention of both industry and government to distinguish and restrict their utilization. In spite of the way that these AI generated fake media produce realistic images upon detailed assessment, the proposed method can find some features that are unnatural which are not visible to the naked eyes. This paper uses a method known as frequency domain analysis after which a classifier will be used to differentiate the real and fake image. This paper evaluates our method on dataset of deepfake images collected from different website. Our work can show promising performance for detecting these deepfake images.
A Residual-Attention Offline Handwritten Chinese Text Recognition Based on Fully Convolutional Neural Networks

Offline handwritten Chinese text recognition is one of the most challenging tasks in that it involves various writing styles, complex character-touching, and large number of character categories. In this paper, we propose a residual-attention offline handwritten Chinese text recognition based on fully convolutional neural networks, which is segmentation-free handwritten recognition that avoids the impact of incorrect character segmentation. By designing a smart residual attention gate block, our model can help to extract important features, and effectively implement the training of deep convolutional neural networks. Furthermore, we deploy an expansion factor to indicate the trade-off between computing resources for model training and the ability of a gradient to propagate across multiple layers, and make our model training adapt to different computing platforms. Experiments on the CASIA-HWDB and ICDAR-2013 competition dataset show that our method achieves a competitive performance on offline handwritten Chinese text recognition. On the CASIA-HWDB test set, the character-level accurate rate and correct rate achieve 97.32% and 97.90% respectively.
A multi-objective gradient optimizer approach-based weighted multi-view clustering

The advancement of technology has enabled the availability of a large amount of data from different sources. In such multi-view datasets, each view provides a particular representation for data objects and produces different partitions. Weighted Multi-view clustering approaches aim to find a suitable consensus clustering taking into consideration both the incompatibility between views and the relevance of features in each view. In this paper, a multi-objective weighted, Multi-view clustering method is presented based on gradient based optimizer. In the developed algorithm, a set of objective functions is considered that optimize the feature weights simultaneously in each view and the cluster centers that provide the optimal partitioning. Each candidate solution in our proposed method is evaluated by the weighted within-cluster compactness of the partitioning obtained from a single view and by the global weighted between-cluster dispersion among the partitioning provided by all views and the negative entropy among all clusters. To validate the clustering performance of developed approach, nine multi-view datasets with different statistical properties were used in this study. In addition, a real-world multi-omics data which contains four multi-omics datasets for cancer subtype discovery with three levels of omics data were considered. Experimental results demonstrate the ability of the new method to generate better clustering results than six popular multi-objective optimizers and ten state-of-the-art multi-view methods according to three measures, which are clustering accuracy, rand index, and normalized mutual information.
A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the “curse of dimensionality” that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a real-world gas turbine (GT) dataset that contains 87620 columns and 56 rows. During the experiment, two issues have been investigated and solved to enhance the results. The first is the dataset class imbalance, which solved using SMOTE technique. The second issue is the poor performance, which can be solved using one of the optimization algorithms. Several optimization algorithms have been investigated and tested, including stochastic gradient descent (SGD), RMSprop, Adam and Adamax. However, Adamax optimization algorithm showed the best results when employed to train the DANN model. The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%, F1-score of 0.9649, Area Under the Curve (AUC) rate of 0.9649, and a minimal loss function during the hybrid model training.
Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals

Inertial sensors are widely used in the field of human activity recognition (HAR), since this source of information is the most informative time series among non-visual datasets. HAR researchers are actively exploring other approaches and different sources of signals to improve the performance of HAR systems. In this study, we investigate the impact of combining bio-signals with a dataset acquired from inertial sensors on recognizing human daily activities. To achieve this aim, we used the PPG-DaLiA dataset consisting of 3D-accelerometer (3D-ACC), electrocardiogram (ECG), photoplethysmogram (PPG) signals acquired from 15 individuals while performing daily activities. We extracted hand-crafted time and frequency domain features, then, we applied a correlation-based feature selection approach to reduce the feature-set dimensionality. After introducing early fusion scenarios, we trained and tested random forest models with subject-dependent and subject-independent setups. Our results indicate that combining features extracted from the 3D-ACC signal with the ECG signal improves the classifier's performance F1-scores by 2.72% and 3.00% (from 94.07% to 96.80%, and 83.16% to 86.17%) for subject-dependent and subject-independent approaches, respectively.
Authenticity, Interactivity, and Collaboration in Virtual Reality Games: Best Practices and Lessons Learned

Virtual reality has become an increasingly important topic in the field of education research, going from a tool of interest to a tool of practice. In this paper, we document and summarize the studies associated with our 4-year design project, Collaborative Learning Environments in Virtual Reality (CLEVR). Our goal is to share the lessons we gleaned from the design and development of the game so that others may learn from our experiences as they are designing, developing, and testing VR for learning. We translate “lessons learned” from our user studies into “best practices” when developing authentic, interactive, and collaborative experiences in VR. We learned that authentic representations can enhance learning in virtual environments but come at a cost of increased time and resources in development. Interactive experiences can motivate learning and enable users to understand spatial relationships in ways that two dimensional representations cannot. Collaboration in VR can be used to alleviate some of the cognitive load inherent in VR environments, and VR can serve as a context for collaborative problem solving with the appropriate distribution of roles and resources. The paper concludes with a summation of best practices intended to inform future VR designers and researchers.
Effective De-identification Generative Adversarial Network for Face Anonymization

The growing application of face images and modern AI technology has raised another important concern in privacy protection. In many real scenarios like scientific research, social sharing and commercial application, lots of images are released without privacy processing to protect people's identity. In this paper, we develop a novel effective de-identification generative adversarial network (DeIdGAN) for face anonymization by seamlessly replacing a given face image with a different synthesized yet realistic one. Our approach consists of two steps. First, we anonymize the input face to obfuscate its original identity. Then, we use our designed de-identification generator to synthesize an anonymized face. During the training process, we leverage a pair of identity-adversarial discriminators to explicitly constrain identity protection by pushing the synthesized face away from the predefined sensitive faces to resist re-identification and identity invasion. Finally, we validate the effectiveness of our approach on public datasets. Compared with existing methods, our approach can not only achieve better identity protection rates but also preserve superior image quality and data reusability, which suggests the state-of-the-art performance.
CSL-SHARE: A Multimodal Wearable Sensor-Based Human Activity Dataset

DATA REPORT article Front. Comput. Sci., 15 October 2021 | https://doi.org/10.3389/fcomp.2021.759136
Declared and Real Level of Digital Skills of Future Teaching Staff

Digital competence is undoubtedly one of the key skills that teaching staff should possess. Currently, there are many theoretical frameworks and ways to measure skills and knowledge related to the use of information and communication technologies (ICT). This article is an attempt to show the real and declared level of digital skills among future teaching staff. The research was conducted in Poland among 128 students of pedagogical faculties (first-year undergraduate studies). The research used a triangulation of research methods and techniques: diagnostic survey and competency tests related to the use of word processors and spreadsheets, and the level of knowledge about the use of ICT. Competency tests were in accordance with the European Computer Skills Certificate (ECDL) standard. The collected data showed the following: (1) more than half of the students rate their own skills in the use of word processors and spreadsheets, and their overall theoretical knowledge as high or very high; (2) in the case of the real assessment of digital competence, only less than 20% reached the passing threshold in the areas of word processors and theoretical knowledge, with only 1.6% passing in the area of spreadsheets; (3) the declared and actual levels of digital competence were moderately related in the surveyed group; (4) attitudes towards new media, self-assessment of digital skills, and previous learning experience in handling ICT are not predictive factors for ECDL test results.
ARROCH: Augmented Reality for Robots Collaborating with a Human

Human-robot collaboration frequently requires extensive communication, e.g., using natural language and gesture. Augmented reality (AR) has provided an alternative way of bridging the communication gap between robots and people. However, most current AR-based human-robot communication methods are unidirectional, focusing on how the human adapts to robot behaviors, and are limited to single-robot domains. In this paper, we develop AR for Robots Collaborating with a Human (ARROCH), a novel algorithm and system that supports bidirectional, multi-turn, human-multi-robot communication in indoor multi-room environments. The human can see through obstacles to observe the robots’ current states and intentions, and provide feedback, while the robots’ behaviors are then adjusted toward human-multi-robot teamwork. Experiments have been conducted with real robots and human participants using collaborative delivery tasks. Results show that ARROCH outperformed a standard non-AR approach in both user experience and teamwork efficiency. In addition, we have developed a novel simulation environment using Unity (for AR and human simulation) and Gazebo (for robot simulation). Results in simulation demonstrate ARROCH’s superiority over AR-based baselines in human-robot collaboration.
Identity-Preserving Face Anonymization via Adaptively Facial Attributes Obfuscation

With the popularity of using computer vision technology in monitoring system, there is an increasing societal concern on intruding people's privacy as the captured images/videos may contain identity-related information e.g. people's face. Existing methods on protecting such privacy focus on removing the identity-related information from faces. However, this would weaken the utility of current monitoring system. In this paper, we develop a face anonymization framework that could obfuscate visual appearance while preserving the identity discriminability. The framework is composed of two parts: an identity-aware region discovery module and an identity-aware face confusion module. The former adaptively locates the identity-independent attributes on human faces, and the latter generates the privacy-preserving faces using original faces and discovered facial attributes. To optimize the face generator, we employ a multi-task based loss function, which consists of discriminator loss, identify preserving loss, and reconstruction loss functions. Our model can achieve a balance between recognition utility and appearance anonymizing by modifying different numbers of facial attributes according to pratical demands, and provide a variety of results. Extensive experiments conducted on two public benchmarks Celeb-A and VGG-Face2 demonstrate the effectiveness of our model under distinct face recognition scenarios.
Transfer learning and siamese neural network based identification of geochemical anomalies for mineral exploration: A case study from the Cu Au deposit in the NW Junggar area of northern Xinjiang Province, China

The identification of geochemical anomaly plays an important role in mineral exploration. The division of geochemical background and anomaly can be treated as a binary classification problem. Deep learning has shown encouraging performance in various fields of classification and prediction, and research has shown that geochemical anomalies can be effectively identified on the base of spatial characteristics and internal relations in geochemical data. However, deep learning models are often limited by the number of training samples, so new improved algorithms are needed to improve the accuracy of the model in anomaly recognition. Considering that the spatial patterns of geochemical usually have nested or hierarchical multi-scale characteristics, it is possible to improve model performance by integrating local and regional geochemical information. In this study, a combination of transfer learning and siamese neural network was used to improve the ability to extract multi-element geochemical anomalies, and multi-scale geochemical data was tried to improve model performance. The model accuracy of using both transfer learning and siamese neural network reached 85%, which showed that the improved deep learning method can greatly improve the ability of anomaly recognition of the model. The integration of multi-scale geochemical data made the model accuracy to 88%, which showed that the deep learning model also had a certain ability to recognize the information of the scale. Therefore, deep learning can effectively depict complex geochemical spatial patterns and implicit anomalies, which can be better applied to the recognition of geochemical anomalies through continuous improvement.