Reviewerly/exHarmony huggingface dataset:
Stress Propagation in Human-Robot Teams Based on Computational Logic Model

Mission teams are exposed to the emotional toll of life and death decisions. These are small groups of specially trained people supported by intelligent machines for dealing with stressful environments and scenarios. We developed a composite model for stress monitoring in such teams of human and autonomous machines. This modelling aims to identify the conditions that may contribute to mission failure. The proposed model is composed of three parts: 1) a computational logic part that statically describes the stress states of teammates; 2) a decision part that manifests the mission status at any time; 3) a stress propagation part based on standard Susceptible-Infected-Susceptible (SIS) paradigm. In contrast to the approaches such as agent-based, random-walk and game models, the proposed model combines various mechanisms to satisfy the conditions of stress propagation in small groups. Our core apporach involves data structures such as decision tables and decision diagrams. These tools are adaptable to human-machine teaming as well.
Trajectories for the Optimal Collection of Information

We study a scenario where an aircraft has multiple heterogeneous sensors collecting measurements to track a target vehicle of unknown location. The measurements are sampled along the flight path and our goals to optimize sensor placement to minimize estimation error. We select as a metric the Fisher Information Matrix (FIM), as “minimizing” the inverse of the FIM is required to achieve small estimation error. We propose to generate the optimal path from the Hamilton-Jacobi (HJ) partial differential equation (PDE) as it is the necessary and sufficient condition for optimality. A traditional method of lines (MOL) approach, based on a spatial grid, lends itself well to the highly non-linear and non-convex structure of the problem induced by the FIM matrix. However, the sensor placement problem results in a state space dimension that renders a naive MOL approach intractable. We present a new hybrid approach, whereby we decompose the state space into two parts: a smaller subspace that still uses a grid and takes advantage of the robustness to non-linearities and non-convexities, and the remaining state space that can be found efficiently from a system of ODEs, avoiding formation of a spatial grid.
Gaussian Mixture Representation of 3- D Phased Array Radar Measurements in Sine Space

When measurement errors are more than two orders of magnitude larger in crossrange (i.e., angle) than in range, the representation of the posterior distribution with a single Gaussian covariance is inaccurate for tracking applications. Measurements are a nonlinear function of the Cartesian track states, and this nonlinearity must be addressed to achieve near optimal estimates with a consistent covariance. Gaussian mixture models have been shown to be an effective tool for modeling the posterior distribution when a single Gaussian is not sufficient [1]. An effective method to find the Maximum Likelihood (ML) solution to the parameters describing the Gaussian mixture model around a converted distribution is Expectation Maximization (EM) [3]. The application of EM tools to radar measurements has been restricted to polar and spherical measurements [8], [10], while phased array measurements are in range and sine space. In this work, the EM algorithm is used to find the ML estimates of the parameters of a Gaussian mixture representation of the 3-D sine space measurements in 3-D Cartesian space. The mixture models are shown to greatly reduce the Kullback-Leibler diver-gence of the true and model distributions when compared to a single Gaussian. The nuances of the distributions are examined, such as the presence of a skew in the distribution, and a lookup table is proposed as a computationally efficient component of a Gaussian mixture filter. The Gaussian mixtures are shown to be effective in reducing the adverse effects of the nonlinearity in sensor measurements when tracking with a Gaussian mixture filter.
Automated Aircraft Recognition via Vision Transformers

Aircraft recognition aims to identify an aircraft type from its external appearance, serving as a vital task in the military field. The development of photography equipment allows technicians to collect images of the aircraft with rich information over a variety of scales and resolutions in a convenient way. However, these images are taken from different light and viewing angles, leading to the aircraft's various shapes, radiations, and colors. Such variance raises challenges for automated aircraft recognition techniques. This paper proposes an accurate and robust automated aircraft recognition technique based on Vision Transformers (ViT) to resist the variation carried by visual images. In particular, the self-attention mechanism in the ViT can better models the long-range dependency of pixels, compared with the existing convolutional neural network (CNN) approaches. We evaluate the effectiveness of our approach on a publicly available benchmark FGVCAircraft over multi-level granularity categories. The suggested ViT model achieves an overall Precision@1 of 0.915, outperforming other baselines, especially in images with complex variations.
An Improved DDPG Algorithm for UAV Navigation in Large-Scale Complex Environments

Autonomous navigation is one of the most critical elementary skills to control UAV navigate around the environment without colliding with obstacles. As UAVs are prevalently applied in many domains, such as goods delivery, search and rescue and intelligent transportation etc, safety and efficiency are two basic requirements while executing tasks. However, autonomous navigation is not a trivial task because it is challenging for UAV to observe, orientate, decide and take actions simultaneously. Specially, in large-scale complex environments, the constrained narrow passages, dense obstacles and dynamic objects pose increasing difficulty. In this article, UAV autonomous navigation is modeled as a Markov decision process and an Improved Deep Deterministic Policy Gradient (ImDDPG) algorithm is proposed to make safe and efficient navigation decisions. In ImDDPG, the actor network is decomposed into two sub-networks to learn a more stable action policy. Next, some reward reshaping functions are developed to solve the sparse reward problem. Then, the convergence speed is further accelerated with a dynamic decay strategy. The targeting neural network is well designed and trained. Simulation experiments are conducted to prove that the proposed ImDDPG algorithm can achieve a success rate more than 95% in large-scale complex environments. Moreover, numerous simulation results are also presented to demonstrate that ImDDPG has better generalization ability than the benchmark DDPG and TD3 algorithms in environments with denser obstacles and dynamic objects.
Finding Feature Relationships and Relevant Features in Large Datasets using FPGAs

Finding relevant features in datasets is critical to designing and implementing effective Machine Learning solutions. For datasets of relatively low dimensions this can be a challenging task and in high-dimensional datasets this can be very difficult. This paper provides a nonparametric approach to find feature relationships in large datasets. Such an approach applies to high-dimensional, time series data, but can be used on any dataset. The approach discussed uses rank correlation and applies a technique to identify feature relationships in the data. This results in finding sets of feature relationships, some of which have relevance (weak, strong, etc.) to a specific feature while other sets of relationships may be irrelevant. The number of feature relationships identified will vary based on the data. The approach in this paper is ultimately limited by the ability to distinguish between random and correlated data (i.e., noise limited). The end goal is identifying relevant features and Knowledge Discovery as these sets of Feature Relationships can reveal a framework that helps explain complex systems described by the data. Furthermore, this paper considers implementing the approach using Field Programmable Gate Arrays (FPGAs). FPGAs support high performance, scalable, high throughput implementations to process big data. Design data is provided to implement this approach in the context of challenging high-dimensional data. The example FPGA design is provided based on the Xilinx Adaptive Compute Acceleration Platform (ACAP) using the Versal Series device.
Trusting Machine-Learning Applications in Aeronautics

A general recommendation from the French office for aeronautical and space standardization (BNAE) is being drawn up by experts from Onera, Thales, Dassault and Safran, with the collaboration of Airbus, MBDA and ADP, the main French aeronautical companies. This document is based on mathematical and statistical elements which are reintroduced within a system and software development process considering the specificities of algorithms based on learning methods from data sets or data generators. For each activity in this development process, whether it is data capitalization or the use of artificial intelligence, risks are identified, and mitigation methods proposed. A few application cases are included in the document to illustrate the particularities of certain types of algorithms. Methods of estimation, classification, categorization or even reinforcement learning are mentioned. This paper gives a summary in English of the general recommendation.
Resolving Ambiguity via Dialogue to Correct Unsynthesizable Controllers for Free-Flying Robots

For human-robot teams that operate in space, safety and robustness are paramount. In situations such as habitat construction, station inspection, or cooperative exploration, incorrect assumptions about the environment or task across the team could lead to mission failure. Thus it is important to resolve any ambiguity about the mission between teammates before embarking on a commanded task. The safeguards guaranteed by formal methods can be used to synthesize correct-by-construction reactive controllers for a robot using Linear Temporal Logic. If a robot fails to synthesize a controller given an instruction, it is clear that there exists a logical inconsistency in the environmental assumptions and/or described interactions. These specifications however are typically crafted in a language unique to the verification framework, requiring the human collaborator to be fluent in the software tool used to construct it. Furthermore, if the controller fails to synthesize, it may prove difficult to easily repair the specification. Language is a natural medium to generate these specifications using modern symbol grounding techniques. Using language empowers non-expert humans to describe tasks to robot teammates while retaining the benefits of formal verification. Additionally, dialogue could be used to inform robots about the environment and/or resolve any ambiguities before mission execution. This paper introduces an architecture for natural language interaction using a symbolic representation that informs the construction of a specification in Linear Temporal Logic. The novel aspect of this approach is that it provides a mechanism for resolving synthesis failure by hypothesizing corrections to the specification that are verified through human-robot dialogue. Experiments involving the proposed architecture are demonstrated using a simulation of an Astrobee robot navigating in the International Space Station.
A Low-Communication Distributed State-Estimation Framework for Satellite Formations

This paper presents a novel distributed state-estimation framework for satellite formations. Existing consensus-based distributed filters have high communication re-quirements that make them unsuitable for resource-constrained small-spacecraft formations. We present a low-communication distributed filtering scheme targeted at small-satellite formations that are limited by inter-satellite communication chan-nel capacity. We assume a chief-deputy topology, similar to the proposed NASA Helioswarm mission. Our novel state-estimation framework is based on decoupling the chief state estimation filter from the deputies and using two separate filter architectures: one chief-specific that accounts for intermittent access to inertial position measurements, and a second deputy-specific one that only incorporates relative range measurements between the spacecraft, having the chief state as an external model input. We introduce a consider-covariance technique to account for the chief's state uncertainty in the deputy's filter. We demonstrate that the proposed distributed filter architecture requires significantly less communication than prior consensus-based methods in the literature while achieving comparable accuracy.
A Single-pass Noise Covariance Estimation Algorithm in Multiple-model Adaptive Kalman Filtering

Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of industrial applications. The reliable algorithms for their estimation are scarce, and the necessary and sufficient conditions for the identifiability of the covariances were in dispute until very recently. In this paper, we present a single-pass stochastic gradient descent (SGD) algorithm for noise covariance estimation for use in multiple-model adaptive Kalman filters. The multiple-model estimation algorithm assumes that the system obeys one of a finite number of models, and each model has its own dynamics. The overall estimate of the multiple-model approach is a convex combination of the estimates from the filters running in parallel based on the individual models, where the weights of the convex combination are the concomitant posterior model probabilities. Here, the identifiability conditions of the multiple-model approach depend on each candidate model since the corresponding Kalman filters are non-interacting. The proposed streaming algorithm reads measurement data exactly once and has an acceptable root mean square error (RMSE) of state estimates. The proposed estimating algorithm is implemented based on its single-pass nature, recursive fading memory estimation of the sample cross-correlations of the innovations, and the root mean square propagation (RMSprop) accelerated SGD algorithm. Our approach enforces the structural assumptions on unknown noise covariances, and ensures the symmetry and positive definiteness of the estimated covariance matrices. The proposed method is applicable to non-stationary systems where the noise covariances continuously change. The validation of the proposed method on several numerical examples demonstrates its accuracy and filter consistency.
Spacecraft Time-Series Online Anomaly Detection Using Deep Learning

Anomaly detection in spacecraft telemetry channels is of great importance, especially considering the extremeness of the spacecraft operating environment. These anomalies often function as precursors for system failure. Currently, domain experts manually monitor telemetry channels, which is time-consuming and limited in scope. An automated approach to anomaly detection would be ideal, considering that each satellite system has thousands of channels to monitor. Deep learning models have been shown to be effective at capturing the normal behavior of the channels and flagging any abnormalities. However, each channel needs a unique model trained on it, and high performing models have been shown to require an increased training time. We instead propose training deep learning models in an online manner to quickly understand the behavior of a given channel and identify anomalies in real-time. This greatly reduces the amount of training time required to obtain a model for each channel. We present the results of our approach to show that we can achieve performance comparable to state-of-the-art spacecraft anomaly detection methods with minimal training time.
A Speed Deviation Feature for Enhanced Target Tracking and Data Association

The probabilistic data association filter (PDAF) is a Kalman-based tracking approach to situations in which non-target (clutter) measurements may disguise the desired tar-get measurements. Throughout recent decades, feature-based modifications have been proposed to provide additional target-clutter discrimination beyond the proximity metric used in the original PDAF. Examples include amplitude information, directional deviation, and doppler deweighting, of which the first has become widely used under the title PDAFAI. This paper proposes a new feature for the PDAF, known as a speed deviation. It is based on the kinematics between a candidate measurement and the track, and aims to yield a more stable and effective mapping of measurements to target probabilities. A large collection of simulated target motion data (1,000 random-ized runs) is used to statistically demonstrate the effectiveness of this feature in reducing kinematic track error. Single-target motion is simulated here as a randomized series of constant velocity and constant rate of turn segments. The true target measurement is then corrupted by Gaussian noise, disguised in a uniformly distributed clutter field, and has the potential to “drop out” at any given time step. The target state is tracked using measurements of position and doppler recorded at regular intervals. Statistical analyses are carried out and demonstrate a significant improvement over the baseline PDAFAI framework. Kinematic error results are also shown across a wide range of target dropout rates, showing a consistent improvement over the baseline across various levels of tracking difficulty. Moreover, performance comparisons are made with respect to the more recent directional deviation and doppler deweighting features, against which the proposed speed deviation shows the single greatest error reduction.
Asynchronous Data Association for Kinematic and Identity Fusion

This paper proposes an asynchronous paradigm for hard data association of kinematic tracks with identity measurements. This addresses a critical task in disparate-sensor fusion that finds application in several operational domains. We demonstrate improved performance using simulated multi-target scenarios. Directions for future work are proposed. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> IEEE Aerospace Conference, Big Sky MT, March 2023. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IEEEAC paper #2741, Final Version, Updated 2022-12-19.
A Measurement Correlation Approach for Multitarget Tracking in a Noisy Environment

In multitarget tracking, measurement correlation uncertainty occurs when remote sensors, such as radars, yield measurements whose origin is uncertain. Using incorrect measurements in multitarget tracking systems leads to tracks loss. In such cases, efficient measurement correlation methods are needed to select measurements from many to be used to update the target tracks of interest in the tracking systems. This paper proposes a measurement correlation approach for multitarget tracking in a noisy environment. In this approach, measurements-to-targets correlation is computed across all targets and measurements based on minimization of weighted total squared errors. For a given track, the measurement that has the maximum correlation is used for updating the target track. The proposed correlation approach is applied to a scenario of multitarget tracking system and performance comparison with other correlation approaches is also presented. The results showed that performance improvement in terms of correct measurements correlation is achieved.
Automatic Code Annotation Generation Based on Heterogeneous Graph Structure

Automatic code annotation generation aims to generate readable annotations that describe the functionality of source code, which may facilitate software developers and programmers. Previous methods follow the encoder-decoder structures where the encoders are based on the abstract syntax trees (ASTs) to encode syntactic structures of code fragments. However, the AST alone cannot fully express complicated control structures, data flows, or dependencies of source code, leading to sub-optimal annotations. On the other hand, a functionality can be implemented in various ways with possibly different structures and token names. Most methods treat code fragments independently and do not exploit these similarities among code fragments. In this paper, we present HANCode2Seq, an automatic code annotation generation method by utilizing the code heterogeneous representation graph. Specifically, we construct the heterogeneous graph by combining multiple code induced graphs, including abstract syntax trees, control flow graphs, data flow graphs, and program dependency graphs. Then a heterogeneous graph attention network is applied to extract the comprehensive semantic meanings and syntactic structures of the source code fragments. Furthermore, we present a novel adaptive code similarity graph with code fragments being nodes. The representation of a code fragment is enhanced by aggregating information from other similar fragments on the graph, which may reduce the ambiguity of the code. The experimental results on real datasets show that our proposed model outperforms other baselines and produces more fluent and readable code annotations.
Security Code Recommendations for Smart Contract

A smart contract is a self-executing program that is stored on the blockchain and runs when predetermined conditions are satisfied. Many frequent transactions involving asset transfers rely on smart contracts deployed on the blockchain, making them highly vulnerable to attack, thus it is essential to ensure the security of smart contracts. Since the smart contract is immutable once deployed, developers must try their best to fix existing vulnerabilities in advance to ensure security. Current approaches for automatic program repair on the smart contracts have mainly adopted the heuristic search algorithms or defined patterns to fix several well-defined types of vulnerabilities. They can only provide security code recommendations for developers in specific scenarios. We explore more general automated program repair of smart contracts in software history.To pave the way for studying code changes related to bug fix of smart contracts in software history, we present a labeled public dataset for method-level program repair task, containing over 12 typical insecure code patterns. Unlike bugs in traditional software, the vulnerabilities of smart contracts are more associated with access control and conditional statements as smart contracts pertain to financial assets. For this problem, we devise a novel double-encoder network and use a code representation designed for the smart contract based on syntax information to repair program. By implementing and evaluating our approach on new dataset comprised of over 10,000 program pairs, we demonstrate the superiority of our approach in both qualitative and quantitative aspects.