Reviewerly/exHarmony huggingface dataset:
Motion sickness and sense of presence in a virtual reality environment developed for manual wheelchair users, with three different approaches

Visually Induced Motion Sickness (VIMS) is a bothersome and sometimes unsafe experience, frequently experienced in Virtual Reality (VR) environments. In this study, the effect of up to four training sessions to decrease VIMS in the VR environment to a minimal level was tested and verified through explicit declarations of all 14 healthy participants that were recruited in this study. Additionally, the Motion Sickness Assessment Questionnaire (MSAQ) was used at the end of each training session to measure responses to different aspects of VIMS. Total, gastrointestinal, and central motion sickness were shown to decrease significantly by the last training session, compared to the first one. After acclimatizing to motion sickness, participants' sense of presence and the level of their motion sickness in the VR environment were assessed while actuating three novel and sophisticated VR systems. They performed up to four trials of the Illinois agility test in the VR systems and the real world, then completed MSAQ and Igroup Presence Questionnaire (IPQ) at the end of each session. Following acclimatization, the three VR systems generated relatively little motion sickness and high virtual presence scores, with no statistically meaningful difference among them for either MSAQ or IPQ. Also, it was shown that presence has a significant negative correlation with VIMS.
A synthetic data generation procedure for univariate circular data with various outliers scenarios using Python programming language

Abstract Synthetic data is artificial data that is created based on the statistical properties of the original data. The aim of this study is to generate a synthetic or simulated data for univariate circular data that follow von Mises (VM) distribution with various outliers scenario using Python programming language. The procedure of formulation a synthetic data generation is proposed in this study. The synthetic data is generated from various combinations of seven sample size, n and five concentration parameters, K. Moreover, a synthetic data will be generated by formulating a data generation procedure with different condition of outliers scenarios. Three outliers scenarios are proposed in this study to introduce the outliers in synthetic dataset by placing them away from inliers at a specific distance. The number of outliers planted in the dataset are fixed with three outliers. The synthetic data is randomly generated by using Python library and package which are ‘numpy’, ‘random’ and von Mises’. In conclusion, the synthetic data of univariate circular data from von Mises distribution is generated and the outliers are successfully introduced in the dataset with three outliers scenarios using Python. This study will be valuable for those who are interested to study univariate circular data with outliers and choose Python as an analysis tool.
3D Modelling and FEM Analysis on Metal Coin Edge Punching Error

Abstract Minting metal coin, mean to impress a given relief model on both coin faces and also on the edge. Due to the high striking pressure, the round blank material is forced to fill the inner space between dies. In the past, for less coin value, the coin edge remained cylindrical, without any inscription. Following the increased coin value, it was introduced a security feature against forgery; on the coin edge model could be toothed or inscribed with relief or incuse text logo. Besides the coin edge basic design, a various amount of coin edge error types are released. In the paper there are studied some errors on the metal coin edge impressed with incuse text by punching. First, there are presented some particular examples and general aspects about the coin edge punching are also introduced. Then, the simplified 3D model of the parts is presented. Also, the finite element analysis on this model is realized and is achieved for different initial conditions. In the final part, the analysis results and conclusions are presented.
Multi-space Knowledge Enhanced Question Answering over Knowledge Graph

Knowledge graph question answering (KG-QA) accesses the substantial knowledge to return a comprehensive answer in a more user-friendly solution. Recently, the embedding-based methods to KG-QA have always been hot issues. Traditional embedding-based methods can not make full use of knowledge since they incorporating the knowledge by using a single semantic translation model to embed entities and relations. Semantic translation models based on non-Euclidean spaces can capture more kinds of latent information because they can focus on the characteristics of different aspects of knowledge. In this paper, we propose the multi-space knowledge enhanced question answering model and mine the latent information of knowledge in different embedding spaces to improve the KG-QA. In addition, Transformer is used to replace the traditional Bi-LSTM to obtain the vector representation of question, and specially designed attention mechanism is used to calculate the score of candidate answers dynamically. The experiment conducted on the WebQuestions dataset shows that compared with other state-of-art QA systems, our method can effectively improve the accuracy.
Seek Common Ground While Reserving Differences: A Model-Agnostic Module for Noisy Domain Adaptation

Noisy domain adaptation aims to solve the problem that the source dataset contains noisy labels in domain adaptation. Previous methods handle noisy labels by selecting the small-loss samples with inconsistent predictions between two models and discarding the consistent samples, resulting in many noises contained in the selected samples. By jointly considering the consistent and inconsistent samples, we propose a model-agnostic module, named Seek Common Ground While Reserving Differences (SCGWRD), to reduce the impact of noisy samples. The proposed SCGWRD module consists of Seek Common Ground (SCG) component and Reserve Differences (RD) component by utilizing the outputs of two symmetrical domain adaptation models. As the common samples with consistent predictions between two models are more likely to be clean samples, the SCG component applies the small-loss strategy to select the reliable samples with consistent predictions. Unlike SCG, the RD component maintains the divergences between two models with mutual learning and reduces the effect of noisy data using the samples with different predictions and small losses. Evaluations on three benchmarks demonstrate the effectiveness and robustness of the proposed SCGWRD module for noisy domain adaptation.
Dynamic Grids for Finite-Difference Schemes in Musical Instrument Simulations

For physical modelling sound synthesis, many techniques are available; time-stepping methods (e.g., finite-difference time-domain (FDTD) methods) have an advantage of flexibility and generality in terms of the type of systems they can model. These methods do, however, lack the capability of easily handling smooth parameter changes while retaining optimal simulation quality and stability, something other techniques are better suited for. In this paper, we propose an efficient method to smoothly add and remove grid points from a FDTD simulation under sub-audio rate parameter variations. This allows for dynamic parameter changes in physical models of musical instruments. An instrument such as the trombone can now be modelled using FDTD methods, as well as physically impossible instruments where parameters such as e.g. material density or its geometry can be made time-varying. Results show that the method does not produce (visible) artifacts and stability analysis is ongoing.
A Novel Method For Segmentation Of Breast Masses Based On Mammography Images

The accurate segmentation of breast masses in mammography images is a key step in the diagnosis of early breast cancer. To solve the problem of various shapes and sizes of breast masses, this paper proposes a cascaded UNet architecture, which is referred to as CasUNet. CasUNet contains six UNet subnetworks, the network depth increases from 1 to 6, and the output features between adjacent subnetworks are cascaded. Furthermore, we have integrated the channel attention mechanism based on CasUNet, hoping that it can focus on the important feature maps. Aiming at the problem that the edges of irregular breast masses are difficult to segment, a multi-stage cascaded training method is presented, which can gradually expand the context information of breast masses to assist the training of the segmentation model. To alleviate the problem of fewer training samples, a data augmentation method for background migration is proposed. This method transfers the background of the unlabeled samples to the labeled samples through the histogram specification technique, thereby improving the diversity of the training data. The above method has been experimentally verified on two datasets, INbreast and DDSM. Experimental results show that the proposed method can obtain competitive segmentation performance.
Action Prediction Using Extremely Low-Resolution Thermopile Sensor Array For Elderly Monitoring

Accident anticipation for monitoring the elderly is an important topic given the global issue of the rapidly aging population. In this work, we propose a novel approach to elderly accident prevention by using a low-resolution (32x32 pixels) infrared camera - thermopile sensor array (TPA) - for the action prediction task. Such a kind of sensor ensures that the monitoring system is cost-effective, does not interfere with daily life of the user, and most importantly fully preserves their privacy which makes it suitable for use in hospitals, nursing homes and private residences. As the majority of accidents involving the elderly occurs when a person attempts to exit the bed unassisted, we concentrate our efforts on predicting that an elderly person will attempt to get off the bed without asking for help. Our system raises an alarm in such a case and informs the caregiver so that they can intervene and offer assistance. Our designed deep-learning model can predict that the elderly patient monitored has the intention to get off the bed by 8.12 seconds at an accuracy of 96.51% (based on our own dataset collected), on average according to experiments, before the action onset is observed.
Deep Learning for Frame Error Prediction Using a DARPA Spectrum Collaboration Challenge (SC2) Dataset

We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge. Four scenarios are considered based on randomizing or fixing the strategy for bandwidth and channel allocation, and either training and testing with different links or using a pilot phase for each link to train the deep neural network. We also investigate the effect of latency constraints, and uncover interesting characteristics of the predictor over different Signal to Noise Ratio (SNR) ranges. The obtained insights open the door for implementing a deep-learning-based strategy that is scalable to large heterogeneous networks, generalizable to diverse wireless environments, and suitable for predicting frame error instances and rates within a congested shared spectrum.
Bi-directional Recurrent MVSNet for High-resolution Multi-view Stereo

Learning-based multi-view stereo regularizes cost volumes containing spatial information to reduce noise and improve the quality of a depth map. Cost volume regularization using 3D CNNs consumes a large amount of memory, making it difficult to scale up the network architecture. Recent work proposed a cost-volume regularization method that applies 2D convolutional GRUs and significantly reduces memory consumption. However, this uni-directional recurrent processing has a narrower receptive field than 3D CNNs because the regularized cost at a time step does not contain information about future time steps. In this paper, we propose a cost volume regularization method using bi-directional GRUs that expands the receptive field in the depth direction. In our experiments, our proposed method significantly outperforms the conventional methods in several benchmarks while maintaining low memory consumption.
Partially scattered linearized polynomials and rank metric codes

A linearized polynomial f(x)∈Fqn[x] is called scattered if for any y,z∈Fqn, the condition zf(y)−yf(z)=0 implies that y and z are Fq-linearly dependent. In this paper two generalizations of the notion of a scattered linearized polynomial are provided and investigated. Let t be a nontrivial positive divisor of n. By weakening the property defining a scattered linearized polynomial, L-qt-partially scattered and R-qt-partially scattered linearized polynomials are introduced in such a way that the scattered linearized polynomials are precisely those which are both L-qt- and R-qt-partially scattered. Also, connections between partially scattered polynomials, linear sets and rank metric codes are exhibited.
Evaluating deep neural networks for image document enhancement

This work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively (i. e. with manual visual inspection) and quantitatively (i. e. using Image Quality Assessment metrics - IQA), and also compared with an existing approach based on traditional computer vision techniques. The best performing architectures generally produced good enhancement compared to the existing algorithm, showing that it is possible to use DNNs for document image enhancement. Furthermore, the best performing architectures could work as a baseline for future investigations on document enhancement using deep learning techniques. The main contributions of this paper are: a baseline of deep learning techniques that can be further improved to provide better results, and a evaluation methodology using IQA metrics for quantitatively comparing the produced images from the neural networks to a ground truth.
Lightweight Convolutional Neural Networks with Model-Switching Architecture for Multi-Scenario Road Semantic Segmentation

A convolutional neural network (CNN) that was trained using datasets for multiple scenarios was proposed to facilitate real-time road semantic segmentation for various scenarios encountered in autonomous driving. However, the CNN inhibited the mutual suppression effect between weights; thus, it did not perform as well as a network that was trained using a single scenario. To address this limitation, we used a model-switching architecture in the network and maintained the optimal weights of each individual model which required considerable space and computation. We, subsequently, incorporated a lightweight process into the model to reduce the model size and computational load. The experimental results indicated that the proposed lightweight CNN with a model-switching architecture outperformed and was faster than the conventional methods across multiple scenarios in road semantic segmentation.
Efficient Facial Reconstruction and Real-time Expression for VR Interaction Using RGB-D Videos

We present an efficient face reconstruction and real-time facial expression for VR interaction driven by RGB-D videos. A RGB-D camera is first used to capture depth images, and a coarse face model is then rapidly reconstructed. The user’s personalized avatar is generated using pre-defined face model template and shape morphing techniques. We track the user’s head motion and face expression using a RGB camera. A set of facial features are located and labelled on the colour images. Corresponding facial features are automatically labelled on the reconstructed face model. The user’s virtual avatar is driven by the set of facial features using Laplacian deformation. We demonstrate that our algorithm is able to rapidly create a personalized face model using depth images and achieve realtime facial expression for VR interaction using live RGB videos. Our algorithm can be used in online learning environments that allow learners to interact with simulated and controlled virtual agents.
ComplexMix: Semi-Supervised Semantic Segmentation Via Mask-Based Data Augmentation

Semantic segmentation using convolutional neural networks (CNN) is a crucial component in image analysis. Training a CNN to perform semantic segmentation requires a large amount of labeled data, where the production of such labeled data is both costly and labor intensive. Semi-supervised learning algorithms address this issue by utilizing unlabeled data and so reduce the amount of labeled data needed for training. In particular, data augmentation techniques such as CutMix and ClassMix generate additional training data from existing labeled data. In this paper we propose a new approach for data augmentation, termed ComplexMix, which incorporates aspects of CutMix and ClassMix with improved performance. The proposed approach has the ability to control the complexity of the augmented data while attempting to be semantically-correct and address the tradeoff between complexity and correctness. The proposed ComplexMix approach is evaluated on a standard dataset for semantic segmentation and compared to other state-of-the-art techniques. Experimental results show that our method yields improvement over state-of-the-art methods on standard datasets for semantic image segmentation.
Mobile Device and Social Media Forensic Analysis: Impacts on Cyber-Crime

This research work has focused on a digital forensic analysis of social media through mobile devices to determine the primary criminal. This proposed system considered the mobile device used by the prime suspect as the main evidence of cybercrime and tried to find out the degree of criminal involvement in terms of probability likelihood. At first, the most critical data elements were obtained, e.g., deleted files and keywords, through the forensic analysis of the mobile device. This will also help in the identification of the main culprits in the investigation of cybercrime. Next, the system classified the criminals in one of three zones based on the analysis of the keywords to determine the level of crime. The system also takes into account the most probable timeframe for the crime. Thus, the proposed system helps to identify the main culprits in investigating cybercrime more efficiently than the traditional approaches. The system is also looking into the most probable timeframe for the crime, for example, it has been observed that most cybercrime happens on the weekends. The proposed system investigates using cookies and the logical image of the device that cyber criminals left behind.
Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images

Unmanned Aerial Vehicles (UAVs) can serve as an ideal mobile platform in various situations. Real-time object detection with on-board apparatus provides drones with increased flexibility as well as a higher intelligence level. In order to achieve good detection results in UAV images with complex ground scenes, small object size and high object density, most of the previous work introduced models with higher computational burdens, making deployment on mobile platforms more difficult.This paper puts forward a lightweight object detection framework. Besides being anchor-free, the framework is based on a lightweight backbone and a simultaneous up-sampling and detection module to form a more efficient detection architecture. Meanwhile, we add an objectness branch to assist the multi-class center point prediction, which notably improves the detection accuracy and only takes up very little computing resources. The results of the experiment indicate that the computational cost of this paper is 92.78% lower than the CenterNet with ResNet18 backbone, and the mAP is 2.8 points higher on the Visdrone-2018-VID dataset. A frame rate of about 220 FPS is achieved. Additionally, we perform ablation experiments to check on the validity of each part, and the method we propose is compared with other representative lightweight object detection methods on UAV image datasets.
Few-Shot Personalized Saliency Prediction using Person Similarity based on Collaborative Multi-Output Gaussian Process Regression

A few-shot personalized saliency prediction method using person similarity based on collaborative multi-output Gaussian process regression is presented in this paper. Contrary to prediction of general saliency maps, that of personalized saliency maps (PSMs), which is a focus of attention owing to its heterogeneity among individuals, is a challenging problem since the amount of training gaze data is limited due to the burden on new persons. Thus, the proposed method focuses on the similarity of gaze tendency between persons. In the proposed method, collaborative Gaussian process regression (CoMOGP) is adopted for PSM prediction. CoMOGP enables to represent similarity of gaze tendency between the target person and other persons as weights, and then consider the similarity for each image by using visual features obtained from images as inputs. The contributions of the few-shot PSM prediction based on CoMOGP are two-folds. 1) CoMOGP, which is one of probabilistic methods, can avoid the overfitting to small amount of training data. 2) Similarity for each image can be considered by using visual features as inputs. In the experiment using the open dataset, the proposed method outperforms comparative methods including the state-of-the-art method.
Unsupervised Learning of Shape-invariant Lie Group Transformer by Embedding Ordinary Differential Equation

Humans can recognize geometric transformations, such as translation or rotation, regardless of the original patterns. Transformation recognition is subsumed under the Lie group theory. As is known, as humans grow, they acquire the transformation recognition ability and mental transformers to apply to any imagined objects. However, the underlying mechanism of transformation recognition remains unclear. Although transformation discovery models have been researched extensively, many of these studies used an unrealistic amount of dataset, which is unsuitable for modeling the transformation recognition ability of humans, who can immediately recognize transformations using only a few examples. Therefore, in this paper, we propose a new approach to learning Lie group transformers from a few sequences of images in an unsupervised way by embedding ordinary differential equation dynamics in the transformer, which can naturally be derived from the Lie group theory. Unlike the conventional models, our model considers objects as a collection of tiny points, to which it locally applies a transformation to ensure the invariance to the shape of objects. We train our model to discover the transformation in a custom dataset of translation, rotation, or scaling transformation and successfully acquire a Lie group operator that is applicable to unseen objects outside the dataset.