Reviewerly/exHarmony huggingface dataset:
Multiview Deep Online Clustering: An Application to Online Research Topic Modeling and Recommendations

In today’s scenario, a large number of scientific articles on various domains are being published everyday, resulting in a rapid change in the trends of research topics. Retrieving the trending topics, evaluating the trends and extracting the scope of topics could be beneficial to young researchers, which can be recommended for future scope. Publication of articles is a continuous process, and so is the evolution of topics as well as the scope. The dynamic behavior of topics can be handled by continuously updating the partitioning of incoming articles arriving in a streaming manner. This article proposes an online clustering approach utilizing the autoencoder, which is capable of handling the online stream of articles. The training is performed by considering the multiple views of articles and posing the topic modeling (TM) problem as a multiview (MV) clustering problem. In addition, we employ an evolutionary-based approach to the latent representation of data to automatically determine the number of clusters. To learn the nonlinear mapping appropriately and to generate clusters effectively, the model simultaneously optimizes the reconstruction loss and clustering loss in an MV framework. The developed method has experimented on ArXiv dataset to determine the trending topics, scope, and recommendation of topics. The superiority of the proposed method in generating clusters as well as in determining scope is shown by comparing the results with many existing and baseline methods.
DynaLAP: Human Activity Recognition in Fixed Protocols via Semi-Supervised Variational Recurrent Neural Networks With Dynamic Priors

Learning the route and order of tasks can be critical to human activity recognition (HAR) for fixed protocols of movement. In this article, we propose a novel framework, DynaLAP, a semi-supervised variational recurrent neural network (VRNN) with a dynamic prior distribution, to perform activity recognition in fixed protocols. DynaLAP takes single tri-axial accelerometry data as input and causally classifies the activity of 10–30-s windows at a time. DynaLAP learns not only a window-specific short-term state, but also a long-term dynamic state iteratively updated throughout the protocol’s measurements. Additionally, instead of using a stationary prior distribution of activity classes, DynaLAP learns a dynamic prior that updates for each window. DynaLAP thereby learns protocol-specific dynamics when trained on data from subjects abiding by a fixed protocol. Two datasets from previously published literature were used to evaluate DynaLAP: the fully labeled MotionSense dataset of 24 subjects and a weakly labeled dataset of 17 subjects collected at the Georgia Institute of Technology. For each dataset, we varied the number of training labels used from a single subject’s data to the entire dataset. DynaLAP outperformed previous supervised and semi-supervised HAR approaches by 6–42 percentage points, with F1 scores that remained above 80%. These results suggest that DynaLAP can achieve state-of-the-art HAR performance in fixed protocols by learning protocol-specific dynamics, especially in weakly and scarcely labeled settings. DynaLAP could ultimately reduce the necessity for labor-intensive annotation efforts in HAR applications involving routine activities (e.g., military training).
A Coarse-to-Fine Human Visual Focus Estimation for ASD Toddlers in Early Screening

Human visual focus is a vital feature to uncover subjects' underlying cognitive processes. To predict the subject's visual focus, existing deep learning methods learn to combine the head orientation, location, and scene content for estimating the visual focal point. However, these methods mainly face three problems: the visual focal point prediction solely depends on learned spatial distribution heatmaps, the reasoning process in post-processing is non-learnable, and the learning of gaze salience representation could utilize more prior knowledge. Therefore, we propose a coarse-to-fine human visual focus estimation method to address these problems, for improving estimation performance. To begin with, we introduce a coarse-to-fine regression module, in which the coarse branch aims to estimate the subject's possible attention area while the fine branch directly outputs the estimated visual focal point position, thus avoiding sequential reasoning and making visual focal point estimation is totally learnable. Furthermore, the human visual field prior is used to guide the learning of gaze salience for better encoding target-related representation. Extensive experimental results demonstrate that our method outperforms existing state-of-the-art methods on self-collected ASD-attention datasets.
Recent Development on Robot Assisted Social Skills Intervention of Children with ASD

Children with autism spectrum disorder (ASD) have significant challenges in social interaction. With the rapid development of intelligent robot technology, robot assisted ASD children therapy has penetrated into the intervention of social skills. In this paper, we mainly focus on reviewing the recent development on robot assisted social skills intervention of children with ASD. First, five kinds of robots for ASD children assisted diagnosis and treatment are introduced and the respective advantages and disadvantages are analyzed. Then, five robot assisted social skill intervention scenarios are reviewed and analyzed. Finally, the future research directions on robot assisted ASD children diagnosis and treatment are proposed.
Citizen Manufacturing: Unlocking a New Era of Digital Innovation

We have all come to expect—if not depend upon—the steady march of technology. All manner of pervasive computing devices, applications, and services increasingly support us at home and work. Thankfully, for those tasked with creating future generations of innovative digital technologies, the design and prototyping process continues to get easier. But for hardware, the transition from device prototype to production presents a bottleneck that is restricting the rate and nature of innovation. Imagine instead a world of citizen manufacturing, where individuals are empowered to turn their ideas not only into working prototypes, but also to evolve them organically and seamlessly into viable products. Such an approach could increase consumer choice, grow local and national economies, and facilitate more socially conscious production. We call on the community to join us in pursuit of this goal! We believe that overcoming the many challenges of transitioning from device prototyping to production will unlock a new era of digital innovation that allows more people to explore and benefit from pervasive computing.
Research on Feature Matching Based on Improved RANSAC Algorithm

Aiming at the problems that the current RANSAC (Random Sample Consensus) algorithm has too large randomness and is susceptible to external point interference, which leads to the reduction of matching accuracy, an improved RANSAC algorithm combining feature matching confidence and grid clustering is proposed. Firstly, rough matching is carried out by the FLANN algorithm, and confidence analysis is carried out on the coarse matching point pairs, then expanding grid clustering around the high confidence point pairs. Multiple local optimal interior points are screened to optimize the global interior points and improve the matching accuracy of feature points. The experimental results show that the improved RANSAC in this paper increases the existence probability of interior points, avoids too many wrong feature matching affecting the model effect of the homography matrix, and improves the accuracy of feature matching.
Improved IMM algorithm based on support vector regression for UAV tracking

With the development of technology, the relevant performance of unmanned aerial vehicles (UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology.Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed, angle, and acceleration.However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation.This paper applies support vector regression (SVR) to the interacting multiple model (IMM) algorithm.The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.
A Self-Adaptive Discriminative Autoencoder for Medical Applications

Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to obtain the highly recognizable representation for the medical images, a self-adaptive discriminative autoencoder (SADAE) is proposed in this paper. The proposed SADAE system is implemented under a deep metric learning framework which consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> local autoencoders, employed to learn the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> subspaces that represent the diverse distribution of the underlying data, and a global autoencoder to restrict the spatial scale of the learned representation of images. Such community of autoencoders is aided by a self-adaptive metric learning method that extracts the discriminative features to recognize the different categories in the given images. The quality of the extracted features by SADAE is compared against that of those extracted by other state-of-the-art deep learning and metric learning methods on five popular medical image data sets. The experimental results demonstrate that the medical image recognition results gained by SADAE are much improved over those by the alternatives.
Knowledge-Enhanced Scene Context Embedding for Object-Oriented Navigation of Autonomous Robots

Object-oriented navigation in unknown environments with only vision as input has been a challenging task for autonomous robots. Introducing semantic knowledge into the model has been proved to be an effective means to improve the suboptimal performance and the generalization of existing end-to-end learning methods. In this paper, we improve object-oriented navigation by proposing a knowledge-enhanced scene context embedding method, which consists of a reasonable knowledge graph and a designed novel 6-D context vector. The developed knowledge graph (named MattKG) is derived from large-scale real-world scenes and contains object-level relationships that are expected to assist robots to understand the environment. The designed novel 6-D context vector replaces traditional pixel-level raw features by embedding observations as scene context. The experimental results on the public dataset AI2-THOR indicate that our method improves both the navigation success rate and efficiency compared with other state-of-the-art models. We also deploy the proposed method on a physical robot and apply it to the real-world environment.
Human Following for Mobile Robots

Human following is an essential function in many robotic systems. Most of the existing human following algorithms are based on human tracking algorithms. However, in practical scenarios, the human subject might easily disappear due to occlusions and quick movements. In order to solve the problem of occlusion, this paper proposed a classification-based human following framework. After using a pre-trained MobileNetV2 model to detect the human subjects, the robot will automatically train a classification model to identify the target person. In the end, the robot is controlled by some rule-based motion commands to follow the target human. Experimental results on several practical scenarios have demonstrated the effectiveness of the algorithm.
Spatiotemporal Feature Hierarchy-Based Blind Prediction of Natural Video Quality via Transfer Learning

In this paper, we propose a pyramidal spatiotemporal feature hierarchy (PSFH)-based no-reference (NR) video quality assessment (VQA) method using transfer learning. First, we generate simulated videos by a generative adversarial network (GAN)-based image restoration model. The residual maps between the distorted frames and simulated frames, which can capture rich information, are utilized as one input of the quality regression network. Second, we use 3D convolution operations to construct a PSFH network with five stages. The spatiotemporal features incorporating the shared features transferred from the pretrained image restoration model are fused stage by stage. Third, with the guidance of the transferred knowledge, each stage generates multiple feature mapping layers that encode different semantics and degradation information using 3D convolution layers and gated recurrent units (GRUs). Finally, five approximate perceptual quality scores and a precise prediction score are obtained by fully connected (FC) networks. The whole model is trained under a finely designed loss function that combines pseudo-Huber loss and Pearson linear correlation coefficient (PLCC) loss to improve the robustness and prediction accuracy. According to the extensive experiments, outstanding results can be obtained compared with other state-of-the-art methods. Both the source code and models are available online. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</xref>
Application of Virtual Reality for Teaching Rock Climbing in Colleges Using Big Data

Since rock climbing was added to the Olympic Games, nations around the globe have started to pay more attention to the instruction of rock climbing and have established it as a program in colleges and universities to train college students and adolescents, who make up the majority of rock climbers. However, there are many problems faced while teaching rock climbing in colleges and universities, such as no standardized teaching materials, short teaching time, and cumbersome teaching content, which are few to mention. As a result, students can only master the basic methods and techniques of climbing on rock while studying at colleges and institutions, and their climbing ability cannot be improved. Most of the current college’s rock climbing teachers only pay attention to the technical problems. The correct methodology of teaching is a vital factor for student’s guidance. To overcome these issues, this research demonstrates the virtual reality in classroom instruction; this has the potential to improve the impact and standard of collegiate rock climbing. The authors focus on the concept of virtual reality technology, technical characteristics, virtual reality system composition, and virtual reality modeling and apply the theory and technology to college rock climbing teaching for building a teaching model basis on virtual reality in this research paper. This model is applied to college rock climbing teaching, and the experimental group and the control group are set to analyze the students’ interest in rock climbing teaching. The comparison demonstrates that employing virtual reality technology dramatically increases the experimental group students’ enthusiasm in rock climbing education. Upon analyzing the students’ recognition rock climbing instruction in colleges and institutions, the result shows that 93.3% of the experimental group and 60% of the control group agree that the rock climbing course can motivate their own learning initiative and enthusiasm. When analyzing the motivation of students to learn rock climbing, the highest proportion is to get credit and recognition, the proportion of which is 35.6%.
PergaNet: A Deep Learning Framework for Automatic Appearance-Based Analysis of Ancient Parchment Collections

Archival institutions and program worldwide work to ensure that the records of governments, organizations, communities and individuals be preserved for the next generations as cultural heritage, as sources of rights, and to hold the past accountable. The digitalization of ancient written documents made of parchment were an important communication mean to humankind and have an invaluable historical value to our culture heritage (CH). Automatic analysis of parchments has become an important research topic in fields of image and pattern recognition. Moreover, Artificial Intelligence (AI) and its subset Deep Learning (DL) have been receiving increasing attention in pattern representation. Interest in applying AI to ancient image data analysis is becoming mandatory, and scientists are increasingly using it as a powerful, complex, tool for statistical inference. In this paper it is proposed PergaNet a lightweight DL-based system for historical reconstructions of ancient parchments based on appearance-based approaches. The aim of PergaNet is the automatic analysis and processing of huge amount of scanned parchments. This problem has not been properly investigated by the computer vision community yet due to the parchment scanning technology novelty, and it is extremely important for effective data recovery from historical documents whose content is inaccessible due to the deterioration of the parchment. The proposed approach aims at reducing hand-operated analysis and at the same time at using manual annotation as a form of continuous learning. PergaNet comprises three important phases: classification of parchments recto/verso, the detection of text, then the detection and recognition of the “signum tabellionis”. PergaNet concerns not only the recognition and classification of the objects present in the images, but also the location of each of them. The analysis is based on data from the ordinary use and does not involve altering or manipulating techniques in order to generate data.
StyleTrendGAN: A Deep Learning Generative Framework for Fashion Bag Generation

Dealing with fashion multimedia big data with Artificial Intelligence (AI) algorithms has become an appealing challenge for computer scientists, since it can serve as inspiration for fashion designers and can also allow to predict the next trendy items in the fashion industry. Moreover, with the global spread of COVID-19 pandemic, social media contents have achieved an increasingly crucial factor in driving retail purchase decisions, thus it has become mandatory for fashion brand analysing social media pictures. In this light, this paper aims at presenting StyleTrendGAN, a novel custom deep learning framework that has the ability to generate fashion items. StyleTrendGAN combines a Dense Extreme Inception Network (DexiNed) for sketches extraction and Pix2Pix for the transformation of the input sketches into the new handbag models. StyleTrendGAN increases the efficiency and accuracy of the creation of new fashion models compared to previous ones and to the classic human approach; it aims to stimulate the creativity of designers and the visualization of the results of a production process without actually putting it into practice. The approach was applied and tested on a newly collected dataset, “MADAME” (iMage fAshion Dataset sociAl MEdia) of images collected from Instagram. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.
Generating High-Resolution 3D Faces Using VQ-VAE-2 with PixelSNAIL Networks

The realistic generation of synthetic 3D faces is an open challenge due to the complexity of the geometry and the lack of large and diverse publicly available datasets. Generative models based on convolutional neural networks (CNNs) have recently demonstrated great ability to produce novel synthetic high-resolution images indistinguishable from the original pictures by an expert human observer. However, applying them to non-grid-like data like 3D meshes presents many challenges. In our work, we overcome the challenges by first reducing the face mesh to a 2D regular image representation and then exploiting one prominent state-of-the-art generative approach. The approach uses a Vector Quantized Variational Autoencoder VQ-VAE-2 to learn a latent discrete representation of the 2D images. Then, the 3D synthesis is achieved by fitting the latent space and sampling it with an autoregressive model, PixelSNAIL. The quantitative and qualitative evaluation demonstrate that synthetic faces generated with our method are statistically closer to the real faces when compared to a classical synthesis approach based on Principal Component Analysis (PCA).
Intelligent Video Surveillance for Animal Behavior Monitoring

The behavior of animals reflects their internal state. Changes in behavior, such as a lack of sleep, can be detected as early warning signs of health issues. Zoologists are often required to use video recordings to study animal activity. These videos are generally not sufficiently indexed, so the process is long and laborious, and the observation results may vary between the observers. This study looks at the difficulty of measuring elephant sleep stages from surveillance videos of the elephant bran at night. To assist zoologists, we propose using deep learning techniques to automatically locate elephants in each camera surveillance, then mapping the elephants detected onto the barn plan. Instead of watching all of the videos, zoologists will examine the mapping history, allowing them to measure elephant sleeping stages faster. Overall, our approach monitors elephants in their barn with a high degree of accuracy.
High-Level Feature Extraction for Crowd Behaviour Analysis: A Computer Vision Approach

The advent of deep learning has brought in disruptive techniques with unprecedented accuracy rates in so many fields and scenarios. Tasks such as the detection of regions of interest and semantic features out of images and video sequences are quite effectively tackled because of the availability of publicly available and adequately annotated datasets. This paper describes a use case scenario with a deep learning models' stack being used for crowd behaviour analysis. It consists of two main modules preceded by a pre-processing step. The first deep learning module relies on the integration of YOLOv5 and DeepSORT to detect and track down pedestrians from CCTV cameras' video sequences. The second module ingests each pedestrian's spatial coordinates, velocity, and trajectories to cluster groups of people using the Coherent Neighbor Invariance technique. The method envisages the acquisition of video sequences from cameras overlooking pedestrian areas, such as public parks or squares, in order to check out any possible unusualness in crowd behaviour. Due to its design, the system first checks whether some anomalies are underway at the microscale level. Secondly, It returns clusters of people at the mesoscale level depending on velocity and trajectories. This work is part of the physical behaviour detection module developed for the S4AllCities H2020 project.
Intra-language: the study of L2 morpheme productivity as within-item variance

Abstract This article suggests a method to appraise L2 morphemes productivity in longitudinal learner data. Traditionally, morpheme productivity is believed to depend on type frequency and on proportion between inflected and uninflected lexemes. However, such measures cannot distinguish between rote-learning and rule-learning of target-like forms. In contrast, the association measure Δ P (delta pi) can quantify the extent to which a morpheme is contingent upon a limited number of lexemes. Decreasing contingency might parallel learners’ increasing awareness of asymmetrical morpheme-lexeme distribution in the input and this might be a cue of developing L2 grammatical competence beyond appearances. The paper presents the rationale and procedure for analyzing within-item variance – or the ‘intra-language’ – and illustrates a case-study concerning the perfective morpheme in L2 Italian.