Reviewerly/exHarmony huggingface dataset:
Design of a low-cost Hand Gesture Controlled Automated Wheelchair

Design and implementation of a low-cost hand gesture controlled automated wheelchair-using Arduino based microcontroller and Node MCU is presented in this paper. The main focus of this study is to control the wheelchair with the movement of the hand-wrist movement. Besides hand gestures, the wheelchair can also be controlled via Bluetooth technology. If any problem occurs during the hand gesture control, Bluetooth technology can help to control the wheelchair through assisting person. The design also has some additional features such as tracking the location of the wheelchair through GPS from anywhere of the world and emergency switching system to send messages to the assisting person through sensor-based network. Arduino Mega, Arduino Nano, NODE MCU, MPU 6050 Gyro sensor, Sonar Sensor, GPS Module, GSM Module, HC-05 Bluetooth Module, nRF Transceiver, and relay-based H-Bridge Motor Driver Circuits are used to design the wheelchair prototype. The designed wheelchair is comparatively simpler in a sense that if any person can able to move their wrist only, then he/she can operate the wheelchair. Some other types of wheelchair designs were proposed in literature which contains complex computing system along with processing biological signals such as EEG, EMG, and EOG etc. The good recording of these bio-signals is quite challenging for very elderly/movement disabled subjects and complex signal processing along with bulky computing systems are required to operate such types of wheelchair. In the proposed design, the hand gesture control system is designed such that it overcomes the necessity of extra sensors and complicated biological signal processing systems. The proposed technique will have a great impact on society because of its easier use and cheaper price compared to other automated wheelchair designs.
Random Forest for Human Daily Activity Recognition

Abstract Machine learning classifiers are often used to evaluate the predicting accuracy of human activity recognition. This study aimed to evaluate the performance of random forest (RF) compared to other classifiers with considering the time taken to build the models. Human activity daily living data, namely walking, walking upstairs, walking downstairs, sitting, standing, and lying down were collected from smartphone-based accelerometer with sampling frequency of 50Hz. The dataset was evaluated using artificial neural network (ANN), k-nearest neighbors (KNN), linear discriminant analysis (LDA), naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The results of the study showed that RF indeed predicted the activities with the highest accuracy. However, the time taken to build the models using RF was the second-longest after ANN.
Internet of Things (IoT) Privacy–Protected, Fall-Detection System for the Elderly Using the Radar Sensors and Deep Learning

The increase in the number world population of elderly citizens, as well as those who live in solitude, needs an immediate solution with an intelligent monitoring system at home. In this work, we present an intelligent fall-detection system based on IoT to monitor the elderly with your privacy-protected. Currently, fall detection has attracted significant research attention and deep learning has shown promising performance in this task using conventional cameras. However, these traditional methods pose a risk of the leakage of personal privacy. This work proposes a novel fall-detection system that uses a continuous-wave Doppler radar sensor to acquisition the elderly movements and sends this information thought the internet to a server with deep learning using a convolutional neural network (CNN) that identifies the fall. The radar sensor is inexpensive, completely camera-free, and collects no personally identifiable information, thereby allaying privacy concerns. Additionally, unlike traditional cameras, it has environmental robustness and dark/light-independence. The proposed system obtained 99.9% accuracy in detecting falls by using the GoogleNet convolutional neural network. The proposed system is also capable of detecting other types of movements in addition to those tested, including the detection of diseases such as COVID-19 through the cough movement.
Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows

Medical image segmentation is inherently an ambiguous task due to factors such as partial volumes and variations in anatomical definitions. While in most cases the segmentation uncertainty is around the border of structures of interest, there can also be considerable inter-rater differences. The class of conditional variational autoencoders (cVAE) offers a principled approach to inferring distributions over plausible segmentations that are conditioned on input images. Segmentation uncertainty estimated from samples of such distributions can be more informative than using pixel level probability scores. In this work, we propose a novel conditional generative model that is based on conditional Normalizing Flow (cFlow). The basic idea is to increase the expressivity of the cVAE by introducing a cFlow transformation step after the encoder. This yields improved approximations of the latent posterior distribution, allowing the model to capture richer segmentation variations. With this we show that the quality and diversity of samples obtained from our conditional generative model is enhanced. Performance of our model, which we call cFlow Net, is evaluated on two medical imaging datasets demonstrating substantial improvements in both qualitative and quantitative measures when compared to a recent cVAE based model.
Overcoming the Channel Estimation Barrier in Massive MIMO Communication via Deep Learning

A new wave of wireless services, including virtual reality, autonomous driving and Internet of Things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive numbers of connections and ultra-low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.
Using interpretability approaches to update “black-box” clinical prediction models: an external validation study in nephrology

Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies.
Fine-Grained Similarity Measurement between Educational Videos and Exercises

In online learning systems, measuring the similarity between educational videos and exercises is a fundamental task with great application potentials. In this paper, we explore to measure the fine-grained similarity by leveraging multimodal information. The problem remains pretty much open due to several domain-specific characteristics. First, unlike general videos, educational videos contain not only graphics but also text and formulas, which have a fixed reading order. Both spatial and temporal information embedded in the frames should be modeled. Second, there are semantic associations between adjacent video segments. The semantic associations will affect the similarity and different exercises usually focus on the related context of different ranges. Third, the fine-grained labeled data for training the model is scarce and costly. To tackle the aforementioned challenges, we propose VENet to measure the similarity at both video-level and segment-level by just exploiting the video-level labeled data. Extensive experimental results on real-world data demonstrate the effectiveness of VENet.
QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms

In quality-diversity algorithms, the behavioral diversity metric is a key design choice that determines the quality of the evolved archives. Although behavioral diversity is traditionally obtained by describing the observed resulting behavior of robot controllers evaluated in a single environment, it is often more easily induced by introducing environmental diversity, i.e., by manipulating the environments in which the controllers are evaluated. This article proposes quality-environment-diversity (QED), an algorithm that repeatedly generates a random environment according to a probability distribution over environmental features (e.g., number of obstacles, arena size and robot sensor and actuator characteristics), evaluates the controller in that environment, and then describes the controller in terms of the features of that environment, the environment descriptor. Our study compares QED to three baseline task-specific and generic behavioral descriptors, in 5 different robot swarm benchmark tasks. For each task, the quality of the evolved archives is assessed by their capability to provide high-performing compensatory behaviors following injection of 250 unique faults to the robots of the swarm. The evolved archives achieve a median 2- to 3-fold reduction in the impact of the faults on the performance of the swarm. A qualitative analysis of evolved archives is done by visualizing the relation between diversity of compensatory behaviors, here called useful behavioral diversity, and fault recovery metrics. The resulting signatures indicate that, due to the diversity of environments inducing useful behavioral diversity, archives evolved by QED provide robot swarm controllers that are capable of recovering from high-impact faults.
Revisiting Popular Speech Recognition Software for ESL Speech

TESOL QuarterlyVolume 54, Issue 4 p. 1086-1097 Brief Research Reports Revisiting Popular Speech Recognition Software for ESL Speech Shannon McCrocklin, Corresponding Author Shannon McCrocklin shannon.mccrocklin@siu.edu orcid.org/0000-0002-5365-2067 Southern Illinois University, Carbondale, Illinois, United States Correspondence author. Email: shannon.mccrocklin@siu.eduSearch for more papers by this authorIdée Edalatishams, Idée Edalatishams orcid.org/0000-0003-2111-9273 Iowa State University, Ames, Iowa, United StatesSearch for more papers by this author Shannon McCrocklin, Corresponding Author Shannon McCrocklin shannon.mccrocklin@siu.edu orcid.org/0000-0002-5365-2067 Southern Illinois University, Carbondale, Illinois, United States Correspondence author. Email: shannon.mccrocklin@siu.eduSearch for more papers by this authorIdée Edalatishams, Idée Edalatishams orcid.org/0000-0003-2111-9273 Iowa State University, Ames, Iowa, United StatesSearch for more papers by this author First published: 29 October 2020 https://doi.org/10.1002/tesq.3006Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume54, Issue4December 2020Pages 1086-1097 RelatedInformation
Airport Detection in SAR Images Via Salient Line Segment Detector and Edge-Oriented Region Growing

Airport detection in synthetic aperture radar (SAR) images has attracted much concern in the field of remote sensing.Affected by other salient objects with geometrical features similar to those of airports, traditional methods often generate false detections.In order to produce the geometrical features of airports and suppress the influence of irrelevant objects, we propose a novel method for airport detection in SAR images.First, a salient line segment detector is constructed to extract salient line segments in the SAR images.Second, we obtain the airport support regions by grouping these line segments according to the commonality of these geometrical features.Finally, we design an edge-oriented region growing (EORG) algorithm, where growing seeds are selected from the airport support regions with the help of edge information in SAR images.Using EORG, the airport region can be mapped by performing region growing with these seeds.We implement experiments on real radar images to validate the effectiveness of our method.The experimental results demonstrate that our method can acquire more accurate locations and contours of airports than several state-of-the-art airport detection algorithms. Index Terms-Airport detection, line segment detector (LSD)
Effects of Immersive Virtual Reality Content Type to Mindfulness and Physiological Parameters

Virtual reality (VR) has been applied as a complimentary way to conventional treatment for mental disorders successfully. On the other hand, it has not been clearly shown what type of immersive media such as VR can directly affect one’s physiological parameters, associated with the state of mindfulness. We sought to assess how being subjected to differently designed VR contents can affect and modulate one’s anxiety both psychologically and more importantly physiologically. We empirically tested the comparative effects of two polarizing VR content types to this effect: (1) “calm/soothing” content and (2) “disturbing”. Twenty-five adults participated and their mental state, anxiety level and physiological signals were measured before and after experiencing the respective VR content type. The experiment found a statistically significant effect of the content type to the changes in these measures and confirmed that the “calm” content was helpful for one to self-regulate to lower heart rate and blood pressure, stable GSR, and the “disturbing” content in the opposite way. We applied this result to calm down and stabilize vital signs of patients during actual coronary angiography and catheterization operations. We were able to observe the same effect with positive comments from the patients and operating team.
Large-scale online multi-view graph neural network and applications

Recently popularized Graph Neural Network (GNN) has been attaching great attention along with its successful industry applications. This paper focuses on two challenges traditional GNN frameworks face: (i) most of them are transductive and mainly concentrate on homogeneous networks considering single typed nodes and edges; (ii) they are difficult to handle the real-time changing network structures as well as scale to big graph data. To address these issues, a novel attention-based Heterogeneous Multi-view Graph Neural Network (aHMGNN) solution is introduced. aHMGNN models a more intricate heterogeneous multi-view network, where various node and edge types co-exist and each of these objects also contain specific attributes. It is end-to-end, and two stages are designed for node embeddings learning and multi-typed node and edge representations fusion, respectively. Experimental studies on large-scale spam detection and link prediction tasks clearly verify the efficiency and effectiveness of our proposed aHMGNN. Furthermore, we have implemented our approach in one of the largest e-commerce platforms which further verifies that aHMGNN is arguably promising and scalable in real-world applications.
DeepIaC: deep learning-based linguistic anti-pattern detection in IaC

Linguistic anti-patterns are recurring poor practices concerning inconsistencies among the naming, documentation, and implementation of an entity. They impede readability, understandability, and maintainability of source code. This paper attempts to detect linguistic anti-patterns in infrastructure as code (IaC) scripts used to provision and manage computing environments. In particular, we consider inconsistencies between the logic/body of IaC code units and their names. To this end, we propose a novel automated approach that employs word embeddings and deep learning techniques. We build and use the abstract syntax tree of IaC code units to create their code embedments. Our experiments with a dataset systematically extracted from open source repositories show that our approach yields an accuracy between 0.785 and 0.915 in detecting inconsistencies.
Improved depth characterization of internal defect using the fusion of shearography and speckle interferometry

A hybrid nondestructive testing (NDT) approach based on the fusion of shearography and digital speckle pattern interferometry (DSPI) is proposed for depth characterization of internal defect. To this end, a novel dual-sensitive speckle interferometry system is developed for both shearography and DSPI measurements. On one hand, shearography imaging results are used to identify the internal defect location, shape, and boundary qualitatively for the preparation of subsequent depth characterization. On the other hand, an improved mechanical model combined with bending theory and DSPI imaging results is built to perform the internal defect depth prediction. Finally, the hybrid NDT experiment for a thin metallic plate with an internal planar defect is conducted for feasibility validation of the proposed fusion method. The results indicate that the relative error of defect depth detection is less than 5% compared to the existing method and show that the proposed fusion method can successfully perform improved depth characterization of internal defect.
Contour and region harmonic features for sub-local facial expression recognition

Expression recognition relies on intensity, edges, and geometry that overlooks the actual shape curvatures of facial regions. This paper presents a novel two-stage approach to distinguish seven expressions on the basis of eleven different facial areas. The combination of contour and region harmonics is used to develop the interrelationship of sub-local areas in the human face for expression recognition. We applied a multi-class support vector machine (SVM) with subject dependent k-fold cross-validation to classify the human emotions into expressions. We tested our proposed method on three public facial expression datasets for sub-local regions in human face and achieved 94.90%, 93.43%, and 92.57% recognition rate for the CK+, CFEE, and MUG datasets respectively. Experiments show that the contour and region harmonics have high classification power and can be computed efficiently. Our method provides higher accuracy, less computing time, and less memory space than existing techniques, including deep learning.
Volumetric capture for narrative films

Volumetric capture is a technique that allows to create “holographic” recordings of actors, sets and props. The technique can be used to create immersive stories that sometimes reflect aspects of reality better than realistic 3D models. For example, volumetric captures of actors do not seem to cause uncanny valley effect. In this paper, we provide an overview of volumetric capture technology and its application to narrative filmmaking.
A Case Study on the Use of Volumetric Video in Augmented Reality for Cultural Heritage

Interest in the use of augmented reality (AR) is currently growing alongside advances in visual display and 3D reconstruction technologies. With the integration of volumetric video (VV), AR can be enhanced with representations of live-action actors. In this paper, we present an exploratory case-study of a museologically focused AR application that evaluated various parameters affecting the overall experience for visitors of the Long Room in the Old Library at Trinity College Dublin. Employing a volumetric 3D representation of Jonathan Swift, the benefits of applying VV in AR for a cultural heritage use-case scenario were explored. Moreover, we compared two AR platforms for this prototype application: a tablet and an HMD. For this purpose, we collected post-task opinions of the application and processed quantitative and qualitative data. The results highlighted differences between the two platforms and showed how the developed VV AR application was initially received by the users.
On oriented supersingular elliptic curves

We revisit theoretical background on OSIDH (Oriented Supersingular Isogeny Diffie-Hellman protocol), which is an isogeny-based key-exchange protocol proposed by Colò and Kohel at NutMiC 2019. We give a proof of a fundamental theorem for OSIDH. The theorem was stated by Colò and Kohel without proof. Furthermore, we consider parameters of OSIDH, give a sufficient condition on the parameters for the protocol to work, and estimate the size of the parameters for a certain security level.
On the Impact of Interlanguage Dependencies in Multilanguage Systems Empirical Case Study on Java Native Interface Applications (JNI)

Nowadays, developers are often using multiple programming languages to exploit the advantages of each language and to reuse code. However, dependency analysis across multilanguage is more challenging compared to mono-language systems. In this article, we introduce two approaches for multilanguage dependency analysis: static multilanguage dependency analyzer) and historical multilanguage dependency analyzer, which we apply on ten open-source multilanguage systems to empirically analyze the prevalence of the dependencies across languages, i.e., interlanguage dependencies and their impact on software quality and security. Our main results show that: the more interlanguage dependencies, the higher the risk of bugs and vulnerabilities being introduced, while this risk remains constant for intralanguage dependencies; the percentage of bugs within interlanguage dependencies is three times higher than the percentage of bugs identified in intralanguage dependencies; the percentage of vulnerabilities within interlanguage dependencies is twice the percentage of vulnerabilities introduced in intralanguage dependencies.
Success-or-Draw: A Strategy Allowing Repeat-Until-Success in Quantum Computation

The repeat-until-success strategy is a standard method to obtain success with a probability that grows exponentially with the number of iterations. However, since quantum systems are disturbed after a quantum measurement, how to perform repeat-until-success strategies in certain quantum algorithms is not straightforward. In this Letter, we propose a new structure for probabilistic higher-order transformation named success-or-draw, which allows a repeat-until-success implementation. For that we provide a universal construction of success-or-draw structure that works for any probabilistic higher-order transformation on unitary operations. We then present a semidefinite programming approach to obtain optimal success-or-draw protocols and analyze in detail the problem of inverting a general unitary operation.