Reviewerly/exHarmony huggingface dataset:
The Effect of Force Sensor Arrays Integration into Textile for a Novel Head-Foot Wheelchair Steering System

In this paper a novel head-foot wheelchair steering system based on force sensor arrays (FSAs) for people diagnosed with dyskinetic cerebral palsy (DCP) is introduced. The user applies pressure on FSAs placed on the head, and foot supports of the electrically powered wheelchair (EPW), based on his/her intention to accelerate, brake, steer right or left. The microcontroller-based electronic system acquires and translates the mean voltage generated by the applied force into wheelchair control signals. In such a system, FSAs are integrated into the head support of the wheelchair using support materials and textiles for the comfort of the user, having an effect on the sensor readings. This work aims to explore the effect of integrating FSAs into the head support of the wheelchair using support material and textiles. Four different sensor integration approaches were examined and compared to baseline readings of a non-integrated FSA. It was found that when a maximum force of 80 N is applied to a single sensing element (sensel) the support material decreases the mean voltage by approximately 70%, and the sensor integration into textile has shown increases between 5.2 and 14.9% compared to the support material. Furthermore, when force is applied over multiple sensels, the support material accounts for a reduce in the mean voltage ranging from approximately 3 to 32%. The addition of textile has exhibited peak decrease in the mean voltage up to roughly 41% for the tested integrations.
An Elastic Gradient Boosting Decision Tree for Concept Drift Learning

In a non-stationary data stream, concept drift occurs when different chunks of incoming data have different distributions. Hence, over time, the global optimization point of a learning model might permanently drift to the point where the model no longer adequately performs the task it was designed for. This phenomenon needs to be addressed to maintain the integrity and effectiveness of a model over the long term. In this paper, we propose a simple but effective drift learning algorithm called elastic Gradient Boosting Decision Tree (eGBDT). Since the prediction of a GBDT model is the sum output of a list of trees, we can easily append new trees to perform incremental learning or delete the last few trees to roll back to a previously known optimization point. The proposed eGBDT incrementally fits new data and detect drift by searching for the tree with the lowest residual. If the rollback deletions required would exceed the initial number of trees, a retraining process is triggered. Comparisons of eGBDT with five state-of-the-art methods on eight data sets show the efficacy of eGBDT.
A Correlation Particle Filter Target Tracking Algorithm Based on Adaptive Feature Fusion

In order to improve the tracking accuracy and success rate of correlation filtering algorithm in the case of severe occlusion and background interference, a correlation particle filter target tracking algorithm based on adaptive feature fusion is proposed. The adaptive fusion of directional gradient histogram features and convolution features improves the expression ability of target features. The correlation filter and particle filter are fused to form the correlation particle filter tracker, which enhances the performance of the tracker. The secondary positioning strategy is introduced to quickly locate the target when judging the failure of target tracking. The algorithm is tested with OTB-100 video sequence and compared with the other two tracking algorithms. The experimental results show that the proposed algorithm performs well in the case of serious occlusion and background interference.
A Confidence-Based Assessment Method for Distinguishing Pyroclastic Density Current Deposits From Other Volcaniclastic Units

Pyroclastic density currents (PDCs) are a destructive volcanic hazard. Quantifying the types, frequency and magnitudes of PDC events is essential for effective risk management, but since historical records at best extend a few hundred years this usually relies on identifying deposits in the geological record. However, small volume unconsolidated PDC deposits have low preservation potential and can be difficult to distinguish from other volcaniclastic units, especially in proximal locations. Consequently many small or poorly exposed deposits can be overlooked. Here, we introduce a structured field method for assessing volcaniclastic deposits of unknown origin with a particular focus on identifying deposits from concentrated PDCs (pyroclastic flows). The method differs from traditional identification schemes in that it does not uniquely attribute a deposit to a single depositional process, but instead assesses how confidently different volcaniclastic processes could explain the observed deposit features. Therefore, the underlying uncertainties in the assessment are explicitly addressed. The method allows consistent, rapid assessment of candidate pyroclastic flow deposits in the field, and the concept could easily be adapted for assessing other types of volcaniclastic deposit. The introduction of confidence levels in deposit interpretations should be useful for carrying though uncertainties into probabilistic assessments of volcanic hazards.
Simulation of Drone Controller using Reinforcement Learning AI with Hyperparameter Optimization

Drone is one of the latest drone technologies that grows with multiple applications; one of the critical applications is for fire-fighting drones such as water hose carrying for firefighting. One of the main challenges of the drone technologies is the non-linear dynamic movement caused by a variety of fire conditions. One solution is to use a nonlinear controller such as Reinforcement Learning. In this paper, Reinforcement Learning has been applied as their key control system to improve the conventional approach, which is the agent (drone) that will interact with the environment without need of the controller for the flying process. This paper is introduced an optimization method for the hyperparameter in order to achieve a better reward. In addition, we only concentrate on the learning rate (alpha) and potential reward factor discount (gamma) for optimization in this paper. From this optimization, the better performance and response from our result by using alpha = 0.1 & gamma = 0.8 with reward produced 6100 and it takes 49 seconds in the learning process.
Real-Time Implementation of Mini Autonomous Car Based on MobileNet - Single Shot Detector

In this paper, in order to realize a prototype of an autonomous vehicle, we present a framework that consists of convolutional neural networks and image processing methods. The study is comprised of two main parts as software and hardware. In the hardware part, a small-sized smart video car kit is used as the prototype of the autonomous car. This programmable tool consists of Raspberry Pi, servo motors and a USB webcam whose angle of vision is equal to 120°. In the software part, we propose an algorithm in which we use Convolutional Neural Networks to detect the objects (vehicles, pedestrians, and traffic signs) and Hough transformation to detect the road lanes. Based on the outputs of the object and lane detections, the system decides the speed and the direction of the car in real-time. In our results, the vehicle performs autonomous driving in the scaled real-world application.
$$\mathsf {SafePILCO}$$: A Software Tool for Safe and Data-Efficient Policy Synthesis

$$\mathsf {Safe PILCO}$$ is a software tool for safe and data-efficient policy search with reinforcement learning. It extends the known $$\mathsf {PILCO}$$ algorithm, originally written in MATLAB, to support safe learning. $$\mathsf {Safe PILCO}$$ is a Python implementation and leverages existing libraries that allow the codebase to remain short and modular, towards wider use by the verification, reinforcement learning, and control communities.
3D Face Reconstruction and Semantic Annotation from Single Depth Image

We introduce a novel data-driven approach for taking a single-view noisy depth image as input and inferring a detailed 3D face with per-pixel semantic labels. The critical point of our method is its ability to handle the depth completions with varying extent of geometric details, managing 3D expressive face estimation by exploiting low-dimensional linear subspace and dense displacement field-based non-rigid deformations. We devise a deep neural network-based coarse-to-fine 3D face reconstruction and semantic annotation framework to produce high-quality facial geometry while preserving large-scale contexts and semantics. We evaluate the semantic consistency constraint and the generative model for 3D face reconstruction and depth annotation in extensive series of experiments. The results demonstrate that the proposed approach outperforms the compared methods not only in the face reconstruction with high-quality geometric details, but also semantic annotation performances regarding segmentation and landmark location.
Evasion from Detection of the Moving Object with Non-Uniform Radiation Pattern

The problem of optimal trajectory planning of the moving object is addressed. The task is to minimize the risk of detection of the moving object by a static sensor while moving between two given points in space. The detection is based on the primary acoustic field radiated by the object with a non-uniform radiation pattern. In the first part of the article, the probability of non-detection is derived. Further it is used as an optimization criterion. Non-uniform radiation pattern of the object differs this work from the previous researches in the area. The optimal trajectory and velocity law of the moving object are found, as well as the criterion value.
Sentential Semantic Dependency Parsing for Vietnamese

Semantic dependency parse is the dependency graph of a sentence. This graph shows the grammatical dependencies between words in a sentence clearer than the dependency parse because it allows one word possibly be the dependant in two or more dependencies in a sentence. Therefore, it has been used to represent the meaning of a sentence. In order to represent the meaning of a sentence in Vietnamese, a method of parsing the sentence into semantic dependencies is proposed in this paper. This rule-based method transforms the result of Vietnamese dependency parser by using semantic constraints existing in a lexicon ontology called VLO. The test result shows that the proposed method can capture more dependencies than the state-of-the-art Vietnamese dependency parser with the precisions respectively being 0.5328 and 0.3113.
Restoring Spatially-Heterogeneous Distortions Using Mixture of Experts Network

In recent years, deep learning-based methods have been successfully applied to the image distortion restoration tasks. However, scenarios that assume a single distortion only may not be suitable for many real-world applications. To deal with such cases, some studies have proposed sequentially combined distortions datasets. Viewing in a different point of combining, we introduce a spatially-heterogeneous distortion dataset in which multiple corruptions are applied to the different locations of each image. In addition, we also propose a mixture of experts network to effectively restore a multi-distortion image. Motivated by the multi-task learning, we design our network to have multiple paths that learn both common and distortion-specific representations. Our model is effective for restoring real-world distortions and we experimentally verify that our method outperforms other models designed to manage both single distortion and multiple distortions.
What Bounds Entrepreneurial Business Modelling? The Impacts of Visual Framing Effects and Cognitive Dispositions

Great uncertainty accompanies entrepreneurs’ processes of designing promising business models (BMs). Therefore, stabilising factors act as important means in this process. In this study, we examined the impact of cognitive dispositions and visual BM frameworks on the BM process and outcomes. By using partial-least-square structural equation modelling (PLS-SEM) and an experimental setting, our results show that the stabilising function of BM frameworks depends on entrepreneurs’ cognitive dispositions. This finding contributes to the cognitive BM perspective and explains how cognitive dispositions and visual framing effects act as boundary conditions for the theory of stabilising factors. This also has important implications for applying frameworks in practice.
3D Texture Feature Extraction and Classification using the BM3DELBP approach

This paper presents a new feature extraction technique for 3D texture classification based on Local Binary Patterns-derived operators and state-of-the-art denoising techniques, with good invariance properties to different transformations and robust to noise. Our approach consists in the computation of textural features obtained using the Block Matching and 3D Filtering Extended Local Binary Patterns for each of the three orthogonal planes and their concatenation. The proposed method achieves a promising classification performance on a publicly available dataset of 3D textured images. The obtained results prove that the presented feature extraction technique is above traditional 2D texture descriptors and typical CNN deep learning-based methods.
Mogens Lesner Glass (1946-2018)

Childhood and becoming a biologist Mogens was born in Aarhus, the second largest city in Denmark, on March 24, 1946 and attended a private school (Foraeldreskolen) and finished high school nearby, in 1965. In high school (Marselisborg Gymnasium), he followed the linguistic line and therefore received little education in mathematics and physics. Thus, after having spent a year at Askov Hojskole, following a century-old Danish tradition of attending a boarding school for additional general education prior to university, he needed [...]
Frozen Factory: A Playful Virtual Experience for Multiple Co-Located Redirected Walking Users

In this exhibit, we present a playful virtual reality (VR) experience for two co-located users who are both re-directed simultaneously. The redirection is seamlessly integrated with gameplay and interactions to perfectly match the given environment. This allows to explore a virtual space by foot which is 5 times as large as the 5 × 3m2 real-world play space that is shared by both users.
Lessons Learned: Revising an Online Introductory Course

Are you considering adapting an existing face-to-face statistics course to an online format? We did! It is a complicated and time-consuming, but ultimately rewarding, process. This article describe...
An improved lightweight model based on Mask R-CNN for satellite component recognition

Satellite component recognition has always been a hot topic in the field of orbital services. However, it is very challenging to segment the components such as satellite body, solar panel, and antenna in pixel-level accurately due to the poor illumination condition and the scarce image for spaceborne observation. Based on the Mask R-CNN, this paper proposes a lightweight instance segmentation model for satellite component segmentation and recognition. It improves residual module by using deep separable convolution, replacing nonlinear activation function with linear one after deep separable convolution and deleting the dimensionality reduction convolution layer in residual module. Also, the training datasets consist of the synthetic images generated by the 3D max software and the C-DCGAN based image generation method through several known satellite CAD models. The simulation experiments are carried out and the results show that the proposed method can effectively recognize the typical satellite components and achieve better performance than the compared model in aspects of accuracy, model parameters, and model size.
Feature Based Human Activity Recognition using Neural Network

In various research areas, human activity recognition system (HAR) becomes more popular. Features is one of the important things to recognize the object or activity in machine learning. Increasing the old age population in our country, the smart nursing home monitoring and heath care supporting system is still needed to develop. The dataset for experiment is collected from 'Cherry Myay' nursing home in Pyin Oo Lwin, Myanmar. We proposed new feature extraction method, new morphological operation method 'Vertical Binary Bridge' pose classification method using SVM and new frequency based feature selection method. Artificial neural network is used to recognize the human activity and abnormal behaviour detection system. Compared with other exiting methods, our proposed system can achieve acceptable result.
Reconstructing Creative Lego Models

Lego is one of the most successful toys in the world. Being able to scan, analyse and reconstruct Lego models has many applications, for example studying creativity. In this paper, from a set of 2D input images, we create a monolithic mesh, representing a physical 3D Lego model as input, and split it in to its known components such that the output of the program can be used to completely reconstruct the input model, brick for brick. We present a novel, fully automatic pipeline to reconstruct Lego models in 3D from 2D images; A-DBSCAN, an angular variant of DBSCAN, useful for grouping both parallel and anti-parallel vectors; and a method for reducing the problem of non-Manhattan reconstruction to that of Manhattan reconstruction. We evaluate the presented approach both qualitatively and quantitatively on a set of Lego duck models from a public data set, and show that the algorithm is able to identify and reconstruct the Lego models successfully.
QR Code and NFC-Based Information System for Southland Tourism Industry-New Zealand

New Zealand is a hub of tourism. Beautiful and serene, this country plays a host to more diverse and enthusiastic travellers from across the globe and tourism is a major boost to New Zealand economy. With technology taking over all the aspects of human life, it simply makes sense to link it with tourism to enhance and augment its features and make it more hassle-free and easier for the tourists. In one such attempt, this research makes use of Quick Response (QR) code and Near Field Communication (NFC) technology to augment the field of tourism in Southland, New Zealand. Quick Response (QR) code and Near Field Communication (NFC) technology are already extensively used in inventory systems, online payment systems, and lots more. This research presents a system named QR Code and NFC Bases Information System (QNBIS) for tourists visiting Southland region of New Zealand and aims at assisting the tourists by providing the information about objects of interest in their surrounding and thereby making their travelling experience easy and enjoyable. By scanning QR codes or reading NFC tags using a mobile device, the system can provide an introduction for the corresponding object and therefore provide much-needed help to a wandering tourist. The danger of malicious replacement of QR codes and NFC tags limits the scope of execution of the proposed study, and therefore to address this risk, a security strategy is implemented where three specific implementation methods are offered. Lastly, in this research, a proof of concept (POS) QNBIS prototype is implemented and evaluated. The evaluation results show that the proposed QNBIS system is a viable method to provide tourists visiting Southland, with a practical and secure way to get information about their surrounding objects of interest.
A Lower Bound for Adaptively-Secure Collective Coin Flipping Protocols

In 1985, Ben-Or and Linial (Advances in Computing Research 1989) introduced the collective coin flipping problem, where n parties communicate via a single broadcast channel and wish to generate a common random bit in the presence of adaptive Byzantine corruptions. In this model, the adversary can decide to corrupt a party in the course of the protocol as a function of the messages seen so far. They showed that the majority protocol, in which each player sends a random bit and the output is the majority value, tolerates O(√n) adaptive corruptions. They conjectured that this is optimal for such adversaries. We prove that the majority protocol is optimal (up to a poly-logarithmic factor) among all protocols in which each party sends a single, possibly long, message. Previously, such a lower bound was known for protocols in which parties are allowed to send only a single bit (Lichtenstein, Linial, and Saks, Combinatorica 1989), or for symmetric protocols (Goldwasser, Kalai, and Park, ICALP 2015).
Optimizing COVID-19 testing capabilities and clinical management using pathology informatics

Abstract Coronavirus disease 2019, first reported in China in late 2019, has quickly spread across the world. The outbreak was declared a pandemic by the World Health Organization on March 11, 2020. Here, we describe our initial efforts at the University of Florida Health for processing of large numbers of tests, streamlining data collection, and reporting data for optimizing testing capabilities and superior clinical management. Specifically, we discuss clinical and pathology informatics workflows and informatics instruments which we designed to meet the unique challenges of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. We hope these results benefit institutions preparing to implement SARS-CoV-2 testing.
AI and 'Equality by Design'

Artificial intelligence (AI) is increasingly offered by proponents of its use as a tool for overcoming human biases, errors in judgment, and irregularities in decision-making, promising to provide a greater measure of predictability and consistency in decisions and, therefore, in the rule of law. For its critics, generating consistency in decision-making is a means of entrenching and amplifying discriminatory practices in a way that reproduces privilege and inequality in human relations through the use of pattern discrimination and predictive analytics, while simultaneously rendering these practices less transparent, explainable, and contestable. In both cases, technology is catapulted to the forefront of law as constitutive of lived realities and the governance of human relations, whether in the private or public sphere, rendering law perhaps nothing more than a technology too soon to become obsolete. Algorithms feed neural networks with neo-liberalism’s relations of power and inequality, and thus have the capacity to “bake in inequality,” to reproduce bias, and effectively to pass off the descriptive output of “code” as law, and such “law” as the normative aspirations of justice. More than ex post remedial mechanisms in law are needed to address such harms: if the rule of law is to retain its comparative advantage, an “Equality by Design” enforceable default for computer programming, consistent with the aim of a “human-centred approach” to AI governance, is required. Borrowing from well-established “Privacy by Design” principles, this chapter argues for similar, strengthened implementation in domestic law and policies for equality as the default value-based inclusive design for machine-learning algorithms and predictive analytics.