Reviewerly/exHarmony huggingface dataset:
Fourier Series Model for Facial Feature Point Land-Marking

The field of digital health apps, combined with intelligent learning systems, is new and expanding to incorporate a wide range of possibilities in different domains. An application in the field of digital therapy is for the incorporation of emotion recognition systems as a tool for therapeutic interventions. Adopting an individually tailored virtual world combined with a novel reward system in a gaming scenario, complemented with the technical affinity of most autism spectrum disorder (ASD) children makes a suitable atmosphere for therapeutic intervention. In this paper the use of image processing techniques coupled with Fourier models is used to generate point land-mark annotations on facial features in an image. The OULU-CASIA database was used for the analysis process. The images were first pre-processed based on previous work to reduce background noise and focus on the face. Afterwards a de-correlation stretch was executed to separate different features. A series of morphological, region detections and boundary traces followed. Fourier series models were used to transition the rough segmented pixel data into a smooth geometric representation. Twenty evenly distributed land-mark points are then selected from a fine mesh. Results showed that the geometric representation adhered to the segmented pixel data with a mean of 81.88% Dice similarity. The positive outlook highlighted the effectiveness of such a technique in automating the land-mark annotation process, which is tedious and time consuming. This method leads to explainable machine learning feature representations, which lead to more robust emotion recognition models.
Capture the flag games: Observations from the 2022 Aquaticus competition

In this paper we illustrate observations collected during the 2022 Aquaticus competition. In particular, we illustrate our experiences in terms of classical controller designs for capture the flag games. The Aquaticus competition is a capture the flag multi-player game where two teams of (autonomous) robots compete with each other. We illustrate ideas of a simple controller that combines ideas from optimal control, model predictive control and path planning using Dubins curves. The corresponding controller outperformed the other controllers in the 2022 Aquaticus competition. The results are meant as a stepping stone to combine classical controller designs with reinforcement learning. While reinforcement learning based control laws might be well suited for competitive controller design in future competitions, based on the authors experience, a good understanding of the problem seems to be inevitable to initiate a reinforcement learning controller design.
Reachability Analysis of Neural Networks with Uncertain Parameters

The literature on reachability analysis methods for neural networks currently only focuses on uncertainties on the network's inputs. In this paper, we introduce two new approaches for the reachability analysis of neural networks with additional uncertainties on their internal parameters (weight matrices and bias vectors of each layer), which may open the field of formal methods on neural networks to new topics, such as safe training or network repair. The first and main method that we propose relies on existing reachability analysis approach based on mixed monotonicity (initially introduced for dynamical systems). The second proposed approach extends the ESIP (Error-based Symbolic Interval Propagation) approach which was first implemented in the verification tool Neurify, and first mentioned in the publication of the tool VeriNet. Although the ESIP approach has been shown to often outperform the mixed-monotonicity reachability analysis in the classical case with uncertainties only on the network's inputs, we show in this paper through numerical simulations that the situation is greatly reversed (in terms of precision, computation time, memory usage, and broader applicability) when dealing with uncertainties on the weights and biases.
Fast Trajectory Generation on a Path using Feedback Linearization*

This paper describes a method for quickly generating feasible trajectories for fully actuated autonomous surface vehicles. It is assumed that a path consisting of a list of positions with associated heading angles is given. The goal is to generate a feasible trajectory that follows a given path and takes into account the nonlinear dynamical model. The trajectory contains the state as well as force-level input variables and can be used as feed-forward control by a trajectory controller. The generation of the trajectory is based on a closed-loop simulation of the nonlinear model in combination with a nonlinear controller and guidance law. Feedback linearization is applied to linearize the motion model of the surface vehicle at the velocity level. A state feedback controller is used to control the pose along the path. The presented method allows for simple and fast trajectory generation and can therefore be applied in sampling-based motion planners where many trajectories have to be generated.
Deep Recurrent Neural Network-Based Observer for Uncertain Nonlinear Systems

Recurrent neural networks (RNNs) have gained popularity in various applications like handwriting recognition, time series prediction, and system identification due to their ability to retain state information for later use through temporal operations present in the RNN architecture. The dynamic behavior of RNNs makes them well-suited for the identification of nonlinear systems. Several training methods for adaptive RNN observers have been previously developed, but those methods are restricted to either RNNs satisfying a linear-in-the-parameters assumption or RNNs with a single hidden layer. While deep neural networks have been shown to yield improved function approximation performance compared to neural networks with a single hidden-layer, their complex structure and nested nonlinearities make real-time weight adaptation a difficult problem. In this paper, an adaptive deep RNN observer is developed for identification of a class of nonlinear systems. The developed deep RNN architecture adjusts in real-time to system uncertainties through a Lyapunov-derived weight adaptation law, where a filtered estimation error is designed and implemented in the weight adaptation law to relax full-state feedback requirements. A Lyapunov-based stability analysis is performed to ensure asymptotic convergence of the estimation errors and stability of the adaptive deep RNN architecture.
A Markov Chain Monte Carlo approach for Pseudo-Input selection in Sparse Gaussian Processes

The effectiveness of non-parametric methods for regression comes at the price of high computational complexity. In fact, these methods scale as O(N3), where N is the number of available data points. One possible option to address this issue consists in introducing a set of fictitious (pseudo-) inputs of size M ≪ N such that the computational effort is reduced to O(M2N). To estimate hyper-parameters and pseudo-inputs, a non-convex optimization problem needs to be solved. As opposed to the conventional gradient-based approach used in the literature, this paper proposes a stochastic scheme leveraging Markov Chain Monte Carlo methods. Numerical comparisons show that the latter returns a more efficient set of pseudo-inputs, leading to a superior performance in terms of mean squared error.
Stackelberg Meta-Learning Based Control for Guided Cooperative LQG Systems

Guided cooperation allows intelligent agents with heterogeneous capabilities to work together by following a leader-follower type of interaction. However, the associated control problem becomes challenging when the leader agent does not have complete information about follower agents. There is a need for learning and adaptation of cooperation plans. To this end, we develop a meta-learning-based Stackelberg game-theoretic framework to address the challenges in the guided cooperative control for linear systems. We first formulate the guided cooperation between agents as a dynamic Stackelberg game and use the feedback Stackelberg equilibrium as the agent-wise cooperation strategy. We further leverage meta-learning to address the incomplete information of follower agents, where the leader agent learns a meta-response model from a prescribed set of followers offline and adapts to a new coming cooperation task with a small amount of learning data. We use a case study in robot teaming to corroborate the effectiveness of our framework. Comparison with other learning approaches also shows that our learned cooperation strategy provides better transferability for different cooperation tasks.
Design of Reward Function on Reinforcement Learning for Automated Driving

This paper proposes a design scheme of reward function that constantly evaluates both driving states and actions for applying reinforcement learning to automated driving. In the field of reinforcement learning, reward functions often evaluate whether the goal is achieved by assigning values such as +1 for success and -1 for failure. This type of reward function can potentially obtain a policy that achieves the goal, but the process by which the goal is reached is not evaluated. However, process to reach a destination is important for automated driving, such as keeping velocity, avoiding risk, retaining distance from other cars, keeping comfortable for passengers. Therefore, the reward function designed by the proposed scheme is suited for automated driving by evaluating driving process. The effects of the proposed scheme are demonstrated on simulated circuit driving and highway cruising. Asynchronous Advantage Actor-Critic is used, and models are trained under some situations for generalization. The result shows that appropriate driving positions are obtained, such as traveling on the inside of corners, and rapid deceleration to turn along sharp curves. In highway cruising, the ego vehicle becomes able to change lane in an environment where there are other vehicles with suitable deceleration to avoid catching up to a front vehicle, and acceleration so that a rear vehicle does not catch up to the ego vehicle.
Optimal and Reactive Control for Agile Drone Flight in Cluttered Environments

We present a control framework for agile and reactive collision avoidance with quadrotor drones, applied to the state-based tier of the DodgeDrone Challenge, a simulation competition where the goal is to reach a finish line as fast as possible without collisions. The approach consists of an optimal control scheme with a Log-Sum-Exponential (LSE) obstacle avoidance formulation for efficient motion planning through cluttered environments with a high number of obstacles. The framework copes with a lower update frequency than required in classical Model Predictive Control by combining low-frequency smooth on-trajectory motion planning updates with computationally inexpensive high-frequency linear feedback control. This control structure is augmented with a monitoring function to allow for emergency reactive control with artificial repulsive potential fields in case of an optimal control failure to find a feasible solution in time. The approach is evaluated in extensive simulation runs, which show (1) the effectiveness of the total approach, (2) the increased added value of the LSE formulation for high numbers of obstacles, and (3) the trade-off between the execution time and the success rate. We show that our performance is competitive with the top five teams, even though the approach is, due to its model-based design, not specifically tailored to or trained for this particular challenge but is generally extendable towards other drone applications.
Automatic Detection Strategy of Multi-Scale Catenary Support Device Based on Improved YOLOv7

In recent years, UAV(Unmanned Aerial Vehicle) has shown great vitality and potential in automated railway inspection operations. The catenary support device is an important infrastructure on the high-speed railway, which guarantees for the electric power system when the high-speed railway is running. However, the current inspection by UAV cannot automatically obtain multi-scale and standardized image data of the catenary support devices. It presents a serious challenge to the faulty diagnosis of the catenary support devices. Concerning this issue, an automatic multi-scale image capture strategy of catenary support device based on improved YOLOv7 is proposed. Due to the characteristics of less foreground and more background in the image detection of catenary support device, the Focal Loss is introduced. Experimental results show that the convergence speed of the model is improved with high detection accuracy. Furthermore, multi-scale and standardized capture strategies for catenary support device data are proposed. In the deployment experiment, improved YOLOv7 achieves 81.3% mAP, the multi-scale, standardized and normalized image data of catenary support device is captured.
Kalman smoother error bounds in the presence of misspecified measurements

The performance of a discrete-time fixed-interval Kalman smoother in the presence of a misspecified measurement equation is considered. An easily calculable numerical bound for the increment in smoothing error energy is provided in an adversarial non-linear setting by formulating a high-dimensional optimization problem which is solvable via its Lagrange dual, a scalar convex optimization problem. The results are validated by an adversarial learner, implemented as a neural network.
A BiLSTM Combining WRELM-Based Method For Online TCP State Prediction

Round-trip time (RTT) and throughput are two of the most important parameters that in networks with transmission control protocol (TCP). Deep learning-based time-series forecasting methods such as long-short term memory (LSTM) have recently been widely applied in TCP state prediction due to their strong pattern recognition and accurate prediction ability. However, the practical network environment can be dynamic and may deviate from the situations in which the deep model has been trained, resulting in deteriorated predictions. Furthermore, online retraining of the deep model to adapt to current working environment is usually unfeasible due to the nature of heavy computational complexity. In this paper, we propose a method which can online rectify the TCP predictions with a very small computational overhead (time consumption) by combining Bidirectional LSTM (BiLSTM) with the weighted regularized extreme learning machine (WRELM). Experiments show that the proposed method can greatly increase the online prediction accuracy of TCP states especially when the knowledge of the trained deep model diverges from the conditions of its original working environment.
Occlusion-free Image-Based Visual Servoing using Probabilistic Control Barrier Certificates

Image based visual servoing (IBVS) is a widely-used approach in robotics that employs visual information to guide robots towards desired positions. However, occlusions in this approach can lead to visual servoing failure and degrade the control performance due to the obstructed vision feature points that are essential for providing visual feedback. In this paper, we propose a control barrier function (CBF) based controller that enables occlusion-free IBVS tasks by automatically adjusting the robot's configuration to keep the feature points in the field of view and away from obstacles. In particular, to account for measurement noise of the feature points, we develop the probabilistic control barrier certificates (PrCBC) using control barrier functions that encode the chance-constrained occlusion avoidance constraints under uncertainty into deterministic admissible control space for the robot, from which the resulting configuration of robot ensures that the feature points stay occlusion free from obstacles with a satisfying predefined probability. By integrating such constraints with a model predictive control (MPC) framework, the sequence of optimized control inputs can be derived to achieve the primary IBVS task while enforcing the occlusion avoidance during robot movements. Simulation results are provided to validate the performance of our proposed method.
A Dual-Based Pruning Method for the Least-Squares Support Vector Machine

The least-squares support vector machine (LS-SVM) is generally parameterized by a large number of support vectors, which slows down the speed of classification. This paper proposes to search for and prune two types of support vectors. The first type is the potential outliers, each of which is misclassified by the model trained on the other samples. The second type is the sample whose removal causes the least perturbation to the dual objective function. Without implicitly implementing the training procedure, the LS-SVM model pertaining to omission of a training sample is derived analytically from the LS-SVM trained on the whole training set. The derivation reduces the computational cost of pruning a sample, which makes the major technical contribution of this paper. Experimental results on six UCI datasets show that, compared with classical pruning methods, the proposed algorithm can enhance the sparsity of the LS-SVM significantly, while maintaining satisfactory generalization performances.
Sign Language Recognition Using Artificial Rabbits Optimizer with Siamese Neural Network for Persons with Disabilities

Sign language recognition is an effective solution for individuals with disabilities to communicate with others. It helps to convey information using sign language. Recent advances in computer vision (CV) and image processing algorithms can be employed for effective sign detection and classification. As hyperparameters involved in Deep Learning (DL) algorithms considerably affect the classification results, metaheuristic optimization algorithms can be designed. In this aspect, this manuscript offers the design of Sign Language Recognition using Artificial Rabbits Optimizer with Siamese Neural Network (SLR-AROSNN) technique for persons with disabilities. The proposed SLR-AROSNN technique mainly focused on the recognition of multiple kinds of sign languages posed by disabled persons. The goal of the SLR-AROSNN technique lies in the effectual exploitation of CV, DL, and parameter tuning strategies. It employs the MobileNet model to derive feature vectors. For the identification and classification of sign languages, Siamese neural network is used. At the final stage, the SLR-AROSNN technique makes use of the ARO algorithm to get improved sign recognition results. To illustrate the improvement of the SLR-AROSNN technique, a series of experimental validations are involved. The attained outcomes reported the supremacy of the SLR-AROSNN technique in the sign recognition process.
A Centralized MongoDB-Based Repository Design for IIoT Data: The ecoKI Project*

One of the challenges of cyber-physical systems is the data acquisition in real- time through different communication protocols within the scope of the Industrial Internet of Things (IIoT). This IIoT data is mainly specified as big in amount, continuous in nature, unstructured, and ambiguous format. It is also enigmatic in terms of structure for an overview of the manufacturing plant and which data belongs to which sensor or machine. Presently, relational database technologies are inadequate to cater to such kinds of IIoT data due to their strict data structure and relations. For this reason, we presented the ontology-based data repository model implementation for the process industry using MongoDB, which is a cross- platform document-oriented, most sought-after ample data storage database system nowadays. Firstly, we have designed a JSON-based data-model schema for the IIoT data repository, which gathers the data with the help of one of the most commonly used communication protocols within the scope of IIoT, OPC UA. Secondly, our schema stores the meta-data and data using embedded referencing through the same data-model. Hence, the scalability, flexibility, and variety of the data storage system have increased. Lastly, with the execution of insertion and search queries, we show how fast to get the desired data element hierarchy or track any particular data, which becomes possible due to the proposed ontology. This effectiveness of the ontology-based data-model also minimize the overhead of the machine learning engineer job.
Design of a Computer Vision Algorithm for Surface Vessel Position Detection in a Laboratory Pool

The growing availability of cameras and advancements in computers hardware that led to increase in computer performance make systems based on computer vision attractive because of its ease of setup and use but at the same time low economic expenses. There are many methods for object detection and recognition, image segmentation that are already in use in many practical applications such as face recognition, industrial production etc. Many of these use cases do not require the knowledge of the exact position of the object as detection methods usually provide axis-aligned bounding box. In order to get more precise borders of the object image segmentation methods could be used, but in some applications such as control systems based on computer vision it is necessary to not only know the exact position of the object, but also its direction. In this study the objective is to find the linear and angular direction of surface vessel using computer vision approach. The proposed methodology is capable of obtaining the center coordinates of the surface vessel as well as the direction the surface vessel is heading. Out findings indicate that this could be done based on just image processing and some transformations without the use of deep learning approaches thus avoiding the need to collect, annotate and clean data etc. The approach considered in this study is based on filtering, edge detection, dilation, contour tracing and Hough Transform.
Fault diagnosis using data-driven residuals for anomaly classification with incomplete training data

Data-driven modeling and machine learning have received a lot of attention in fault diagnosis and system monitoring research. Since faults are rare events, conventional multi-class classification is complicated by incomplete training data and unknown faults. One solution is anomaly classification which can be used to detect abnormal behavior when only training data from the nominal operation is available. However, data-driven fault isolation is still a non-trivial task when training data is not representative of nominal and faulty behavior. In this work, the importance of redundancy for a set of known variables that are fed to a data-driven anomaly classification is discussed. It is shown that residual-based anomaly detection can be used to reject the nominal class which is not possible with one-class classifiers, such as one-class support vector machines. Based on these results, it is also discussed how data-driven residuals can be integrated with model-based fault isolation logic.
Path Planning over Visibility Maps

This paper examines the generation of visibility maps for the purposes of robotic path planning. The paper proposes a map construction technique that extends a version of the viewshed problem to calculate the rays to and from all discrete points on the map efficiently. The introduced form is parallelizable, making it suitable for fast GPU computation necessary for realtime calculation during path planning. New algorithms are introduced to generate worst- and best-case visibility maps by bounding precision uncertainties in the discretized map. Visibility theory is then applied to quantify the detectability of each viewpoint in the space based on atmospheric visibility models. Path planning is executed on the generated cost map illustrating its usability for visibility path planning.