Reviewerly/exHarmony huggingface dataset:
Automatic Detection and Prediction of Cybersickness Severity using Deep Neural Networks from user’s Physiological Signals

Cybersickness is one of the primary challenges to the usability and acceptability of virtual reality (VR). Cybersickness can cause motion sickness-like discomforts, including disorientation, headache, nausea, and fatigue, both during and after the VR immersion. Prior research suggested a significant correlation between physiological signals and cybersickness severity, as measured by the simulator sickness questionnaire (SSQ). However, SSQ may not be suitable for automatic detection of cybersickness severity during immersion, as it is usually reported before and after the immersion. In this study, we introduced an automated approach for the detection and prediction of cybersickness severity from the user's physiological signals. We collected heart rate, breathing rate, heart rate variability, and galvanic skin response data from 31 healthy participants while immersed in a VR roller coaster simulation. We found a significant difference in the participants' physiological signals during their cybersickness state compared to their resting baseline. We compared a support vector machine classifier and three deep neural classifiers for cybersickness severity detection and prediction in two minutes' future, given the previous two minutes of physiological signals. Our proposed simplified convolutional long short-term memory classifier achieved an accuracy of 97.44% for detecting current cybersickness severity and 87.38% for predicting future cybersickness severity from the physiological signals.
Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer

Generating high-quality data (e.g., images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such tasks to potentially enhance conventional machine-learning algorithms has emerged as a promising application but poses big challenges due to the limited number of qubits and the level of gate noise in available devices. In this work, we provide the first practical and experimental implementation of a quantum-classical generative algorithm capable of generating high-resolution images of handwritten digits with state-of-the-art gate-based quantum computers. In our quantum-assisted machine-learning framework, we implement a quantum-circuit-based generative model to learn and sample the prior distribution of a generative adversarial network. We introduce a multibasis technique which leverages the unique possibility of measuring quantum states in different bases, hence enhancing the expressivity of the prior distribution. We train this hybrid algorithm on an ion-trap device based on Yb+171 ion qubits to generate high-quality images and quantitatively outperform comparable classical generative adversarial networks trained on the popular MNIST dataset for handwritten digits.1 MoreReceived 26 November 2021Revised 19 April 2022Accepted 1 June 2022DOI:https://doi.org/10.1103/PhysRevX.12.031010Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum algorithmsQuantum Information
Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps

Historic topographic maps, which are georeferenced and made publicly available by the United States Geological Survey (USGS) and the National Map’s Historical Topographic Map Collection (HTMC), are a valuable source of historic land cover and land use (LCLU) information that could be used to expand the historic record when combined with data from moderate spatial resolution Earth observation missions. This is especially true for landscape disturbances that have a long and complex historic record, such as surface coal mining in the Appalachian region of the eastern United States. In this study, we investigate this specific mapping problem using modified UNet semantic segmentation deep learning (DL), which is based on convolutional neural networks (CNNs), and a large example dataset of historic surface mine disturbance extents from the USGS Geology, Geophysics, and Geochemistry Science Center (GGGSC). The primary objectives of this study are to (1) evaluate model generalization to new geographic extents and topographic maps and (2) to assess the impact of training sample size, or the number of manually interpreted topographic maps, on model performance. Using data from the state of Kentucky, our findings suggest that DL semantic segmentation can detect surface mine disturbance features from topographic maps with a high level of accuracy (Dice coefficient = 0.902) and relatively balanced omission and commission error rates (Precision = 0.891, Recall = 0.917). When the model is applied to new topographic maps in Ohio and Virginia to assess generalization, model performance decreases; however, performance is still strong (Ohio Dice coefficient = 0.837 and Virginia Dice coefficient = 0.763). Further, when reducing the number of topographic maps used to derive training image chips from 84 to 15, model performance was only slightly reduced, suggesting that models that generalize well to new data and geographic extents may not require a large training set. We suggest the incorporation of DL semantic segmentation methods into applied workflows to decrease manual digitizing labor requirements and call for additional research associated with applying semantic segmentation methods to alternative cartographic representations to supplement research focused on multispectral image analysis and classification.
Nonconvex and Bound Constraint Zeroing Neural Network for Solving Time-Varying Complex-Valued Quadratic Programming Problem

Many methods are known to solve the problem of real-valued and static quadratic programming (QP) effectively. However, few of them are still useful to solve the time-varying QP problem in the complex domain. In this study, a nonconvex and bound constraint zeroing neural network (NCZNN) model is designed and theorized to solve the time-varying complex-valued QP with linear equation constraint. Besides, we construct several new types of nonconvex and bound constraint complex-valued activation functions by extending real-valued activation functions to the complex domain. Subsequently, corresponding simulation experiments are conducted, and the simulation results verify the effectiveness and robustness of the proposed NCZNN model. Moreover, the model proposed in this article is further applied to solve the issue of small target detection in remote sensing images, which is modeled to QP problem with linear equation constraint by a serial of conversions based on constrained energy minimization algorithm.
Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-view Geometry

Epipolar constraints are at the core of feature matching and depth estimation in current multi-person multi-camera 3D human pose estimation methods. Despite the satisfactory performance of this formulation in sparser crowd scenes, its effectiveness is frequently challenged under denser crowd circumstances mainly due to two sources of ambiguity. The first is the mismatch of human joints resulting from the simple cues provided by the Euclidean distances between joints and epipolar lines. The second is the lack of robustness from the naive formulation of the problem as a least squares minimization. In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation. Our method consists of two key components: a graph model for fast cross-view matching, and a maximum a posteriori (MAP) estimator for the reconstruction of the 3D human poses. We demonstrate the effectiveness and superiority of our proposed method on four benchmark datasets. Our code is available at: https://github.com/HeCraneChen/3D-Crowd-Pose-Estimation-Based-on-MVG .
Blockchain-Based Payment Channel Networks: Challenges and Recent Advances

Blockchain technology has been developed with the vision to enable trusted collaboration between untrusted parties, without the need for a central authority.Despite its many promising applications, the technology suffers from a scalability problem.In order to increase transaction throughput and decrease transaction confirmation latency, payment channel networks have been proposed.Payment channel networks introduce a layer on top of the main chain, in which transactions can happen in a safe manner between only the transacting parties without burdening the entire network.In this paper, we present and highlight the many interesting research aspects this new type of network introduces.We first provide background on the mechanics of the operation of payment channel networks, and then proceed to present a plethora of research problems of networking and/or economics flavor arising in this context, including routing, scheduling, rebalancing, network design and topology analysis, and fee optimization.This work is within the scope of both the networking and network economics communities.
The Solar System Algorithm: A Novel Metaheuristic Method for Global Optimization

A novel metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is presented. The proposed algorithm imitates the orbiting behavior of some objects found in the solar system: i.e., Sun, planets, moons, stars, and black holes. SSA is used to solve five well-known engineering design problems: three-bar truss, pressure vessel, tension/compression spring, welded beam, and gear train. The obtained results are compared to 16 state-of-the-art metaheuristic algorithms. They show that SSA is very competitive in solving the considered engineering problems. In addition, the performance of SSA is evaluated on the benchmarks CEC 2014 and CEC 2020. The experimental results are compared to 27 (12 for CEC 2014 and 15 for CEC 2020) metaheuristic algorithms. They demonstrate that SSA is very promising in finding efficient solutions.
Skills2Job: A recommender system that encodes job offer embeddings on graph databases

We propose a recommender system that, starting from a set of users' skills, identifies the most suitable jobs as they emerge from a large dataset of Online Job Vacancies (OJVs). To this aim, we process 2.5M+ OJVs posted in three different countries (United Kingdom, France, and Germany), training several embeddings and performing an intrinsic evaluation of their quality. Besides, we compute a measure of skill importance for each occupation in each country, the Revealed Comparative Advantage (rca). The best vector model, one for each country, together with the rca, is used to feed a graph database, which will serve as the keystone for the recommender system. Results are evaluated through a user study of 10 labor market experts, using [email protected] and nDCG as scores. Results show a high precision for the recommendations provided by skills2job, and the high values of nDCG (0.985 and 0.984 in a [0,1] range) indicate a strong correlation between the experts' scores and the rankings generated by skills2job.
PEGASUS: Bridging Polynomial and Non-polynomial Evaluations in Homomorphic Encryption

Homomorphic encryption (HE) is considered as one of the most important primitives for privacy-preserving applications. However, an efficient approach to evaluate both polynomial and non-polynomial functions on encrypted data is still absent, which hinders the deployment of HE to real-life applications. To address this issue, we propose a practical framework PEGASUS. PEGASUS can efficiently switch back and forth between a packed CKKS ciphertext and FHEW ciphertexts without decryption, allowing us to evaluate arithmetic functions efficiently on the CKKS side, and to evaluate look-up tables on FHEW ciphertexts. Our FHEW → CKKS conversion algorithm is more practical than the existing methods. We improve the computational complexity from linear to sublinear. Moreover, the size of our conversion key is significantly smaller, e.g., reduced from 80 gigabytes to 12 megabytes. We present extensive benchmarks of PEGASUS, including sigmoid/ReLU/min/max/division, sorting and max-pooling. To further demonstrate the capability of PEGASUS, we developed two more applications. The first one is a private decision tree evaluation whose communication cost is about two orders of magnitude smaller than the previous HE-based approaches. The second one is a secure K-means clustering that is able to run on thousands of encrypted samples in minutes that outperforms the best existing system by 14 × – 20×. To the best of our knowledge, this is the first work that supports practical K-means clustering using HE in a single server setting.
Complexity, accuracy, and fluency as indices of college-level L2 writers’ proficiency

Several studies have explored complexity, accuracy, and fluency (CAF) as an index of language development, as an index of language performance, and as an index of writing quality. Although there have been studies that dealt with complexity measures as an index of proficiency, none so far have examined it in the company of accuracy and fluency as indices of proficiency. Thus, this study investigates whether CAF measures differentiate the four proficiency levels of L2 writers and whether relationships exist among the 18 CAF measures. More than 5000 essays from the International Corpus Network of Asian Learners of English (ICNALE) were analyzed using the L2 Syntactic Complexity Analyzer. Overall findings reveal that an interaction exists between language proficiency and CAF measures. Specifically, the length of production unit indices, degree of phrasal sophistication indices, weighted clause ratio, and words per text were found to best separate proficiency levels. The study also presents the patterns of relationship among the CAF measures which provided insights into how multiple measures may be selected and reclassified in so far as language proficiency is concerned. Implications for L2 writing assessment, pedagogy, use of automated text analyzers, and future studies are discussed.
Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings

Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adaptation techniques able to transfer learned knowledge from label-abundant domains to unlabeled ones. In this paper we propose an effective Unsupervised Domain Adaptation (UDA) strategy, based on a feature clustering method that captures the different semantic modes of the feature distribution and groups features of the same class into tight and well-separated clusters. Furthermore, we introduce two novel learning objectives to enhance the discriminative clustering performance: an orthogonality loss forces spaced out individual representations to be orthogonal, while a sparsity loss reduces class-wise the number of active feature channels. The joint effect of these modules is to regularize the structure of the feature space. Extensive evaluations in the synthetic-to-real scenario show that we achieve state-of-the-art performance.
Autonomous Social Distancing in Urban Environments Using a Quadruped Robot

Corona Virus Disease 2019 (COVID-19) pandemic has become a global challenge faced by people all over the world. Social distancing has been proved to be an effective practice to reduce the spread of COVID-19. Against this backdrop, we propose that the surveillance robots can not only monitor but also promote social distancing. Robots can be flexibly deployed and they can take precautionary actions to remind people of practicing social distancing. In this paper, we introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fully autonomous</i> surveillance robot based on a quadruped platform that can promote social distancing in complex urban environments. Specifically, to achieve autonomy, we mount multiple cameras and a three dimensional light detection and ranging sensor (3D LiDAR) on the legged robot. The robot then uses an onboard real-time social distancing detection system to track nearby pedestrian groups. Next, the robot uses a crowd-aware navigation algorithm to move freely in highly dynamic scenarios. The robot finally uses a crowd-aware routing algorithm to effectively promote social distancing by using human-friendly verbal cues to send suggestions to over-crowded pedestrians. We demonstrate and validate that our robot can be operated autonomously by conducting several experiments in various urban scenarios.
Crypto Pharmacy – Digital Medicine: A Mobile Application Integrated With Hybrid Blockchain to Tackle the Issues in Pharma Supply Chain

Mobile application integrated with cryptocurrency would be an innovative solution for making medicine purchase in the future. This research work fixates on the design and implementation of a crypto pharmacy - digital medicine utilizing hybrid blockchain technology. We present the concept of crypto medicine that eliminates third-party presence (buy/sell) in a medicine purchase. The proposed design integrates the mobile application with the NEM blockchain. The manufacturer engenders the digital medicines with NEM blockchain namespace, all the pharma products are assigned with NEM cryptocurrency XEM, labeled with a QR code. Our archetype model (perspicacious detector) executes the astute contract that connects all the stakeholders in a pharma supply chain. The proposed mobile application has been prosperously integrated with NEM cryptocurrency by assigning XEM for the medicines, all the transactions are monitored in a blockchain from manufacturer to culminate-utilizer. The mobile application designed utilizing apple iOS integrated with nem2sdk swift framework. This system evades a few millions worth of substandard medicine integrated in the ecumenical market every year. In additament to crypto medicine, the genuine-time monitoring of liquid medicine has been tested with the IoT platform. It fetches the temperature data into the blockchain and authenticates the reliability of medicine for intensive care unit patients.
Architectural patterns for the design of federated learning systems

Federated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning system can be viewed as a large-scale distributed system with different components and stakeholders as numerous client devices participate in federated learning. Designing a federated learning system requires software system design thinking apart from the machine learning knowledge. Although much effort has been put into federated learning from the machine learning technique aspects, the software architecture design concerns in building federated learning systems have been largely ignored. Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems. Architectural patterns present reusable solutions to a commonly occurring problem within a given context during software architecture design. The presented patterns are based on the results of a systematic literature review and include three client management patterns, four model management patterns, three model training patterns, four model aggregation patterns, and one configuration pattern. The patterns are associated to the particular state transitions in a federated learning model lifecycle, serving as a guidance for effective use of the patterns in the design of federated learning systems.
Multi-Scale Mixed Dense Graph Convolution Network for Skeleton-Based Action Recognition

In skeleton-based action recognition, the approaches based on graph convolutional networks(GCN) have achieved remarkable performance by modeling spatial-temporal graphs to explore the physical dependencies between body joints.However, these methods mostly apply hierarchical GCNs to aggregate wider-range neighborhood information, which makes joint features be weakened during long diffusion.In this paper, we design a multi-scale mixed dense graph convolutional network (MMDGCN) to overcome both shortcomings.We propose a dense graph convolution operation to enhance the local context information of joints, and then the spatial and temporal attention modules with a larger receptive field are introduced to help the model strengthen the discriminative features to adaptively refine the intermediate feature maps.We also design a multi-scale mixed temporal convolution module, which provides a flexible temporal graph through the combination of different scale convolution kernels.Extensive experiments on the three real-world datasets (NTU-RGB+D, NTU-RGB+D120 and Kinetics) demonstrate that the performance of the proposed MMDGCN in skeleton-based action recognition. INDEX TERMS Dense graph convolution, spatial and temporal attention module
Understanding Software-2.0

Enabled by a rich ecosystem of Machine Learning (ML) libraries, programming using learned models , i.e., Software-2.0 , has gained substantial adoption. However, we do not know what challenges developers encounter when they use ML libraries. With this knowledge gap, researchers miss opportunities to contribute to new research directions, tool builders do not invest resources where automation is most needed, library designers cannot make informed decisions when releasing ML library versions, and developers fail to use common practices when using ML libraries. We present the first large-scale quantitative and qualitative empirical study to shed light on how developers in Software-2.0 use ML libraries, and how this evolution affects their code. Particularly, using static analysis we perform a longitudinal study of 3,340 top-rated open-source projects with 46,110 contributors. To further understand the challenges of ML library evolution, we survey 109 developers who introduce and evolve ML libraries. Using this rich dataset we reveal several novel findings. Among others, we found an increasing trend of using ML libraries: The ratio of new Python projects that use ML libraries increased from 2% in 2013 to 50% in 2018. We identify several usage patterns including the following: (i) 36% of the projects use multiple ML libraries to implement various stages of the ML workflows, (ii) developers update ML libraries more often than the traditional libraries , (iii) strict upgrades are the most popular for ML libraries among other update kinds, (iv) ML library updates often result in cascading library updates, and (v) ML libraries are often downgraded (22.04% of cases). We also observed unique challenges when evolving and maintaining Software-2.0 such as (i) binary incompatibility of trained ML models and (ii) benchmarking ML models. Finally, we present actionable implications of our findings for researchers, tool builders, developers, educators, library vendors, and hardware vendors.