Reviewerly/exHarmony huggingface dataset:
Torso-mounted Vibrotactile Interface to Experimentally Induce Illusory Own-body Perceptions

Recent developments in virtual reality and robotic technologies have allowed investigating the behavioural and brain mechanisms that grounds self-consciousness in the multi-sensory (e.g. vision and touch) and sensorimotor processing of bodily signals. Yet, previous technological solutions to apply tactile stimuli for body illusion induction limit participants movements, do not allow for stimulations in dynamic environments (e.g., the subject walking), and can hardly be integrated into real-life settings and complex, interactive, virtual reality environments. Here, we present the development and first validation of a new semi-wearable haptic system, based on vibration technology, to induce a range of bodily illusions that are of relevance for research in psychiatry. This is a first step towards the development of wearable haptic systems able to administer touch and induce specific bodily illusions under dynamic conditions and in real-life settings.
Cognitive rehabilitation using virtual reality for children with rare diseases

The aim of public and private health institutions is to prevent, mitigate or suppress the effects that endanger the lives and health of populations. Rare diseases are generally genetic and hereditary diseases that appear in pediatric age. Rare diseases are chronic diseases with a risk of causing death or physical, neurological and/or cognitive impairment.
Petrographic Microscope Digital Image Processing Technique for Texture and Microstructure Interpretation of Earth Materials

The technique of description and characterization of rocks with the aid of a polarized light microscope is a well-established practice in the fields of mineralogy and petrology. However, because geological materials are inherently highly variable on a small scale, capturing good-quality images, particularly of the fine details present in the mineral grains that compose the rock, is the main difficulty encountered when a thin section is examined under a petrographic microscope. Combining petrographic concepts and digital image processing methods, the principal aim of this paper is to provide a practical approach to digital image treatment with specific software, and its immediate application in the micromorphological characterization of minerals. In addition to the basic calibration of color, brightness, and contrast, three different methods of digital image processing in the spatial domain, following the principles of embossed surface, negative image, and edge detection techniques, were applied to the images. The use of these primary filters was found to be efficient for detailed characterization of the mineralogical phases involved in the different types of microstructures. However, special care must be taken regarding the sensitivity and accuracy parameters to avoid the exclusion of information or the addition of noise to the image. Although research has focused on the distinction of several types of textural features in rock-forming minerals, these techniques can be employed in other areas of investigation, in both academic and industrial settings, to diagnose textures of microtectonic deformation, soil micromorphological features, the proportions of the original ingredients in concretes, and the mineralogical modal determination of ceramics of archeological origin and to characterize mineral raw materials for the manufacture of technological products.
Classification of Traffic Vehicle Density Using Deep Learning

The volume density of vehicles is a problem that often occurs in every city, as for the impact of vehicle density is congestion. Classification of vehicle density levels on certain roads is required because there are at least 7 vehicle density level conditions. Monitoring conducted by the police, the Department of Transportation and the organizers of the road currently using video-based surveillance such as CCTV that is still monitored by people manually. Deep Learning is an approach of synthetic neural network-based learning machines that are actively developed and researched lately because it has succeeded in delivering good results in solving various soft-computing problems, This research uses the convolutional neural network architecture. This research tries to change the supporting parameters on the convolutional neural network to further calibrate the maximum accuracy. After the experiment changed the parameters, the classification model was tested using K-fold cross-validation, confusion matrix and model exam with data testing. On the K-fold cross-validation test with an average yield of 92.83% with a value of K (fold) = 5, model testing is done by entering data testing amounting to 100 data, the model can predict or classify correctly i.e. 81 data.
A Visual Sensitivity Analysis for Parameter-Augmented Ensembles of Curves

Abstract Engineers and computational scientists often study the behavior of their simulations by repeated solutions with variations in their parameters, which can be, for instance, boundary values or initial conditions. Through such simulation ensembles, uncertainty in a solution is studied as a function of various input parameters. Solutions of numerical simulations are often temporal functions, spatial maps, or spatio-temporal outputs. The usual way to deal with such complex outputs is to limit the analysis to several probes in the temporal/spatial domain. This leads to smaller and more tractable ensembles of functional outputs (curves) with their associated input parameters: augmented ensembles of curves. This article describes a system for the interactive exploration and analysis of such augmented ensembles. Descriptive statistics on the functional outputs are performed by principal component analysis (PCA) projection, kernel density estimation, and the computation of high density regions. This makes possible the calculation of functional quantiles and outliers. Brushing and linking the elements of the system allows in-depth analysis of the ensemble. The system allows for functional descriptive statistics, cluster detection, and finally, for the realization of a visual sensitivity analysis via cobweb plots. We present two synthetic examples and then validate our approach in an industrial use-case concerning a marine current study using a hydraulic solver.
Design and Development of an Integrated Platform for Management of Homecare Providers

The paper describes the development process of an integrated platform, called CDMS, that offers services to homecare providers (HCPs). The platform, through its' software and hardware components, is designed to be implemented by entities that offer services to HCPs and optimizes their activity and the offered services. A CDMS platform offers services to HCPs for keeping electronic health records, for supplies management, supervision of the nursing staff and point of care documentation among other services described in the paper. A pilot of the platform is currently under test in Alba County and further improvements and testing activities are planned before deploying the platform as a product.
Generation of Low Distortion Adversarial Attacks via Convex Programming

As deep neural networks (DNNs) achieve extraordinary performance in a wide range of tasks, testing their robustness under adversarial attacks becomes paramount. Adversarial attacks, also known as adversarial examples, are used to measure the robustness of DNNs and are generated by incorporating imperceptible perturbations into the input data with the intention of altering a DNN's classification. In prior work in this area, most of the proposed optimization based methods employ gradient descent to find adversarial examples. In this paper, we present an innovative method which generates adversarial examples via convex programming. Our experiment results demonstrate that we can generate adversarial examples with lower distortion and higher transferability than the C&W attack, which is the current state-of-the-art adversarial attack method for DNNs. We achieve 100% attack success rate on both the original undefended models and the adversarially-trained models. Our distortions of the L_inf attack are respectively 31% and 18% lower than the C&W attack for the best case and average case on the CIFAR-10 data set.
Blended Learning dengan Macromedia Flash untuk Melatih Kemandirian Belajar Mahasiswa

Learning independence among students is still low. It can be seen from the students being less active in finding learning resources and only waiting from the lecturers, from the task of making learning media given by students there are still many who only copy and edit it. To practice learning independence, innovation is needed in learning, one of which uses learning that combines face-to-face and virtual learning, namely blended learning. This study aims to train the independence of student learning in making learning media based on Macromedia flash through blended learning. The research method used is quantitative descriptive method. The sample in this study were 28 students of the mathematics education program at the PGRI University of Palembang. The research instrument was a student learning independence questionnaire given before and after learning. The quantitative data obtained were analyzed descriptively. The results showed that student learning independence changes before and after learning, with the largest percentage change in the indicators of self-efficacy (self-concept), while the smallest percentage of changes in the indicators evaluating the learning process and results. Besides, the efforts that can be made to train the independence of student learning with blended learning are providing a discussion forum; giving assignments to each material so that students know clearly the learning objectives; conducting discussions on each assignment given, and; do not limit students in access learning resources.
Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

The synthesis of high-performance computing (particularly graphics processing units), cloud computing services (like Google Colab), and high-level deep learning frameworks (such as PyTorch) has powered the burgeoning field of artificial intelligence. While these technologies are popular in the computer science discipline, the physics community is less aware of how such innovations, freely available online, can improve research and education. In this tutorial, we take the Hopfield network as an example to show how the confluence of these fields can dramatically accelerate physics-based computer simulations and remove technical barriers in implementing such programs, thereby making physics experimentation and education faster and more accessible. To do so, we introduce the cloud, the GPU, and AI frameworks that can be easily repurposed for physics simulation. We then introduce the Hopfield network and explain how to produce large-scale simulations and visualizations for free in the cloud with very little code (fully self-contained in the text). Finally, we suggest programming exercises throughout the paper, geared towards advanced undergraduate students studying physics, biophysics, or computer science.
FRIEND: Feature selection on inconsistent data

With the explosive growth of information, inconsistent data are increasingly common. However, traditional feature selection methods are lack of efficiency due to inconsistent data repairing beforehand. Therefore, it is necessary to take inconsistencies into consideration during feature selection to not only reduce time costs but also guarantee accuracy of machine learning models. To achieve this goal, we present FRIEND, a feature selection approach on inconsistent data. Since features in consistency rules have higher correlation with each other, we aim to select a specific amount of features from these. We prove that the specific feature selection problem is NP-hard and develop an approximation algorithm for this problem. Extensive experimental results demonstrate the efficiency and effectiveness of our proposed approach.
Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate Predictions

It is evident that machine learning algorithms are being widely impacting industrial applications and platforms. Beyond typical research experimentation scenarios, there is a need for companies that wish to enhance their online data and analytics solutions to incorporate ways in which they can select, experiment, benchmark, parameterise and choose the version of a machine learning algorithm that seems to be most appropriate for their specific application context. In this paper, we describe such a need for a big data platform that supports food data analytics and intelligence. More specifically, we introduce Agroknow’s big data platform and identify the need to extend it with a flexible and interactive experimentation environment where different machine learning algorithms can be tested using a variation of synthetic and real data. A typical usage scenario is described, based on our need to experiment with various machine learning algorithms to support price prediction for food products and ingredients. The initial requirements for an experimentation environment are also introduced.
Research on Optimization of Visual Navigation Algorithm for Real-Time Obstacle Avoidance of UAV Moving Target Tracking

In recent years, UAV target tracking and real-time obstacle avoidance are urgent problems in UAV industry. In the UAV visual navigation system, how to realize real-time obstacle avoidance in the process of moving target tracking is an important part of successful tracking. In this study, the image of the tracked object is collected by the monocular camera, and the motion target is predicted and corrected based on the Kalman filter algorithm to determine the size and direction of the search and tracking object. The binocular camera collects the real-time image information of the UAV in the forward direction. The object's distance can be determined by reliable disparity values and spatial depth information obtained through the camera. The moving speed of the obstacle relative to the camera at each moment can be obtained through the SIFT-based optical flow method. Experimental results show that the method can effectively guide the UAV to continuously and accurately track the target object, and avoid obstacles in the forward direction in the tracking process.
Awareness of Ethical Issues when using an e-Learning System

Transformation to the digital system has made life easier, and the acceptance of the e-learning system in the academic life of students is a fact.Therefore, many educational organizations use the e-learning environment for teachinglearning activities.The present study has been conducted to evaluate the awareness of the undergraduate students to ethics and to determine if there is a difference according to gender and academic level variables when using an e-learning system at Al-Balqa Applied University in Jordan.A self-questionnaire has been designed to measure the participant's awareness of ethics.It consists of 20 items classified in three ethical categories; Intellectual property rights, vandalism and Privacy.The results show that the awareness of students is low in all three categories regarding their commitment to the ethical issues when using an e-learning system.Result also show that there are no significant differences between undergraduate students' gender and academic level related to the awareness of ethical issues.Therefore, undergraduate students should be fully knowledgeable about ethical issues to avoid unethical behavior while using of the e-learning system.
From Voice to Knowledge: A Proposal for a Voice Annotation System to Support Collaborative Engineering Design Processes

This paper describes a novel voice interaction mechanism for capturing and managing design knowledge within a collaborative Computer-Aided Design (CAD) environment.We present a software module for speech recognition that integrates with a CAD application to allow the automatic creation of textual annotations in a 3D model directly from voice data.Audio is transcribed automatically, resulting in a textual note that is searchable and available to other users via a Product Data Management (PDM) system, providing an intuitive mechanism to document modeling processes and design knowledge.The system consists of three functional blocks: (1) audio recording, (2) speech recognition, and ( 3) query management against a cloud-based service.In this paper, we justify the need for our system from a human-computer interaction standpoint and discuss the rationale of its design and implementation in the context of collaborative design communication.Finally, we discuss some application spaces that demonstrate the capability of voice annotations for capturing knowledge.
An AR-based Gamified English Course in Vocational College through Interest-driven Approach

In recent years, augmented reality (AR) technology's application has been explored in numerous disciplines.Many studies claimed that AR technology had significant advantages in enhancing learning interesting and boosting the understanding and collaborative learning etc.However, in China, most college students lack learning interest and adopt a passive method to learn English.In order to improve the learning situation, the researcher utilized the interest-driven creator (IDC) theory as the basis to design an AR-based gamified English course in vocational college.The study aimed to answer the following questions: (1) How does the AR-based gamified learning environment impact students' learning motivation in English course?(2) How does the AR-based gamified learning environment impact students' collaborative learning in English course?This study was conducted in a vocational college of China and the in-depth interviews were adopted to collect data.The results of the investigation revealed that students' learning motivation was enhanced through the change of teacher' role and using AR technology's virtual content.Additionally, the AR-based gamified learning environment can effectively enhance collaborative learning through strengthening the willingness of discussion and providing more opportunities, and the relaxed learning atmosphere.
Dog Breed Identification Using Convolutional Neural Networks on Android

Identifying the breed of a dog, is a challenging image classification problem.In this paper, we implement an android application that identifies the breed of a dog via image analysis, using a Convolutional Neural Network (CNN) and transfer learning model.The android application lets the user click or upload a picture of a dog.It then pre-processes the image and extracts the features required for testing.Prediction of dog breed is done using CNN and transfer learning.We have used Stanford's standard dog dataset for training the model and achieved an accuracy of 94% on the testing data.
Metric-Based Semi-Supervised Regression

Regression problems are present in many industrial applications, and many supervised learning algorithms have been devised over decades. However, available labeled examples are limited in some application settings; meanwhile, enormous unlabeled examples are relatively easy to collect. Thus, this work proposes a simple but effective method to cope with semi-supervised regression problems. We propose to use deep neural networks to develop our proposed method as deep learning has shown promising results in recent years. Our proposed method is a metric-based approach, and the goal is to learn an embedding space by metric learning with few labeled examples and enormous unlabeled examples. The regression estimation of the target data point is performed on the new space. We generate an artificial dataset based on several criteria to investigate whether the proposed model could make accurate predictions on the data samples that have specific properties. The experimental results point that our proposed model could capture the trend of a non-linear function and normally predict well even though this dataset comprises extreme outliers. Moreover, we conduct experiments on four datasets and compare our proposed work with several alternatives. The experimental results indicate that our proposed method achieves promising results. Besides performance evaluation, detailed analysis about our proposed method is also provided in this work.
Neural Decision Tree Towards Fully Functional Neural Graph

Though the traditional algorithms could be embedded into neural architectures with the proposed principle of [H. Xiao, Hungarian layer: Logics empowered neural architecture, arXiv: 1712.02555], the variables that only occur in the condition of branch could not be updated as a special case. To tackle this issue, we multiply the conditioned branches with Dirac symbol (i.e., [Formula: see text]), then approximate Dirac symbol with the continuous functions (e.g., [Formula: see text]). In this way, the gradients of condition-specific variables could be worked out in the back-propagation process, approximately, making a fully functional neural graph. Within our novel principle, we propose the neural decision tree (NDT), which takes simplified neural networks as decision function in each branch and employs complex neural networks to generate the output in each leaf. Extensive experiments verify our theoretical analysis and demonstrate the effectiveness of our model.
Survival Prediction from Longitudinal Health Insurance Data using Graph Pattern Mining

Survival prediction based on health insurance data is a promising research direction, since through health insurance data, we may be able to uncover the implicit information reflecting the health status of patients. Deep learning models, especially the Recurrent Neural Networks (RNNs), are powerful tools. However, at this stage, it is often difficult to explain the semantics of the features and evolution laws learned by RNNs. Therefore, this paper proposes a survival prediction model based on graph pattern mining. First, each patient's health insurance data are built as a Heterogeneous Information Network (HIN). Then, frequent patterns are mined from these HINs and each frequent pattern is regarded as a feature, called “pattern feature”. At last, the predictive survival time is given by an improved random forest, which is able to take into account the censored data. We conducted experiments on a real health insurance data set. The experimental results show that the model has better predictive ability than the traditional survival prediction models.
Safer UAV Piloting: A Robust Sense-and-Avoid Solution for Remotely Piloted Quadrotor UAVs in Complex Environments

Current commercial UAVs are to a large extent remotely piloted by amateur human pilots. Due to lack of teleoperation experience or skills, they often drive the UAVs into collision. Therefore, in order to ensure safety of the UAV as well as its surroundings, it is necessary for the UAV to boast the capability of detecting emergency situation and acting on its own when facing imminent threat. However, the majority of UAVs currently available in the market are not equipped with such capability. To fill in the gap, in this paper we present a complete sense-and-avoid solution for assisting unskilled pilots in ensuring a safe flight. Particularly, we propose a novel nonlinear vehicle control system which takes into account of sensor characteristics, an emergency evaluation policy and a novel optimization-based avoidance control strategy. The effectiveness of the proposed approach is demonstrated and validated in simulation with multiple moving objects.