Reviewerly/exHarmony huggingface dataset:
Duality-Based Stochastic Policy Optimization for Estimation with Unknown Noise Covariances

Duality of control and estimation allows mapping recent advances in data-guided control to the estimation setup. This paper formalizes and utilizes such a mapping to consider learning the optimal (steady-state) Kalman gain when process and measurement noise statistics are unknown. Specifically, building on the duality between synthesizing optimal control and estimation gains, the filter design problem is formalized as direct policy learning. In this direction, the duality is used to extend existing theoretical guarantees of direct policy updates for Linear Quadratic Regulator (LQR) to establish global convergence of the Gradient Descent (GD) algorithm for the estimation problem–while addressing subtle differences between the two synthesis problems. Subsequently, a Stochastic Gradient Descent (SGD) approach is adopted to learn the optimal Kalman gain without the knowledge of noise covariances. The results are illustrated via several numerical examples.
Guidance of Quadrotor Unmanned Aerial Vehicles via Adaptive Multiple-Surface Sliding Mode Control

In many application domains, navigation of unmanned aerial vehicles (UAVs) requires a planar flight to move along a desired path or to track a moving object under uncertain conditions. In this paper, we propose a robust control approach for quadrotor UAVs performing a nonholonomic-like navigation with a predefined velocity based guidance law. Specifically, the quadrotor model is first recast in the framework of nonholonomic systems, and then an adaptive multiple-surface sliding mode approach, with suboptimal second order sliding mode control, is applied. The robustness features of the proposed approach are discussed and assessed in simulation.
State and Parameter Estimation for Stochastic Open Two-Level Quantum Systems

Research in the field of quantum control usually assumes complete knowledge of the state and parameters. In this paper, a driven and damped two-level quantum system under continuous observation is considered. Its time evolution is governed by an operator-valued stochastic master equation and conditioned on the recorded measurement data. The system is transformed to state space representation and its observability is proven analytically. An extended Kalman filter is employed in order to both reconstruct the density operator describing the full state of the system, and to dynamically identify key parameters. Convincing results are obtained in numerical simulations. Overall, a practical engineering approach to quantum control is presented.
Chance-Constrained Control with Imperfect Perception Modules

Autonomous systems are required to operate in different environments, but recognizing the current environment is often challenging. For example, an autonomous vehicle should stop or obey a speed limit according to a traffic sign, but state-of-the-art perception modules (e.g., neural networks) do not guarantee the correctness of their reading of the traffic sign. Considering such uncertain outputs of a perception module, which in effect determines modes, we propose a chance-constrained control formulation that with high probability guarantees the satisfaction of a set of constraints associated with the possible modes. To do this, we present a method based on the Bayes rule and sampling to calculate the probability of each mode. We prove that our approach can ensure satisfying constraints of novel situations, which have not been used during training of the perception module. Also, to account for the error due to limited data, we present a robust formulation that guarantees constraint satisfaction with high confidence. In an autonomous vehicle example, we train a neural network that classifies traffic signs and show that given each output of the neural network, our motion planning approach guarantees the constraint satisfaction with high probability.
Distributed Connectivity Keeping Supervision Scheme with Collision and Obstacle Avoidance Requirements

This paper investigates a connectivity preserving coordination problem for a group of dynamically decoupled fully actuated Unmanned Surface Vehicles (USVs) that are subject to both collision and obstacle avoidance constraints. An existing supervision scheme based on Distributed Command Governor ideas is here extended in order to obtain motion coordination of vehicles. To this end, the proposed method enforces the existence of a specific minimum spanning tree at each time instant while fulfilling obstacle avoidance and collision avoidance constraints, by using a separation hyperplane and solving online a linearly constrained optimization problem involving a mixture of continuous and integer variables. Conditions that formally ensure the feasibility of the proposed strategy are included along with simulations that show the effectiveness of the proposed method.
A Minimax-Based Decision-Making Approach for Safe Maneuver Planning in Automated Driving

This paper proposes a novel game-theoretic decision-making algorithm for safe maneuver planning in highway driving. The problem is formulated as a two-player extensive-form game for safety between the ego vehicle and the environment (all the other vehicles around). In order to make a decision (i.e., plan maneuver), the ego vehicle builds a game tree in the current state. The tree is expanded for each possible maneuver of the ego vehicle and the observations from the environment. For evaluation, we quantify the safety value of a maneuver by computing and over-approximating its trajectory and checking for the worst-case spatiotemporal overlap with possible trajectories of other vehicles. The ego vehicle tries to maximize the safety value, assuming that others will act to minimize it. Among equally safe maneuvers, it chooses the one that travels the longest distance. The minimax solution of the game tree yields a sequence of maneuvers up to a predefined depth. The ego vehicle applies the first maneuver in a receding horizon fashion and repeats the process in constant planning cycles. For validation, we simulated highway driving scenarios and compared our minimax-based planning approach to a rule-based planner and an online look-ahead planner in terms of safety, traveled distance, and computation time. We have shown that our approach incorporates a higher safety level than the baseline planners at the cost of traveled distance and computation time.
A Physics-Informed Neural Network Approach Towards Cyber Attack Detection in Vehicle Platoons

Connected and Autonomous Vehicles (CAVs) are seen as a promising solution to reduce traffic congestion, improve passenger comfort and fuel economy. Although CAVs address such needs in an effective way, they are vulnerable to cyber attacks due to their extensive utilization of communication networks. In light of this problem, we present a cyber attack detection framework for a vehicle platoon based on physics-informed neural network (PINN) framework. The proposed algorithm exploits the physics based model of the platoon as well as limited available data to detect and distinguish cyber-attacks from various sources, namely, attacks affecting communication network and attacks affecting local vehicular sensors. Essentially, the PINN framework learns an uncertain parameter from the physics model and utilizes the learned parameter knowledge to infer attack scenarios. Finally, as shown through the simulation studies, the proposed algorithm is able to detect and distinguish various cyber attacks showing its potential.
Graduated Moving Window Optimization as a Flexible Framework for Multi-Object Tracking

Continuous optimization methods for multiple object tracking allow to jointly estimate continuous object trajectories and perform implicit data association. However, the local minima that arise from including data association in a continuous optimization problem pose challenges. In addition, optimization is usually performed either over a fixed or an indefinitely growing time frame. This either discards valuable past information or is computationally unsustainable. Hence, in this work, a flexible continuous optimization based framework for multiple object tracking that accounts for these issues is proposed. The framework provides a unified approach to not only include data association, but also multiple motion models and temporary interactions between objects in a continuous optimization problem. It leverages the concept of graduated optimization, a heuristic, which allows avoiding local minima. The proposed framework’s performance is benchmarked on a synthetic dataset, showing its capabilities and indicating areas of possible improvement.
Quantifying the Estimation Error for a Constant-Gain Tracker

We present a new method of calculating approximate probabilistic bounds for the state estimation error of a constant-gain Kalman filter in nonlinear multivariate systems. A critical part of the method is an analysis of the convergence in distribution and the mean-square error of the algorithm. Based on such analysis, a surrogate error process is constructed to mimic the dynamics of the unknown tracking error. We analyze the distance between the true filtering error and the surrogate process and show that it can be bounded under some circumstances. The surrogate process provides a means of computing probabilistic bounds and confidence regions for the tracking error.
How Aphasia Influences Written and Spoken Texts

Aphasia is disorder of the brain influencing language processing which occurs after the brain damage (such as stroke or brain tumor). People with aphasia have problems in communication in both spoken and written language. Their abilities to understand conversations or written sentences are strongly affected too. In this paper we analyze both written and spoken texts with a help of the graph theoretical, statistics and linguistics methods. We compare several statistical measures and other properties in texts, written or spoken, by people with and without aphasia.
Text classification technique for discovering country-based publications from international COVID-19 publications

The significant increase in the number of COVID-19 publications, on the one hand, and the strategic importance of this subject area for research and treatment systems in the health field, on the other hand, reveals the need for text-mining research more than ever. The main objective of the present paper is to discover country-based publications from international COVID-19 publications with text classification techniques.The present paper is applied research that has been performed using text-mining techniques such as clustering and text classification. The statistical population is all COVID-19 publications from PubMed Central® (PMC), extracted from November 2019 to June 2021. Latent Dirichlet allocation (LDA) was used for clustering, and support vector machine (SVM), scikit-learn library, and Python programming language were used for text classification. Text classification was applied to discover the consistency of Iranian and international topics.The findings showed that seven topics were extracted using the LDA algorithm for international and Iranian publications on COVID-19. Moreover, the COVID-19 publications show the largest share in the subject area of "Social and Technology in COVID-19" at the international (April 2021) and national (February 2021) levels with 50.61% and 39.44%, respectively. The highest rate of publications at international and national levels was in April 2021 and February 2021, respectively.One of the most important results of this study was discovering a common trend and consistency of Iranian and international publications on COVID-19. Accordingly, in the topic category "Covid-19 Proteins: Vaccine and Antibody Response," Iranian publications have a common publishing and research trend with international ones.
Belief State Actor-Critic Algorithm from Separation Principle for POMDP

Partially observable Markov decision process (POMDP) is a general framework for decision making and control under uncertainty. A large class of POMDP algorithms follows a two-step approach, in which the first step is to estimate the belief state, and the second step is to solve for the optimal policy taking the belief state as input. The optimality guarantee of their combination relies on the so-called separation principle. In this paper, we propose a new path to prove the separation principle for infinite horizon general POMDP problems under both discounted cost and average cost. We use a nominal horizon to split a virtual objective function into two parts and prove that it converges to the optimal state-value function. Based on the separation principle, we design a two-step POMDP algorithm called Belief State Actor-Critic (BSAC), which first estimates the belief state and then takes it as input to solve for the optimal policy. The belief state is learned using variational inference, and the policy is learned through model-based reinforcement learning. We test our algorithm in a partially observable multi-lane autonomous driving task. Results show that our algorithm achieves lower costs than the baselines and learns safe, efficient, and smooth driving behaviors.
Device for the Sound Quality Measurement of Reeds

The test device was recently developed and realized, which meets the specific requirements for the reproducible sound quality measurement of reeds. The air flow through the reed with a small negative pressure is controlled by means of pressure sensor in the feedback. The device provides an objective assessment of A-weighted sound pressure level, relative harmonic spectrum, inharmonicity, air consumption and average intake pressure. The evaluated values are stored in the database and are available for further processing and comparison. The measurement is controlled and evaluated by means of PC application. The device functionality is verified by a regular use in R&D and production quality activities.
A New Spatial Registration Algorithm of Aerial Moving Platform to Sea Target Tracking

Spatial registration is the primary challenge affecting target tracking accuracy, especially for the aerial moving platform and sea target tracking. In this environment, it is important to account for both the errors in sensor observations and the variations in platform attitude. In order to solve the problem of complex types of errors in the tracking of sea targets by aerial moving platforms, a new spatial registration algorithm is proposed. Through separating and analyzing observation data, the influence of sensor observation error and attitude error on observation data is obtained, and a systematic error consistency matrix is established. Based on observation information from multiple platforms, accurate tracking of sea targets can be accomplished without estimating systematic error. In order to verify the effectiveness of the algorithm, we carried out simulation experiments and practical experiments on the lake, which showed that the new algorithm was more efficient than traditional algorithms.
Robust Text Clustering To Cluster The Text Documents In A Meta-Heuristic Optimization

The conventional text clustering models lacks with various issues that includes processing of redundant information even using a simplest technique. It is hence necessary to optimize the process of data clustering using soft computing techniques that should be in a robust manner. This research optimises the clustering of text documents using a meta-heuristic approach, which requires pre-processing and clustering of data. The first step in data pre-processing is a data cleaning process, which eliminates any extraneous or missing information. Selecting the most useful attribute to enhance data extraction in response to a given query is the goal of feature selection. The procedure converts the raw data (int, numeric, nominal, or text) into a more usable form. The features are broken down into discrete categories according to the input query, associations are made between them, and the relative contributions of each feature are calculated. The features with the shortest distance to the answer attribute are chosen. In this way, we may get rid of all the other features within the clusters.
Application of Manta Ray Foraging Optimization with Gradient-based Mutation (cMRFO) for Solving Power System Problem

In this paper, the Manta Ray Foraging optimization (MRFO) algorithm is applied to solve real parameter constrained optimization problems, using the Gradient-based Mutation MRFO (cMRFO) variant. The cMRFO algorithm integrates the MRFO strategy, which emulates the foraging behavior of Manta Rays, with the Gradient-based Mutation strategy, inspired by the $\varepsilon$-Matrix-Adaptation Evolution Strategy $(\varepsilon$ MAgES), to enhance solution feasibility and repair during the search process. Previous studies have demonstrated the effectiveness of MRFO in solving artificial benchmark-function tests, and GbM in improving solution feasibility during the search. This study found cMRFO to be a competitive optimization algorithm for solving constrained optimization problems. To validate the performance of the cMRFO algorithm, it was applied to a power system problem of sizing single-phase distributed generation with reactive power support for phase balancing at the main transformer/grid. The analysis revealed that cMRFO outperformed $\varepsilon$ MAgES and COLSHADE in terms of overall performance.
TxMirror: When the Dynamic EVM Stack Meets Transactions for Smart Contract Vulnerability Detection

Smart contract vulnerability can be exploited to cause serious financial loss. While there are many logic-based techniques for detecting vulnerabilities, few have focused on the dynamic stack of the Ethereum virtual machine (EVM) in the process of transactions. This motivates us to raise an intriguing question: What will happen when the dynamic EVM stack meets a transaction for smart contract vulnerability detection? To answer it, we propose a novel data-driven framework, dubbed TxMirror, to detect smart contract vulnerability at the bytecode level by simulating transactions symmetrically. Beyond logic-based wisdom, TxMirror customizes EVM for smart contract vulnerability detection, and stores its dependency between the stack data and logic relation in a new manner, that is, all the data are stored in a double link forest and can index the logic that creates them; it directly inspects customized EVM stack data in a transaction without replaying history transactions repeatedly or recording EVM bytecode-level traces. Furthermore, it extends detection rules defined by user interests, possessing good adaptability for developers. Extensive experiments demonstrate that TxMirror effectively detects attacks and vulnerabilities in unpredictable smart contracts.
Exploring Quantum Machine Learning for Electroencephalogram Classification

Quantum machine learning (QML) is a relatively new discipline emerging from the concepts of machine learning and quantum computing, whereby quantum algorithms are used to solve machine learning tasks. This paper explores the use of quantum machine learning for electroencephalogram (EEG) classification. In particular, a previously proposed EEG feature extraction method and classification framework for classifying dementia subjects were followed in this study. A quantum classifier replaced the classical classifier component of the framework, and the classification accuracies between the quantum and classical classifiers were compared. This study has demonstrated that applying QML in healthy-dementia classification can be implemented using near-term quantum devices or quantum simulators with moderate performance. The quantum classifier achieved an overall classification accuracy of 81.67% and 79.17% in a train-test split performance test and an n $\times$ k-fold cross-validation test, respectively. However, the quantum approach did not produce higher classification accuracies than the classical classifier. Despite the promise of quantum advantages, further investigation and optimization are required to improve its effectiveness.
Visualization of Word Similarity Measurement for Messages in Sequence Diagram using Heatmap

The sequence diagrams emphasize time-based ordering and are very helpful for understanding the specifications and complex activities in each use case. This is important to analysts in compiling sequence-messages that create interactions between objects in the sequence diagram during the analysis stage. Later, during the design stage, these sequence-messages will be converted to operations in both the sequence diagram and class diagram. Reusing existing sequence messages is possible when analysts need to deal with complex sequences of messages in sequence diagrams. Analysts can apply reference fragments to reuse parts of one sequence diagram in another sequence diagram. In order to apply reusability in developing a sequence diagram, measure similarity among the sequence of messages using appropriate similarity models. Furthermore, it is more difficult for the analyst to trace the similarities of messages in a complicated sequence diagram based on the numerical representation of the results. Therefore, this study measured similarity between messages in each sequence diagram for the create, read, and update processes using Word2Vec, Bag of Words, and the TF-IDF web embedding tool. Then, the results of these similarity measurements will be visualized in heatmaps. A heatmap is a type of plot that is often used in multiple correspondence analysis. Based on the results, the comparison heatmaps showed the biggest differences between the cosine similarity and Euclidean distance using the Word2Vec method. This study will help analysts refine the most similar messages in the sequence diagram in the analysis stage.
Road Hazard Detection for the Motorcycle based on EfficientNet-Lite0

With the limitation of shorter stopping distance, the motorcycle needs more space to break. The existence of static road hazards has a higher potential for road crashes which leads to a higher risk of serious injury to motorcyclists. Road hazards can be identified and located using the Motorcycle Object Detection system or simply known as MOD. The MOD system has the ability to detect and classify multiple objects in real-time. It employs the TensorFlow Lite framework on edge devices i.e., Raspberry Pi 4. TensorFlow Lite is the best preference for the Raspberry Pi 4 for deploying a pre-trained neural network; EfficientNet-Lite0 model. The MOD system utilizes an 8MP camera to capture the presence of the trained objects such as potholes, cones, barriers, and branches from the opposite direction which enter the motorcyclist’s Region of Interest (RoI). RoI is designed based on the motorcyclist’s point of view (30 meters ahead) which can reduce false object detection. Likewise, RoI can prevent excessive alert noise to the motorcyclist. MOD system serves to alert the motorcyclist to the presence of the trained objects and activates the audible and visual warning system through the built-in speaker in the helmet and the warning light installed on the handlebar of the motorcycle.