Reviewerly/exHarmony huggingface dataset:
Implementation of the Modern Immersive Learning Model CPLM

The digitalization of industrial processes is being driven forward worldwide. In parallel, the education system must also be transformed. Currently, education does not follow the opportunities and development of technologies. We can ask ourselves how we can integrate technologies into a traditional learning process or how we can adapt the learning process to these technologies. We focused on robotics education in secondary vocational education. The paper contains research results from a modern learning model that addresses student problem-solving using cyber–physical systems. We proposed a reference model for industrial robotics education in the 21st century based on an innovative cyber-physical didactic model (CPLM). We conducted procedure time measurements, questionnaire evaluations, and EEG evaluations. We could use VR to influence the improvement of spatial and visual memory. The more intense representation of the given information influences multiple centers in the brain and, thus, the formation of multiple neural connections. We can influence knowledge, learning more effectively with short-term training in the virtual world than with classical learning methods. From the studied resources, we can conclude that the newer approach to teaching robotics is not yet available in this form. The emerging modern technologies and the possibility of developing training in this area should be investigated further.
Curriculum Design of Art Higher Vocational Education Based on Artificial Intelligence Assisted Virtual Reality Technology

In order to improve the effect of higher vocational art teaching, this paper applies artificial intelligence virtual reality technology to the art education design of higher vocational schools to improve the immersion of art education, change the traditional higher vocational art teaching mode, and effectively improve the efficiency of higher vocational art teaching. Furthermore, the teaching system’s presentation of virtual reality algorithms has been enhanced. This paper elaborates the realisation process of the system’s homework management function, virtual scene function, personal space function, resource management function, online examination function, and teacher-student communication function, which is primarily displayed in the form of interface diagrams and flowcharts, in the realisation of the state-of-the-art higher vocational education curriculum design system based on virtual reality technology. Furthermore, this work uses experimental research to verify the system’s dependability. The results show that the art higher vocational education curriculum design system constructed in this paper based on artificial intelligence assisted virtual reality technology basically meets the needs of contemporary higher vocational art teaching and can effectively improve the art higher vocational education curriculum design.
Implementation of a Person Following Robot in ROS-gazebo platform

This research work involves the implementation of a person follower robot in ROS_Gazebo platform As part of this research work, we designed a mobile robot (for simulation)that can follow human beings. It assists people carrying heavyweights to their desired locations by following them. The entire simulation work is carried out with the help of ROS2 (Robot Operating System Version2). We used Gazebo and RVIZ as simulation tools to verify the outputs. MakeHuman and Blender software&#x0027;s assisted in creating an animated person and verify the rendering in the gazebo. The mobile robot created makes use of a Depth camera (Kinect) to detect a person and Lidars to calculate the distance between the robot and person. YOLOv3 algorithm has been used for image recognition. With this mobile robot, we simulated with multiple persons in gazebo software to make sure the robot is following only one particular person. The Kinect sensor uses object detection algorithms to identify and follow a particular person.
ILLIXR: An Open Testbed to Enable Extended Reality Systems Research

We present Illinois Extended Reality testbed (ILLIXR), the first fully open-source XR system and research testbed.ILLIXR enables system innovations with end-to-end co-designed hardware, compiler, OS, and algorithms, and driven by end-user perceived Quality-of-Experience (QoE) metrics.Using ILLIXR, we provide the first comprehensive quantitative analysis of performance, power, and QoE for a complete XR system and its individual components.We describe several implications of our results that propel new directions in architecture, systems, and algorithms research for domain-specific systems in general, and XR in particular.
A Cell-Based Fast Memetic Algorithm for Automated Convolutional Neural Architecture Design

Neural architecture search (NAS) has attracted much attention in recent years. It automates the neural network construction for different tasks, which is traditionally addressed manually. In the literature, evolutionary optimization (EO) has been proposed for NAS due to its strong global search capability. However, despite the success enjoyed by EO, it is worth noting that existing EO algorithms for NAS are often very computationally expensive, which makes these algorithms unpractical in reality. Keeping this in mind, in this article, we propose an efficient memetic algorithm (MA) for automated convolutional neural network (CNN) architecture search. In contrast to existing EO algorithms for CNN architecture design, a new cell-based architecture search space, and new global and local search operators are proposed for CNN architecture search. To further improve the efficiency of our proposed algorithm, we develop a one-epoch-based performance estimation strategy without any pretrained models to evaluate each found architecture on the training datasets. To investigate the performance of the proposed method, comprehensive empirical studies are conducted against 34 state-of-the-art peer algorithms, including manual algorithms, reinforcement learning (RL) algorithms, gradient-based algorithms, and evolutionary algorithms (EAs), on widely used CIFAR10 and CIFAR100 datasets. The obtained results confirmed the efficacy of the proposed approach for automated CNN architecture design.
DMCNet: Diversified model combination network for understanding engagement from video screengrabs

Engagement is an essential indicator of the Quality-of-Learning Experience (QoLE) and plays a major role in developing intelligent educational interfaces. The number of people learning through Massively Open Online Courses (MOOCs) and other online resources has been increasing rapidly because they provide us with the flexibility to learn from anywhere at any time. This provides a good learning experience for the students. However, such learning interface requires the ability to recognize the level of engagement of the students for a holistic learning experience. This is useful for both students and educators alike. However, understanding engagement is a challenging task, because of its subjectivity and ability to collect data. In this paper, we propose a variety of models that have been trained on an open-source dataset of video screengrabs. Our non-deep learning models are based on the combination of popular algorithms such as Histogram of Oriented Gradient (HOG), Support Vector Machine (SVM), Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). The deep learning methods include Densely Connected Convolutional Networks (DenseNet-121), Residual Network (ResNet-18) and MobileNetV1. We show the performance of each models using a variety of metrics such as the Gini Index, Adjusted F-Measure (AGF), and Area Under receiver operating characteristic Curve (AUC). We use various dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) to understand the distribution of data in the feature sub-space. Our work will thereby assist the educators and students in obtaining a fruitful and efficient online learning experience.
High‐resolution knee plain radiography image synthesis using style generative adversarial network adaptive discriminator augmentation

In this retrospective study, 10,000 anteroposterior (AP) radiography of the knee from a single institution was used to create medical data set that are more balanced and cheaper to create. Two types of convolutional networks were used, deep convolutional GAN (DCGAN) and Style GAN Adaptive Discriminator Augmentation (StyleGAN2-ADA). To verify the quality of generated images from StyleGAN2-ADA compared to real ones, the Visual Turing test was conducted by two computer vision experts, two orthopedic surgeons, and a musculoskeletal radiologist. For quantitative analysis, the Fréchet inception distance (FID), and principal component analysis (PCA) were used. Generated images reproduced the features of osteophytes, joint space narrowing, and sclerosis. Classification accuracy of the experts was 34%, 43%, 44%, 57%, and 50%. FID between the generated images and real ones was 2.96, which is significantly smaller than another medical data set (BreCaHAD = 15.1). PCA showed that no significant difference existed between the PCs of the real and generated images (p > 0.05). At least 2000 images were required to make reliable images optimally. By performing PCA in latent space, we were able to control the desired PC that show a progression of arthritis. Using a GAN, we were able to generate knee X-ray images that accurately reflected the characteristics of the arthritis progression stage, which neither human experts nor artificial intelligence could discern apart from the real images. In summary, our research opens up the potential to adopt a generative model to synthesize realistic anonymous images that can also solve data scarcity and class inequalities.
Lung CT image enhancement based on total variational frame and wavelet transform

Abstract Benefitting from the development of computer vision, computed tomography (CT) images have been used for assisting doctor's clinical diagnosis and improving the diagnostic efficiency. However, there exist some issues in medical images, such as low contrast, obscure detail, and complex noise due to the restriction of the system and the equipment in the process of imaging, medical images. To resolve these issues, a novel lung CT image enhancement method based on total variational framework combined with wavelet transform is proposed. Firstly, low‐frequency structure layer with low contrast and high‐frequency details layer with complex noise signals are acquired by decomposing the original image using total variational framework. Then, through the analysis of the histogram distribution characteristics of CT image, structure layer requires contrast enhancement; at the same time, the detail layer performs wavelet transform adaptive threshold denoising to remove noise. Finally, weight fusion of processed structure layer and details layer is performed to obtain the final fusion enhancement CT images. Experimental results show that the proposed method can enhance the contrast of clinical lung CT images, improve the clarity of details, and effectively suppress artifacts and complex noises. Contrast and sharpness—objective indicators—prove the proposed method's advantages. Subjectively, the proposed method performs superior over other existing CT enhancement methods, which achieves a better visual recognition.
Architectural Design Decisions for the Machine Learning Workflow

We conducted a qualitative investigation of architectural decisions faced by practitioners and modeled current practices in machine learning. We describe a subset of the architectural decisions that were modeled, discuss uses for the model, and outline areas in which further research may be pursued.
A Taxonomy for Augmented and Mixed Reality Applications to Support Physical Exercises in Medical Rehabilitation—A Literature Review

In the past 20 years, a vast amount of research has shown that Augmented and Mixed Reality applications can support physical exercises in medical rehabilitation. In this paper, we contribute a taxonomy, providing an overview of the current state of research in this area. It is based on a comprehensive literature review conducted on the five databases Web of Science, ScienceDirect, PubMed, IEEE Xplore, and ACM up to July 2021. Out of 776 identified references, a final selection was made of 91 papers discussing the usage of visual stimuli delivered by AR/MR or similar technology to enhance the performance of physical exercises in medical rehabilitation. The taxonomy bridges the gap between a medical perspective (Patient Type, Medical Purpose) and the Interaction Design, focusing on Output Technologies and Visual Guidance. Most approaches aim to improve autonomy in the absence of a therapist and increase motivation to improve adherence. Technology is still focused on screen-based approaches, while the deeper analysis of Visual Guidance revealed 13 distinct, reoccurring abstract types of elements. Based on the analysis, implications and research opportunities are presented to guide future work.
Anomaly Detection in Player Performances in Multiplayer Online Battle Arena Games

Esports are digital video games that are played professionally. In recent years there has been a growing need to improve the broadcast experience by incorporating real-time data-driven analytics. In these same games, when played by the general public, there is a growing issue of cheating. Using the popular esport and video game DOTA 2 as a case study, we present a novel application of Archetype Analysis that can be used for anomaly detection in player performance. We show how these anomalies can be utilised for both esports broadcasting and cheat detection.
Revisiting Cluster Vulnerabilities towards Information and Communication Technologies in the Eastern Island of Indonesia Using Fuzzy C Means

Design/methodology/approach: In the present digital era, technology infrastructure plays an important role in the development of digital literacy in various sectors that can provide various important information on a large scale. Purpose: The use of information and communication technology (ICT) in Indonesia in the last five years has shown a massive development of ICT indicators. The population using the internet also experienced an increase during the period 2016–2020, as indicated by the increasing percentage of the population accessing the internet in 2016 from around 25.37 percent to 53.73 percent in 2020. This study led to a review of the level of ICT vulnerability in eastern Indonesia through a machine learning-based cluster analysis approach. Implications: Data were collected in this study from Badan Pusat Statistik (BPS) through SUSENAS to obtain an overview of the socioeconomic level and SAKERNAS to capture the employment side. This study uses 15 variables based on aspects of business vulnerability covering 174 districts/cities. Practical implications: Cluster analysis using Fuzzy C Means (FCM) was used to obtain a profile of ICT level vulnerability in eastern Indonesia by selecting the best model. The best model is obtained by selecting the validation value such as Silhouette Index, Partition Entropy, Partition Coefficient, and Modified Partition Coefficient. Social implication: For some areas with a very high level of vulnerability, special attention is needed for the central or local government to support the improvement of information technology through careful planning. Socio-economic and occupational aspects have been reflected in this very vulnerable cluster, and the impact of the increase in ICT will provide a positive value for community development. Originality/value: From the modelling results, the best cluster model is two clusters, which are categorized as high vulnerability and low vulnerability. For each cluster member who has a similarity or proximity to each other, there will be one cluster member.
A Blockchain-based Security Architecture for the Internet of Things

The Internet of Things (IoT) is growing at a very fast pace and being increasingly adopted in many scenarios of industrial applications such as energy (smart grid), automobile (smart cars), healthcare (smart healthcare), manufacturing (smart manufacturing and supply chain) and other application such as homes (smart home) and cities (smart city). Nonetheless, these IoT technologies (devices, systems, protocols, and applications) are faced with many security-related issues. IoT Systems have different layers that are vulnerable to various kinds of attacks. To defend against these attacks, one must consider appropriate security approaches and mechanisms to ensure privacy, security, and trust within the various components and layers that make up the IoT system. Appropriate security mechanism is needed at every layer of an IoT system to keep them secure. Hence, finding suitable mechanism for each IoT layer is a necessity to keep IoT systems secure in the 21st-century applications and implementations. The paper first investigates the IoT layers and protocols, their vulnerability issues, and methods to resolve the issues from existing literatures. We then present a blockchain-based security architecture for the internet of things and practically investigated its security feature through implementation of various security mechanism. Analysis and discussion of the blockchain implementation results are carried for the purpose of meeting the security, privacy, and trust requirements of IoT.
A two-stage detection method of copy-move forgery based on parallel feature fusion

Abstract The copy-move forgery refers to the copying and pasting of a region of the original image into the target region of the same image, which represents a typical tampering method with the characteristics of easy tampering and high-quality tampering. The existing single feature-based methods of forgery detection have certain shortcomings, such as high false alarm rate, low robustness, and low detection accuracy. To address these shortcomings, this paper proposes an improved two-stage detection method based on parallel feature fusion and an adaptive threshold generation algorithm. Firstly, the SLIC super-pixels segmentation algorithm is used for image preprocessing, and a similar region extraction algorithm without threshold is employed to obtain the suspected tampering regions with high similarity. Secondly, the parallel fusion feature is obtained based on the SIFT and HU features to express the characteristics of local regions. Then, the corresponding threshold value is generated based on the histogram of oriented gradient (HOG) to describe the texture characteristics of the obtained regions, which acts as a criterion to judge whether a region has been forged or not. The experimental results show that the proposed method outperforms the existing methods, achieving the accuracy of 99.01% and 98.5% on the MICC-F220 and MICC-F2000 datasets respectively. In addition, the proposed method has stronger robustness performance on COMOFOD dataset than the comparison methods.
Intelligent control system of smart home for context awareness

In recent years, the rapid development of Internet of Things smart hardware has increased the demand for intelligent control of devices, mainly in the smart home industry. The framework to solve the problem of equipment intelligent control is studied. The advantages and disadvantages of existing context modeling strategies are analyzed. According to the characteristics of household context activities, combined with the design principles of graph databases, a new context modeling method based on object and attribute graph is proposed, which is suitable for Internet of Things scenarios with limited resources. Rule control, mode control, and voice control approaches of smart home interaction are designed. An inference engine is used to map the data of context awareness to the Internet of Things control services that executed automatic control of the system, and a framework of smart control system based on context awareness of Internet of Things is proposed. Considering the behavior habits of users, the concept of user preference is introduced to provide more personalized services. Performance tests with simulated data show that the new context modeling method has a faster system response time than the ontology modeling control mode.
Combining multiple survey sources: A reproducible workflow and toolbox for survey data harmonization

Ex-post harmonization of survey data creates new opportunities for research by extending the geographical and/or time coverage of analyses. Researchers increasingly combine data from different survey projects to analyze them as a single dataset, and while teams engaged in data harmonization continue to expand the information they provide to end users, there are still no commonly agreed standards for the documentation of data processing. Existing harmonization project typically opt for recode scripts that are generally hard to read, modify, and reuse, although some projects make efforts to facilitate verification and reproduction. This paper describes an alternative procedure and a set of simple tools for the exploration, recoding, and documentation of harmonization of survey data, relying on crosswalks. The presented tools are flexible and software-agnostic. The illustrative example uses the programming language R and spreadsheets—both common software choices among social scientists. Harmonization of variables on trust in institutions from four major cross-national survey projects serves as an illustration of the proposed workflow and of opportunities harmonization creates.
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines

Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep the training processes busy. Maximizing resource utilization is becoming more challenging as the throughput of training processes increases with hardware innovations (e.g., faster GPUs, TPUs, and inter-connects) and advanced parallelization techniques that yield better scalability. At the same time, the amount of training data needed in order to train increasingly complex models is growing. As a consequence of this development, data preprocessing and provisioning are becoming a severe bottleneck in end-to-end deep learning pipelines. In this paper, we provide an in-depth analysis of data preprocessing pipelines from four different machine learning domains. We introduce a new perspective on efficiently preparing datasets for end-to-end deep learning pipelines and extract individual trade-offs to optimize throughput, preprocessing time, and storage consumption. Additionally, we provide an open-source profiling library that can automatically decide on a suitable preprocessing strategy to maximize throughput. By applying our generated insights to real-world use-cases, we obtain an increased throughput of 3x to 13x compared to an untuned system while keeping the pipeline functionally identical. These findings show the enormous potential of data pipeline tuning.
Hybrid Terrain Traversability Analysis in Off-road Environments

There is a significant growth in autonomy level in off-road ground vehicles. However, unknown off-road environments are often challenging due to their unstructured and rough nature. To find a path that the robot can move smoothly to its destination, it needs to analyse the surrounding terrain. In this paper, we present a hybrid terrain traversability analysis framework. Semantic segmentation is implemented to understand different types of the terrain surrounding the robot; meanwhile geometrical properties of the terrain are assessed with the aid of a probabilistic terrain estimation. The framework represents the traversability analysis on a robot-centric cost map, which is available to the path planners. We evaluated the proposed framework with synchronised sensor data captured while driving the robot in real off-road environments. This thorough terrain traversability analysis will be crucial for autonomous navigation systems in off-road environments.