Reviewerly/exHarmony huggingface dataset:
Coarse-grained Control Flow Integrity Check for Processors with Sliding Register Windows

Control Flow Integrity (CFI) checks arean effective way to detect control flow manipulation attacks. Protection isachieved by combining a static analysis to create a control flow graph and codeinstrumentation for runtime checks. A coarse-grained CFI check in contrastrelies on a ruleset to check for the validity of the control flow. It can beused alongside with other techniques to protect a processor fromreturn-oriented-programming attacks. This paper presents the firstimplementation of a coarse-grained CFI check specifically for processors withsliding register window, instrumenting interrupt handles instead of theapplication itself. Additionally, the security of the method is improved bycombining it with a simple XOR obfuscation technique to make it harder forattackers to target remaining legal addresses. Two (software-only andhardware-assisted) implementations are presented and evaluated.
Development, model, simulation, and real test of a new fully actuated quadrotor

This study presents the design, construction, and testing of a fully actuated quadrotor UAV prototype named FlapPyr that utilizes the "+" arrangement to produce horizontal forces. Four flaps are installed beneath each main motor to capture the airflow from propellers and generate aerodynamic forces perpendicular to them. A control allocation matrix is determined by modeling the complete structure and forces. This UAV suits applications requiring position control while maintaining zero tilting angles. Both software in the loop simulations and real-world tests are conducted to evaluate the system’s performance. The real-world tests highlighted the system’s sensitivity to external air disturbances, which can be addressed through robust control laws. The developed code for this work is publicly available on GitHub, and a video showcasing the experiments is also provided.
A Fully-Actuated Drone with Rotating Seesaws

Standard drones are generally underactuated systems, an attribute that limits their maneuvering ability. This limitation is because of the inherent coupling between the total thrust direction and the angular state of the drone body. To decouple these quantities, we suggest using seesaws, which allow controlling the thrust direction independently. Unlike other structures based on additional actuators to tilt the thrust, our solution is not based on any extra actuator that does not contribute to the lifting force. The presented configuration is an octocopter with eight propellers and four seesaws. These results extend a former suggested structure based on a single seesaw.
H2AMI: Intuitive Human to Aerial Manipulator Interface

Aerial manipulators are unmanned aerial vehicles (UAVs) coupled with robotic manipulators (RMs). Their ability to interact with the environment and perform service and maintenance tasks in hard-to-reach places makes them interesting for a wide range of applications and industries. Human pose estimation relates to the problem of estimating human body pose. In this paper, we present an intuitive aerial manipulator human interface which enables the control of the aerial manipulator based on the operator’s movements. The operator’s motions are captured with an affordable depth camera and pose estimation is performed with a neural network. We have developed a system that enables intuitive decoupled control of the unmanned aerial vehicle and robotic manipulator. We compared two neural network architectures for the human pose estimation as a part of the H2AMI. System performance was verified in the simulation environment.
Quantum Resilient Public Key Cryptography in Internet of Things

With Internet of Things (IoT) becoming increasingly popular and widespread, the somewhat secure devices could potentially become completely insecure with the emergence of quantum computers. The problem is that a lot of IoT devices in crucial places are not secured and use unencrypted communication or use the current recommended encryption algorithms. This paper explores public key encryption (PKE) with post quantum cryptography (PQC) algorithm Kyber, despite it being usually used as key-encapsulation mechanism (KEM). The proposed approach has been evaluated experimentally. The conducted experiment encrypts the data on one scenario on the IoT device itself and in the other scenario the data is encrypted on a fog node. Obtained results are promising and suggest that quantum resilient public key cryptography is applicable in internet of things.
Multicopters Obstacle Avoidance by Learning Optical Flow with a Balance Strategy

Obstacle avoidance using onboard sensors is an important part of the safe and reliable navigation of autonomous aerial vehicles. For Micro aerial vehicles (MAVs), due to the extremely limited payload, it is a better choice to equip only one monocular camera. Although much attention had been paid to using optical flow to avoid obstacles mimicking the behavior of flying insects, these methods have met only limited success. Here, we propose a recognize-and-avoid method drawing lessons from the reactive obstacle avoidance methods. To let MAVs recognize the environmental conditions, we build an optical flow dataset for obstacle avoidance in the simulation environment and use a deep neural network to classify optical flow images into 5 labels. Then an avoidance policy is designed to mimic the "optical flow balance" strategy of flying insects. We analyze the proposed method in different simulation scenes and demonstrate the generalization of our method.
A PX4 Integrated Framework for Modeling and Controlling Multicopters with Til table Rotors

This paper presents a general control framework for multicopters equipped with tiltable rotors (tilting multicopters). Differently from classical flat multicopters, tilting multicopters can be fully actuated systems able to decouple position and attitude control. The proposed framework has been transparently integrated into the widely used PX4 control stack, an open-source controller for ground and aerial systems, to fully exploit its high-level interfaces and functionalities and, at the same time, simplify the creation of new devices with tilting propellers. Simulation tools have been also added to the PX4 simulation framework, based on its Software-In-The-Loop (SITL) system and a set of simulated experiments in a dynamic robotic simulator have been carried out to demonstrate the effectiveness of this system. Moreover, to demonstrate the usability of the proposed framework, initial experiments with a real platform have been carried out. The proposed control framework is accessible at the following link: https://github.com/prisma-lab/PX4_tilting_multicopters
Technological Approach in University Educational Activity

In the context of modern education, special attention is paid to the quality of pedagogical process, to the quality of a graduate student. To achieve this quality modern educational technologies within a technological approach are used in universities. The technological approach to education, which provides precise instrumental management of the pedagogical university process and guarantees educational aim achievement, is now being actively developed by the scientists. To improve the quality of educational process university teachers try to use different modern innovative technologies that are a system in which a pre-designed educational process is consistently realized to ensure the achievement of pedagogical goals, different pedagogical tasks, to increase the effectiveness of educational process, and to guarantee its high level. In this article different innovative technologies will be presented, such as the technology of collective creative activity by I.P. Ivanov that enables to develop creative characteristics of students, their ability to work in a team and others. An interactive technology is a type of the communicative technology that provides a feedback between subjects and objects of qualitative education. It's a special dialog form of pedagogical process, the purpose of which is to create comfortable conditions for learning, where a student feels his success, his intellectual consistency. The examples of using interactive technologies at English lessons in a technical university are given in the article. The results of the survey about the students' achievements when they are taught with the help of interactive technologies are presented in the article.
Bandwidth-aware Coverage Path Planning for Swarm of UAVs with Aerial Base Station

Coverage path planning (CPP) by multiple UAVs has been widely studied for its advantage in wide-area searches. For time-critical missions, a swarm of UAVs equipped with a high-resolution camera can be used to transmit live video streams to a ground control station (GCS). However, as the number of UAVs increases, communication between GCS and the UAVs becomes a challenge due to limited channel bandwidth. This paper presents a bandwidth-aware coverage path planning algorithm for UAV swarm that utilizes an aerial base station to efficiently handle the bandwidth limitations while ensuring aerial video quality, as measured by ground sample distance (GSD). By the proposed method, bandwidth-aware mission altitude and width for swarm CPP are found. The effectiveness of the proposed approach is demonstrated through simulations under various scenarios.
Time Series Approach for Visual Servoing Using Transformers

We introduce Visual servoing using a novel deep-learning time-series architecture to control an unmanned aerial vehicle (UAV) with a mounted camera to track a target consisting of a finite set of stationary points lying in a plane. Many visual servoing approaches use computer vision along with estimation algorithms, sensors, and actuators' feedback to solve tasks like, tracking, obstacle avoidance, and localization. Nowadays, deep neural networks are gaining popularity in such tasks owing to their accuracy, adaptability, and flexibility. We propose a solution that employs a time-series architecture to learn temporal data from sequential values to output the control cues to the flight controller. Because of its low computational expense, the solution is deployable on less powerful onboard computers present on the UAV, ensuring real-time tracking of the target. The solution is tested both in a simulation environment and in real life, outperforming the current state-of-the-art in terms of time efficiency and accuracy.
A pseudo-labelled ensemble inference for fast and efficient real field photovoltaic system’s power prediction

Clean energy solutions like solar power plants have been at the forefront of the collaborative global efforts of the world towards the sustainable development of society. This research paper explores the use of machine learning in predicting photovoltaic (PV) power generation. An accurate and reliable prediction of solar power generation is crucial for the efficient integration of clean energy solutions into the existing power grid. The most common approach for PV forecasting is the statistical approach, which captures the local weather, seasonal, and geographical effects on the PV system but has limited accuracy due to limited data. The physical approach uses complex physical models to simulate solar radiation and predict energy output, but it is time-consuming and unsuitable for short-term forecasting. This study proposed a 3-stage machine learning architecture that uses pseudo labelling to increase accuracy and efficiency in predicting power generated by PV modules. The architecture uses a light gradient boosting machine (LGBM) and Cuda random forest (cuRF) together, which improved the model performance and achieved a high accuracy score of 99.98% R2. This study addresses missing data points and improves accuracy using imputation and weighted ensemble techniques with a year's worth of institutional data. The proposed model outperforms other models tested in this study.
Program logic for higher-order probabilistic programs in Isabelle/HOL

The verification framework PPV (Probabilistic Program Verification) verifies functional probabilistic programs supporting higher-order functions, continuous distributions, and conditional inference. PPV is based on the theory of quasi-Borel spaces which is introduced to give a semantics of higher-order probabilistic programming languages with continuous distributions. In this paper, we formalize a theory of quasi-Borel spaces and a core part of PPV in Isabelle/HOL. We first construct a probability monad on quasi-Borel spaces based on the Giry monad in the Isabelle/HOL library. Our formalization of PPV is extended so that integrability of functions can be discussed formally. Finally, we prove integrability and convergence of the Monte Carlo approximation in our mechanized PPV.
A Novel Web Scraping Technique Used for Phuket Travel Industry Decision Support System

The Internet can provide an individual with a broad area of facts and data sources. However, data available on the Internet also brings a great challenge with its volume, high-speed generation, and unorganized characteristics. Moreover, the quality and validity of Internet data vary from different sources. This leads to difficulties in collecting and processing Internet data. Nevertheless, several data collection techniques have recently been developed in order to convert entire business to consumer (B2C) and business to business (B2B) systems into well-organized data. This paper presents web scraping with Python for collecting information on accommodations, hospitals, restaurants, spas, and gyms in Phuket. The paper presents scraping processes both with and without data cleaning. The difference between these two types is that web scraping without data cleaning does not need data to be stored all the time, which can make the process shorter. However, this shorter algorithm may carry a risk in case of computer failure during processing which will result in data loss, but the risk is very unlikely when considering the speed of data processing which is worth more. The information gathered from web scraping in this research was stored in a data warehouse for further data analysis and decision support for stakeholders in Phuket.
Comparing a friction-based uni-directional treadmill and a slip-style omni-directional treadmill on first-time HMD-VR user task performance, cybersickness, postural sway, posture angle, ease of use, enjoyment, and effort

Treadmills allow users to translate their real world walking into movement in virtual worlds. Conventional treadmills used for fitness can be repurposed for users to move around in virtual reality, but permit walking in only one direction (uni-directional). Treadmills that allow walking in any direction (omni-directional) are available, in which users walk-in-place and their feet slip on a low-friction surface. Given that users can experience cybersickness and postural instability in virtual reality, this study explored user performance and side effects of using two different walking platforms: a conventional uni-directional treadmill and a slip-style omni-directional treadmill (Virtuix OMNI). All participants (n = 46) played a game in which they collected items distributed around a virtual world. Participants moved around the virtual world by walking on the conventional uni-directional treadmill (for 10 minutes) and on the omni-directional treadmill (for 10 minutes), whilst in HMD-VR, with a 5-minute break between platforms. Measurements of cybersickness, task achievement, distance covered, posture, postural sway and user experience (ease, enjoyment, and effort) were collected. Participants experienced significantly higher total cybersickness after walking on the slip-style omni-directional treadmill compared to after walking on the uni-directional treadmill (p=.003), and significantly higher Nausea and Oculomotor cybersickness after walking on the slip-style omni-directional treadmill compared to after walking on the uni-directional treadmill (p<.001 and p=.005 respectively). Participants had lowest postural sway after using the uni-directional treadmill compared to after using the omni-directional treadmill (p=.052). Participants completed significantly more of the task (p<.001) and walked further (p<.001) on the uni-directional treadmill compared to the omni-directional treadmill, and their posture angle (back tilt angle) was significantly more forward on the omni-directional treadmill compared to the uni-directional treadmill (p<.001). Significant differences were found with participants reporting walking on the omni-directional treadmill to be more difficult (p<.001), require more effort (p<.001) and be less enjoyable (p=.005), and changing direction to require more effort (p=.010), than compared to the uni-directional treadmill. This study recommends that alternatives to slip-style locomotion treadmills be developed due the non-natural walking action required to use slip-style platforms and the consequent negative psychophysiological impacts of their use with HMD-VR.
Privacy-centered authentication: A new framework and analysis

The usage of authentication schemes is increasing in our daily life with the ubiquitous spreading Internet services. The verification of user's identity is still predominantly password-based, despite being susceptible to various attacks and openly disliked by users. Bonneau et al. presented a framework, based on Usability, Deployability, and Security criteria (UDS), to evaluate authentication schemes and find a replacement for passwords. Although the UDS framework is a mature and comprehensive evaluation framework and has been extended by other authors, it does not analyse privacy aspects in the usage of authentication schemes. In the present work, we extend the UDS framework with a privacy category to allow a more comprehensive evaluation, becoming the UDSP framework. We provide a thorough, rigorous assessment of sample authentication schemes, including the analysis of novel behavioural biometrics. Our work also discusses implementation aspects regarding the new privacy dimension and current gaps to be addressed in the future research.
Self-supervised learning based domain regularization for mask-wearing speaker verification

Automatic speaker verification (ASV) faces an unprecedented problem due to mask-wearing speakers, a consequence of COVID-19. Masked speakers unconsciously alter their normal speaking styles to compensate for the mask transfer effect, changing the statistical distribution of speech and resulting in a domain mismatch that can be dealt with by domain adaptation (DA) algorithms. However, most DA algorithms align speaker embedding distributions to reduce domain shift, which inevitably reduces the discriminative power of speaker representations for the target domain. To improve the adaptation performance, we propose a self-supervised learning-based domain regularization (SDR) algorithm that focuses on the discriminative class structure by removing the domain effect. Different from most DA algorithms, SDR is a co-training structure with two modules, which uses a self-supervised learning strategy to explore domain variables and discriminative speaker representations in the target domain. We collected speech data from 271 mask-wearing speakers, and carried out ASV experiments, whose results showed that the proposed SDR improved performance by 10%–20% based on the equal error rate (EER), compared with state-of-the-art adversarial DA frameworks.
Consequences of the minimum specificity principle on conditioning and on independence in possibility theory

The consequences of Dubois and Prade's minimum specificity principle are shown under a continuous t-norm T, when dealing with conditioning and independence in possibility theory. The minimum specificity principle singles out a particular sub-class of T-conditional possibilities (referred to as T-DP-conditional possibilities) adhering to a suitable axiomatic definition that relies on the residuum of T. Such a sub-class differentiates from the larger class of T-conditional possibilities in terms of non-closure with respect to pointwise limits, non-connectedness of extension sets, and Kolmogorov-like representation. We then switch to coherence for a partial assessment in both frameworks, highlighting that, under T-DP-conditioning, coherence of the global assessment cannot be characterized in terms of coherence on every finite sub-family. Finally, both for T-conditioning and T-DP-conditioning, we introduce an independence notion that implies logical independence and we investigate the differences due to minimum specificity.
Low-rank constraint-based multiple projections learning for cross-domain classification

Transfer learning aims to apply previously learned knowledge to new unknown domains by mining the potential relationships between data from different domains. Because of its ability to process data from different domains, the projection transfer learning methods have attracted much attention. However, most of the methods only focus on the reconstruction between different domains and do not fully use the data. Here, we put forward a novel transfer learning method called low-rank constraint-based multiple projections learning (LRMPL) for cross-domain classification. LRMPL works on both the overall reconstruction of two domains and aligns the data sharing the same label but from different domains by imposing low-rank constraints on the reconstruction coefficient matrix. In this way, the obtained feature representation is transferrable and discriminative. Moreover, LRMPL considers the information preserving during the feature representation learning by integrating a modified PCA-like item into the objective function. To learn a suitable classifier parameter and feature representation, LRMPL also unifies the classifier and feature learning into a single optimization objective. Extensive experiments on several benchmark datasets show LRMPL outperforms the existing traditional transfer learning methods in cross-domain classification. The demo code is available in https://github.com/892384750/LRMPL.
Autoencoders for a manifold learning problem with a jacobian rank constraint

We formulate the manifold learning problem as the problem of finding an operator that maps any point to a close neighbor that lies on a "hidden" k-dimensional manifold. We call this operator the correcting function. Under this formulation, autoencoders can be viewed as a tool to approximate the correcting function. Given an autoencoder whose Jacobian has rank k, we deduce from the classical Constant Rank Theorem that its range has a structure of a k-dimensional manifold. A k-dimensionality of the range can be forced by the architecture of an autoencoder (by fixing the dimension of the code space), or alternatively, by an additional constraint that the rank of the autoencoder mapping is not greater than k. This constraint is included in the objective function as a new term, namely a squared Ky-Fan k-antinorm of the Jacobian function. We claim that this constraint is a factor that effectively reduces the dimension of the range of an autoencoder, additionally to the reduction defined by the architecture. We also add a new curvature term into the objective. To conclude, we experimentally compare our approach with the CAE+H method on synthetic and real-world datasets.
The Mealy-machine reduction functions of Spot

We present functions for reducing Mealy machines, initially detailed in our FORTE'22 article. These functions are now integrated into Spot 2.11.2, where they are used as part of the ltlsynt tool for reactive synthesis. Of course, since Spot is a library, these functions can also be used on their own, and we provide Python bindings for easy experiments. The reproducible capsule benchmarks these functions on Mealy machines from various sources, and compare them to the MeMin tool.