Reviewerly/exHarmony huggingface dataset:
Automated grading of non-small cell lung cancer by fuzzy rough nearest neighbour method

Lung cancer is one of the most lethal diseases across the world. Most lung cancers belong to the category of non-small cell lung cancer (NSCLC). Many studies have so far been carried out to avoid the hazards and bias of manual classification of NSCLC tumors. A few of such studies were intended towards automated nodal staging using the standard machine learning algorithms. Many others tried to classify tumors as either benign or malignant. None of these studies considered the pathological grading of NSCLC. Automated grading may perfectly depict the dissimilarity between normal tissue and cancer affected tissue. Such automation may save patients from undergoing a painful biopsy and may also help radiologists or oncologists in grading the tumor or lesion correctly. The present study aims at the automated grading of NSCLC tumors using the fuzzy rough nearest neighbour (FRNN) method. The dataset was extracted from The Cancer Imaging Archive and it comprised PET/CT images of NSCLC tumors of 211 patients. Accelerated segment test (FAST) and histogram oriented gradients methods were used to detect and extract features from the segmented images. Gray level co-occurrence matrix (GLCM) features were also considered in the study. The features along with the clinical grading information were fed into four machine learning algorithms: FRNN, logistic regression, multi-layer perceptron, and support vector machine. The results were thoroughly compared in the light of various evaluation-metrics. The confusion matrix was found balanced, and the outcome was found more cost-effective for FRNN. Results were also compared with various other leading studies done earlier in this field. The proposed FRNN model performed satisfactorily during the experiment. Further exploration of FRNN may be very helpful for radiologists and oncologists in planning the treatment for NSCLC. More varieties of cancers may be considered while conducting similar studies.
kdensity: An R package for kernel density estimation with parametric starts and asymmetric kernels

It is often necessary to estimate a probability density non-parametrically, that is, without making strong parametric assumptions such as normality.This R (R Core Team, 2019) package provides a non-parametric density estimator that can take advantage of some of the knowledge the user has about the probability density.Kernel density estimation (Silverman, 2018) is a popular method for non-parametric density estimation based on placing kernels on each data point.Hjort & Glad (1995) extended kernel density estimation with parametric starts.The parametric start is a parametric density that is multiplied with the kernel estimate.When the data-generating density is reasonably close to the parametric start density, kernel density estimation with that parametric start will outperform ordinary kernel density estimation.
A quantitative method for evaluation of 6 degree of freedom virtual reality systems

Modern virtual reality systems such as the HTC Vive enable users to be immersed in a virtual world. Validation of the HTC Vive and other contemporaneous systems for use in clinic, research, and industry applications will assure users and developers that games and applications made for these systems are accurate representations of the real world. The purpose of this study was to develop a standardized method for testing the translational and rotational capabilities of VR systems such as the HTC Vive. The translational and rotational capabilities of the HTC Vive were investigated using an industry grade robot arm and a gold standard motion capture system. It was found that the average difference between reported translational distances traveled was 0.74 ± 0.42 mm for all room-scale calibration trials and 0.63 ± 0.27 mm for all standing calibration trials. The mean difference in angle rotated was 0.46 ± 0.46° for all room-scale calibration trials and 0.66 ± 0.40° for all standing calibration trials. When tested using human movement, the average difference in distance traveled was 3.97 ± 3.37 mm. Overall, the HTC Vive shows promise as a tool for clinic, research, and industry and its controllers can be accurately tracked in a variety of situations. The methodology used for this study can easily be replicated for other VR systems so that direct comparisons can be made as new systems become available.
Optimal Design of PID Controller for 2-DOF Drawing Robot Using Bat-Inspired Algorithm

Tuning process which is used to find the optimum values of the proportional integral derivative (PID) parameters, can be performed automatically using meta-heuristics algorithms such as BA (Bat Algorithm), PSO (Particle Swarm Optimization) and ABC (Artificial Bee Colony). This paper presented a theoretical and practical implementation of a drawing robot using BA to tune the PID controller governing the robotic arm which is a non linear system difficult to be controlled using classical control. In line with the above and in order to achieve this aim and meet high performance feedback and robust dynamic stability of the system, the PID controller is designed considering the realistic constraints. For faster tuning of the controller parameters, ten individuals and five iterations have been selected. BA, ABC and PSO have been compared and it's noticed that BA is the best choice to achieve good performance control. In the proposed design, MATLAB was used for trajectory reckoning. Afterwards, the value of coordinate position of the shape to be drawn is translated into a joint angle by applying the inverse kinematics to control the two DC motors through the ATMEGA 2560 microcontroller. The suggested technique reveals via simulations and hardware implementation the high efficiency of the applied algorithm. The PID controller approach presents an impressive stability and robustness results. The achieved results demonstrated that high performance can be obtained by tuning a PID controller using nature inspired based algorithms.
A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement

Deep neural networks for image quality enhancement typically need large quantities of highly-curated training data comprising pairs of low-quality images and their corresponding high-quality images. While high-quality image acquisition is typically expensive and time-consuming, medium-quality images are faster to acquire, at lower equipment costs, and available in larger quantities. Thus, we propose a novel generative adversarial network (GAN) that can leverage training data at multiple levels of quality (e.g., high and medium quality) to improve performance while limiting costs of data curation. We apply our mixed-supervision GAN to (i) super-resolve histopathology images and (ii) enhance laparoscopy images by combining super-resolution and surgical smoke removal. Results on large clinical and pre-clinical datasets show the benefits of our mixed-supervision GAN over the state of the art.
Road Layout Understanding by Generative Adversarial Inpainting

Autonomous driving is becoming a reality, yet vehicles still need to rely on complex sensor fusion to understand the scene they act in. The ability to discern static environment and dynamic entities provides a comprehension of the road layout that poses constraints to the reasoning process about moving objects. We pursue this through a GAN-based semantic segmentation inpainting model to remove all dynamic objects from the scene and focus on understanding its static components such as streets, sidewalks and buildings. We evaluate this task on the Cityscapes dataset and on a novel synthetically generated dataset obtained with the CARLA simulator and specifically designed to quantitatively evaluate semantic segmentation inpaintings. We compare our methods with a variety of baselines working both in the RGB and segmentation domains.
Measurement of the Three-Dimensional Shape of Discontinuous Specular Objects Using Infrared Phase-Measuring Deflectometry

Phase-measuring deflectometry (PMD)-based methods have been widely used in the measurement of the three-dimensional (3D) shape of specular objects, and the existing PMD methods utilize visible light. However, specular surfaces are sensitive to ambient light. As a result, the reconstructed 3D shape is affected by the external environment in actual measurements. To overcome this problem, an infrared PMD (IR-PMD) method is proposed to measure specular objects by directly establishing the relationship between absolute phase and depth data for the first time. Moreover, the proposed method can measure discontinuous surfaces. In addition, a new geometric calibration method is proposed by combining fringe projection and fringe reflection. The proposed IR-PMD method uses a projector to project IR sinusoidal fringe patterns onto a ground glass, which can be regarded as an IR digital screen. The IR fringe patterns are reflected by the measured specular surfaces, and the deformed fringe patterns are captured by an IR camera. A multiple-step phase-shifting algorithm and the optimum three-fringe number selection method are applied to the deformed fringe patterns to obtain wrapped and unwrapped phase data, respectively. Then, 3D shape data can be directly calculated by the unwrapped phase data on the screen located in two positions. The results here presented validate the effectiveness and accuracy of the proposed method. It can be used to measure specular components in the application fields of advanced manufacturing, automobile industry, and aerospace industry.
Score Following as a Multi-Modal Reinforcement Learning Problem

Score following is the process of tracking a musical performance (audio) in a corresponding symbolic representation (score). While methods using computer-readable score representations as input are able to achieve reliable tracking results, there is little research on score following based on raw score images. In this paper, we build on previous work that formulates the score following task as a multi-modal Markov Decision Process (MDP). Given this formal definition, one can address the problem of score following with state-of-the-art deep reinforcement learning (RL) algorithms. In particular, we design end-to-end multi-modal RL agents that simultaneously learn to listen to music recordings, read the scores from images of sheet music, and follow the music along in the sheet. Using algorithms such as synchronous Advantage Actor Critic (A2C) and Proximal Policy Optimization (PPO), we reproduce and further improve existing results. We also present first experiments indicating that this approach can be extended to track real piano recordings of human performances. These audio recordings are made openly available to the research community, along with precise note-level alignment ground truth.
Generation of fashionable clothes using generative adversarial networks

Purpose There are various style options available when one buys clothes on online shopping websites, however the availability the new fashion trends or choices require further user interaction in generating fashionable clothes. The paper aims to discuss this issue. Design/methodology/approach Based on generative adversarial networks (GANs) from the deep learning paradigm, here the authors suggest model system that will take the latest fashion trends and the clothes bought by users as input and generate new clothes. The new set of clothes will be based on trending fashion but at the same time will have attributes of clothes where were bought by the consumer earlier. Findings In the proposed machine learning based approach, the clothes generated by the system will personalized for different types of consumers. This will help the manufacturing companies to come up with the designs, which will directly target the customer. Research limitations/implications The biggest limitation of the collected data set is that the clothes in the two domains do not belong to a specific category. For instance the vintage clothes data set has coats, dresses, skirts, etc. These different types of clothes are not segregated. Also there is no restriction on the number of images of each type of cloth. There can many images of dresses and only a few for the coats. This can affect the end results. The aim of the paper was to find whether new and desirable clothes can be created from two different domains or not. Analyzing the impact of “the number of images for each class of cloth” is something which is aim to work in future. Practical implications The authors believe such personalized experience can increase the sales of fashion stores and here provide the feasibility of such a clothes generation system. Originality/value Applying GANs from the deep learning models for generating fashionable clothes.
On-Line Obstacle Detection, Avoidance, and Mapping of an Outdoor Quadrotor Using EKF-Based Fuzzy Tracking Incremental Control

The unknown two-dimensional obstacles, including different shapes, numbers, locations, and in the vicinity of the planning path for an outdoor quadrotor, are on-line detected by a 360-degree Li-Dar Sweep Scanse.Based on the comparison among detected triangles containing extreme points of each obstacle, the central orientation of the triangle with the maximum detected distance and the angle larger than the specific threshold is assigned to fly a specific distance for obstacle avoidance.Nevertheless, the quadrotor probably encounters a dead end and is then navigated to a specific altitude for the continuous procedure of obstacle detection.By on-line detection at different waypoints for the same obstacle, an obstacle mapping is also established by all the obstacle detections.Since the sensing signals of an outdoor quadrotor are easily affected by stochastic noise and the quadrotor is operated in ''x'' configuration, an extended Kalman filter based fuzzy tracking incremental control (EKF-FTIC) is employed to simultaneously execute the path tracking, obstacle avoidance, and target approach.Finally, the experiments with 5 unknown obstacles in the neighborhood of a planning path connecting start point with end point validate the effectiveness and feasibility of the proposed method.
Help! I Need a Remote Guide in My Mixed Reality Collaborative Environment

The help of a remote expert in performing a maintenance task can be useful in many situations and can save time as well as money. In this context, augmented reality (AR) technologies can improve remote guidance thanks to the direct overlay of 3D information onto the real world. Furthermore, virtual reality (VR) enables a remote expert to virtually share the place in which physical maintenance is being carried out. In a traditional local collaboration, collaborators are face-to-face and are observing the same artifact, while being able to communicate verbally and use body language such as gaze direction or facial expression. These interpersonal communication cues are usually limited in remote collaborative maintenance scenarios, in which the agent uses an AR setup while the remote expert uses VR. Providing users with adapted interaction and awareness features to compensate for the lack of essential communication signals is, therefore, a real challenge for remote MR collaboration. However, this context offers new opportunities for augmenting collaborative abilities, such as sharing an identical point of view, which is not possible in real life. Based on the current task of the maintenance procedure, such as navigation to the correct location or physical manipulation, the remote expert may choose to freely control his/her own viewpoint of the distant workspace or instead may need to share the viewpoint of the agent in order to better understand the current situation. In this work, we first focus on the navigation task, which is essential to complete the diagnostic phase and to begin the maintenance task in the correct location. We then present a novel interaction paradigm, implemented in an early prototype, in which the guide can show the operator the manipulation gestures required to achieve a physical task that is necessary to perform the maintenance procedure. These concepts are evaluated, allowing us to provide guidelines for future systems targeting efficient remote collaboration in MR environments.
Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds

Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation &amp; answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime.
Investigating a Physical Dial as a Measurement Tool for Cybersickness in Virtual Reality

This study explores ways to increase comfort in Virtual Reality by minimizing cybersickness. Cybersickness is related to classical motion sickness and causes unwanted symptoms when using immersive technologies. We developed a dial interface to accurately capture momentary user cybersickness and feed this information back to the user. Using a seated VR roller coaster environment, we found that the dial is significantly positively correlated with post-immersion questionnaires and is a valid tool compared to verbal rating approaches.
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning

In sequence to sequence learning, the self-attention mechanism proves to be highly effective, and achieves significant improvements in many tasks. However, the self-attention mechanism is not without its own flaws. Although self-attention can model extremely long dependencies, the attention in deep layers tends to overconcentrate on a single token, leading to insufficient use of local information and difficultly in representing long sequences. In this work, we explore parallel multi-scale representation learning on sequence data, striving to capture both long-range and short-range language structures. To this end, we propose the Parallel MUlti-Scale attEntion (MUSE) and MUSE-simple. MUSE-simple contains the basic idea of parallel multi-scale sequence representation learning, and it encodes the sequence in parallel, in terms of different scales with the help from self-attention, and pointwise transformation. MUSE builds on MUSE-simple and explores combining convolution and self-attention for learning sequence representations from more different scales. We focus on machine translation and the proposed approach achieves substantial performance improvements over Transformer, especially on long sequences. More importantly, we find that although conceptually simple, its success in practice requires intricate considerations, and the multi-scale attention must build on unified semantic space. Under common setting, the proposed model achieves substantial performance and outperforms all previous models on three main machine translation tasks. In addition, MUSE has potential for accelerating inference due to its parallelism. Code will be available at https://github.com/lancopku/MUSE
User experience design for mixed reality: a case study of HoloLens in museum

In recent years, the applications of mixed reality (MR) processing have become highly apparent in academia and the manufacturing industry with the release of innovative technologies such as the Microsoft HoloLens. However, crucial design issues with the HoloLens' restricted field of view (FOV) to a narrow window of 34 degrees inhibited the user's natural peripheral vision (Kress and Cummings, 2017). This visual limitation results in a loss of pre-set functions and projected visualisations in the AR application window. This paper presents an innovative methodology in designing a spatial user interface (UI), to minimise the adverse effects associated with the HoloLens' narrow FOV. The spatial UI is a crucial element towards developing a museum-based MR system, which was evaluated by nine experts in human-computer interaction (HCI), visual communication and museum studies. Results of this study indicate a positive user reaction towards the accessibility of the spatial UI system and enhancing the user experience. This approach can help current and future HoloLens developers to extend their application functions without visual restrictions and missing content.
Are Cryptocurrencies Affected by Their Asset Class Movements or News Announcements?

This study analyses whether returns of top market capitalised cryptocurrencies are affected by their movements or major global macroeconomic news. Daily data are collected for the leading 10 cryptocurrencies from July 2017–December 2018. This study, (i) tests whether lagged variables can help predict other variables' returns through a vector autoregression (VAR) model, (ii) analyses the response of cryptocurrencies to one standard deviation shock on Bitcoin's returns, and (iii) decomposes factors that contribute to variance and tests for structural breaks. Findings show that most cryptocurrencies do not significantly affect other variances, except for Monero, which represented between 19% and 45% of the variances of five cryptocurrencies. Autoregressive (AR) models are superior in forecasting one day ahead return forecasts, compared to the VAR model, whereas the random walk (RW) model ranked last. Although remarkable structural breaks are observed via impulse response functions during December 2017–January 2018, no major news announcements were released on the same day the breaks occurred. Overall, this study suggests the need for high-frequency cryptocurrency prices to tackle the issue of the relationship between intraday news release and cryptocurrencies.
Temporal Coherence for Active Learning in Videos

Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets.