Reviewerly/exHarmony huggingface dataset:
The TAO CI ontology of vases of the Ming and Qing dynasties

The advent of the Semantic Web and Linked Data initiative has contributed to new perspectives and opportunities regarding cultural heritage conservation. Museums have extensive collections of Chinese ceramic vases in China. Although some data sources have been digitized, the vision of cultural heritage institutions is not only to display objects and simple descriptions (drawn from metadata), but also to allow for understanding relationships between objects (created by semantically interrelated metadata). The key to achieving this goal is to utilize the technologies of the Semantic Web, whose core is Ontology. The focus of this paper is to describe the construction of the TAO CI (“ceramics” in Chinese) ontology and terminology of the domain of ceramic vases of the Ming (1368–1644) and Qing (1644–1911) dynasties. The theoretical framework relies on the notion of essential characteristics. This notion is compliant with the ISO principles on Terminology (ISO 1087 and 704), according to which a concept is defined as a combination of essential characteristics, and with the Aristotelian definition in terms of genus and differentia. This approach is intuitive for domain experts and requires identifying essential characteristics, combining them into concepts, and translating the result into a Semantic Web language. This article proposes an approach based on a morphological analysis of the Chinese terms for vases to identify essential characteristics and a term-guided method for defining concepts. Such a term-and-characteristic guided approach makes ontology engineering less dependent on formal languages and does not require a background in Description Logics. The research presented in this article aims to publish the resulting structured data on the Semantic Web for the use of anybody interested, including museums hosting collections of these vessels, and enrich existing domain ontology building methodologies. To our knowledge, there are no comprehensive ontologies for Chinese ceramic vases. TAO CI ontology remedies this gap and provides a reference for ontology building in other domains of Chinese cultural heritage. The TAO CI ontology is openly accessible here: http://www.dh.ketrc.com/otcontainer/data/otc.owl.
Online non-parametric changepoint detection with application to monitoring operational performance of network devices

Motivated by a telecommunications application where there are few computational constraints, a novel nonparametric algorithm, NUNC, is introduced to perform an online detection for changes in the distribution of data. Two variants are considered: the first, NUNC Local, detects changes within a sliding window. Conversely, NUNC Global, compares the current window of data to all of the historic information seen so far and makes use of an efficient update step so that this historic information does not need to be stored. To explore the properties of both algorithms, both real and simulated datasets are analysed. Furthermore, a theoretical result for the choice of test threshold to control the false alarm rate is presented, a result that could be applied in other binary segmentation change detection settings.
Assessment of heavy metals and nutrients availability in oil palm plantation effected by bauxite mining using geostatistical and multivariate analyses

Abstract The study aimed at evaluation of soil heavy metals in oil palm plantation and selected nutrients availability effected by mining. It was investigated using multivariate and geostatistical analyses followed by assessment using environmental indices. Samples were collected from both mining and oil palm cultivated area, prepared, and analyzed using ICPMS. Semi-variogram and kriging were done by using GS + and ArcGIS 10.8, respectively. Content of Cu, Cr and Zn were higher compared to the Dutch target values and the 95% ‘Investigation Levels’ determined for Malaysia soil, while Pb showed a lower value. Analysis of Principal Component suggested that the heavy metals were from one source of contamination, particularly the mining activities and long-term agricultural practices. Geostatistics analyses revealed that Zn, Cu, Pb and Fe confront to a strong spatial dependence structure and in line with multivariate and statistical analysis, except for Cr, which had a moderate spatial dependence. Geoaccumulation Index demonstrated contamination occurred in the order of Cr&gt;Cu&gt;Fe&gt;Zn&gt;Ni&gt;Pb. The heavy metals contamination has impacted the micronutrients contents as lower K, Ca and Mg were observed. These findings highlight that combining multivariate and geostatistical analysis can be valuable tools for assessing environmental contamination.
Solving constrained optimization problems via multifactorial evolution

As a new paradigm in the field of evolutionary computation, multifactorial evolution has become more and more popular since its inception. It attempts to solve multiple optimization problems simultaneously using a single evolving population. Due to the implicit knowledge transfer, multifactorial evolution exhibits the potential to solve complex optimization problems. This paper tries to take advantage of multifactorial evolution to solve constrained optimization problems (COPs). To this end, we derive two different optimization problems from the considered COP. Theoretical analysis reveals that the optima of these two problems are exactly identical to the feasible optima of the original COP. Thus, the advantages of knowledge transfer can be used adequately. In addition, these two problems focus more on the objective function and the constraints, respectively. By solving them concurrently, we can achieve the balance between constraints and objective function, which is of essential importance in constrained evolutionary optimization. Moreover, a multifactorial differential evolution is developed, which can leverage the merits of multifactorial evolution and differential evolution effectively. To tackle complex COPs, a diversity strategy is designed for population diversity maintenance. Extensive experiments on benchmark test sets and engineering optimization problems have demonstrated the effectiveness of the proposed method.
Embed2Sym - Scalable Neuro-Symbolic Reasoning via Clustered Embeddings

Neuro-symbolic reasoning approaches proposed in recent years combine a neural perception component with a symbolic reasoning component to solve a downstream task. By doing so, these approaches can provide neural networks with symbolic reasoning capabilities, improve their interpretability and enable generalization beyond the training task. However, this often comes at the cost of poor training time, with potential scalability issues. In this paper, we propose a scalable neuro-symbolic approach, called Embed2Sym. We complement a two-stage (perception and reasoning) neural network architecture designed to solve a downstream task end-to-end with a symbolic optimisation method for extracting learned latent concepts. Specifically, the trained perception network generates clusters in embedding space that are identified and labelled using symbolic knowledge and a symbolic solver. With the latent concepts identified, a neuro-symbolic model is constructed by combining the perception network with the symbolic knowledge of the downstream task, resulting in a model that is interpretable and transferable. Our evaluation shows that Embed2Sym outperforms state-of-the-art neuro-symbolic systems on benchmark tasks in terms of training time by several orders of magnitude while providing similar if not better accuracy.
Privacy of Personal Information

Each online interaction with an external service creates data about the user that is digitally recorded and stored. These external services may be credit card transactions, medical consultations, census data collection, voter registration, etc. Although the data is ostensibly collected to provide citizens with better services, the privacy of the individual is inevitably put at risk. With the growing reach of the Internet and the volume of data being generated, data protection and, specifically, preserving the privacy of individuals, have become particularly important. In this article we discuss the data privacy concepts using two fictitious characters, Swara and Betaal, and their interactions with a fictitious entity, namely Asha Hospital.
Experiments on Collaborative Transport of Cable-suspended Payload with Quadrotor UAVs

The use of drones to transport cargo is an important application of unmanned aerial vehicles. Given the limited payload capacity of a typical small drone, the notion of utilizing multiple drones to transport heavy payloads presents a promising alternative. This article describes an easy to deploy system of multiple drones with a cable-suspended payload to enable flight testing of guidance, navigation, and control strategies for such systems in realistic operating conditions, outside of a laboratory. A unique aspect of our system is the use of Ethernet cables to ensure fast and reliable communications between vehicles. Deploying the system with a basic leader- follower guidance strategy and the PX4 flight stack for low-level control of each vehicle, we demonstrate collaborative payload transport through an extensive experimental campaign. We are able to autonomously transport payloads up to 2kg with two vehicles and up to 3kg with three off-the-shelf, 1kg vehicles. The paper also presents a brief discussion of failure cases and points to worthwhile directions for further research on this topic.
Leader-Follower UGV-UAV Formation as Control Paradigm for Package Delivery

This paper addresses the problem of package delivery by a drone taking off of a ground vehicle, flying towards a desired delivery point and flying back to land on the ground vehicle. Specifically, the final step, when the drone approaches the ground vehicle and lands on it is dealt with here. To do that a ground mobile robot, a Pioneer 3-DX unicycle platform, is used to play the role of the delivery truck, whereas the drone is the Bebop 2 quadrotor. To control such a formation the leader-follower control paradigm is adopted, in which the Pioneer 3-DX is the leader and the quadrotor is the follower, which mimics the movement of the leader. According to such a control paradigm, the two vehicles are independently controlled. A first controller is responsible for the leader and a second one is responsible for the follower. Aiming at a package delivery application, the leader robot is programmed to follow a given path, thus playing the role of a delivery truck, whereas a trajectory-tracking controller is designed for the follower, having the leader as the trajectory to be tracked. Therefore, the leader-follower control paradigm fits quite well the application in mind, which is the reason for this proposal. Simulation results considering such a system are presented here, which validate the proposed approach.
Task Allocation and Path Planning for Collaborative Swarm Guidance in Support of Artillery Mission

The last few decades have seen the emergence of autonomous actors for various uses on the battlefield. New methods to safely and efficiently guide these actors have therefore been developed. This paper considers a complete military scenario involving autonomous actors cooperating with artillery systems. Then, focusing on the path planning and task allocation aspects, a brief review and classification of available methods is given in order to select the most promising ones for our case study. Based on existing works, these chosen methods include the Voronoi diagram, the satisficing paradigm, the Simulated Annealing algorithm and the k-means method. Combinations of these methods in four different approaches are implemented and compared to assess which one shows the best ratio between computation time and flying time to complete a mission.
SwarmHawk: Self-Sustaining Multi-Agent System for Landing on a Moving Platform through an Agent Supervision

Heterogeneous teams of mobile robots and UAVs are offering a substantial benefit in an autonomous exploration of the environment. Nevertheless, although joint exploration scenarios for such systems are widely discussed, they are still suffering from low adaptability to changes in external conditions and faults of swarm agents during the UAV docking. We propose a novel vision-based drone swarm docking system for robust landing on a moving platform when one of the agents lost its position signal.The proposed SwarmHawk system relies on vision-based detection for the mobile platform tracking and navigation of its agents. Each drone of the swarm carries an RGB camera and AprilTag3 QR-code marker on board. SwarmHawk can switch between two modes of operation, acting as a homogeneous swarm in case of global UAV localization or assigning leader drones to navigate its neighbors in case of a camera fault in one of the drones or global localization failure.Two experiments were performed to evaluate SwarmHawk’s performance under the global and local localization with static and moving platforms. The experimental results revealed a sufficient accuracy in the swarm landing task on a static mobile platform (error of 4.2 cm in homogeneous formation and 1.9 cm in leader-follower formation) and on moving platform (error of 6.9 cm in homogeneous formation and 4.7 cm in leader-follower formation). Moreover, the drones showed a good landing on a platform moving along a complex trajectory (average error of19.4 cm) in leader-follower formation.The proposed SwarmHawk technology can be potentially applied in various swarm scenarios, including complex environment exploration, inspection, and drone delivery.
Obstacle Avoidance for a Heterogeneous Formation Using the Null Space-based Behavioral Approach and Potential Fields

This paper proposes an obstacle avoidance approach based on null space behavioral control to autonomously guide a formation of two mobile robots, a differential-drive wheeled platform and an unmanned aerial vehicle, to overtake an obstacle modeled as a potential field. The controller is designed considering the paradigm of virtual structure, in this case the tridimensional straight line linking the two robots. Using such a paradigm the agents in the formation are controlled in such a way that the formation reaches and then preserve a desired shape as well as move according the task being accomplished. When the point of interest for control gets close to a potential higher than a predefined threshold the controller, which is based on the null space behavioral control approach works on the control signals sent to the robots, modifying them to assure that the formation deviate from the obstacle. The use of the null space technique allows giving higher priority to the subtask of avoiding the obstacle, leaving the subtask of moving the formation and preserve its shape with a lower priority, thus generating a way to move around an obstacle following a path of low potential. Note that if the priority were changed the formation would continue moving ahead, what certainly would cause a collision. In this article just fixed obstacles, with well known positions, and a family of Gaussian bell potential curves associated to them, are considered.
Distributed Trajectory Planning for a Formation of Aerial Vehicles Inspecting Wind Turbines

This paper proposes a distributed method for optimal trajectory planning in formations of multiple Unmanned Aerial Vehicles supporting inspection and maintenance tasks of wind turbines. Since wind turbines are usually located at places of difficult access and exposed to strong wind gusts, UAVs become a valuable asset for these risky operations. The system consists of a leader-follower formation where one UAV visits a series of inspection points indicated by a human operator on the ground, while the others follow it keeping relative angles to provide alternative inspection views. The distance of the formation to the inspection points and the relative angles can be modified by the operator online, who can command the multi-camera system as a whole. Our optimization scheme results in trajectories that allow the formation to adapt its shape in real time by means of smooth and safe trajectories, avoiding jerky movements to produce pleasant images and collisions. We present experimental results in realistic simulations.
Soft Actor-Critic with Inhibitory Networks for Retraining UAV Controllers Faster

Active research in autonomous unmanned aerial vehicles (UAVs) seeks to combine the agility of Proportional-Integral-Derivative (PID) systems for low-level control with the adaptability of Deep Reinforcement Learning (DRL) to navigate through challenging, non-stationary environments. In the real world, there is often a need to quickly adapt trained DRL agents to more difficult tasks with conflicting rewards. For efficient retraining, the ability to leverage previously learned skills becomes critical. Unfortunately, using traditional DRL algorithms like soft actor critic (SAC) for retraining a policy can lead to catastrophic forgetting of the policy’s known skills. In this work, inspired by neuroscience research, we propose a novel approach using SAC with inhibitory networks to allow separate and adaptive state value evaluations, as well as distinct automatic entropy tuning. We validate our method through experiments using a quadcopter in a realistic simulation environment and demonstrate the advantage of retraining. Moreover, we present the superiority of our approach compared to baseline methods with respect to both sample efficiency and cumulative success.
Evenly Weighted Particle Filter for Terrain-referenced Navigation using Gaussian Mixture Proposal Distribution

The irreversible problematic situation of bootstrap particle filter that it is subject to the weight collapse, is tackled with an evenly weighted setup especially in application to the terrain-referenced navigation problem of unmanned aerial systems. The paper is featured with the Gaussian mixture proposal density taking multimodal noise characteristics of terrain clearance sensor into account. Each particle explores further towards the region of high likelihood in addition to its original motion model, while the amount of transition of the introduced proposal density is calculated from a superposition of a couple of optimal data assimilation methods. Numerical local terrain elevation gradient in conjunction with the parameters that describe the multimodality realize the calculation of transition gain by which the innovation is multiplied. The proposed approach significantly reduces the variance of particle weight and reinforces the diversity of particles by locating them exploiting both the terrain measurement and its noise characteristic.
Predictive optimal collision avoidance for a swarm of fixed-wing aircraft in 3D space

In this paper, we propose a predictive and cooperative optimal control based autonomous navigation and collision avoidance for multiple fixed-wing aircraft (FWA) that also takes into account the physical constraints of the aircraft. The proposed method is implemented in a cooperative framework, analogous to the Reciprocal Velocity Obstacle (RVO) framework for autonomous navigation and collision avoidance for FWA swarm moving in a three dimensional (3D) space. Also, the change in performance with respect to varying prediction horizon is analyzed through numerical simulations. As a result of predictive optimal control and cooperation, we get less conservative maneuvers, reduced path lengths, and less deviation from the shortest path when compared to the conventional method without prediction and cooperation. Simulation results are provided, highlighting the advantages of the proposed method when compared to a popular method (Optimal reciprocal collision avoidance) for collision avoidance in 3D for FWA swarm and also against FGA algorithm (Fast geometric avoidance algorithm).
Modified Constrained Wavefront Expansion Path Planning Algorithm for Tilt-Wing UAV

This paper presents a path planning algorithm for tilt-wing UAV that considers the flight dynamic constraints while planning the path. Being able to perform thrust-borne vertical take-off and landing as well as efficient wing-borne flight, tilt-wing aircraft offer multiple benefits in unmanned aerial vehicle applications. Applications that involve flying in airspaces not used exclusively by the tilt-wing UAV require the ability to avoid collisions with other manned and unmanned airspace users to ensure reliable and safe operation. Known approaches for collision avoidance either do not consider flight dynamic constraints during the path planning or use avoidance controllers instead of a planned path. The presented algorithm modifies the Constrained Wavefront Expansion (COWEX) algorithm to estimate the movement of the tilt-wing UAV as well as the intruder aircraft to be avoided at each planning step. This allows the modified COWEX algorithm to expand its avoidance capabilities from only static obstacles to moving aircraft. The algorithm is implemented for a simulated tilt-wing UAV and validated by solving a variety of standard and non standard avoidance scenarios.
Federated Learning with Anomaly Client Detection and Decentralized Parameter Aggregation

Federated learning is a framework for machine learning that is dedicated to data privacy protection. In federated learning, system cannot fully control the behavior of clients which can be faulty. These behaviors include sharing arbitrary faulty gradients and delaying the process of sharing due to Byzantine attacks or clients’ own software and hardware failures. In federated learning, the parameter server may also be faulty during gradient collection and aggregation, mainly including gradient-based training data inference and model parameter faulty update. The above problems may lead to reduced accuracy of federated learning model training, leakage of client privacy, etc. Existing research enhances the robustness of federated learning by exploiting the decentralization and immutability of Blockchain. For untrusted clients, most research is based on Byzantine fault tolerance to defend against clients indiscriminately, and may cause model accuracy reduction. In addition, most of the research focus on unencrypted gradients, and there is insufficient research on dealing with client anomalies in the case of gradient encryption. For untrusted parameter servers, existing research has problems in energy overhead and scalability. Aiming at the problems above, this paper studies the robustness of federated learning, and proposes a blockchain-based federated learning parameter update architecture PUS-FL. Through experiments simulating distributed machine learning on neural networks, we demonstrate that the anomaly detection algorithm of PUS-FL outperforms conventional gradient filters including geometric median, Multi-Krum and trimmed mean. In addition, our experiments also verify that the scalability-enhanced parameter aggregation consensus algorithm proposed in this paper(SE-PBFT) improves consensus scalability by reducing communication complexity.