Reviewerly/exHarmony huggingface dataset:
Temporal Waypoint Navigation of Multi-UAV Payload System using Barrier Functions

Aerial package transportation often requires complex spatial and temporal specifications to be satisfied in order to ensure safe and timely delivery from one point to another. It is usually efficient to transport versatile payloads using multiple UAVs that can work collaboratively to achieve the desired task. The complex temporal specifications can be handled coherently by applying Signal Temporal Logic (STL) to dynamical systems. This paper addresses the problem of waypoint navigation of a multi-UAV payload system under temporal specifications using higher-order time-varying control barrier functions (HOCBFs). The complex nonlinear system of relative degree two is transformed into a simple linear system using input-output feedback linearization. An optimization-based control law is then derived to achieve the temporal waypoint navigation of the payload. The controller’s efficacy and real-time implementability are demonstrated by simulating a package delivery scenario inside a high-fidelity Gazebo simulation environment.
Consolidating user data from social networks using Machine Learning and Serverless Cloud

Social Networks are integral part of our lives. Each of them has application programming interface (API) to access its data. People willingly store their private information, photos, thoughts and locations in numerous forums. We decided to determine how much information we could obtain about a person using automatic cloud serverless architectures, social network’s APIs and advanced Data Science models and algorithms. The paper shows how vulnerable privacy is and how easy it is to consolidate users’ information using modern cloud technologies. We were able to obtain geolocations, friends, similar profiles, predict influencers, and even predict missing friends using Machine Learning graph models. At the end of the day, people should always guard their private information and innovative social platform corporations should carefully think about what data could be given to third parties.
A Robust Diabetes Mellitus Prediction System Based on Federated Learning Strategies

In recent years, diabetes mellitus has emerged as one of the most prevalent diseases worldwide. This disease can be caused by various factors, and individuals often visit diagnostic centers and consult with doctors to identify the root cause. Fortunately, Machine Learning (ML) techniques have proven effective in predicting and detecting diabetes. However, developing robust ML models requires access to large datasets obtained from various healthcare systems. This poses significant privacy and confidentiality concerns, as sharing personal health data can be risky. The primary objective of this study is to design a robust diabetes mellitus prediction system using an ML-based binary classification approach along with federated learning strategies. The aim is to develop a system that can deliver performance comparable to centralized learning while ensuring data privacy and confidentiality. The results demonstrate that federated learning can provide comparable performance to centralized learning, thus making it a promising candidate for developing robust diabetes prediction models. Additionally, the use of federated learning strategies can help overcome the privacy and confidentiality concerns associated with large healthcare datasets from various healthcare systems. The findings of this study suggest that machine learning, particularly federated learning, can significantly contribute to the development of effective diabetes prediction models while addressing privacy and confidentiality concerns.
Data Falsification Attacks on Distributed Multi-Object Tracking Systems

The paper considers the problem of propagation and detection of false information in a distributed sensor network tasked with multi-object tracking. Leveraging the framework of multi-object tracking by means of the Probability Hypothesis Density (PHD) filter, the papers contributes twofold. First, we proposed a new byzantine attack called the overconfident data falsification attack that exploits the knowledge of the data fusion protocol to feed the network with false low uncertainty estimates. Second, we devise a defense strategy within the fusion protocol by introducing time-varying fusion weights that use an inter-agent trust measure based on the Beta reputation system to decide the level of information merging from each neighbouring agent.
Differentially Private Set-Based Estimation Using Zonotopes

For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise from disclosing sensitive measurement signals to a cloud estimator that predicts the system state. To solve this issue, we propose a differentially private set-based estimation protocol that preserves the privacy of the measurement signals. Compared to existing research, our approach achieves less privacy loss and utility loss using a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach. Zonotopes, a less conservative form of set representation, are used to represent estimation sets, giving set operations a computational advantage. The privacy-preserving noise anonymizes the centers of these estimated zonotopes, concealing the precise positions of the estimated zonotopes.
A Novel Line of Sight Constraint for Mixed-Integer Programming Models with Applications to Multi-Agent Motion Planning

Mixed-Integer Programming (MIP) is an established method used to develop Multi-Agent System (MAS) motion planning algorithms under particular requirements, such as Line of Sight (LOS) connectivity. However, maintaining the tractability of the ensuing models is a known issue. In this paper, we propose a novel sufficient condition for LOS between agents of a MAS and discuss its implementation in a (MIP) model as constraints. The functionality of the proposal is demonstrated through simulations in a connectivity-constrained motion planning and decision-making problem. A comparison between the novel method and an earlier approach shows that the new proposal requires fewer optimization variables and constraints to be implemented. Finally, we employ Monte Carlo simulations to demonstrate that this reduction in the complexity of the models consistently decreases the time required to solve them.
An adaptive mesh dynamic programming algorithm for robotic manipulator trajectory planning

An adaptive mesh initialisation algorithm is proposed to compute a collision free trajectory of a robotic manipulator. To this end, we model the problem as a discrete optimal control problem in the joint space. Constraints are imposed on the joint positions and velocities. Moreover, nonlinear geometric collision avoidance constraints account for a detailed obstacle and robot geometry, imposing additional intricacies on the problem. The problem is solved numerically using a backward Semi-Lagrangian dynamic programming method in conjunction with a penalization of the nonconvex collision constraints, as proposed in [1]. Further, to adhere to the curse of dimensions an iterative algorithm is proposed to locally refine the initial state space discretisation. The main contribution of this paper is the introduction of a refinement criterion, which is independent of the value function and solely based on a signed distance function estimated from the collision geometry. Optimal trajectories resulting from adaptive and equidistant grid structures are compared and evaluated for the problem of a robotic manipulator operating in a dynamic environment.
Distributed finite-time observer for LTI systems: A kernel-based approach

The problem of distributed state estimation of a linear-time-invariant (LTI) system is addressed in this paper. Given a directed communication network, the full state vector can be reconstructed at an appointed node under the assumption of an open Hamiltonian path. By a suitably designed coordinate transformation, the initial conditions of the subsystems can be estimated successively along the path. Thanks to the Volterra integral operator induced by non-asymptotic kernel functions, the estimation task at each agent can be achieved within a predefined finite time and transmitted to the next node. As such, the instant state vector can be reconstructed at the end node after a finite time interval. Such a scheme is prone to reduce the communication burden. Extensive numerical examples are conducted to verify the effectiveness of the proposed observer in both noise-free and noisy scenarios.
Can random proximal coordinate descent be accelerated on nonseparable convex composite minimization problems?

In this paper we consider convex composite optimization problems, where first term is smooth, while the second term is proximal easy but nonseparable (possibly non-smooth). For this problem we adapt the accelerated proximal coordinate descent algorithm from [7], initially developed for convex composite problems having the second term separable. We study convergence to a coordinate-wise minimizer point, and derive convergence rate in expected function values of order $\mathcal{O}((\mathrm{C}+(\mathbb{E}[S_{k}^{\#}])_{+})/k^{2})$. The first term, C, coincides with the usual constant appearing in the rate of accelerated gradient type methods, while the second one, $S_{k}^{\#}$, measures the nonseparability of the second term in the objective along the iterates. We conjecture that the second term, $S_{k}^{\#}$, is bounded as this is what we observe in all our numerical simulations and that coordinate descent can be accelerated.
Symbolic control applied to miniature quadcopter mission guidance

Being a fully algorithmic procedure, symbolic controller synthesis offers weighty advantages over other established synthesis procedures. In fact, the returned controllers provably enforce the given specification in the control loop making verification steps obsolete. However, the curse-of-dimensionality prevents this scheme from being applied to industrial problems. Applications to real experiments are indeed rare. In this note, we demonstrate how to utilize symbolic optimal control in order to control miniature quadcopters at the level of mission guidance. Specifically, a firefighting scenario using a Crazyflie 2.1 drone is considered, which involves reach-avoid and reach-and-stay control tasks. Furthermore, we present a runtime monitor, automatically derived from the synthesized symbolic controller. Based on the methodologies of plan recognition, this monitor observes the drone’s flightpath and infers the current controller mode. Thus, it is able to predict the upcoming manoeuvres of the drone.
Coverage patterns generated by two unicycles pursuing each other

This paper studies the trajectory of a pair of unicycles pursuing each other. The control law depends only on the distance between them. The resulting paths trace hypotrochoidal patterns. These patterns are suitable for coverage path planning and can be utilized in surveillance, patrolling, demining, and similar applications. We find the conditions under which unicycles generate the patterns. We also design the controller gain that ensures the patterns cover a region with a specified maximum and minimum radius. The results are validated in simulations.
Online Learning of Safety function for Markov Decision Processes

In this paper, we aim to study safety specifications for a Markov decision process with stochastic stopping time in an almost model-free setting. Our approach involves characterizing a proxy set of the states that are near in a probabilistic sense to the set of unsafe states - forbidden set. We also provide results that relate safety function with reinforcement learning. Consequently, we develop an online algorithm based on the temporal difference method to compute the safety function. Finally, we provide simulation results that demonstrate our work in a simple example.
Robust Phased Array Navigation in Reflective Prone Areas

Navigation with a Phased Array Radio System (PARS) is prone to performance degradation due to radio wave reflections of the directed communication beam on reflective surfaces. This work presents a navigation algorithm, based on the Error State Kalman Filter (ESKF) and an outlier rejection scheme, to better handle such outliers, and thus improve navigation robustness. We show, with experimental data, that this algorithm results in an improved state estimation quality when radio beam reflections are present.
Discrete-Time Kalman Filter Error Bounds in the Presence of Misspecified Measurements

The performance of a discrete time Kalman filter in the presence of a misspecified measurement equation is considered. Analytical and easily calculable numerical bounds for the increment in filtering error energy are provided in an adversarial setting by formulating a high-dimensional optimization problem which is solvable via its Lagrange dual, a scalar convex optimization problem. The performance bounds are obtained for finite and infinite horizons.
Snacks: a fast large-scale kernel SVM solver

Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear statistical learning tools, such as Ridge Regression or Support Vector Machines. However, standard kernel methods suffer from a quadratic time and memory complexity in the number of data points and thus have limited applications in large-scale learning. In this paper, we propose Snacks, a new large-scale solver for Kernel Support Vector Machines. Specifically, Snacks relies on a Nyström approximation of the kernel matrix and an accelerated variant of the stochastic subgradient method. We demonstrate formally through a detailed empirical evaluation, that it competes with other SVM solvers on a variety of benchmark datasets.
Distributed Regression by Two Agents from Noisy Data

We consider the problem of learning functions by two agents and a fusion center from noisy data. True data comprises of samples of an independent variable (input) and the corresponding value of a dependent variable (output) collectively labeled as (input, output) data. The data received by the agents, both the input and output data, are corrupted by noise. The objective of the agents is to learn a mapping from the true input to the true output. We formulate a general regression problem for the agents followed by the least squares regression (LS) problem. We prove a stochastic representer theorem for the general regression problem and subsequently solve the LS problem. The functions learned by the agents are transmitted to the fusion center where an optimization problem is formulated to fuse the functions together, which is then declared as the mapping. As an example, the methodology developed has been applied to the data generated from a transcendental function.
Machine Learning based Cybersecurity Technique for Detection of Upcoming Cyber Attacks

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, Significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. Several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks.
Next Word Prediction While Typing using LSTM

Predicting the next word in a sentence is the basic goal of word prediction. Its benefits, such as auto-correction, which is frequently utilized in emails and text messages, are already well-known to many of us. It is also employed in Google Search and MS Word, where the following word is predicted based on past searches or a worldwide search is performed. One application of natural language processing (NLP), commonly referred to as language modeling, is next-word prediction. This model was created with the intention of swiftly predicting the third word that would come after the input sentence. Long short-term memory (LSTM) equipped recurrent neural networks (RNNs) are able to recognize previous context and anticipate terms that may be useful in generating sentences. This method makes use of letter-by-letter prediction, which assumes that a word will be formed by the letters in the correct order.
Navigation in Time-Varying Densities: An Operator Theoretic Approach

This paper considers the problem of optimizing robot navigation with respect to a time-varying objective encoded into a navigation density function. We are interested in designing state feedback control laws that lead to an almost everywhere stabilization of the closed-loop system to an equilibrium point while navigating a region optimally and safely (that is, the transient leading to the final equilibrium point is optimal and satisfies safety constraints). Though this problem has been studied in literature within many different communities, it still remains a challenging non-convex control problem. In our approach, under certain assumptions on the time-varying navigation density, we use Koopman and Perron-Frobenius Operator theoretic tools to transform the problem into a convex one in infinite dimensional decision variables. In particular, the cost function and the safety constraints in the transformed formulation become linear in these functional variables. Finally, we present some numerical examples to illustrate our approach, as well as discuss the current limitations and future extensions of our framework to accommodate a wider range of robotics applications.
Content Restricting Age Predictor System using Artificial Intelligence

Online safety, particularly for children, is a significant concern in today’s digital age. The proposed project aims to develop a combined framework to prevent children from accessing the age restricted content on the internet. The system involves utilizing a computer vision technique called Blob from image to detect face in the image. To achieve accurate age detection, the proposed approach employs a deep learning method called a convolutional neural network (CNN). CNN is particularly good at processing and recognizing images and has multiple layers, including convolutional, pooling, and fully linked layers, which extract the binary values and compare it with available data to provide the user’s age. The proposed approach uses the open-source automation tool, Selenium, to control access to websites based on the predicted age of the user. By restricting access to inappropriate content based on age, this approach provides a reliable way to safeguard children’s online experience. The combined framework of face recognition and age detection, in conjunction with the OpenCV and the Selenium tool, offers a reliable and effective approach to age detection and access control on the internet.
Unifying Governance, Risk and Controls Framework Using SDLC, CICD and DevOps

This paper aims to study different software development frameworks and propose an efficient and comprehensive framework for handling Software Development Life Cycle (SDLC) in an IT Project. Risks and controls, work products and IT Audit risk parameters for each phase are also analysed. Furthermore, it covers Continuous Integration Continuous Deployment/Deliver (CICD) during support to the project along with management of code, branching strategies, storage of code, and CICD Pipelining. The paper also introduces Development-Operations (DevOps) and teaming structures to orchestrate project’s success. It also depicts the importance of cross functional teams in a DevOps environment.
Image Aesthetics Evaluation Algorithm based on Visual Attention Model

In recent years, many researchers at home and abroad have paid more and more attention to the in-depth study of the task of image aesthetic quality evaluation, because image aesthetic quality evaluation can play a potential role in many practical applications. Then, this paper proposes the novel image aesthetics evaluation algorithm based on visual attention model. Firstly, the novel 2D OTSU image segmentation algorithm is proposed to conduct the pre-processing taks of the captured images. Then, the novel visual attention model is designed, the proposed model uses the visual association and context double attention mechanism, uses visual association attention to ensure the visual coherence, and uses the context attention to obtain more complete semantic information, and the first step is the image segmentation. Furthermore, the novel evaluation of the image aesthetics is designed and tested. Through the visual model simulation and the ROC testing, the proposed algorithm is proven to be efficient.
Image Display using FPGA with BRAM and VGA Interface for Multimedia Applications

Using a Field-Programmable Gate Array, this work aims to develop a multimedia system, on-chip Block RAM (BRAM), and a Video Graphics Array (VGA) interface. The system will be centered around a VGA monitor that will display multimedia information. The BRAM will temporarily store the image data while the FPGA converts it to a format compatible with the VGA display. The system can produce 24-bit color at a resolution of 100x100. Verilog will be used to construct the hardware, whilst Xilinx will be used for simulation. To demonstrate how the system operates, it will be tested on an FPGA development board. This work aims to create a low-cost, high-performance approach for rendering and processing pictures for use in multimedia applications.
DeepASD Framework: A Deep Learning-Assisted Automatic Sarcasm Detection in Facial Emotions

The vibrant human-machine research provides space for assessing sentiments in facial emotions. Capturing apt sarcasm-related emotions, especially in online meetings or stress interviews, is a challenging aspect. The purpose of this research is to apply deep learning algorithms to effectively assess the sarcasm in human facial emotions in an automatic fashion using the proposed Deep Learning-Assisted Automatic Sarcasm Detection (DeepASD) framework. Our framework trains facial sarcasm-related emotions from internet sources and applies deep learning algorithms to perform visual sarcasm detections. The proposed framework processes algorithms on edge-enabled compute nodes, including GPU-based machines. We evaluated the DeepASD framework using various deep learning algorithms such as EfficientNet, XceptionNet, InceptionNet, ResNet, DenseNet, ConvNext, MobileNet, and their variants; and, we observed that Mobilenetv3 achieved a better learning accuracy of 96.44 percent and energy consumption of 7959 Joules using minimal trainable/non-trainable parameters while detecting sarcasm in facial emotions. Our work will be beneficial for online interviewers, business enthusiasts, or future robotic machine developers for accomplishing accurate decisions considering sarcasm in facial emotions.