Reviewerly/exHarmony huggingface dataset:
Domain-driven models yield better predictions at lower cost than reservoir computers in Lorenz systems

Recent advances in computing algorithms and hardware have rekindled interest in developing high-accuracy, low-cost surrogate models for simulating physical systems. The idea is to replace expensive numerical integration of complex coupled partial differential equations at fine time scales performed on supercomputers, with machine-learned surrogates that efficiently and accurately forecast future system states using data sampled from the underlying system. One particularly popular technique being explored within the weather and climate modelling community is the echo state network (ESN), an attractive alternative to other well-known deep learning architectures. Using the classical Lorenz 63 system, and the three tier multi-scale Lorenz 96 system (Thornes T, Duben P, Palmer T. 2017 Q. J. R. Meteorol. Soc. 143 , 897–908. ( doi:10.1002/qj.2974 )) as benchmarks, we realize that previously studied state-of-the-art ESNs operate in two distinct regimes, corresponding to low and high spectral radius (LSR/HSR) for the sparse, randomly generated, reservoir recurrence matrix. Using knowledge of the mathematical structure of the Lorenz systems along with systematic ablation and hyperparameter sensitivity analyses, we show that state-of-the-art LSR-ESNs reduce to a polynomial regression model which we call Domain-Driven Regularized Regression (D2R2). Interestingly, D2R2 is a generalization of the well-known SINDy algorithm (Brunton SL, Proctor JL, Kutz JN. 2016 Proc. Natl Acad. Sci. USA 113 , 3932–3937. ( doi:10.1073/pnas.1517384113 )). We also show experimentally that LSR-ESNs (Chattopadhyay A, Hassanzadeh P, Subramanian D. 2019 ( http://arxiv.org/abs/1906.08829 )) outperform HSR ESNs (Pathak J, Hunt B, Girvan M, Lu Z, Ott E. 2018 Phys. Rev. Lett. 120 , 024102. ( doi:10.1103/PhysRevLett.120.024102 )) while D2R2 dominates both approaches. A significant goal in constructing surrogates is to cope with barriers to scaling in weather prediction and simulation of dynamical systems that are imposed by time and energy consumption in supercomputers. Inexact computing has emerged as a novel approach to helping with scaling. In this paper, we evaluate the performance of three models (LSR-ESN, HSR-ESN and D2R2) by varying the precision or word size of the computation as our inexactness-controlling parameter. For precisions of 64, 32 and 16 bits, we show that, surprisingly, the least expensive D2R2 method yields the most robust results and the greatest savings compared to ESNs. Specifically, D2R2 achieves 68 × in computational savings, with an additional 2 × if precision reductions are also employed, outperforming ESN variants by a large margin. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Catch the Ball: Accurate High-Speed Motions for Mobile Manipulators via Inverse Dynamics Learning

Mobile manipulators consist of a mobile platform equipped with one or more robot arms and are of interest for a wide array of challenging tasks because of their extended workspace and dexterity. Typically, mobile manipulators are deployed in slow-motion collaborative robot scenarios. In this paper, we consider scenarios where accurate high-speed motions are required. We introduce a framework for this regime of tasks including two main components: (i) a bi-level motion optimization algorithm for real-time trajectory generation, which relies on Sequential Quadratic Programming (SQP) and Quadratic Programming (QP), respectively; and (ii) a learning-based controller optimized for precise tracking of high-speed motions via a learned inverse dynamics model. We evaluate our framework with a mobile manipulator platform through numerous high-speed ball catching experiments, where we show a success rate of 85.33%. To the best of our knowledge, this success rate exceeds the reported performance of existing related systems [1], [2] and sets a new state of the art.
Blockchain-Based Federated Learning Against End-Point Adversarial Data Corruption

With the approach of 5G Society, more and more devices have been connected to the Internet, where information is stored, analyzed, and shared. Federated learning allows participants to train a machine learning model through sharing the parameters of it based on local training, instead of raw private data at local. In this research, we propose the implementation of the blockchain in federated learning for local parameters evaluation and global parameter aggregation, thus alleviating the influence of end-point adversarial training data. Besides, all updates of local parameters are encrypted and stored in a block of the blockchain after the consensus by the committee. We evaluate the performance of the scheme when adopting various types of corruption to the adversary's dataset, including noise with various degrees and circle occlusion with various diameters. At last, it shows robust and resilient performance compared with the traditional federated learning, achieving a validation accuracy rate of 0.957 when adding noise with a degree of 1.0, and one of 0.944 when adopting circle occlusion with a diameter of 28 pixels for the classification.
Fast Single-Shot Ship Instance Segmentation Based on Polar Template Mask in Remote Sensing Images

Object detection and instance segmentation in remote sensing images is a fundamental and challenging task, due to the complexity of scenes and targets. The latest methods tried to take into account both the efficiency and the accuracy of instance segmentation. In order to improve both of them, in this paper, we propose a single-shot convolutional neural network structure, which is conceptually simple and straightforward, and meanwhile makes up for the problem of low accuracy of single-shot networks. Our method, termed with SSS-Net, detects targets based on the location of the object's center and the distances between the center and the points on the silhouette sampling with non-uniform angle intervals, thereby achieving a balanced sampling of lines in mask generation. In addition, we propose a non-uniform polar template IoU based on the contour template in polar coordinates. Experiments on both the Airbus Ship Detection Challenge dataset and the ISAID-ships dataset show that SSS-Net has strong competitiveness in precision and speed for ship instance segmentation.
CCAMP: An Integrated Translation and optimization Framework for OpenACC and OpenMP

Heterogeneous computing and exploration into specialized accelerators are inevitable in current and future supercomputers. Although this diversity of devices is promising for performance, the array of architectures presents programming challenges. High-level programming strategies have emerged to face these challenges, such as the OpenMP offloading model and OpenACC. However, the varying levels of support for these standards within vendor-specific and open-source tools, as well as the lack of performance portability across devices, have prevented the standards from achieving their goals. To address these shortcomings, we present CCAMP, an OpenMP and OpenACC interoperable framework. CCAMP provides two primary facilities: language translation between the two standards and device-specific directive optimization within each standard. We show that by using the CCAMP framework, programmers can easily transplant non-portable code into new ecosystems for new architectures. Additionally, by using CCAMP's device-specific directive optimizations, users can achieve optimized performance across architectures using a single source code.
Novel security models, metrics and security assessment for maritime vessel networks

In the past few years, maritime vessels have become computerized and connected to the internet. However, with this technology, critical systems based on-board ships that manage the vessel’s navigation system, radar, cargo management system, etc have become more prone to cyber-attacks. Moreover, attackers are now becoming aware of the vulnerabilities associated with such maritime vessel systems. As a result, it is of vital importance to manage and secure the maritime vessel networks against cyber-attacks, but there is a lack of capabilities to efficiently manage the identifications of vulnerabilities, security risk assessment, and evaluate the effectiveness of countermeasures. Hence, we propose a novel framework and security risk modeling and assessment method to evaluate the security of maritime vessel networks. We develop (1) a security model for maritime vessels to capture probabilistic events, vulnerabilities, and network configurations of vessel components; (2) propose an approach to assess the network with a single function, multiple functions, and the inter-dependencies between the functions as attack goal(s); (3) adopt three well-defined security metrics with the proposed model to evaluate possible attacks and/or threats; and (4) compare the effectiveness of cyber-defense strategies based on different attack scenarios on the maritime vessel network. Besides, we perform sensitivity analysis based on temporal and permanent connections that are associated with the vessels’ systems to understand the effect of the connections on security. Our results demonstrate the applicability and usability of the proposed model for finding potential attack paths, assessing security, and mitigating the impact of cyber-attacks and threats on a vessel network.
REFORM: Recognizing F-formations for Social Robots

Recognizing and understanding conversational groups, or F-formations, is a critical task for situated agents designed to interact with humans. F-formations contain complex structures and dynamics, yet are used intuitively by people in everyday face-to-face conversations. Prior research exploring ways of identifying F-formations has largely relied on heuristic algorithms that may not capture the rich dynamic behaviors employed by humans. We introduce REFORM (REcognize F-FORmations with Machine learning), a data-driven approach for detecting F-formations given human and agent positions and orientations. REFORM decomposes the scene into all possible pairs and then reconstructs F-formations with a voting-based scheme. We evaluated our approach across three datasets: the SALSA dataset, a newly collected human-only dataset, and a new set of acted human-robot scenarios, and found that REFORM yielded improved accuracy over a state-of-the-art F-formation detection algorithm. We also introduce symmetry and tightness as quantitative measures to characterize F-formations.
Maximizing minority accuracy for imbalanced pattern classification problems using cost-sensitive Localized Generalization Error Model

Traditional machine learning methods may not yield satisfactory generalization capability when samples in different classes are imbalanced. These methods tend to sacrifice the accuracy of the minority class to improve the overall accuracy without regarding the fact that misclassifications of minority samples usually costs more in many real world applications. Therefore, we propose a neural network training method via a minimization of the cost-sensitive localized generalization error-based objective function (c-LGEM) to achieve a better balance of error yielded by the minority and the majority classes. The c-LGEM emphasizes the minimization of the generalization error of the minority class in a cost-sensitive manner. Experimental results obtained on 16 UCI datasets show that neural networks trained by the c-LGEM yield better performance in comparison to the performance yielded by some existing methods.
SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from multiple sources when direct collection is difficult or costly. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this paper, we study a specific synthetic data generation task called downscaling, a procedure to infer high-resolution, harder-to-collect information (e.g., individual level records) from many low-resolution, easy-to-collect sources, and propose a multi-stage framework called SynC (Synthetic Data Generation via Gaussian Copula). For given low-resolution datasets, the central idea of SynC is to fit Gaussian copula models to each of the low-resolution datasets in order to correctly capture dependencies and marginal distributions, and then sample from the fitted models to obtain the desired high-resolution subsets. Predictive models are then used to merge sampled subsets into one, and finally, sampled datasets are scaled according to low-resolution marginal constraints. We make four key contributions in this work: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-of-the-art machine learning and statistical techniques, 2) perform simulation studies to validate SynC's performance as a synthetic data generation algorithm, 3) demonstrate its value as a feature engineering tool, as well as an alternative to data collection in situations where gathering is difficult through two real-world datasets, 4) release an easy-to-use framework implementation for reproducibility and scalability at the production level that easily incorporates new data.
An Online Training Method for Augmenting MPC with Deep Reinforcement Learning

Recent breakthroughs both in reinforcement learning and trajectory optimization have made significant advances towards real world robotic system deployment. Reinforcement learning (RL) can be applied to many problems without needing any modeling or intuition about the system, at the cost of high sample complexity and the inability to prove any metrics about the learned policies. Trajectory optimization (TO) on the other hand allows for stability and robustness analyses on generated motions and trajectories, but is only as good as the often over-simplified derived model, and may have prohibitively expensive computation times for real-time control, for example in contact rich environments. This paper seeks to combine the benefits from these two areas while mitigating their drawbacks by (1) decreasing RL sample complexity by using existing knowledge of the problem with real-time optimal control, and (2) allowing online policy deployment at any point in the training process by using the TO (MPC) as a baseline or worst-case scenario action, while continuously improving the combined learned-optimized policy with deep RL. This method is evaluated on tasks of successively navigating a car model to a series of goal destinations over slippery terrains as fast as possible, in which drifting will allow the system to more quickly change directions while maintaining high speeds.
Performance analysis of electricity generation from grid-connected photovoltaic system using All-Sky Index for Smart City projects in Thailand

Promotion of solar energy as a new promising renewable energy source for Smart City projects in Thailand plays an important role in driving the economy, society and the environment. A novel conceptual framework with different solar radiation indexes was developed to investigate and evaluate the efficiency of photovoltaic (PV) energy production by comparing predicted and actual results for energy acquired from the reference project. The trend of potential energy production was predicted from solar radiation data as the basis for further calculations to evaluate PV system efficiency through a techno-economic analysis of project development. A mathematical model was developed to predict the obtained electrical energy by considering the suitable location-specific solar radiation indexes including direct normal radiation, All-Sky insolation incident on a horizontal surface, direct normal irradiation and global horizontal irradiance. The developed model was tested with three existing Smart City projects at various locations in Thailand. The All-Sky index was determined as the most suitable option to evaluate new PV development projects. Highest energy production from the PV projects occurred in March and April. Energy production efficiencies of the PV systems were estimated in the range of 3.97–4.32 kWh/kWp. The techno-economic analysis suggested that the PV project had an internal rate of return of 14.30–15.75% and payback period of 6.18–6.74 years. The proposed conceptual framework was a promising tool for decision-making regarding Smart City PV projects.
Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse, and Fragmented Functional Data

In many applications, smooth processes generate data that is recorded under a variety of observational regimes, including dense sampling and sparse or fragmented observations that are often contaminated with error. The statistical goal of registering and estimating the individual underlying functions from discrete observations has thus far been mainly approached sequentially without formal uncertainty propagation, or in an application-specific manner by pooling information across subjects. We propose a unified Bayesian framework for simultaneous registration and estimation, which is flexible enough to accommodate inference on individual functions under general observational regimes. Our ability to do this relies on the specification of strongly informative prior models over the amplitude component of function variability using two strategies: a data-driven approach that defines an empirical basis for the amplitude subspace based on training data, and a shape-restricted approach when the relative location and number of extrema is well-understood. The proposed methods build on the elastic functional data analysis framework to separately model amplitude and phase variability inherent in functional data. We emphasize the importance of uncertainty quantification and visualization of these two components as they provide complementary information about the estimated functions. We validate the proposed framework using multiple simulation studies and real applications.
WideScan: Exploiting Out-of-Band Distortion for Device Classification Using Deep Learning

Wireless device classification techniques play a vital role in supporting spectrum awareness applications, such as spectrum access policy enforcement and unauthorized network access monitoring. Recent works proposed to exploit distortions in the transmitted signals caused by hardware impairments of the devices to provide device identification and classification using deep learning. As technology advances, the manufacturing impairment variations among devices become extremely insignificant, and hence the need for more sophisticated device classification techniques becomes inescapable. This paper proposes a scalable, RF data-driven deep learning-based device classification technique that efficiently classifies transmitting radios from a large pool of bit-similar, high-end, high-performance devices with same hardware, protocol, and/or software configurations. Unlike existing techniques, the novelty of the proposed approach lies in exploiting both the in-band and out-of-band distortion information, caused by inherent hardware impairments, to enable scalable and accurate device classification. Using convolutional neural network (CNN) model for classification, our results show that the proposed technique substantially outperforms conventional approaches in terms of both classification accuracy and learning times. In our experiments, the testing accuracy obtained under the proposed technique is about 96% whereas that obtained under the conventional approach is only about 50% when the devices exhibit very similar hardware impairments. The proposed technique can be implemented with minimum receiver design tuning, as radio technologies, such as cognitive radios, can easily allow for both in-band and out-of band sampling.
User Behaviour Analysis of Mixed Reality Remote Collaboration with a Hybrid View Interface

In this paper, we present a Mixed Reality (MR) remote collaboration system that enables hybrid view sharing from the local worker to the remote expert for real-time remote guidance. The local worker can share either a 2D first-person view, a 360° live view with a fixed viewpoint, or a 3D free view within a point-cloud reconstruction of the local environment. The remote expert can access these views and freely switch between them in Virtual Reality (VR) for better awareness of the local worker's surroundings. We investigate the remote experts' behaviours while using the hybrid view interface during typical pick-and-place remote guiding tasks. We found that the remote experts prefer to learn the local physical layout and search for the targets with a global perspective from the 3D free view. The results also showed that the experts chose to use the 360° live view with independent view control rather than the 2D first-person view with high-resolution to control the task procedures and check the local worker's actions. Our study informs the viewpoint interface design for a MR remote collaboration system in various guiding scenarios.
Computationally Efficient Obstacle Avoidance Trajectory Planner for UAVs Based on Heuristic Angular Search Method

For accomplishing a variety of missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement for UAVs in real applications. In this paper, we proposed such a computationally efficient obstacle avoidance trajectory planner that can be used in unknown cluttered environments. Because of the narrow view field of single depth camera on a UAV, the information of obstacles around is quite limited thus the shortest entire path is difficult to achieve. Therefore we focus on the time cost of the trajectory planner and safety rather than other factors. This planner is mainly composed of a point cloud processor, a waypoint publisher with Heuristic Angular Search(HAS) method and a motion planner with minimum acceleration optimization. Furthermore, we propose several techniques to enhance safety by making the possibility of finding a feasible trajectory as large as possible. The proposed approach is implemented to run onboard in real-time and is tested extensively in simulation and the average control output calculating time of iteration steps is less than 18 ms.