Reviewerly/exHarmony huggingface dataset:
Successful Simple Side Channel Analysis: Vulnerability of an atomic pattern $kP$ algorithm implemented with a constant time crypto library to simple electromagnetic analysis attacks

Elliptic curve cryptosystem is a widely-used public key cryptosystem that is vulnerable to physical attacks, especially to Side Channel Analysis (SCA) attacks. This paper investigates a version of the atomic patterns <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{kP}$</tex> algorithm for elliptic curves over prime finite fields. The atomic patterns for Elliptic Curve (EC) point doublings and additions are well-known countermeasures against simple SCA attacks. We implemented a binary left-to-right <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{kP}$</tex> algorithm corresponding to the atomic patterns as proposed by Rondepierre and using the FLECC open-source cryptographic library. The library provides constant-time functions for the implementation of different mathematic operations in prime finite fields, making it suitable for SCA-aware implementations of cryptographic protocols, at a theoretical level. We were able to reveal the scalar k processed during a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{kP}$</tex> execution analyzing the measured electromagnetic trace of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{kP}$</tex> execution. We determined at least two parts in the measured trace where the distinction between EC point doublings and EC point additions is good observable.
An overview of reinforcement learning techniques

Writing control code for a system where the optimal solution is not known in advance can be a very time-consuming process. The Artificial Intelligence (AI) methods typically involve designing a set of rules which can be effective in situations where the problem is precisely defined and well understood. As in real world problems the optimal solution is rarely known, the reinforcement learning framework which incorporates trial and error attempts can be used. Reinforcement learning (RL) is a machine learning technique that involves training an agent to make decisions which are based on the feedback it receives from the environment. One important decision to make when designing an RL system is whether to use a single or multiple agents. This decision depends on the type of problem that needs to be solved as well the environment complexity. Having a goal that can be achieved by a single agent (one player) it is recommended to use single-agent RL while if there is a need for coordination between multiple agents (players) then a multi-agent approach is recommended. In this article, the differences between single agent RL and multi agent RL techniques, as well as their advantages and disadvantages have been presented, and insights are provided into when one approach may be more appropriate than the other.
A comparative analysis on prototype-based clustering methods

In the machine learning domain, clustering is a fundamental unsupervised learning operation which aims to partition the instances of a dataset into clusters (i.e, groups, subsets) such that instances within the same cluster are much similar to each other and much different from the other clusters. In the broad spectrum of clustering methods, prototype-based methods characterize each cluster through a prototype (i.e. centroid) and a relocation scheme is employed to iteratively redistribute the instances into the clusters, guided by an objective function. In this paper, several prototype-based methods are brought into focus, including K-means, K-medoids, K-medians, Fuzzy C-means and Kernel K-means. These algorithms are experimentally analyzed on several original benchmark datasets, distorted benchmark datasets and synthetic datasets. The comparative analysis is oriented in two main aspects: accuracy and sensitivity to noise and outliers.
On Verifying Temporal Properties of Configurable Software

Configurable software systems today are becoming very popular. They can produce a set of related programs by choosing preferable features (configuration options) at compile-time. Many configurable programs are applied in safety-critical areas, so their formal verification is of primary importance. In this paper, we introduce a new, interesting approach for verifying temporal properties of configurable programs that contain numerical features. We extend an abstract interpretation-based static analysis framework for proving temporal properties of single programs that uses a decision tree abstract domain. In particular, we define a novel lifted decision tree domain for proving temporal properties of configurable programs. We have developed a lifted static analyzer for proving temporal properties of #if-enriched C configurable programs. The evaluation on a range of loop benchmarks from SV-COMP confirms the effectiveness of our approach.
A Multi-Objective Approach for Unmanned Aerial Vehicle Mapping

Many commercial applications require aerial mapping with multiple UAVs. Mapping is a mission planning problem which requires meeting a set of constraints while optimizing key factors that may conflict with each other, such as fuel/battery consumption, make-span, and the associated risks. Solving this Multi-Objective Optimization (MOO) will therefore result in a set of trade-offs (Pareto optimal solutions) that will be supplied to a decision-maker. Given that the Pareto set can be of a very large size, we propose a Multi-criteria Decision Making (MCDM) system that relies on user’s preferences to bring down this set to a manageable size. More precisely, the proposed system captures user’s qualitative preferences and uses them through the Fuzzy Vikor to filter and rank Pareto optimal solutions. The designed system is able to work with both or either fixed-wing and multi-rotor UAVs. To evaluate the performance of our system, we conducted a set of experimental simulations considering several scenarios. The findings show that fixed-wing UAVs have higher energy consumption and mission time than multi-rotors due to Dubin’s turns, assuming both types have the same charging/fueling endurance and the same velocity. Lastly, it is found that heterogeneity will not always lead to a better mission duration than homogeneous UAV fleets.
Obstacle Avoidance Based on the Null Space Control Approach for a Formation of an Aerial and a Ground Robot

This paper complements a previous study on obstacle avoidance using the null space-based behavioral approach to autonomously guide a formation composed of a differential-drive wheeled platform and an unmanned aerial vehicle, to overtake obstacles modeled as potential fields. The highest priority, regarding the null space behavioral control, is assigned to the task of overcoming an obstacle, with the lowest priority assigned to moving the formation to a destination point. The controller is designed considering the paradigm of virtual structure, which is the three-dimensional straight line linking the robots. This approach allows controlling the robots to move in a coordinate way, leading the formation to reach the desired point while keeping the proposed rigid structure. The obstacle avoidance proposal is adopted for the ground and the aerial robots. When the ground robot maneuver to avoid an obstacle in the ground the position of the point of interest for control also varies, since it is in the ground vehicle, so that the aerial vehicle does not need to break the formation, continuing "attached" to the ground vehicle during the maneuver. However, when the aerial robot faces an obstacle, the formation behaves differently. The formation shape is not guaranteed to be preserved during the maneuver of the aerial robot to avoid the obstacle. This is the behavior this paper proposes to discuss: the effect of the null space-based behavioral control over the navigation of the formation. The scenario for this case study is an automated warehouse, inside which several ground platforms and aerial vehicles are moving to suitably store goods, possibly with boxes in the ground, also obstacles for the ground vehicle.
Optimized Compression Algorithm Based on Binary Analysis of Independent Components

In modern conditions, the creation of methods for optimizing data storage and transmission is of great relevance. One of these methods can be considered a binary analysis of independent components, which allows to process information and optimize the process during data compression. This, in turn, makes it possible to obtain a methodology of data compression. In this paper, various optimization methods are considered, such as the Huffman method, splain-wavelet transform, binary analysis of independent components, on the basis of which the compression method is built.
SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm

Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field Programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).
Applicability of Deep Learning Model Trainings on Embedded GPU Devices: An Empirical Study

The wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-Things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. The results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. The results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. These findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU.
Experimental analysis of Radar/Optical track-to-track fusion for non-cooperative Sense and Avoid<sup>*</sup>

In the framework of non-cooperative Sense and Avoid solutions, major attention is reserved to the design of sensing strategies which can enable fast and reliable identification of possible near collision threats, by exploiting passive or active exteroceptive sensors. To overcome the limits of standalone technological architectures and provide more accurate tracking solutions, which can be used to improve conflict detection and thus better support avoidance strategies, data fusion approaches can be considered. Hence, this work proposes a radar/visual fusion method based on the track-to-track fusion approach. The strategy is tested on data gathered during ground-to-air experimental flight tests involving a small UAV commanded to fly near collision approach geometries with respect to a multi-sensor setup placed on the ground. Results collected analyzing three different encounters show that the fusion solution allows retrieving meter and sub-degree level accuracies in the intruder range and bearing estimation, respectively, while ensuring declaration ranges of about 500 meters.
Deep Learning-Based Real-Time Body Measurements Using Device Camera

In recent years, the use of deep learning techniques has gained widespread popularity in the field of computer vision, especially for tasks such as object detection and recognition. In this research paper, we present a deep learning-based approach for the real-time estimation of human body measurements using device cameras, intending to enhance the online shopping experience and reduce concerns related to size selection. The proposed method based on convolutional neural networks (CNNs) is evaluated and the results are presented.
Solving Vehicle Routing Problem for Unmanned Heterogeneous Vehicle Systems using Asynchronous Multi-Agent Architecture (A-teams)

Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to cooperatively perform tasks over wide areas. Such a cooperation can be achieved efficiently by solving a path planning problem. On top of solving a path planning problem, the problem of routing an heterogeneous set of vehicles in an optimal fashion is quite challenging. In order to solve the computationally expensive path-planning problem in a reasonable time, we created a two-level optimization approach with heuristics. At the outer level, the UGV route is parameterized by considering which set of locations to visit in the scenario and the UGV wait times to recharge UAVs and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judiciously in order to create high quality solutions. We explore two methods for tuning the free UGV parameters: (1) a Genetic Algorithm (GA), and (2) Asynchronous Multi-agent architecture (A-teams). The A-teams uses multiple agents to create, improve, and destroy solutions. The parallel asynchronous architecture enables A-teams to quickly optimize the parameters. Our results on test cases show that the A-teams produces similar solutions as GA but is 2-3 times faster.
Creating Trustworthy AI for UAS using Labeled Backchained Behavior Trees

Unmanned Aerial Systems (UAS) have the potential to provide cost effective solutions to many problems, but their control systems need to be safe and trustworthy in order to realize this potential. In this paper we show how behavior trees (BTs), created using backward chaining and using a particular way of labelling subtrees, can be used to meet the requirements of trustworthy autonomy described in a US air force (USAF) report. Behavior Trees represent a modular, reactive and transparent way of structuring a control system that is receiving increasing interest in the UAS community. While their safety and efficiency have been investigated in prior research, their connection to trustworthy autonomy has not been explored. A set of guidelines for trustworthy autonomy, taken from a USAF report, include items such as: being similar to how humans parse problems, being able to explain its reasoning in a concise way, and being able to be visualized at different levels of resolution. We propose a new way of deriving explanations that conform to these guidelines, using a particular labeling of subtrees in the BT combined with a structured design methodology called backward chaining. The proposed approach is illustrated in a detailed example.
Improved path planning algorithm of an informed RRT algorithm in 3D space

The main purpose of drone flight is to find an optimal path without colliding with obstacles. The key point is to design a search algorithm. Path planning for searching a 2-dimensional (2D) map has been studied extensively and reached a mature stage. For a higher-dimensional configuration space, it is quite challenging. In this paper, a sampling based path planning method is proposed. It uses the rapidly-exploring random trees (RRT) concept. An improved guidance is proposed for reducing the search space in the present algorithm in a 3D clutter environment. Simulation is given to show the effectiveness of the proposed method.
New Design Methodology of an Advanced Optimal Space/Spatial-Frequency Filter

New methodology in the creation of the region of support of the optimal (Wiener) filter used in the estimation of two-dimensional (2D) nonstationary signals is considered. The methodology is based on the symmetrical spreading of the filter's region of support around the detected local frequency (LF) of the estimated 2D signal and on simultaneous inclusion (in the region of support creation) of points having equal distance from the detected LF. The proposed filter provides signal reconstruction free of distortion of the original (noiseless) signal.
Intelligent Traffic Light Solution for Green and Sustainable Smart City

This research aims to develop a smart traffic light system that can improve traffic flow in urban areas. The proposed system uses sensors, cameras, and software to adjust the timing of traffic signals based on real-time traffic conditions. In this study, a Raspberry Pi 4 and MATLAB software was used to build the smart traffic controller. The detection part of the system involves several steps, including removing noise and retrieving information to calculate the number of cars detected. The system then switches traffic lights based on the detected car count. The MATLAB Image Acquisition and Computer Vision toolboxes were used to obtain and analyze the video frames received from the connected cameras. The detector uses the Gaussian Mixture Model to suppress frequently occurring features and to detect abnormal features, which are then used to detect changes caused by moving objects. Morphological operations are used to remove noise from the output. Finally, the system counts the cars detected by the Foreground Detector and switches the traffic lights accordingly. The proposed approach can help reduce traffic congestion and improve the overall flow of traffic in urban areas.
Surveying the security of access systems in Uppsala, Sweden

Today, many people use several access systems on a daily basis without paying attention to the fact that many of the technologies in use are obsolete and insecure. For example, there are published attacks against all generations of MIFARE Classic cards and cloning a MIFARE Ultralight card is trivial. In this paper, we look into the security of several access systems in a student town Uppsala in Sweden. We evaluate the security of the cards or tags used for access as well as some of the security of the systems themselves. We present a detailed report on the configurations, including any vulnerabilities, while also presenting attacks exploiting these vulnerabilities, as well as real-life examples of how these attacks can be dangerous to the end user. We compare these systems to a well-designed system in the same city and suggest fixes for all vulnerabilities we found. When presenting the potential fixes, we pay attention to the ease and cost of the fixes.
Enhancing Human-Drone Interaction with Human-Meaningful Visual Feedback and Shared-Control Strategies

Recent developments in the capabilities of unmanned aerial vehicles (UAVs) have made them suitable for use in various industrial settings. Their ability to access difficult and remote locations, as well as providing remote manipulation and visual inspection capabilities, make them valuable for various industrial applications. However, operating UAVs can be challenging, particularly in cluttered environments. This research aims to enhance the teleoperation experience by providing human-meaningful information on the remote user interface, thereby improving the operator’s situational awareness. Shared autonomy routines utilizing the previously collected information are also developed to further assist the operator with challenging control tasks. The proposed system has been tested in simulated environments and on actual hardware.
Side-Channel Analysis of Cryptographic Processor CEC 1702

Cryptography is omnipresent in our daily life, as we need it for trusted authentication (e.g., in access systems), secure communication, ensuring data integrity and confidentiality, and many more. However, even if mathematically secure ciphers are used, the device running the cryptographic algorithms is still vul-nerable to side-channel attacks that may reveal the secrets. These attacks exploit the fact that power consumption or electromagnetic emanation of the device depends on processed data. To prevent such an attack, the designer must employ countermeasures, such as masking, hiding, or shuffling. In this paper, we focus on Microchip CEC1702 microcontroller that supports common cryptographic operations in hardware. We analyze the resistance of its AES accelerator against correlation power analysis (CPA). We analyzed 100 million power traces by first-order CPA and univariate second-order CPA. In neither case did we find any vulnerability.
Package Delivery Based on the Leader-Follower Control Paradigm for Multirobot Systems

This paper proposes the adoption of the leader-follower control paradigm as the support to control an unmanned aerial vehicle (UAV), in this case a quadrotor, in the accomplishment of a package-delivery task. The UAV takes-off from a ground vehicle, here an unmanned ground vehicle (UGV) represented by a unicycle robot, goes to the delivery point, over which it stays hovering for a while, and then goes back to the current position of the ground vehicle and lands on it, thus completing the delivery task. In the first step of the delivery task, to go to the delivery point, the delivery drone considers the delivery point as the formation leader, and composes a leader-follower formation with it, suitable to the delivery procedure. After, the delivery drone establishes a leader-follower formation with the ground vehicle, suitable to allow it to land on the ground vehicle. The main difference between these two leader-follower formations is that in the first case the leader is a static one, whereas in the second case the leader is a moving platform. Results of a real experiment, in lab scale, are also shown, which validate the adopted strategy.
Hierarchical Cooperative Assignment Algorithm (CAA) for mission and path planning of multiple fixed-wing UAVs based on maximum independent sets

Mission planning can be solved as a combinatorial optimization problem which involves computing the path and selecting the agents that will be assigned to a given task. In scenarios with multiple UAVs, the proper control of the vehicle to achieve the proposed path is also a relevant task. This paper proposes a solution to the mission planning problem that involves probabilistic search and optimization of path planning and a graph-based combinatorial solution of task assignment. In addition, we propose an invariant model predictive controller based on the SO(2) manifold to deal with the execution of UAV missions. Our results demonstrate that the algorithm is capable of assigning all agents to tasks and computing a viable and smooth trajectory for the UAVs to follow. Also, the control strategy is capable of guiding the vehicle through the trajectories generated from a start position to the task location.