Reviewerly/exHarmony huggingface dataset:
User Identification through Hidden Markov Model-based Touch Keystroke Dynamics

The biometric feature for user identification based on user typing patterns on touchscreen devices, modeled by Hidden Markov Models (HMMs), has shown to be highly effective and accurate. HMMs are well-suited for this purpose due to their stochastic nature, which allows for revealing pattern distributions and predicting possible keystroke sequences. To assess the performance of our proposed approach, we conducted experiments on two publicly available datasets. The results showed that our method achieved high accuracy rates and effectiveness in identifying users based on their typing patterns. In fact, the accuracy rates achieved by HMMs were significantly higher than those obtained by well-known machine learning algorithms, reaching up to 94% and 98%. Overall, our results demonstrate that the proposed biometric feature, based on user typing patterns on touchscreen devices and modeled by HMMs, is a powerful and reliable tool for user identification. This approach could be useful in a variety of applications, such as authentication and access control systems, where accurate user identification is critical.
Automotive Security System Integrating Machine Learning and Internet-of-Things

The primary objective of this article is the design and implementation of a key-less automotive security system, which allows owners enhanced convenience and security when accessing their vehicles. This is made possible through leveraging the power of machine learning for biometric authentication and the internet of things (IoT) for seamlessly integrating three primary subsystems: (i) biometric authentication and access control subsystem, (ii) global positioning system (GPS) tracking unit, and (ii) a mobile application with graphical user interface (GUI). Both system and subsystem level designs are presented.
Privacy-Preserving Market-Driven Transactive Energy System Using Homomorphic Encryption

Transactive energy paradigm targets creating a way for producers and consumers to match and balance energy supply and demand on a real time basis. An approach towards realizing this target is through price signals generated by energy market mechanisms. Pilot projects have confirmed that transactive control technology is effective in various ways, such as improving energy efficiency and reliability, reducing energy cost and encouraging integration of renewable energy sources. However, a main challenge towards the broad adoption of smart grid technologies, such as transactive energy, is the protection of the participants’ privacy. This paper presents the design and the implementation of a uniform price double auction mechanism which preserves the privacy of its participants leveraging homomorphic encryption. Market participants exchange encrypted information among them and the market entity. The auction clearing is jointly computed and the computations are performed on encrypted data. Large-scale experimentation, simulating the Olympic Peninsula pilot project, enable us to measure the effectiveness and scalability of the proposed distributed mechanism. The analysis of the results suggests that the proposed protocol provides an effective mechanism for the implementation of a privacy preserving real-time energy market.
Generative Lossy Sensor Data Reconstructions for Robust Deep Inference

Limited communication bandwidth in modern net-works of wireless sensors, popularly known as the Internet of Things, may require lossy compression of the data. The sensor data, intended for inference by a deep learning (DL) model, will be reconstructed with some distortion by the remote user who received its compressed representation over a wireless channel. We study the robustness of the remote DL (RDL) model to the distortion-inducing lossy compression of the input, including the robustness under an adersarial attack. We are particularly interested in a novel data compression, known as learned compression (LC) due to the use of DL. Starting from MNIST images, we compare conventional compression (JPEG) with a published generative LC model under different compression ratios (CR). The generative LC, a hierarchical vector-quantized variational autoencoder, is state-of-the-art. With a CR up to 4 times that of JPEG’s, its reconstructions are achieving the same accuracy on a RDL MNIST classifier as with JPEG or uncompressed data. Also, RDL accuracy degrades gracefully with LC-induced information loss up to a remarkable CR. High compression allows for multiple generative LC descriptions: a single image generates many conditionally independent compressed representations of the same low rate. Their decompression creates randomized image reconstructions contributing the salient features needed in downstream RDL, and making it more robust.
QoE Estimation for the Wi-Fi Edge with Gradient Boosting-based Machine Learning

An integral part of the Intent-Based Networking paradigm is estimating and improving the end-user quality of experience (QoE). Estimating user experience from the (wide-area) network data alone does not accurately represent the performance at customer premises since Wi-Fi at the edge also significantly affects the perceived QoE. We propose machine learning-based estimation of the end-users’ perceived QoE for web browsing and video streaming applications, based on Wi-Fi statistics. We implement support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), XGBoost, and CatBoost algorithms and compare their performance. To the best of our knowledge, our CatBoost-based model yields the highest accuracy to date, 0.92 R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , in estimating the QoE for web browsing based on Wi-Fi statistics. Our experiments also show that the XGBoost-based QoE estimator outperformed the neural network-based model in estimating the QoE for video streaming. Our work demonstrates that network operators can infer the user-perceived QoE in a Wi-Fi network through telemetry data obtained by passive measurements.
Challenges of Blockchain in Higher Education Institutions for Protection Against Diploma Forgery

Blockchain technology, thanks to its characteristics which are being seen as indispensable in many fields, is also trying to find application in the field of education, especially in higher education institutions to prevent different types of misuse of data and falsification of diplomas. Although there are attempts for a wider application in all areas of higher education institutions, blockchain is encountering various difficulties, where we will try to describe some of them through this paper and clarify where are the problems based on the various researches that have been done in this direction. One thing is evident that the falsification of documents, especially diplomas in particular, is a phenomenon that even today exists, and methods must be taken to prevent such a phenomenon which is harmful to society. Part of the paper will also present some general models of how blockchain can be applied in higher education institutions and the same can be expanded with other institutions of the similar nature, forming a common chain for the protection against the possible falsification of diplomas. The paper describes the challenges that blockchain faces when applying to higher education institutions, and proposes a model for the protection against diploma forgery, theoretically offering a diploma management software based on blockchain. The practical implementation of blockchain in higher education institutions is encountering many difficulties, despite efforts, due to the immutability of data and the possibility of updating them.
Supervised Wireless Communication: An Analytic Framework for Real-Time Model Inference in the 5G Core Network

The base for providing intelligent management is evolving towards Beyond 5G (B5G) and Sixth Generation (6G) networks. The increasing demand for data traffic, and the deployment of a significant number of network slices, create an essential need to improve the performance of resource utilization and allocation. Deployment strategies for real-time network optimization become challenging with the trends in heterogeneity and diversity. This work proposes the Fifth Generation (5G) wireless communication’s real-time prediction framework by analyzing the traffic of each Network Function (NF) in the Core Network (CN) architecture, simulated in a containerized infrastructure. Based on a varying range of hyperparameters, regressive training is conducted, and an optimal model is chosen for the inference phase through model tracking and registry support. During the real-time prediction stage, if the comparison results in a larger difference, a messaging system is implemented to notify a specific authority for further investigation. Finally, the experimental result shows the feasibility of this proposal to forecast with high accuracy.
Graph Neural Networks Based Anomalous RSSI Detection

In today’s world, modern infrastructures are being equipped with information and communication technologies to create large IoT networks. It is essential to monitor these networks to ensure smooth operations by detecting and correcting link failures or abnormal network behaviour proactively, which can otherwise cause interruptions in business operations. This paper presents a novel method for detecting anomalies in wireless links using graph neural networks. The proposed approach involves converting time series data into graphs and training a new graph neural network architecture based on graph attention networks that successfully detects anomalies at the level of individual measurements of the time series data. The model provides competitive results compared to the state of the art while being computationally more efficient with $\approx$171 times fewer trainable parameters.
False Positive Identification in Intrusion Detection Using XAI

With the increase in the use of the Internet to access sensitive data, intrusion detection has become an essential security measure. The evolution that took place in Artificial Intelligence in the last decades, notably in Machine Learning techniques, combined with the availability of network traffic datasets, opened a vast field for research and development in Intrusion Detection Systems based on anomalies. Published studies on this subject, nonetheless, are unanimous in stating that this type of detection is more prone to the occurrence of false positives. In order to mitigate this problem, we propose a more effective method of identifying them, compared to using only the algorithms confidence. For this, we hypothesize that the relevance given by the algorithm to certain attributes may be related to whether the detection is true or false. The method consists, therefore, in obtaining these features relevance through eXplainable Artificial Intelligence (XAI) and, together with a confidence measure, identifying detections that are more likely to be false. By using the LYCOS-IDS2017 dataset, it is possible to eliminate more than 65% of the total false positives, with a loss of only 0.38% of true positives. Conversely, by using only a confidence measure, the elimination of false positives is approximately just 50%, with a loss of 0.42% of true positives.
INS-GNSS Navigation for Large Attitude Uncertainties with the Matrix Fisher–Gaussian Distribution

In this paper, we present a new recursive Bayesian filter for loosely coupled INS-GNSS navigation to handle large attitude uncertainty. The filter replaces the Gaussian distribution assumed in Kalman filters with the matrix Fisher–Gaussian (MFG) distribution, which is defined intrinsically on the product manifold of the three dimensional special orthogonal group and the Euclidean space of an arbitrary dimension. The MFG models large attitude uncertainty accurately, which can be frequently encountered in robot and pedestrian localization, for example when the robot or person enters a building where the heading direction is unobservable. It is validated by simulation studies illustrating that the proposed filter has a substantially faster convergence rate, when compared with the extended Kalman filter.
An Interleaved Algorithm for Integration of Robotic Task and Motion Planning

We propose an interleaved method for robotic task and motion planning (TAMP) problems, which involves optimizing both continuous and discrete dynamic behaviors. The coupling between the task planning and motion planning results in a large search space, causing challenge for computing the optimal solution. To address this challenge, we develop a novel bi-level algorithm leveraging the Depth First Search (DFS) algorithm and the Monte Carlo Tree Search (MCTS) algorithm to solve the TAMP. Incorporating task completion cost estimation from the motion planning level, we solve the task planning problem in a computationally efficient manner. We prove that our proposed TAMP algorithm is complete, i.e., it always finds the optimal solution if there exists one. Finally, we present simulation results to demonstrate that the proposed algorithm can find the optimal solution of the TAMP problem with lower computation cost than existing algorithms.
Non-Sequential Decentralized Stochastic Control and Static Reduction

In decentralized stochastic control (or stochastic team theory) and game theory, if there is a pre-defined order in a system in which agents act, the system is called sequential, otherwise it is non-sequential. Much of the literature on stochastic control theory, such as studies on the existence analysis, approximation methods, and on dynamic programming or other analytical or learning theoretic methods, have focused on sequential systems. The static reduction method for sequential control problems (via change of measures or other techniques), in particular, has been shown to be very effective in arriving at existence, structural, approximation and learning theoretic results. Many practical systems, however, are non-sequential where the order of agents acting is random, and dependent on the realization of solution paths and prior actions taken. The study of such systems is particularly challenging as tools applicable for sequential models are not directly applicable. In this paper, we will study static reducibility of non-sequential stochastic control systems, including by change of measure methods. We revisit the notion of Causality (a definition due to Witsenhausen and which has been refined by Andersland and Tekenetzis), and provide an alternative representation using imaginary agents. Via this representation, we show that Causality, under an absolute continuity condition, allows for an equivalent static model whose reduction is policy-independent. This facilitates much of the stochastic analysis available for sequential systems to also be applicable for non-sequential systems. We further show that under more relaxed conditions on the model, such as solvability, such a reduction, when possible at all, is policy-dependent or includes policies as parameters in the cost of the reduced model, and thus has limited utility. We will also present a further reduction method for partially nested causal non-sequential systems.
Geometry of Radial Basis Neural Networks for Safety Biased Approximation of Unsafe Regions

Barrier function-based inequality constraints are a means to enforce safety specifications for control systems. When used in conjunction with a convex optimization program, they provide a computationally efficient method to enforce safety for the general class of control-affine systems. One of the main assumptions when taking this approach is the a priori knowledge of the barrier function itself, i.e., knowledge of the safe set. In the context of navigation through unknown environments where the locally safe set evolves with time, such knowledge does not exist. This manuscript focuses on the synthesis of a zeroing barrier function characterizing the safe set based on safe and unsafe sample measurements, e.g., from perception data in navigation applications. Prior work formulated a supervised machine learning algorithm whose solution guaranteed the construction of a zeroing barrier function with specific level-set properties. However, it did not explore the geometry of the neural network design used for the synthesis process. This manuscript describes the specific geometry of the neural network used for zeroing barrier function synthesis, and shows how the network provides the necessary representation for splitting the state space into safe and unsafe regions.
Rapid Construction of Safe Search-Trees for Spacecraft Attitude Planning

This paper adapts the rapidly-exploring variant of invariant-set motion planner (ISMP) for spacecraft attitude motion planning and control. The ISMP is a motion-planning algorithm that uses positive-invariant sets of the closed-loop dynamics to find a constraint admissible path to a desired target through an obstacle filled environment. We present four mathematical results that enable the sub-routines used to rapidly construct a search-tree for the ISMP. These mathematical results describe how to uniformly sample safe quaternions, how to find the nearest orientation in the search-tree, how to move the sampled orientation to form an edge, and how to scale the invariant set to guarantee constraint admissibility. We present simulation results that demonstrate the ISMP for spacecraft attitude motion planning.
Certified Robust Control under Adversarial Perturbations

Autonomous systems increasingly rely on machine learning techniques to transform high-dimensional raw inputs into predictions that are then used for decision-making and control. However, it is often easy to maliciously manipulate such inputs and, as a result, predictions. While effective techniques have been proposed to certify the robustness of predictions to adversarial input perturbations, such techniques have been disembodied from control systems that make downstream use of the predictions. We propose the first approach for composing robustness certification of predictions with respect to raw input perturbations with robust control to obtain certified robustness of control to adversarial input perturbations. We use a case study of adaptive vehicle control to illustrate our approach and show the value of the resulting end-to-end certificates through extensive experiments.
Differentially Private Games via Payoff Perturbation

In this paper, we study network games where players are involved in information aggregation processes subject to the differential privacy requirement for players’ payoff functions. We propose a Laplace linear-quadratic functional perturbation (LLQFP) mechanism, which perturbs players’ payoff functions with linear-quadratic functions whose coefficients are produced from truncated Laplace distributions. For monotone games, we show that the LLQFP mechanism maintains the concavity property of the perturbed payoff functions, and produces a perturbed NE whose distance from the original NE is bounded and adjustable by Laplace parameter tuning. We focus on linear-quadratic games, which is a fundamental type of network games with players’ payoffs being linear-quadratic functions, and derive explicit conditions on how the LLQFP mechanism ensures differential privacy with a given privacy budget. Lastly, numerical examples are provided for the verification of the advantages of the LLQFP mechanism.
Distributed Point-Mass Filter with Reduced Data Transfer Using Copula Theory

This paper deals with distributed Bayesian state estimation of generally nonlinear stochastic dynamic systems. In particular, distributed point-mass filter algorithm is developed. It is comprised of a basic part that is accurate but data intense and optional step employing advanced copula theory. The optional step significantly reduces data transfer for the price of a small accuracy decrease. In the end, the developed algorithm is numerically compared to the usually employed distributed extended Kalman filter.
Sensor Safety and Multi-Objective Satellite Control under Nonlinear Dynamics

The safe operation of satellites is critical as the space domain becomes more cluttered with resident objects. Controller synthesis is a technique used to automatically generate correct-by-construction controllers that guarantee a system will satisfy some requirements, such as safety. In this work, we cast the safe satellite operation problem as a controller synthesis problem, and propose an algorithm that synthesizes full-state control of a satellite. This is done by decoupling the translational control from the attitude control. We deploy this algorithm in a close-proximity scenario and show that the synthesized controller satisfies our requirements and guarantees the safety of a chaser satellite.
NAPVIG: Local Generalized Voronoi Approximation for Reactive Navigation in Unknown and Dynamic Environments

In this paper, we propose a novel online approach for reactive local navigation of a robotic agent, based on a fast approximation of the Generalized Voronoi Diagram in a neighborhood of the robot’s position. We consider the context of an unknown environment characterized by some narrow passages and a dynamic configuration. Given the uncertainty and unpredictability that affect the scenario, we aim at computing trajectories that are farthest away from every obstacle: this is obtained by following the Voronoi diagram. To ensure full autonomy, the navigation task is performed relying only upon onboard sensor measurement without any a-priori knowledge of the environment. The proposed technique builds upon a smooth free space representation that is spatially continuous and based on some raw measurements. In this way, we ensure an efficient computation of a trajectory that is continuously re-planned according to incoming sensor data. A theoretical proof shows that in ideal conditions the outlined solution exactly computes the local Generalized Voronoi Diagram. Finally, we assess the reactiveness and precision of the proposed method with realistic real-time simulations and with real-world experiments.
Distributed Interval Type-2 Fuzzy Filtering for Wireless Sensor Networks With Intermittent Measurements

Though wireless sensor networks (WSNs) help to enhance data fusion accuracy, measurement exchanges between sensors are not always reliable due to inherent open communication channels. In this paper, a distributed interval type-2 (IT2) fuzzy filter is presented in the context of WSNs with intermittent measurements. Firstly, IT2 fuzzy models are employed to formulate one type of nonlinear systems with parameter uncertainties. Then, dual random data packet dropouts phenomena are considered, including the measurements transmitting from sensors to data fusion centers, and the measurements transmitting from data fusion centers to distributed filters. Bernoulli variables are adopted to depict the random data packet dropouts. Furthermore, to guarantee the robust mean-square asymptotic stability of the filtering error system, less conservative sufficient conditions are derived to seek for distributed filter gains. Finally, simulation results on Henon mapping systems with parameter uncertainties verify the robustness of the presented distributed fuzzy filter for WSNs with intermittent measurements.