Reviewerly/exHarmony huggingface dataset:
A blockchain-based architecture and smart contracts for an interoperable Physical Internet

Transport and Logistics stakeholders utilise Blockchain to interact within their networks in a transparent and secure manner. This creates islands of disconnected communities which prohibits visibility across the entire supply chain. This paper introduces a framework, which aims at unifying multiple proprietary Blockchain systems, offering an opportunity to empower stakeholders across the entire supply chain to collaborate and exchange information seamlessly. The Blockchain Interoperability framework employs smart contracts, which aspire to automate previously cumbersome processes and bring value to the Physical Internet (PI) paradigm. Smart contracts guarantee a trustworthy and distributed process of contract negotiation and execution that significantly reduces time, administrative overheads, and costs which are currently typically spent on manual inter-organisational processes.
Remote driving and command of trains: The Shift2Rail approach.

One of the purposes of the Shift2Rail TAURO project is to define and propose standardized remote driving and command functionalities for this type of operations in the future. To achieve it, the ongoing research encompasses the definition of a specification to enable the remote driving and command for three relevant use cases, carefully selected: Remote driving under ETCS, remote driving in shunting yard and remote driving in depots for tramways. The process of defining the specification is divided in three phases. First, a deep analysis of the functions which are suitable for their remote command is performed. This analysis is based on the actual driver tasks based on ERA_ERTMS_0155660 and the EN15380-4 Functional Breakdown Structure. For each function some parameters (including performance and safety concerns) are described. In a second step, the three use cases are thoroughly defined using SysML, describing the actors, steps and outcome, among other aspects. Finally, a large set of functional and non-functional requirements are identified. Based on the specification this paper also presents a draft architecture for the use case of remotely commanded tramways in depots. This architecture targets having a safe and secured tramway under any circumstance by implementing several modules and functions that enable the tramway protecting itself from failures or remote driving errors. The final target of this research activity leads with the definition of an application profile (SysML syntactic and semantic definition of an interface) to be standardized and ensure interoperability.
Real-time learning for real-time data: online machine learning for predictive maintenance of railway systems

In the railway, an unexpected fault reduces service availability and may cause fatalities. Therefore, maintenance must be timely. Nowadays, the omnipresence of onboard sensors generates data enormously, thus enabling data-driven predictive maintenance. For this, machine learning has come into prominence. Traditionally, a machine learning model is trained on a batch of data. However, this approach lags behind on fast-paced data streams. For real-time learning on real-time data, we propose a pipeline that employs online machine learning to address predictive maintenance of sensorized railway systems. We showcase the implementation and experimental results of two modules automating data preprocessing to demonstrate the potentials of online learning. We also discuss the intuition of another module using online clustering to monitor the evolution of system health.
A Comparison of the Best Generative Adversarial Network Architectures for Synthetic Data Generation

Generative Adversarial Networks (GANs), a prominent generative modeling technique, employ deep learning to train two competing networks: a generative network that creates synthetic data and a discriminative network that distinguishes real from synthetic data. In this adversarial game, the generative network strives to improve its ability to generate realistic data that can deceive the discriminative network into believing it is authentic. It is popularly employed to generate pictures and other forms of information based on a bank of existing data.By employing the MNIST dataset, this paper delves into the application of GANs in the generation of handwritten digital images. Additionally, we compared the existing architectures for generating synthetic data such GAN and DCGAN. From our research, we gave the best optimization parameters for generating handwritten digits. After the experimentations, we proposed the further use case for this in medical imagery applications.
Real-time implementable intelligent trajectory controller design for a light commercial vehicle

Light Commercial Vehicles (LCVs) are widely used for last-mile delivery of goods to manufacturers and consumers due to their ease of manoeuvrability, better mileage, and lower operating costs. They account for over 80% of total commercial vehicle sales in the European Union. Therefore, automation of LCVs has a significant socio-economic impact. A fuzzy trajectory controller with a minimum number of rules is designed for a hypothetical Level 3 autonomous vehicle (L3AV) and tested in a narrow road scenario. The analysis of the simulations showed that the maximum tracking error in the angular position was 4.1° at 4 m/s, 6.3° at 6 m/s, and 6.7° at 8 m/s, while the maximum lateral error was 0.1 m in all scenarios. The results have shown that the proposed controller achieves acceptable performance at different speeds with a minimum number of rules, leading to real-time implementation.
A Systematic Review for Cyber Security Awareness Platforms: Recent approaches and Research Gaps

Cybersecurity remains one of the major challenges facing computer systems in the modern age. Traditional hacking methods, reliant on technical intrusion, are being replaced by more human-centric attack vectors (Chang & Coppel, 2020). Consequently, enhancing user awareness has become a crucial defensive measure. The 21st-century surge in remote work and digital transition, exacerbated by the COVID-19 pandemic, amplifies the relevance and urgency for effective cybersecurity awareness platforms. This paper systematically reviews current strategies in this domain, spotlighting: i) Definitions of cyber security awareness platforms, ii) Contemporary Cyber Security Awareness Platform methodologies, iii) Essential components, and iv) Existing research voids.
Model Validation of Articulated Heavy-Duty Vehicle via IPG for Tracking Controller Design

Articulated heavy-duty vehicles (HDVs) exhibit complex dynamic behavior during operation, leading to several control challenges. A critical aspect that plays an important role in the vehicle's performance is the vehicle model used in the development of autonomous functions. A model that adequately represents the response of the vehicle is essential to obtain the desired dynamic simulations and implement control and planning algorithms. This paper focuses on the hybrid-Kinematic (hK) vehicle model for modelling autonomous HDVs, and the validation of its behaviour behaviour via IPG TruckMaker®. A comparison between the hK and a dynamic model was conducted to analyze and evaluate its precision and performance in reversing. The results have shown that the hK model effectively mimics the behavior of the articulated truck, thus facilitating to solve the tracking control problem.
Digital image correlation of brittle materials based on ultrashort pulse laser imaging

Based on the extremely narrow half-height width (FWHM) characteristics of ultrashort pulsed lasers, a DIC imaging technique that can realize picosecond time resolution is proposed by combining the traditional digital image correlation (DIC) technique with it. This method can solve the problems of blurring and dragging of scattering images caused by the sudden crack initiation and fast crack expansion in the fracture process of brittle materials. The crack opening displacement (COD) and the full-field displacement of the sample at the instant of cracking of brittle materials can also be obtained. In addition, the error in spatial resolution of the traditional DIC technique using a continuous light source can be greatly reduced by using this method. In this paper, this method is used to record the tip displacement field of a tuff sample containing a type I prefabricated crack under semicircular disk three-point bending (SCB) experimental conditions. The experimental results show that the recording of crack extension behavior on the order of picoseconds can be achieved using this method, and the key parameters of fracture of brittle materials are calculated more accurately.
Depth layer slicing optimization method based on hybrid compressive light field

Compressive light field (CLF) is a promising light field display technology, and the traditional multiplicative CLF limits the number of layers due to the low transmittance of liquid crystals, which results in a small depth of field. Therefore, this paper proposes a three-dimensional display structure with a hybrid CLF. This structure utilizes a semi-transparent and semi-reflective mirror to superimpose two sets of multiplicative CLFs, each of which consists of two identical liquid crystal displays and a uniform backlight. The hybrid CLF has a greater depth of field and higher brightness, further improving image quality. Due to the properties of the hybrid CLF structure and the non-negative tensor (NTF) decomposition algorithm, the reconstructed image can suffer from layered image crosstalk, which leads to image quality degradation. We propose a method to reduce the hybrid CLF layered image crosstalk, and we validate the proposed method through computer simulations and optical experiments.
Defocus-enhanced technique for real-world scenarios using generative models

In recent years, significant progress has been made in deep learning-based image deblurring. These approaches utilize deep neural networks to learn the map between blurry and clear images or jointly learn the blurry kernel and clear image. They have demonstrated effectiveness in enhancing image quality, preserving details, and handling various types and degrees of blur. The objective of this study is to develop a defocus enhancement technique for real-world scenarios using score-based generative models. Stochastic differential equations (SDE) are employed to gradually introduce noise, thereby smoothing the data distribution towards a known prior distribution. The Score-Matching Langevin Dynamics (SMLD) model estimates the score for each noise scale, while Diffusion Models (DDPM) train the target model for score computation. This process constructs a score-based model capable of reversing the SDE over time. A predictor-corrector framework corrects the evolution of the reverse-time SDE, and the prior distribution is transformed back to the data distribution by removing the noise. By leveraging score-based generative models, accurate score estimation and sample generation are achieved using neural networks and numerical SDE solvers. This technique effectively restores clarity and details in defocused images, thereby enhancing overall image quality.
Conceptual Groundings of Intermediary Practices

Research on intermediaries is not new. In this chapter, we present a short history of how the discourse on intermediation practice has evolved over time, and discuss why it is timely to now look at the role of the intermediary and their practice in a more explicit manner. We also trace and connect different lineages and lines of thoughts that have developed in different disciplines—often with few lateral linkages. We connect these developments with debates and knowledge gaps in design thinking and the wider creative industries.
Intermediation in Design: Designers as Intermediaries

This chapter connects the lines of thought presented in the previous chapters—both theoretically and empirically grounded—with current issues, challenges, and opportunities in design research and practice. Design practice often seeks to engage communities and institutional partners with designers working as or with intermediaries. This chapter argues for the merits of a more explicit engagement with intermediation as part of the toolbox of design practice. The chapter discusses some of the utility of intermediation skills and techniques and calls for intermediation to be better embedded and integrated in design practices.
A Model Predictive Path Integral Method for Fast, Proactive, and Uncertainty-Aware UAV Planning in Cluttered Environments

Current motion planning approaches for autonomous mobile robots often assume that the low level controller of the system is able to track the planned motion with very high accuracy. In practice, however, tracking error can be affected by many factors, and could lead to potential collisions when the robot must traverse a cluttered environment. To address this problem, this paper proposes a novel receding-horizon motion planning approach based on Model Predictive Path Integral (MPPI) control theory – a flexible sampling-based control technique that requires minimal assumptions on vehicle dynamics and cost functions. This flexibility is leveraged to propose a motion planning framework that also considers a data-informed risk function. Using the MPPI algorithm as a motion planner also reduces the number of samples required by the algorithm, relaxing the hardware requirements for implementation. The proposed approach is validated through trajectory generation for a quadrotor unmanned aerial vehicle (UAV), where fast motion increases trajectory tracking error and can lead to collisions with nearby obstacles. Simulations and hardware experiments demonstrate that the MPPI motion planner proactively adapts to the obstacles that the UAV must negotiate, slowing down when near obstacles and moving quickly when away from obstacles, resulting in a complete reduction of collisions while still producing lively motion.
MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization

Supervised 3D Object Detection models have been displaying increasingly better performance in single-domain cases where the training data comes from the same environment and sensor as the testing data. However, in real-world scenarios data from the target domain may not be available for finetuning or for domain adaptation methods. Indeed, 3D object detection models trained on a source dataset with a specific point distribution have shown difficulties in generalizing to unseen datasets. Therefore, we decided to leverage the information available from several annotated source datasets with our Multi-Dataset Training for 3D Object Detection (MDT3D) method to increase the robustness of 3D object detection models when tested in a new environment with a different sensor configuration. To tackle the labelling gap between datasets, we used a new label mapping based on coarse labels. Furthermore, we show how we managed the mix of datasets during training and finally introduce a new cross-dataset augmentation method: crossdataset object injection. We demonstrate that this training paradigm shows improvements for different types of 3D object detection models. The source code and additional results for this research project will be publicly available on GitHub for interested parties to access and utilize: https://github.com/LouisSF/MDT3D
Risk-Tolerant Task Allocation and Scheduling in Heterogeneous Multi-Robot Teams

Effective coordination of heterogeneous multi-robot teams requires optimizing allocations, schedules, and motion plans in order to satisfy complex multi-dimensional task requirements. This challenge is exacerbated by the fact that real-world applications inevitably introduce uncertainties into robot capabilities and task requirements. In this paper, we extend our previous work on trait-based time-extended task allocation to account for such uncertainties. Specifically, we leverage the Sequential Probability Ratio Test to develop an algorithm that can guarantee that the probability of failing to satisfy task requirements is below a user-specified threshold. We also improve upon our prior approach by accounting for temporal deadlines in addition to synchronization and precedence constraints in a Mixed-Integer Linear Programming model. We evaluate our approach by benchmarking it against three baselines in a simulated battle domain in a city environment and compare its performance against a state-of-the-art framework in a pandemic-inspired multi-robot service coordination problem. Results demonstrate the effectiveness and advantages of our approach, which leverages redundancies to manage risk while simultaneously minimizing makespan.
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection

The adversarial robustness of a model is its ability to resist adversarial attacks in the form of small perturbations to input data. Universal adversarial attack methods such as Fast Sign Gradient Method (FSGM) [1] and Projected Gradient Descend (PGD) [2] are popular for LiDAR object detection, but they are often deficient compared to task-specific adversarial attacks. Additionally, these universal methods typically require unrestricted access to the model's information, which is difficult to obtain in real-world applications. To address these limitations, we present a black-box Scaling Adversarial Robustness (ScAR) method for LiDAR object detection. By analyzing the statistical characteristics of 3D object detection datasets such as KITTI, Waymo, and nuScenes, we have found that the model's prediction is sensitive to scaling of 3D instances. We propose three black-box scaling adversarial attack methods based on the available information: model-aware attack, distribution-aware attack, and blind attack. We also introduce a strategy for generating scaling adversarial examples to improve the model's robustness against these three scaling adversarial attacks. Comparison with other methods on public datasets under different 3D object detection architectures demonstrates the effectiveness of our proposed method.
Dodging Like A Bird: An Inverted Dive Maneuver Taking by Lifting-Wing Multicopters

It is crucial for hybrid unmanned aerial vehicles, such as lifting-wing multicopters, to plan a continuous, smooth, and collision-free trajectory to avoid obstacles. Unlike quad-copters, which typically work in indoor environments, lifting-wing multicopters typically fly at a high altitude with a high cruising speed, requiring higher maneuverability in the vertical direction. Inspired by birds, lifting-wing multicopters can take an inverted flight maneuver to gain more maneuverability than the corresponding multicopter owing to the additional lifting wing. In this paper, a rotation-aware collision-free motion planning strategy is proposed that takes aerodynamics into consideration and allows lifting-wing multicopters to fly at large rotation angles, even in inverted postures. Specifically, a collision-free state sequence is found using rotation-aware primitives by solving a graph search problem. The sequence is then refined with B-spline into smooth trajectories to be tracked by the differential flatness-based controller for lifting-wing multicopters. We analyze the proposed motion planning algorithm in different scenarios and demonstrate the feasibility of the generated trajectories in simulation and real-world experiments. Video: https://youtu.be/n87jK81zg_I
CLF-CBF Constraints for Real-Time Avoidance of Multiple Obstacles in Bipedal Locomotion and Navigation

This paper presents a reactive planning system that allows a Cassie-series bipedal robot to avoid multiple non-overlapping obstacles via a single, continuously differentiable control barrier function (CBF). The overall system detects an individual obstacle via a height map derived from a LiDAR point cloud and computes an elliptical outer approximation, which is then turned into a CBF. The QP-CLF-CBF formalism developed by Ames et al. is applied to ensure that safe trajectories are generated. Safe planning in environments with multiple obstacles is demonstrated both in simulation and experimentally on the Cassie biped.
From Crowd Motion Prediction to Robot Navigation in Crowds

We focus on robot navigation in crowded environments. To navigate safely and efficiently within crowds, robots need models for crowd motion prediction. Building such models is hard due to the high dimensionality of multiagent domains and the challenge of collecting or simulating interaction-rich crowd-robot demonstrations. While there has been important progress on models for offline pedestrian motion forecasting, transferring their performance on real robots is nontrivial due to close interaction settings and novelty effects on users. In this paper, we investigate the utility of a recent state-of-the-art motion prediction model (S-GAN) for crowd navigation tasks. We incorporate this model into a model predictive controller (MPC) and deploy it on a self-balancing robot which we subject to a diverse range of crowd behaviors in the lab. We demonstrate that while S-GAN motion prediction accuracy transfers to the real world, its value is not reflected on navigation performance, measured with respect to safety and efficiency; in fact, the MPC performs indistinguishably even when using a simple constant-velocity prediction model, suggesting that substantial model improvements might be needed to yield significant gains for crowd navigation tasks. Footage from our experiments can be found at https://youtu.be/mzFiXgSKsZ0.
Reinforced Potential Field for Multi-Robot Motion Planning in Cluttered Environments

Motion planning is challenging for multiple robots in cluttered environments without communication, especially in view of real-time efficiency, motion safety, distributed computation, and trajectory optimality, etc. In this paper, a reinforced potential field method is developed for distributed multi-robot motion planning, which is a synthesized design of reinforcement learning and artificial potential fields. An observation embedding with a self-attention mechanism is presented to model the robot-robot and robot-environment interactions. A soft wall-following rule is developed to improve the trajectory smoothness. Our method belongs to reactive planning, but environment properties are implicitly encoded. The total amount of robots in our method can be scaled up to any number. The performance improvement over a vanilla APF and RL method has been demonstrated via numerical simulations. Experiments are also performed using quadrotors to further illustrate the competence of our method.
Object Rearrangement Planning for Target Retrieval in a Confined Space with Lateral View

In this paper, we perform an object rearrangement task for target retrieval in an environment with a confined space and limited observation directions. The agent must create a collision-free path to bring out the target object by relocating the surrounding objects using the prehensile action, i.e., pick-and-place. Object rearrangement in a confined space is a non-monotone problem, and finding a valid plan within a reasonable time is challenging. We propose a novel algorithm that divides the target retrieval task, which requires a long sequence of actions, into sequential sub-problems and explores each solution through Monte Carlo tree search (MCTS). In the experiment, we verify that the proposed algorithm can find safe rearrangement plans with various objects efficiently compared to the existing planning methods. Furthermore, we show that the proposed method can be transferred to a real robot experiment without additional training.