Reviewerly/exHarmony huggingface dataset:
Resilient edge machine learning in smart city environments

Distributed Machine Learning (DML) has emerged as a disruptive technology that enables the execution of Machine Learning (ML) and Deep Learning (DL) algorithms in proximity to data generation, facilitating predictive analytics services in Smart City environments. However, the real-time analysis of data generated by Smart City Edge Devices (EDs) poses significant challenges. Concept drift, where the statistical properties of data streams change over time, leads to degraded prediction performance. Moreover, the reliability of each computing node directly impacts the availability of DML systems, making them vulnerable to node failures. To address these challenges, we propose a resilience framework comprising computationally lightweight maintenance strategies that ensure continuous quality of service and availability in DML applications. We conducted a comprehensive experimental evaluation using real datasets, assessing the effectiveness and efficiency of our resilience maintenance strategies across three different scenarios. Our findings demonstrate the significance and practicality of our framework in sustaining predictive performance in smart city edge learning environments. Specifically, our enhanced model exhibited increased generalizability when confronted with concept drift. Furthermore, we achieved a substantial reduction in the amount of data transmitted over the network during the maintenance of the enhanced models, while balancing the trade-off between the quality of analytics and inter-node data communication cost.
DNA dynamic coding-based encryption algorithm for vector map considering global objects

With the rapid development of digitalization and networking, copying and sharing vector map data has become convenient, but it also brings security risks such as data interception and tampering. Current encryption methods focus on partially encrypting objects, which may leave some sensitive and confidential objects unencrypted. Additionally, the encryption effect for the point layers is not satisfactory. This paper proposes an algorithm for encrypting vector maps based on DNA dynamic encoding. Initially, global scrambling is performed on all object coordinates using double random position permutation, and a four-dimensional hyperchaotic system is selected to ensure the complexity of the chaotic sequence. Next, DNA dynamic coding operations are applied to whole layers of the vector map to encrypt all data. Finally, the encrypted data can be decrypted and restored according to the DNA coding rules and the double random position permutation mapping relationship, with the decrypted data being consistent with the original. Experimental results indicate that the proposed algorithm can be applied to the encryption protection of various vector map elements, especially to improve the performance on encrypting point layer data compared with existing encryption algorithms. It improves the security of vector data in the process of storage and transmission, and has potential application value in the protection of vector map.
OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control

This paper presents OpTaS, a task specification Python library for Trajectory Optimization (TO) and Model Predictive Control (MPC) in robotics. Both TO and MPC are increasingly receiving interest in optimal control and in particular handling dynamic environments. While a flurry of software libraries exists to handle such problems, they either provide interfaces that are limited to a specific problem formulation (e.g. TracIK, CHOMP), or are large and statically specify the problem in configuration files (e.g. EXOTica, eTaSL). OpTaS, on the other hand, allows a user to specify custom nonlinear constrained problem formulations in a single Python script allowing the controller parameters to be modified during execution. The library provides interface to several open source and commercial solvers (e.g. IPOPT, SNOPT, KNITRO, SciPy) to facilitate integration with established workflows in robotics. Further benefits of OpTaS are highlighted through a thorough comparison with common libraries. An additional key advantage of OpTaS is the ability to define optimal control tasks in the joint-space, task-space, or indeed simultaneously. The code for OpTaS is easily installed via pip, and the source code with examples can be found at github.com/cmower/optas.
Heading for the Abyss: Control Strategies for Exploiting Swinging of a Descending Tethered Aerial Robot

The use of aerial vehicles for exploration and data collection has the potential to significantly aid environmental monitoring in environments which are dangerous and hard to navigate. However, within these environments navigation can often be restricted by overhangs which are challenging to navigate, particularly so with the high payloads required for environmental monitoring. We propose utilizing a tethered bicopter with horizontal propellers. This spherical pendulum like system can exploit the tether, not only as a means of powering and recovering the robot, but also to assist its motion, i.e. by swinging to increase the workspace of the robot. Using PD-based control, we demonstrate how the system can be stabilized and bang-bang control to excite the system to achieve large amplitude swinging. By combining these controllers, we show how the system can be used to navigate in a glacial-inspired scenario where there are overhangs and obstacles through which the robot must navigate.
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation

Domain generalization for semantic segmentation is highly demanded in real applications, where a trained model is expected to work well in previously unseen domains. One challenge lies in the lack of data which could cover the diverse distributions of the possible unseen domains for training. In this paper, we propose a WEb-image assisted Domain GEneralization (WEDGE) scheme, which is the first to exploit the diversity of web-crawled images for generalizable semantic segmentation. To explore and exploit the real-world data distributions, we collect web-crawled images which present large diversity in terms of weather conditions, sites, lighting, camera styles, etc. We also present a method which injects styles of the web-crawled images into training images on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training. Moreover, we use the web-crawled images with their predicted pseudo labels for training to further enhance the capability of the network. Extensive experiments demonstrate that our method clearly outperforms existing domain generalization techniques.
ReachLipBnB: A branch-and-bound method for reachability analysis of neural autonomous systems using Lipschitz bounds

We propose a novel Branch-and-Bound method for reachability analysis of neural networks in both open-loop and closed-loop settings. Our idea is to first compute accurate bounds on the Lipschitz constant of the neural network in certain directions of interest offline using a convex program. We then use these bounds to obtain an instantaneous but conservative polyhedral approximation of the reachable set using Lipschitz continuity arguments. To reduce conservatism, we incorporate our bounding algorithm within a branching strategy to decrease the over-approximation error within an arbitrary accuracy. We then extend our method to reachability analysis of control systems with neural network controllers. Finally, to capture the shape of the reachable sets as accurately as possible, we use sample trajectories to inform the directions of the reachable set over-approximations using Principal Com-ponent Analysis (PCA). We evaluate the performance of the proposed method in several open-loop and closed-loop settings.
A Probabilistic Model of Activity Recognition with Loose Clothing

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. With comfortable electronic-textiles, sensors can be embedded into clothing so that it is possible to record human movement outside the laboratory for long periods. However, a long-standing issue is how to deal with motion artifact introduced by movement of clothing with respect to the body. Surprisingly, recent empirical findings suggest that cloth-attached sensor can actually achieve higher accuracy of activity recognition than rigid-attached sensor, particularly when predicting from short time-windows. In this work, a probabilistic model is introduced in which this improved accuracy and resposiveness is explained by the increased statistical distance between movements recorded via fabric sensing. The predictions of the model are verified in simulated and real human motion capture experiments, where it is evident that this counterintuitive effect is closely captured.
On Legible and Predictable Robot Navigation in Multi-Agent Environments

Legible motion is intent-expressive, which when employed during social robot navigation, allows others to quickly infer the intended avoidance strategy. Predictable motion matches an observer's expectation which, during navigation, allows others to confidently carryout the interaction. In this work, we present a navigation framework capable of reasoning on its legibility and predictability with respect to dynamic interactions, e.g., a passing side. Our approach generalizes the previously formalized notions of legibility and predictability by allowing dynamic goal regions in order to navigate in dynamic environments. This generalization also allows us to quantitatively evaluate the legibility and the predictability of trajectories with respect to navigation interactions. Our approach is shown to promote legible behavior in ambiguous scenarios and predictable behavior in unambiguous scenarios. In a multi-agent environment, this yields an increase in safety while remaining competitive in terms of goal-efficiency when compared to other robot navigation planners in multi-agent environments. The code of this work is made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Trajectory Planning for the Bidirectional Quadrotor as a Differentially Flat Hybrid System

The use of bidirectional propellers provides quadrotors with greater maneuverability which is advantageous in constrained environments. This paper addresses the development of a trajectory planning algorithm for quadrotors with bidirectional motors. Previous work has shown that the property of differential flatness can be leveraged for efficient trajectory planning. However, planners that leverage flatness for quadrotors fail at points where the acceleration of the center of mass is equal to gravity, i.e., when the vehicle experiences free fall. The central contribution of this paper is a flatness-based trajectory planning method that allows quadrotors to use bidirectional propellers and pass through the so-called free-fall singularity. We model our system as a differentially flat hybrid system with the aid of coordinate charts derived from the Hopf fibration and develop an algorithm that computes forward and reverse thrusts for each propeller, resulting in smooth trajectories everywhere in SE(3). We demonstrate the planner's versatility by planning knife-edge maneuvers and trajectories passing through the free-fall singularity, while transitioning from forward to reverse thrust.
Meta-Reinforcement Learning via Language Instructions

Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamental reasons causing this limitation lies in the nature of the trial-and-error learning paradigm of reinforcement learning, where the agent communicates with the environment and pro-gresses in the learning only relying on the reward signal. This is implicit and rather insufficient to learn a task well. On the con-trary, humans are usually taught new skills via natural language instructions. Utilizing language instructions for robotic motion control to improve the adaptability is a recently emerged topic and challenging. In this paper, we present a meta-RL algorithm that addresses the challenge of learning skills with language instructions in multiple manipulation tasks. On the one hand, our algorithm utilizes the language instructions to shape its in-terpretation of the task, on the other hand, it still learns to solve task in a trial-and-error process. We evaluate our algorithm on the robotic manipulation benchmark (Meta-World) and it significantly outperforms state-of-the-art methods in terms of training and testing task success rates. Codes are available at https://tumi6robot.wixsite.com/million.
Density Planner: Minimizing Collision Risk in Motion Planning with Dynamic Obstacles using Density-based Reachability

Uncertainty is prevalent in robotics. Due to measurement noise and complex dynamics, we cannot estimate the exact system and environment state. Since conservative motion planners are not guaranteed to find a safe control strategy in a crowded, uncertain environment, we propose a density-based method. Our approach uses a neural network and the Liouville equation to learn the density evolution for a system with an uncertain initial state. We can plan for feasible and probably safe trajectories by applying a gradient-based optimization procedure to minimize the collision risk. We conduct motion planning experiments on simulated environments and environments generated from real-world data and outperform baseline methods such as model predictive control and nonlinear programming. While our method requires offline planning, the online run time is 100 times smaller compared to model predictive control. The code and supplementary material can be found at https://mit-realm.github.io/density_planner/.
Optimal Multi-Robot Coverage Path Planning for Agricultural Fields using Motion Dynamics

Coverage path planning (CPP) is the task of computing an optimal path within a region to completely scan or survey the area of interest by using robotic sensor footprints. In this work, we propose a novel approach to find the multi-robot optimal coverage path of an agricultural field using motion dynamics while minimizing the mission time. Our approach consists of three steps: (i) divide the agricultural field into convex polygonal areas to optimally distribute them among the robots, (ii) generate an optimal coverage path to ensure minimum coverage time for each of the polygonal areas, and (iii) generate the trajectory for each coverage path using Dubins motion dynamics. Several experiments and simulations were performed to check the validity and feasibility of our approach, and the results and limitations are discussed.
Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space

Motion planning framed as optimisation in structured latent spaces has recently emerged as competitive with traditional methods in terms of planning success while significantly outperforming them in terms of computational speed. However, the real-world applicability of recent work in this domain remains limited by the need to express obstacle information directly in state-space, involving simple geometric primitives. In this work we address this challenge by leveraging learned scene embeddings together with a generative model of the robot manipulator to drive the optimisation process. In addition, we introduce an approach for efficient collision checking which directly regularises the optimisation undertaken for planning. Using simulated as well as real-world experiments, we demonstrate that our approach, AMP-LS, is able to successfully plan in novel, complex scenes while outperforming traditional planning baselines in terms of computation speed by an order of magnitude. We show that the resulting system is fast enough to enable closed-loop planning in real-world dynamic scenes.
DFR-FastMOT: Detection Failure Resistant Tracker for Fast Multi-Object Tracking Based on Sensor Fusion

Persistent multi-object tracking (MOT) allows autonomous vehicles to navigate safely in highly dynamic environments. One of the well-known challenges in MOT is object occlusion when an object becomes unobservant for subsequent frames. The current MOT methods store objects information, such as trajectories, in internal memory to recover the objects after occlusions. However, they retain short-term memory to save computational time and avoid slowing down the MOT method. As a result, they lose track of objects in some occlusion scenarios, particularly long ones. In this paper, we propose DFR-FastMOT, a light MOT method that uses data from a camera and LiDAR sensors and relies on an algebraic formulation for object association and fusion. The formulation boosts the computational time and permits long-term memory that tackles more occlusion scenarios. Our method shows outstanding tracking performance over recent learning and non-learning benchmarks with about 3% and 4% margin in MOTA, respectively. Also, we conduct extensive experiments that simulate occlusion phenomena by employing detectors with various distortion levels. The proposed solution enables superior performance under various distortion levels in detection over current state-of-art methods. Our framework processes about 7,763 frames in 1.48 seconds, which is seven times faster than recent benchmarks. The framework will be available at https://github.com/MohamedNagyMostafa/DFR-FastMOT.
Computing Confidence Score for Neural Network Predictions from Latent Features

As machine learning (ML)-based systems become more commonplace in our daily lives, users should be able to trust the predictions made by the ML models. Inappropriate predictions may have disastrous effects depending on the context in which the ML model is applied. To decide whether to trust a classifier prediction or not, current methods use the probabilities associated with the model predictions or nearest neighbors of the predicted label. In this study, we present experiments that show how the distribution statistics of the latent space features can be used to accurately compute the output confidence of a model.Our method involves first mapping the latent space feature values from the penultimate layer of a model to a hypothesized prior distribution and then storing the parameters of the prior distributions for each node. The degree to which a trained model’s penultimate layer activations for a given sample match the previously stored distribution parameters for the predicted label determines the level of confidence associated with that prediction. We demonstrate the effectiveness of utilizing different statistical properties of the latent space activations in our confidence scoring algorithm and compare our algorithm’s results against Softmax probabilities based confidence scores on the CIFAR-10 and MNIST datasets. The results of the experiment show that the confidence score computation based on latent space activation statistics performs better than the Softmax based approach.
A Novel Approach for Underwater Image Enhancement Using Style Transfer Technique

In ocean engineering and research, underwater imaging is a developing field. Usually, underwater images exhibit low contrast, distorted colour, and blurred features due to factors like refraction, absorption, and scattering of light by water-borne suspended particles. In order to address these challenges, we have proposed a novel style transfer based deep learning approach for the enhancement of underwater images. The suggested method consists of two stages: i) segmentation stage and ii) enhancement stage. The segmentation stage is implemented using SUIM-Net, which does the semantic segmentation of underwater images. The second stage is the enhancement stage, where we use a style transfer technique, which consists of an encoder-decoder circuit with whitening and coloring transforms. It also uses wavelet pooling which preserves the structural features while stylizing the distorted underwater images. These aids in reconstructing the signals more accurately. To evaluate the performance of the suggested method, various metrics such as Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR), Underwater Image Quality Measure (UIQM) and Underwater Color Image Quality Evaluation (UCIQE) are used. Better UIQM values were obtained using the suggested method when it was compared to other cutting-edge methodologies.
Conceptualising Software Development Lifecycle for Engineering AI Planning Systems

Given the prominence of AI planning in research and industry, the development of AI planning software and its integration into production architectures are becoming important. However, building and managing planning software is a complex and expertise-dependent process without methodological support that would ensure AI planning applications have high quality and industrial strength. To that end, we propose a lifecycle for developing AI planning systems that consists of ten phases related to the design, development, and operation of planning systems.
Simulated uav dataset for object detection

Unmanned Aerial Vehicles (UAVs) have become increasingly popular for various applications, including object detection. Novel detector algorithms require large datasets to improve, as they are still evolving. Additionally, in countries with restrictive drone policies, simulated datasets can provide a cost-effective and efficient alternative to real-world datasets for researchers to develop and test their algorithms in a safe and controlled environment. To address this, we propose a simulated dataset for object detection through a Gazebo simulator that covers both indoor and outdoor environments. The dataset consists of 11,103 annotated frames with 27,412 annotations, of persons and cars as the objects of interest. This dataset can be used to evaluate detector proposals for object detection, providing a valuable resource for researchers in the field. The dataset is annotated using the Dark Label software, which is a popular tool for object annotation. Additionally, we assessed the dataset’s performance using advanced object detection systems, with YOLOv3 achieving 86.9 mAP50-95, YOLOv3-tiny achieving 79.5 mAP50-95, YOLOv5 achieving 82.2 mAP50-95, YOLOv7 achieving 61.8 mAP50-95 and YOLOv8 achieving 87.8 mAP50-95. Overall, this simulated dataset is a valuable resource for researchers working in the field of object detection.