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This article addresses the problem of cooperative object transportation for multiple underwater vehicle manipulator systems (UVMSs) in a constrained workspace involving static obstacles. We propose a nonlinear model predictive control (NMPC) approach for a team of UVMSs in order to transport an object while avoiding significant constraints and limitations, such as kinematic and representation singularities, obstacles within the workspace, joint limits, and control input saturation. More precisely, by exploiting the coupled dynamics between the robots and the object and using certain load sharing coefficients, we design a predictive controller for each UVMS in order to cooperatively transport the object within the workspace's feasible region. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. In addition, the feedback relies on each UVMS's onboard measurements and no explicit data are exchanged online among the robots, thus reducing the required communication bandwidth. Finally, realistic simulation results conducted in the UwSim dynamic simulator running in robot operating system (ROS) environment as well as real-time experiments employing two small UVMSs and demonstrated the effectiveness of the proposed control strategy.

In social and industrial facilities of the future such as hospitals, hotels, and warehouses, teams of robots will be deployed to assist humans in accomplishing everyday tasks like object handling, transportation, or pickup and delivery operations. In such a context, different robots (e.g., mobile platforms, static manipulators, or mobile manipulators) with different actuation, manipulation, and perception capabilities must be coordinated to achieve various complex tasks (e.g., cooperative parts assembly in the automotive industry or loading and unloading of palettes in warehouses) that require collaborative actions with each other and with human operators (Figure 1).

In this work, we propose a novel reinforcement learning algorithm to solve the optimal motion planning problem. Particular emphasis is given on the rigorous mathematical proof of safety, convergence as well as optimality w.r.t. to an integral quadratic cost function, while reinforcement learning is adopted to enable the cost function's approximation. Both offline and online solutions are proposed, and an implementation of the offline method is compared to a state-of-the-art RRT $^{\star }$ approach. This novel approach inherits the strong traits from both artificial potential fields, i.e., reactivity, as well as sampling-based methods, i.e., optimality, and opens up new paths to the age-old problem of motion planning, by merging modern tools and philosophies from various corners of the field.

This article addresses the problem of cooperative object transportation for multiple underwater vehicle manipulator systems (UVMSs) in a constrained workspace with static obstacles, where the coordination relies solely on implicit communication arising from the physical interaction of the robots with the commonly grasped object. In this article, we propose a novel distributed leader–follower architecture, where the leading UVMS, which has knowledge of the object's desired trajectory, tries to achieve the desired tracking behavior via an impedance control law, navigating in this way, the overall formation toward the goal configuration while avoiding collisions with the obstacles. On the other hand, the following UVMSs estimate locally the object's desired trajectory via a novel prescribed performance estimation law and implement a similar impedance control law that achieves tracking of the desired trajectory despite the uncertainty and external disturbance in the object and the UVMS dynamics, respectively. The feedback relies on each UVMS's force/torque measurements and no explicit data is exchanged online among the robots, thus reducing the required communication bandwidth and increasing robustness. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. Finally, various simulation studies clarify the proposed method and verify its efficiency.

This article presents a robust nonlinear model predictive control (NMPC) scheme for autonomous navigation of underwater robotic vehicles operating in a constrained workspace including the static obstacles. In particular, the purpose of the controller is to guide the vehicle toward specific way points with guaranteed input and state constraints. Various constraints, such as obstacles, workspace boundaries, predefined upper bounds for the velocity of the robotic vehicle, and thruster saturations, are considered during the control design. Moreover, the proposed control scheme is designed at dynamic level, and it incorporates the full dynamics of the vehicle in which the ocean currents are also involved. Hence, taking the thrusts as the control inputs of the robotic system and formulating them accordingly, the vehicle exploits the ocean current dynamics when these are in favor of the way-point tracking mission, resulting in reduced energy consumption by the thrusters. The robustness of the closed-loop system against parameter uncertainties has been analytically guaranteed with convergence properties. The performance of the proposed control strategy is experimentally verified using a 4 degrees of freedom (DoF) underwater robotic vehicle inside a constrained test tank with sparse static obstacles.

The rapid growth in computation and processing power of end-user devices has transitioned network functionalities from the core network to the Fog, thus, reducing response times and ultimately improving user experience. The Information-Centric Networking paradigm aims to transform conventional content caching and delivery approaches by enabling Fog nodes to participate in both forwarding and caching. In this work, a Fog caching design scheme is presented for applications such as Internet-of-Things, which integrates three novel design attributes. Firstly, a Fog cluster-based scheme is proposed that utilises both in-network and end-user devices to cache content closer to the edge network, according to the increasing popularity of the content. Secondly, this work proposes a near-path approach that leverages caching nodes near the content delivery path. Finally, by craftily integrating reactive and proactive caching, congestion during network peak-time is averted. Simulations are conducted in the Icarus environment and evaluated against eight popular benchmark techniques. The results indicate significant improvement in internal link load and path stretch metrics. Finally, the cache hit ratio metric is consistently better than all other benchmarks, while the latency performance of the proposed scheme is competitive when the content distribution is more concentrated.

SUMMARYCooperative transportation by human and robotic coworkers constitutes a challenging research field that could lead to promising technological achievements. Toward this direction, the present work demonstrates that, under a leader–follower architecture, where the human determines the object’s desired trajectory, complex cooperative object manipulation with minimal human effort may be achieved. More specifically, the robot estimates the object’s desired motion via a prescribed performance estimation law that drives the estimation error to an arbitrarily small residual set. Subsequently, the motion intention estimation is utilized in the object dynamics to determine the interaction force between the human and the object. Human effort reduction is then achieved via an impedance control scheme that employs the aforementioned estimations. The feedback relies exclusively on the robot’s force/torque, position as well as velocity measurements at its end effector, without incorporating any other information on the task. Moreover, an adaptive control scheme is adopted to relax the need for exact knowledge of the object dynamics. Finally, an extension for multiple robotic coworkers is studied and verified via simulation, while extensive experimental results for the single robot case clarify the proposed method and corroborate its efficiency.

Although Harmonic Potential Fields constitute a powerful tool for tackling the autonomous robot exploration problem, yet their applicability is limited by the heavy computational load involved in solving the Laplace equation in real time. In this letter, we propose a computationally efficient exploration scheme employing a Fast Multipole accelerated Boundary Element Method, which enjoys both linear complexity w.r.t. the boundary's size as well as linear memory requirements. Furthermore, we devise an adaptive control law for the specified boundary conditions that allows us to tune the robot's behavior without affecting the inherent safety and convergence properties of the underlying potential field. Finally, we validate the performance of the proposed exploration scheme through extensive realistic simulations.

A few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations among the protocols, produce extreme variations leading to misdiagnosis of brain atrophy. While brain white matter loss is a characteristic lesion during neurodegeneration, the main objective of this study was to create a computational tool for high precision measuring structural brain changes using the fractal dimension (FD) definition. The validation of the BrainFD software is based on T1-weighted MRI images from the Open Access Series of Imaging Studies (OASIS)-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with the main functionality of the FD calculation using the box-counting algorithm, for different subjects on the same brain regions, with high accuracy and resolution, offering the ability to compare brain data regions from different subjects and on multiple sessions, creating different imaging profiles based on the Clinical Dementia Rating (CDR) scores of the participants. Two experiments were executed. The first was a cross-sectional study where the data were separated into two CDR classes. In the second experiment, a model on multiple heterogeneous data was trained, and the FD calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that the FD variation efficiently describes the structural complexity of the brain and the related cognitive decline. Additionally, the FD efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and the size of the dataset. Therefore, the FD calculation for identifying intracranial brain volume loss could be applied as a potential low-cost personalized imaging biomarker. Furthermore, the possibilities measuring different brain areas and subregions could give robust evidence of the slightest variations to imaging data obtained from repetitive measurements to Physicians and Radiologists.

The global surge of cyber-attacks in the form of sequential network attacks has propelled the need for robust intrusion detection and prediction systems. Such attacks are difficult to reveal using current intrusion detection systems, since each individual attack phase may appear benign when examined outside of its context. In addition, there are challenges in building supervised learning models for such attacks, since there are limited labelled datasets available. Hence, there is a need for updating already built models to specific operational environments and for addressing the concept drift. Hidden Markov models (HMMs) is a popular framework for sequential modelling, however, in addition to the above challenges, the model parameters are difficult to optimise. This paper proposes a transfer learning (TL) approach that exploits already learned knowledge, gained from a labelled source dataset, and adapts it on a different, unlabelled target dataset. The datasets may be from a different but related domain. Five unsupervised HMM techniques are developed utilising a TL approach and evaluated against conventional machine learning approaches. Baum-Welch (BW), Viterbi training, gradient descent, differential evolution (DE) and simulated annealing, are deployed for the detection of attack stages in the network traffic, as well as, forecasting both the next most probable attack stage and its method of manifestation. Specifically, for the prediction of the three next most likely states and observations, TL with DE achieved a maximum accuracy improvement of 48.3%, and 27.4%, respectively. Finally, the actual detection prediction for the three next most probable states and methods of manifestation reaches 78.9% and 96.3% using TL with BW and DE, respectively.

In this paper, we propose a visual servoing scheme that imposes predefined performance specifications on the image feature coordinate errors and satisfies the visibility constraints that inherently arise owing to the camera's limited field of view, despite the inevitable calibration and depth measurement errors. Its efficiency is demonstrated via comparative experimental and simulation studies.

In this work, we tackle the problem of systematic population of a bank of residual generators for model-based fault diagnosis in Unmanned Aerial Vehicles (UAVs). Intended for detailed, large and non-linear system models, Structural Analysis (SA) is applied to produce a graph-based abstraction of the problem in the form of a bipartite graph. The Branch and Bound Integer Linear Programming (BBILP) algorithm is employed, properly adapted to seek a solution for the constrained graph matching problem. Appropriate causality constraints are formulated, which link the structure of the system graph with the analytical form of the residual generators and certify that all resulting residual generators can be implemented automatically using numerical processes. An extensive performance investigation of the proposed approach is carried out, which is shown to be more efficient than other similar algorithms. Benchmarks of UAV models taken from the literature are presented and a simulated response of the diagnostic system against a fault in the roll-rate sensor is showcased.

This paper presents an unsupervised strategy for online system identification of underwater robotic vehicles. The proposed method consists of four operating modules, namely the state estimation module, the collision avoidance module, the excitation module, and the parameter estimation module, that collaborate online in a closed-loop architecture. The excitation inputs are formulated online, based on the convergence achieved for each actuated degree of freedom, thus speeding up the identification process while concurrently evading overfitting. Additionally, the proposed algorithm guarantees safe operation by avoiding collisions with the workspace boundaries; hence, no human supervision is required during the identification procedure. Moreover, the overall scheme is of low complexity and can be easily integrated into the real-time embedded system framework of underwater robotic vehicles. Finally, the efficacy of the proposed strategy was experimentally verified via an online system identification of a small underwater robotic vehicle, and the accuracy of the estimated parameters was further experimentally evaluated via a trajectory tracking task, by comparing the performance of a PID control scheme that employed feedforward compensation of the identified dynamics, with the corresponding performance of a conventional model-free PID control scheme.

Despite the recent developments in robotic technology that have introduced robots in various fields of industry, agriculture, service, security, etc., complex applications that involve multiple robots performing a task in physical interaction with humans, require further investigation. Towards this direction, we propose an impedance control scheme for multiple mobile manipulators assisting a human in transporting/manipulating a rigidly grasped object. The human operator plans the object’s motion, whereas the robot-team imposes predefined impedance properties on the object to reduce the human effort required to execute the task, without assuming either knowledge of the desired object trajectory or explicit inter-robot communication. Moreover, to achieve the desired impedance properties two novel decentralized estimation schemes are employed to derive the human motion intention and the human exerted wrench. Finally, a simulated paradigm verifies the theoretical findings.

In this work a vertically integrated fault-tolerant control scheme for fixed-wing Unmanned Aerial Vehicles (UAVs) is presented. At its core, an online approximate Trim Flight Envelope generator yields the motion constraints of the UAV. Given fault information, it remains always up-to-date in view of emerging faults. The controller stack comprises of Nonlinear Model Predictive Controllers for angular velocity, linear velocity and position. Path Planning is achieved by Simple Sparse Rapidly-exploring Random Trees (SST). Both the controllers and the planner are aware of the flight constraints and are hence tolerant to faults. A large set of sensor and actuator faults, common to UAVs are considered and the controllability of the UAV is examined. Detailed simulations using real-time implementations of the controllers are carried out.

In this work, we consider the motion control problem for a platoon of unicycle robots operating within an obstacle-cluttered workspace. Each robot is equipped with a proximity sensor that allows it to perceive nearby obstacles as well as a camera to obtain its relative position with respect to its preceding robot. Additionally, no robot other than the leader of the team is able to localize itself within the workspace and no centralized communication network exists, i.e., explicit information exchange between the agents is unavailable. To tackle this problem, we adopt a leader–follower architecture and propose a novel, decentralized control law for each robot-follower, based on the Prescribed Performance Control method, which guarantees collision-free tracking and visual connectivity maintenance by ensuring that each follower maintains its predecessor within its camera field of view while keeping static obstacles out of the line of sight for all time. Finally, we verify the efficacy of the proposed control scheme through extensive simulations.

Background For Severe Acute Respiratory Syndrome Coronavirus-2, the investigation of the heterogeneity of individual infectiousness is important due to the recorded widespread cross reactive immunity of general population that can alter transmission dynamics. We therefore aimed to understand how SARS-COV-2 transmits in the general population in relation to age.DesignUsing a sample of infected population with SARS-COV-2 in close geographical proximity to the initial Severe Advanced Respiratory Syndrome-1 (SARS-1) outbreak, we explored the association between infector’s age and dispersion (or heterogeneity) of individual infectiousness (k) in order to investigate the relatedness with the age of an individual’s capability to disperse SARS-COV-2.ResultsWe have found a negative association between k and increase of infector’s age. Significantly this becomes more evident for the age group of 20-60 years comparing with the infectors of younger age.ConclusionsNon pharmaceutical interventions can be effective to age group between 20-60 years whereas in youngsters and older patients containment of spreading must be made by other means to be effective. Immunity differences between age groups may reflect their differences in heterogeneity predicted by variance in dispersion parameter (k).

The problem of missing data, usually absent incurated and competition-standard datasets, is an unfortunate reality for most machine learning models used in industry applications. Recent work has focused on understanding the nature and the negative effects of such phenomena, while devising solutions for optimal imputation of the missing data, using both discriminative and generative approaches. We propose a novel mechanism based on multi-head attention which can be applied effortlessly in any model and achieves better downstream performance without the introduction of the full dataset in any part of the modeling pipeline. Our method inductively models patterns of missingness in the input data in order to increase the performance of the downstream task. Finally, after evaluating our method against baselines for a number of datasets, we found performance gains that tend to be larger in scenarios of high missingness., Comment: 8 pages, 7 figures

Risk-based and automatic security systems require to monitor passengers’ whereabouts in a terminal discretely to allow timely detection of suspicious behaviors and preventing malicious actions. In a series of two papers Thomopoulos et al. have introduced a methodology providing real-time risk assessment for airport passengers based on their trajectories. The proposed methodology implements a deep learning architecture. It is fully automated, reducing the workload of the video surveillance operators leading to less error-prone conclusions. Furthermore, the proposed methodology has been integrated with the OCULUS Command & Control (C2) System and the i-Crowd Simulator, a crowd simulation platform developed in the Integrated Systems Lab (ISL) of the Institute of Informatics and Telecommunications at NCSR “Demokritos.” In this paper we extend our previous work by introducing noise in both training and testing data used for tracking passengers and detecting anomalies in their tracks. Extensive testing of the anomaly detection system in the presence of noise demonstrates that the system is extremely resilient in noise. Furthermore, we consider the case of missing data in both training and testing data in order to model a realistic scenario of tracking with cameras with gaps in the passengers tracks from camera to camera due to missing data from transmission delays and/or data overflow. Extensive testing with the i- Crowd simulator demonstrates considerable robustness in the performance of the anomaly detection system in both noisy and missing data. The experimental results indicate that the proposed anomaly detection system is robust to both noisy and missing data and thus a very promising risk assessment scheme that can reliably be used for risk-based security under realistic operational conditions.

The EcoCAR Mobility Challenge is the latest iteration of the Department of Energy (DOE) Advanced Vehicle Technology Competitions organized by Argonne National Laboratory (ANL). EcoCAR’s new focus on Connected and Automated Vehicle (CAV) technology will require the development of new methods and tools for fairly assessing energy usage for each of the university prototype vehicles. This paper serves to introduce potential methods for assessing CAV technology energy impacts in controlled urban and highway proving ground environments. In addition, it describes the development process of a target vehicle test system in collaboration with HORIBA MIRA. The system, based on HORIBA MIRA’s DigiCAV platform, will accelerate test system development for EcoCAR and produce a test environment with both real and simulated target vehicles for accurately assessing EcoCAR prototype vehicle implementations of hybrid powertrains and CAV features. The authors developed and validated a Hardware-in-the-Loop (HIL) test setup to perform initial calibrations of vehicle-specific DigiCAV controller implementations and will be testing those implementations in the next phase of development.

This paper presents a novel control strategy for the compensation of the slippage effect during non-rigidly grasped object manipulation. A detailed dynamic model of the interconnected system composed of the robotic manipulator, the object and the internal forces and torques induced by the slippage effect is provided. Next, we design a model-based variable impedance control scheme, in order to achieve simultaneously zero convergence for the trajectory tracking error and the slippage velocity of the object. The desired damping and stiffness matrices are formulated online, by taking into account the measurement of the slippage velocity on the contact. A formal Lyapunov-based analysis guarantees the stability and convergence properties of the resulting control scheme. A set of extensive simulation studies clarifies the proposed method and verifies its efficacy.

In this work, a novel solution to the optimal motion planning problem is proposed, through a continuous, deterministic and provably correct approach, with guaranteed safety and which is based on a parametrized Artificial Potential Field (APF). In particular, Reinforcement Learning (RL) is applied to adjust appropriately the parameters of the underlying potential field towards minimizing the Hamilton-Jacobi-Bellman (HJB) error. The proposed method, outperforms consistently a Rapidly-exploring Random Trees (RRT*) method and consists a fertile advancement in the optimal motion planning problem.

We propose a human-robot interface based on potentials recorded through surface Electroencephalographic sensors, aiming to decode human visual attention into motion in three-dimensional space. Low-frequency components are extracted and processed in real time, and subspace system identification methods are used to derive the optimal, in mean squared sense, linear dynamics generating the position vectors. This results in a human-robot interface that can be used directly in robot teleoperation or as part of a shared-control robotic manipulation scheme, feels natural to the user, and is appropriate for upper extremity amputees, since it requires no limb movement. We validate our methodology by teleoperating a redundant, anthropomorphic robotic arm in real time. The system's performance outruns similar EMG-based systems, and shows low long-term model drift, indicating no need for frequent model re-training.

In this paper, we present a distance-based formation control strategy for multiple multi-rotor aerial vehicles participating in a target interception mission. The target moves arbitrarily, following a smooth 3D trajectory, while the intercepting vehicles aim at establishing a predefined enclosing formation around it, by attaining specific distances among them and simultaneously avoiding inter-agent collisions and connectivity breaks. More specifically, we propose a decentralized motion control protocol based on the prescribed performance control notion, which is of low computational complexity and is able to achieve robust and accurate formation establishment. Each agent requires local and relative state feedback, which can be acquired by a common onboard sensor suite without necessitating for explicit network communication. A realistic simulation study with one target and four multi-rotor interceptors was conducted to prove the efficacy of the proposed strategy.

In this paper, we present an image-based visual servo control scheme for tracking a moving target using a multirotor aerial vehicle. The proposed scheme is based on the prescribed performance control notion and it is able to impose appropriately selected performance specifications on the image feature errors to satisfy visibility constraints arising from the camera limited field of view, while exhibiting robustness against calibration and depth estimation errors. The proposed controller is of low complexity and computational cost and thus can be easily implemented on the embedded control unit of an autonomous aerial vehicle. The resulting scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are verified via a realistic simulation scenario, where a quadrotor successfully tracks a visual target located on top of a moving mobile robot.

Fault-tolerant control is currently the most important step towards increased autonomy for Unmanned Aerial Vehicles (UAVs). In this work, we consider actuator and airframe faults in fixed-wing UAVs and show that an online Flight Envelope (FE) calculation can interpret their effect on the achievable trim trajectories. Subsequently, we design a fully integrated control scheme with a RRT-based planner and 3 Nonlinear Model Predictive Control (MPC) layers. The FE is provided at each control layer with the beneficial result that non-trimmable, unstable trajectories are avoided. Results from high-fidelity simulations are provided.

The detection of anomalies in network traffic is an important task in today’s Internet. Among various anomaly detection methods, the techniques based on examination of the long-range dependence (LRD) behavior of network traffic stands out to be powerful. In this paper, we reveal anomalies in aggregated network traffic by examining the LRD behavior based on the cross-correlation function of the bidirectional control and data planes traffic. Specifically, observing that the conventional cross-correlation function has a low measure of dissimilarity between the two planes, which leads to a reduced anomaly detection performance, we propose a modification of the cross-correlation function to mitigate this issue. The performance of the proposed method is analyzed using a relatively recent Internet traffic captured at King Saud University. The results demonstrate that using the modified cross-correlation function has the ability to detect low volume and short duration attacks. It also compensates for some misdetections exhibited by using the autocorrelation structures of the bidirectional traffic of the control, data, and WHOLE (combined control and data) planes traffic.

The first section of this chapter presents an NMPC strategy for underwater robotic vehicles operating under various constraints. The purpose of the controller is to guide the vehicle towards specific way -points. Various constraints such as obstacles, workspace boundaries and control input saturation as well as predefined upper bound of the vehicle velocity (requirements for several underwater tasks such as seabed inspection scenario and mosaicking) are considered during the control design. The proposed scheme incorporates the full dynamics of the vehicle in which the ocean currents are also involved. The controller is designed in order to find the optimal thrusts required for minimizing the way -point tracking error. Moreover, the controlinputs calculated by the proposed approach are formulated in a way that the vehicle will exploit the ocean currents, when they are in favor of the way -point tracking mission, which results in reduced energy consumption by the thrusters. In the second part of this chapter, novel position- and trajectory -tracking control schemes for AUVs are presented. The proposed controllers do not utilize the vehicle's dynamic model parameters and guarantee prescribed transient and steady-state performance despite the presence of external disturbances and kinematic constraints for the case of underactuated vehicles. Moreover, through the appropriate selection of certain performance functions, the proposed scheme can also guarantee the satisfaction of motion and performance constraints imposed by the desired task.

In this work we present a novel, approximate, efficient algorithm for determining the Trim Flight Envelope of a fixed-wing UAV, based on a generic, nonlinear numerical model. The resulting Flight Envelope is expressed as a convex intersection of half-spaces. Subsequently, a Model Predictive Controller (MPC) is designed which takes into account the Flight Envelope constraints, to avoid Loss-of-Control. The overall system is shown to operate in real-time in a simulation environment.

The flexible nature of elastic optical networks allows advanced resource management. Algorithms facilitating the procedure of resource allocation can provide connectivity that is energy-efficient. Also, the network's performance is slightly affected and remains at a high level. There are innovative developments during the last years concerning the physical layer that increase the performance even further. Such a technique which provides spectrum sharing is called Signal Overlap. Specifically, the modulation includes two independent data flows that rely on the same spectrum resources and propagate combined in the optical fiber. When energy-efficient algorithms are designed to exploit the Signal Overlap technique for building the virtual topology of the network, less energy-consuming components operate in the network. This procedure also preserves the performance at a stable level. In this paper, the Signal Overlap technique is used to design a new algorithm that increases energy efficiency in elastic optical networks. To the best of authors' knowledge, it is the first time that the Signal Overlap technique is used for that purpose. Also, the execution time is low upon mid-to-large topologies, so the algorithm is candidate for real-time execution.

Willingness to invest in renewable energy sources (RES) is predictable under data mining classification methods. Data was collected from the area of Evia in Greece via a questionnaire survey by using a sample of 360 respondents. The questions focused on the respondents&rsquo, perceptions and offered benefits for wind energy, solar photovoltaics (PVs), small hydro parks and biomass investments. The classification algorithms of Bayesian Network classifier, Logistic Regression, Support Vector Machine (SVM), C4.5, k-Nearest Neighbors (k-NN) and Long Short Term Memory (LSTM) were used. The Bayesian Network classifier was the best method, with a prediction accuracy of 0.7942. The most important variables for the prediction of willingness to invest were the level of information, the level of acceptance and the contribution to sustainable development. Future studies should include data on state incentives and their impact on willingness to invest.

With increasing global demand for learning English as a second language, there has been considerable interest in methods of automatic assessment of spoken language proficiency for use in interactive electronic learning tools as well as for grading candidates for formal qualifications. This paper presents an automatic system to address the assessment of spontaneous spoken language. Prompts or questions requiring spontaneous speech responses elicit more natural speech which better reflects a learner’s proficiency level than read speech. In addition to the challenges of highly variable non-native, learner, speech and noisy real-world recording conditions, this requires any automatic system to handle disfluent, non-grammatical, spontaneous speech with the underlying text unknown. To handle these, a strong deep learning based speech recognition system is applied in combination with a Gaussian Process (GP) grader. A range of features derived from the audio using the recognition hypothesis are investigated for their efficacy in the automatic grader. The proposed system is shown to predict grades at a similar level to the original examiner graders on real candidate entries. Interpolation with the examiner grades further boosts performance. The ability to reject poorly estimated grades is also important and measures are proposed to evaluate the performance of rejection schemes. The GP variance is used to decide which automatic grades should be rejected. Back-off to an expert grader for the least confident grades gives gains.

In this letter, we consider the predecessor-following control problem for a platoon of car-like vehicles moving on a planar surface with cyclic obstacles. Each vehicle is equipped with an on-board camera that detects its preceding vehicle, and a laser scanner that detects the obstacles around it. Within this framework, we design a fully decentralized control scheme, in the sense that each vehicle calculates its own control signal incorporating only local information, acquired by its on-board camera and laser scanner. Collisions with obstacles, collisions between successive vehicles and connectivity breaks owing to the limited field of view of the camera as well as to visual occlusions raised by static obstacles are provably avoided. Moreover, the transient and steady-state response of the closed-loop system is a priori determined by certain designer-specified functions and is fully decoupled by the number of vehicles in the platoon and the control gains selection. Finally, a simulation study is carried out in MATLAB and Coppelia Robotics V-REP to prove the control protocol's efficiency.

In this paper, we consider the formation control problem for uncertain homogeneous Lagrangian nonlinear multi-agent systems in a leader-follower scheme under a directed communication protocol. A distributed adaptive control protocol of minimal complexity is proposed that achieves prescribed, arbitrarily fast and accurate formation establishment as well as synchronization of the parameter estimates of all followers. The estimation and control laws are distributed in the sense that the control signal and the update laws are calculated based solely on local relative state information. Moreover, provided that the communication graph is strongly connected and contrary to the related works on multi-agent systems, the controller-imposed transient and steady state performance bounds are fully decoupled from: (i) the underlying graph topology, (ii) the control gains selection and (iii) the agents’ model uncertainties. Finally, extensive simulation studies on the attitude control of flying spacecrafts clarify and verify the approach.

Efficiently coordinating different types of robots is an important enabler for many commercial and industrial automation tasks. Here, we present a distributed framework that enables a team of heterogeneous robots to dynamically generate actions from a common, user-defined goal specification. In particular, we discuss the integration of various robotic capabilities into a common task allocation and planning formalism, as well as the specification of expressive, temporally-extended goals by non-expert users. Models for task allocation and execution both consider non-deterministic outcomes of actions and thus, are suitable for a wide range of real-world tasks including formally specified reactions to online observations. One main focus of our paper is to evaluate the framework and its integration of software modules through a number of experiments. These experiments comprise industry-inspired scenarios as motivated by future real-world applications. Finally, we discuss the results and learnings for motivating practically relevant, future research questions.

Complex networks play an important role in human society and in nature. Stochastic multistate processes provide a powerful framework to model a variety of emerging phenomena such as the dynamics of an epidemic or the spreading of information on complex networks. In recent years, mean-field type approximations gained widespread attention as a tool to analyze and understand complex network dynamics. They reduce the model’s complexity by assuming that all nodes with a similar local structure behave identically. Among these methods the approximate master equation (AME) provides the most accurate description of complex networks’ dynamics by considering the whole neighborhood of a node. The size of a typical network though renders the numerical solution of multistate AME infeasible. Here, we propose an efficient approach for the numerical solution of the AME that exploits similarities between the differential equations of structurally similar groups of nodes. We cluster a large number of similar equations together and solve only a single lumped equation per cluster. Our method allows the application of the AME to real-world networks, while preserving its accuracy in computing estimates of global network properties, such as the fraction of nodes in a state at a given time.

In this paper, we present a mixed-initiative motion control strategy for multiple quadrotor aerial vehicles. The proposed approach incorporates formation specifications and motion-planning commands as well as inputs by a human operator. More specifically, we consider a leader–follower aerial robotic system, which autonomously attains a specific geometrical formation, by regulating the distances among neighboring agents while avoiding inter-robot collisions. The desired formation is realized by a decentralized prescribed performance control strategy, resulting in a low computational complexity implementation with guaranteed robustness and accurate formation establishment. The multi-robot system is safely guided towards goal configurations, by employing a properly defined navigation function that provides appropriate motion commands to the leading vehicle, which is the only one that has knowledge of the workspace and the goal configurations. Additionally, the overall framework incorporates human commands that dictate the motion of the leader via a teleoperation interface. The resulting mixed-initiative control system has analytically guaranteed stability and convergence properties. A realistic simulation study, considering a team of five quadrotors operating in a cluttered environment, was carried out to demonstrate the performance of the proposed strategy.

With the rapid deployment of socio-technical systems into all aspects of daily life, we need to be prepared for their failures. It is inherently impractical to specify all the lawful interactions of these systems, in turn, the possibility of invalid interactions cannot be excluded at design time. As modern systems might harm people, or compromise assets if they fail, they ought to be accountable. Accountability is an interdisciplinary concept that cannot be easily described as a holistic technical property of a system. Thus, in this paper, we propose a bottom-up approach to enable accountability using goal-specific accountability mechanisms. Each mechanism provides forensic capabilities that help us to identify the root cause for a specific type of events, both to eliminate the underlying (technical) problem and to assign blame. This paper presents the different ingredients that are required to design and build an accountability mechanism and focuses on the technical and practical utilization of causality theories as a cornerstone to achieve our goal. To the best of our knowledge, the literature lacks a systematic methodology to envision, design, and implement abilities that promote accountability in systems. With a case study from the area of microservice-based systems, which we deem representative of modern complex systems, we demonstrate the effectiveness of the approach as a whole. We show that it is generic enough to accommodate different accountability goals and mechanisms.

In recent years, there has been an immense research interest in applying Machine Learning for defending networked systems from cyber threats. A particular challenge in this domain is the identification and selection of appropriate features that ensure prompt and correct cyber threat detection. This work proposes a novel approach that leverages recent advances in evidence theory to provide a deep insight on the effect of each feature’s uncertainty on the overall classification decision. As a result, a network security analyst may rank the features in a dataset from the most to the least ambiguous, without requiring expert domain knowledge in cyber threats. Ultimately, this enables the creation of cyber threat phenotypes, which may be used to detect and differentiate between similarly manifested cyber threats. The proposed approach is evaluated on a recent, challenging scenario of network security attacks and compared against multiple feature selection techniques. Based on the selected features, cyber threat classification analysis is performed using seven state-of-the-art ML classification algorithms. The results indicate the proposed evidence-based feature selection method performs better, or, at least as good, to the state-of-the-art. Against the best performing state-of-the-art technique, Decision Tree, the proposed technique’s features enabled the classification process to take place in 93.25% of the time, whilst maintaining a high F1 Score of 0.99. Furthermore, the proposed technique’s features enable a faster classification process requiring, on average, just 29.25% of the time compared to the average across other evaluated techniques.

In this paper, we consider a networked multi-robot system operating in an obstacle populated planar workspace under a single leader-multiple followers architecture. We propose a distributed reconfiguration strategy of the set of connectivity and formation specifications that assures convergence to the desired point, while guaranteeing global connectivity. In particular, we construct a low-level distributed navigation functions based controller that encodes the goals and safety requirements of the system. However, owing to topological obstructions, stable critical points other than the desired one may appear. In such case, we employ a high-level distributed discrete procedure which attempts to solve a distributed constraint satisfaction problem on a local Voronoi partition, providing the necessary reconfiguration for the system to progress towards its goal. Eventually, we show that the system either converges to the desired point or attains a tree configuration with respect to the formation topology, in which case the system switches to a novel controller based on the prescribed performance control technique, that eventually guarantees convergence. Finally, multiple simulation studies clarify and verify the approach.

Multi-robot motion planning from local connectivity specifications in unknown workspaces necessitates for reconfigurability, since real-time obstacle detection is likely to raise contradictions between collision avoidance and local connectivity specifications. Thus, a key issue in control synthesis is to allow topological changes in the communication graph in case of infeasibility, while maintaining the global connectivity of the robotic network and guaranteeing collision-free motion. Towards this direction, we propose: i) a distributed algorithm that coordinates robots, whenever feasible, based on local connectivity as well as collision avoidance specifications and ii) a novel decentralized algorithm that dynamically reconfigures the communication topology in case of conflicts with the collision avoidance specifications, without compromising the global connectivity of the communication graph. In the continuous control system, provably correct collision avoidance and robust synchronized motion are secured, employing the prescribed performance control methodology that ensures predefined transient and steady state response. Finally, simulated examples clarify the proposed methodology and verify its efficiency.

A bandwidth allocation method that incorporates the characteristics of the underlying XG-PON access architecture in its computational logic, for improving bandwidth efficiency and ensuring fairness among served end-user units, is presented and evaluated. It is designed to perform efficiently in this generation of optical access networks. Under linear variation in its upstream latency during the varying network load, and linear optical unit scaling, it provides fair treatment to bandwidth allocation entities that are under the same service contract. The TCONT notation (control messaging) is used to create logical user groups, ensuring fair allocation among them. Also, traffic prediction is utilized for adaptively allocating the bandwidth opportunities.

The State-level approach to safeguards aims to draw safeguards conclusions about the State as a whole in contrast to facility-specific classical safeguards. To do so, the integrated safeguards approach aims to integrate all safeguards-relevant information about each State. To achieve this goal procedures and metrics are needed for generating standardized and measurable results on the basis of the available data. While the information pertaining to the declared nuclear fuel cycle is quantitative and measurable, activities outside the declared nuclear fuel cycle may either not be well defined or quantifiable. Some of that information may be quantitative while other may be descriptive and qualitative. Also information about possible clandestine undeclared activities, by its nature, includes uncertainties about its validity and accuracy. The uncertainties inherent in the information available for evaluating the State as a whole present a challenge to the development of a safeguards system that is objective and transparent. This chapter examines the challenges associated with the development of such a system and specifies the necessary attributes and metrics of the information used in the development of such a system. It associates a set of metrics for the attributes of correctness, completeness, transparency and timeliness that can be used to measure the relevance of the information to the evaluation of the State as a whole.

Arms control treaties rely on extensive collection, processing and analysis of data to ascertain compliance with treaty obligations. Monitoring and evaluation relies on a broad range of technologies for sensing, communications and processing to collect, transmit and analyze the data necessary to detect non-compliance. The effectiveness of a treaty verification system depends on the ability of the information management system to collect and evaluate the information necessary to ascertain compliance with the treaty obligations. The information management systems for treaty verification have the same architecture because they are similar in concept and operation. They collect, transmit, store, process and analyze data. The effectiveness of each of these operations is a function of the capabilities of the associated technology. This chapter presents an overview of the technologies available for use in each of the elements of a treaty information management system. In sensing, these range from low frequency seismic sensors to multispectral and hyperspectral imaging. Data can be transmitted in the order of hundreds of Gigabits per second through fiber optic cables for terrestrial communications and tens of Megabits per second for satellite channels, while available technologies allow for storing petabytes of data and processing speeds of tens of Gigabytes per second. These capabilities are more than sufficient to collect the necessary data for use in sophisticated analysis algorithms for detecting treaty violations.

The paper dwells upon Russian and non-Russian approaches to the assessment of innovation. Since the analyzed approaches are subjective, the paper proposes an assessment algorithm. The theory behind this research focuses on the fuzzy inference algorithms. The implementation section is to clarify the capabilities of such algorithms. It is proposed to use ANNs based on preexisting knowledge to evaluate the conditions of technological entrepreneurship, which is integral to innovation. As part of this research, a standard neural network has been modified to account for external and internal factors affecting such entrepreneurship, as such modifications enable more flexible assessment.

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models, namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar&rsquo, s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.