Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Stateless Position-based Packet Routing Algorithms for FANETs

Scalable routing for wireless communication systems is a compelling and challenging task. To this aim, routing algorithms exploiting geographic information have been proposed. These algorithms refer to nodes by their location, rather than their address, and use those coordinates to route greedily towards a destination. With the advent of unmanned airborne vehicle (UAV) technology, a lot of research effort has been devoted to extend position-based packet routing proposals to three dimensional environments. In this context, Flying Ad-hoc Networks (FANETs), comprised of autonomous flying vehicles, pose several issues. This work focuses on the state-of-the-art, stateless geographic packet routing protocols conceived or adapted for three-dimensional network scenarios. Proposals are evaluated over a common scenario through a comprehensive comparative analysis.
Euclidean Wormholes, Baby Universes, and Their Impact on Particle Physics and Cosmology

The euclidean path integral remains, in spite of its familiar problems, an important approach to quantum gravity. One of its most striking and obscure features is the appearance of gravitational instantons or wormholes. These renormalize all terms in the Lagrangian and cause a number of puzzles or even deep inconsistencies, related to the possibility of nucleation of "baby universes". In this review, we revisit the early controversies surrounding these issues as well as some of the more recent discussions of the phenomenological relevance of gravitational instantons. In particular, wormholes are expected to break the shift symmetries of axions or Goldstone bosons non-perturbatively. This can be relevant to large-field inflation and connects to arguments made on the basis of the Weak Gravity or Swampland conjectures. It can also affect Goldstone bosons which are of physical interest in the context of the strong CP problem or as dark matter.
Disruption-Oriented Product Planning: Towards a Framework for a Planning Process for Disruptive Products

Established companies are unmatched in developing sustaining innovations, but when it comes to disruptive products, new entrants often outperform them. Despite the implementation of ‘agile methods’ to enable shorter development cycles and continuous customer feedback, the success score of most corporates still lacks market-changing innovations. Disruption is a continuous process that should start with niche products focusing on insufficiently addressed customer needs and — given a positive market feedback — continue with new variants to gain market share step-by-step. Hence, a needs-oriented definition of the initial product offer followed by a need-based planning of future product variants are essential for a disruptive product strategy. Yet, companies' failure rates of ∼40% for new products show that the need-based definition of minimum viable products and the planning of later product generations is insufficient. In this paper, the authors present a framework for a disruption-oriented product planning process. Based on a literature analysis focused on methodologies supporting the development process of potentially disruptive innovations, basic criteria are derived and deficits of existing approaches are identified. The framework will help companies as a guideline to understand the overall process of how to develop disruptive innovations. It further explains the essential activities and their interactions.
A Flexure-Based 2-DOF Microgripper for Handling Micro-Objects

In this paper, a new Piezoelectric Actuator (PEA) microgripper with two degrees of freedom (2-DOF) is proposed for grasping and micro-positioning of micro-objects. It incorporates two types of parallel mechanism, namely, flexible and rigid body based. The flexure-based gripper is intended to improve the dexterity and flexibility of the grasping and release of micro-objects. Finite Element Analysis (FEA) was employed to evaluate the performance of the microgripper's design parameters. The workspace of proposed mechanism was obtained using a kinematic model. The FEA results showed that the maximum stroke of grasping and transferring are 476 11m and 88 11m, respectively. The displacement amplification ratios are 15.87 and 2.93 for grasping and transferring, respectively. In the worst-case scenario, the maximum equivalent (von-Mises) stress and minimum safety factor of the designed microgripper were obtained as 3.83 MPa and 13.1, respectively, this confirms that the microgripper performs well without any failure. The concept of inverse kinematics was implemented on the proposed mechanism in FEA model and utilized to investigate the mi-crogrippers motion and coupling effects. Computational results indicated that the proposed 2-DOF microgripper can achieve accurate motion trajectory during grasping and releasing micro-objects.
AFSNN: A Classification Algorithm Using Axiomatic Fuzzy Sets and Neural Networks

In this study, we present a comprehensible classifier AFSNN that embeds a new type of coherence membership function, which builds upon the theoretical findings of the axiomatic fuzzy set (AFS) theory into the hidden layer of neural network with random weights (NNRWs). Borrowing from the idea of NNRWs that employs the random initialization technique, the relation among attributes, simple concepts, and complex concepts are randomly determined. Complex concepts are generated through the combination of randomly selected simple concepts by AFS logic operation. The output weights of NNRWs are utilized to evaluate the confidence of each complex concept for every target class, which means that the feasibility of complex concepts for every class is determined analytically rather than through the tuning parameters of constraint conditions such as in conventional AFS-based classifiers. For the proposed method, compared to other neural-network-based classification methods, the fuzzy descriptions generated from complex concepts in hidden layer make classification result human understandable. We have experimented with several benchmark datasets and compared the results with other neural network-based classifiers. We show that our method outperforms Ensemble, EvRBFN, NNEP, LVQ, and iRProp+ in the seven out of ten datasets. The results show that the performance of AFSNN is competitive in terms of classification accuracy and the network shows a distinctive capability of providing explicit knowledge in the form of linguistic description.
Six-Port Calibration Utilizing Matched Load and Unknown Calibration Loads

A six-port calibration technique utilizing matched load and unknown calibration loads having arbitrary magnitude and phase is proposed. A detailed description of the proposed calibration procedure is given and illustrated with the aid of exemplary simulated values. Its performance has been validated with the use of a six-port measurement system operating over 2.5–3.5-GHz bandwidth. This system has been calibrated using the proposed calibration and a reference procedure which utilizes the same calibration loads as known values. Furthermore, five reflection coefficients of broadband one-port devices have been measured. The results obtained for both cases are in good agreement in terms of magnitude and phase. Moreover, they are very close to the values measured using a commercial vector network analyzer.
A Wideband Dual-Polarized Dipole Antenna With Folded Metallic Plates

A wideband dual-polarized dipole antenna with fourfolded metallic plates is proposed in this letter. The folded metallic plates are vertically placed on the ground plane, under the dipole arms. Owing to the coupling with the nearby dipoles, an additional resonance beside the original impedance passband of the dipole antenna can be generated by the parasitic plates, leading to a very wide operating bandwidth. A prototype was fabricated and tested to verify the design. The measured results show that the prototype has a −10 dB impedance bandwidth of about 92%, a port isolation of over 20 dB, and an average gain of 6.2 dBi within the operating band.
User Identification by the Free-Text Keystroke Dynamics

The article discusses the possibility of user identification based on free-text keystroke dynamics for the Russian language. Article describes software for receiving of free-text keystroke dynamics characteristics and designed database for them storage. The using of a naive Bayesian classifier for the task is described. Preliminary quantitative assessment of the user identification quality. The requirements and material for the future studies was accumulated.
Synchrotron radiation microtomography of brain hemisphere and spinal cord of a mouse model of multiple sclerosis revealed a correlation between capillary dilation and clinical score

Multiple sclerosis is a neurological disorder in which the myelin sheaths of axons are damaged by the immune response. We report here a three‐dimensional structural analysis of brain and spinal cord tissues of a mouse model of multiple sclerosis, known as experimental autoimmune encephalomyelitis (EAE). EAE‐induced mice were raised with or without administration of fingolimod, which is used in the treatment of multiple sclerosis. Brains and spinal cords dissected from the EAE mice were lyophilized so as to reconstitute the intrinsic contrast of tissue elements, such as axons, in X‐ray images. Three‐dimensional structures of the brain hemispheres and spinal cords of the EAE mice were visualized with synchrotron radiation microtomography. Microtomographic cross sections reconstructed from the X‐ray images revealed dilation of capillary vessels and vacuolation in the spinal cord of the EAE mice. Vacuolation was also observed in the cerebellum, suggesting that the neuroinflammatory response progressed in the brain. The vessel networks and vacuolation lesions in the spinal cords were modelled by automatically tracing the three‐dimensional image in order to analyze the tissue structures quantitatively. The results of the analysis indicated that the distribution of vacuolations was not uniform but three‐dimensionally localized. The mean vessel diameter showed a linear correlation with the clinical score, indicating that vasodilation is relevant to paralysis severity in the disease model. We suggest that vasodilation and vacuolation are related with neurological symptoms of multiple sclerosis.
Probabilistic Rank-One Discriminant Analysis via Collective and Individual Variation Modeling

Linear discriminant analysis (LDA) is a classical supervised subspace learning technique that has wide applications. However, it is designed for vector only, which cannot exploit the tensor structures and may lead to suboptimal results when dealing with tensorial data. To address this problem, several multilinear LDA (MLDA) methods have been proposed to learn the subspaces from tensors. By exploiting the tensor structures, they achieve compact subspace representations, reduced parameter sizes, and improved robustness against the small sample size problem. However, existing MLDA methods do not take data uncertainty into account, fail to converge properly, or have to introduce additional tuning parameters for good convergence properties. In this paper, we therefore solve these limitations by proposing a probabilistic MLDA method for matrix inputs. Specifically, we propose a new generative model to incorporate structural information into the probabilistic framework, where each observed matrix is represented as a linear combination of collective and individual rank-one matrices. This provides our method with both the expressiveness of capturing discriminative features and nondiscriminative noise, and the capability of exploiting the 2-D tensor structures. To overcome the convergence problem of existing MLDAs, we develop an EM-type algorithm for parameter estimation, which has closed-form solutions with convergence guarantees. Experimental results on real-world datasets show the superiority of the proposed method to other probabilistic and MLDA variants.
Competition-Based Distributed Differential Evolution

Differential evolution (DE) is a simple and efficient evolutionary algorithm for global optimization. In distributed differential evolution (DDE), the population is divided into several sub-populations and each sub-population evolves independently for enhancing algorithmic performance. Through sharing elite individuals between sub-populations, effective information is spread. However, the information exchanged through individuals is still too limited. To address this issue, a competition-based strategy is proposed in this paper to achieve comprehensive interaction between sub-populations. Two operators named opposition-invasion and cross-invasion are designed to realize the invasion from good performing sub-populations to bad performing subpopulations. By utilizing opposite invading sub-population, the search efficiency at promising regions is improved by opposition-invasion. In cross-invasion, information from both invading and invaded sub-populations is combined and population diversity is maintained. Moreover, the proposed algorithm is implemented in a parallel master-slave manner. Extensive experiments are conducted on 15 widely used large-scale benchmark functions. Experimental results demonstrate that the proposed competition-based DDE (DDE-CB) could achieve competitive or even better performance compared with several state-of-the-art DDE algorithms. The effect of proposed competition-based strategy cooperation with well-known DDE variants is also verified.
Performance Evaluation and Comparison of Software for Face Recognition, Based on Dlib and Opencv Library

Overview and investigate time complexity of computer vision algorithms for face recognition. Main article idea is to compare two popular computer vision librarieobjs, they are OpenCV and dlib, explore features, analyze pros and cons each of them and understand in what situation each of them suit the best. Method. The technologies of computer vision, which are used for face recognition was worked out. Research of two popular computer vision libraries was conducted. Their features are analyzed and the advantages and disadvantages of each of them are estimated. Examples of building recognition application based on histogram-oriented gradients for face finding, face landmark estimation for face orientation, and deep convolutional neural network to compare with known faces. The article generalizes the concept of face recognition. The scientific basis for facial recognition and the construction of a complete recognition system was described. The basic principles of the programs for face recognition are formulated. A comparative analysis of the productivity of both libraries in relation to - the time of execution to the number of iterations of the applied algorithms was presented. Also built two simple applications for face recognition based on these libraries and comparing their performance.
Communication using robots: a Perception-action scenario in moderate ASD

ABSTRACT ASD children are characterised by a lack of intentionality. We analysed nonverbal and verbal information, associated with heart rate and emotional feeling, respectively, in ASD and neurotypical children. Analogies of heart rate between ASD and neurotypical children were expressed when the human was ‘passive’ actor and the robot was ‘active’ actor; disanalogies were released when the human was ‘active’ actor. Only ASD children reported better emotional feeling ‘after’ than ‘before’ the interaction with the robot. These results suggest that ASD children might be more reliable to low-level intentionality represented by robots, than to high-level intentionality associated with humans.
An Automated Image Processing Method for Segmentation and Quantification of Rust Disease in Maize Leaves

$A$ In the agro-ecological domain, maize is one of the most dominantcrops of the world. The disease of the maize plantation not only affect the nutritional balance but also the economics related to the crop. In this paper, an automated image processing method is proposed to identify the rust affected maize leaves and differentiate them from the healthy maize leaves. Segmentation and quantification of the rusted portion from the images of the maize leaf is done. The rust affected portion is accurately quantified of the maize leaf using morphological operations and area based thresholding to make the algorithm computationally efficient. Quantification of the segmented rusted spots is done to measure the degree of damage done by the crop disease. The results obtained from the proposed methodology are encouraging and can be used in agricultural industry for some real-time detection of diseases affecting the productivity of crops.
Intelligent Hospitals based on IOT

Ambient Intelligence (AmI) is the technology appropriate for modern buildings, homes and public infrastructure. The WSN is recognized as one of the technological corner stone of AmI. This paper proposes the design of a WSN based multi-Communication standard network for the intelligent monitoring of the fragile hospital environment. The proposed network includes sensor nodes to monitor air quality, water quality and crowd inflow inside various hospital buildings across the state of J&K. The outcome would be a system composed of ad-hoc sensor nodes providing the hospital administration with adequate non intrusive and intelligent monitoring to improve the quality of hospital hygiene, cleanliness and sanitation. The proposed design integrates WSN with packet switched networks/ web service communication into a knowledge and information service platform. Such a platform can support health administrators/ managers to improve the quality of ambient surroundings of hospitals.
Wikistat 2.0: Educational Resources for Artificial Intelligence

Big data, data science, deep learning, artificial intelligence are the key words of intense hype related with a job market in full evolution, that impose to adapt the contents of our university professional trainings. Which artificial intelligence is mostly concerned by the job offers? Which methodologies and technologies should be favored in the training programs? Which objectives, tools and educational resources do we needed to put in place to meet these pressing needs? We answer these questions in describing the contents and operational resources in the Data Science orientation of the specialty Applied Mathematics at INSA Toulouse. We focus on basic mathematics training (Optimization, Probability, Statistics), associated with the practical implementation of the most performing statistical learning algorithms, with the most appropriate technologies and on real examples. Considering the huge volatility of the technologies, it is imperative to train students in seft-training, this will be their technological watch tool when they will be in professional activity. This explains the structuring of the educational site github.com/wikistat into a set of tutorials. Finally, to motivate the thorough practice of these tutorials, a serious game is organized each year in the form of a prediction contest between students of Master degrees in Applied Mathematics for IA.
An Artifact-Type Aware DIBR Method for View Synthesis

Depth-image-based rendering is a popular way to produce content for three-dimensional television and free viewpoint video, allowing the synthesis of numerous viewpoints using a single reference view and its depth map. Due to the synthesis process and nature of the input, artifacts and holes appear, and solving these problems becomes a challenge. In this letter, we propose solutions to remove those artifacts and apply different filling strategies depending on the nature of each hole. Cracks are identified and filled using very local neighborhood information. Regions classified as ghosts are projected to their correct place. The remaining holes are classified as disocclusions or out-of-field areas, and filled with an appropriate adaptation of a popular inpainting method. In both adaptations, patch matching explores the spatial locality concept, using dynamically adaptive patch sizes from the reference image. For disocclusions we propose a filling order using depth and background terms, and a searching process that considers only background patches. We show that our method outperforms several view synthesis methods in the quantitative evaluation, besides presenting consistent visual results for both large baselines and severely occluded scenes.
Robust Nonlinear Model Predictive Control for Robot Manipulators with Disturbances

T0641. The robust NMPC algorithm is developed based on tube methodology. First, by making use of the upper-bound of the disturbances, the tube cross-sections are calculated to contain all possible states of the actual systems, which are centred on a nominal trajectory optimized online. We then use the tube cross-sections to tighten the constraints on the nominal predictions and design a tailored terminal region and a corresponding terminal controller. By implementing the proposed algorithm, robust constraint satisfaction and recursive feasibility are ensured even in the presence of disturbances. In addition, the perturbed closed-loop system is input-to-state stable (ISS) and converges asymptotically to a neighborhood of the reference trajectory.
Robust Free-Space Detection in Urban Roads based on MSER Extraction Using Gradient Images

In this paper, a robust algorithm to detect free-spaces in urban roads using video sequences captured on-board is proposed, mainly aiming at the complex urban road conditions such as the change of road structure and dense traffic conditions, as well as the changing illumination. This algorithm is based on MSER (Maximally Stable Extremal Region) extraction using gradient images, which includes the following two aspects. Firstly, the gradient images are calculated by the Sobel operator formed as the spatial filter template. MSERs are extracted from these gradient images with an improved MSER extraction algorithm with linear complexity. Then some shape analysis is applied on each MSER after shape representation is computed by the contour extraction. On the basis of selecting the proper pixel location of the initial seed point, the region of the road area is determined where the initial seed point falls, based on the assumption that the region directly in front of the host vehicle is just the road area. Experimental results show that this method can run well in various traffic scenarios, and achieve real-time and robust road detection results.
Investigation on Degradation of Common Mode Noise Suppression with Electrostatic Discharge Protection Array

In this paper, it is proposed that the electrostatic discharge protection devices may degrade the common-mode noise suppression. The mode conversion of ESD protection devices for single and multiple differential transmission lines are investigated. Experiments are set up to measure the scattering parameters and common-mode current of common-mode filter cascading different ESD protection devices. From the experimental results, the cases with four-line ESD protection array perform worse mode conversion and common-mode noise suppression.