Miracle-dz/UniFAR-FTUs huggingface dataset:
Gait Energy Image Projections based on Gender Detection using Support Vector Machines

Ascertaining the gender of a person has a eminent role in many social interactions. Recognition of gender gives positive outcomes in certain applications like the interaction of human and computer, scrutinizing systems, Demographic research. Biological and Gait dependent are two possible cases of gender perception. Due to the distinctive advantages of gender perception based on gait, such as non-invasive, non-contact and obtained easily at a distance, there is an increase in the curiosity in researchers in the perception of gender-based on gait. In the proposed work, the first procedural step is determining Gait energy image projection model(GPM) which is our new proposed descriptor for gender perception and in the second step; the proposed descriptors are merged with the better existing ones such as GEI, Gait Entropy Images, Dynamic Gait Energy Image (D-GEI), Gradient Gait Energy Image (GGEI) to enhance the performance. Finally, for classification, the SVM classifier is given to the proposed descriptors for differentiating the gender. The OU-ISIR database and CASIA datasets are considered for the gender evaluation and comparison of different performance evaluation parameters like recall, precision along with accuracy.
A 3D printed wearable device for sweat analysis

This work presents a wearable microfluidic device for sweat analysis. The purpose of this device is to collect sweat from the skin and convey it to the sensor. The device is printed by a 3D printer with Multi-Jet Modelling (MJM) technology using flexible materials. The performance of the microfluidic device in electrochemical sensing of two types of molecular analytes is verified. We investigated the electrochemical measurements of Paracetamol as a model of a drug, and of ferro-ferricyanide as model of ionic analyte. We checked the performance of the device with both unmodified carbon and gold screen-printed electrode. The device is then proposed for several potential analyses in sweat for in-vivo and in-situ applications.
Production Optimization Monitoring System Implementing Artificial Intelligence and Big Data

The proposed work describes an architecture able to optimize production supporting wheat transformation processes, developed within the framework of an industry research project. The architecture is designed in order to integrate and enable different systems and technologies including big data systems and artificial intelligence algorithms. Preliminary results about detected field data and data flow implementation are discussed. A particular attention is focused on predictive maintenance of production machines by infrared thermography imaging and accelerometer signal processing.
Inferring temporal patterns of preservation, origination, and extinction from taxonomic survivorship analysis

Abstract Apparent variation in rates of origination and extinction reflects the true temporal pattern of taxonomic rates as well as the distorting effects of incomplete and variable preservation, effects that are themselves exacerbated by true variation in taxonomic rates. Here I present an approach that can undo these distortions and thus permit estimates of true taxonomic rates, while providing estimates of preservation in the process. Standard survivorship probabilities are modified to incorporate variable taxonomic rates and rates of fossil recovery. Time series of these rates are explored by numerical optimization until the set of rates that best explains the observed data is found. If internal occurrences within stratigraphic ranges are available, or if temporal patterns of fossil recovery can otherwise be assumed, these constraints can be exploited, but they are by no means necessary. In its most general form, the approach requires no data other than first and last appearances. When tested against simulated data, the method is able to recover temporal patterns in rates of origination, extinction, and preservation. With empirical data, it yields estimates of preservation rate that agree with those obtained independently by tabulating internal occurrences within stratigraphic ranges. Moreover, when empirical occurrence data are artificially degraded, the method detects the resulting gaps in sampling and corrects taxonomic rates. Preliminary application to data on Paleozoic marine animals suggests that some features of the apparent record, such as the forward smearing of true origination events and the backward smearing of true extinction events, can be detected and corrected. Other features, such as the end-Ordovician extinction, may be fairly accurate at face value.
Human-Machine Interaction Personalization: a Review on Gender and Emotion Recognition Through Speech Analysis

The increasing spread of pervasive technology has led to the fast development of human-centered connected systems, such as cloud-based voice services, assisted driving systems, domotics control systems, personal digital assistants. The user interacts with these systems by speaking to an artificial intelligence, which interprets the speaker's requests and takes decision accordingly. In such scenario, the real-time collection of personal information from the speaker's voice is a key-function to develop in order to offer personalized services. Gender is part of the basic information needed to customize the user experience. Furthermore, knowledge about the sex of the speaker also proves useful in automatic speaker recognition and voice-based identity recognition systems, since it restricts the search space to individuals of one gender, thus speeding up the system response. Therefore, gender recognition techniques through speech analysis have largely attracted the researchers' attention. Speech analysis is usually performed by extracting some features from the speech signal that can be affected by additional factors other than the gender: emotional state of the speaker, for example, is conveyed in the speech by altering some parameters that take part to the gender recognition process. At the same time, the outcome of emotion recognition systems based on speech analysis can be affected by the speaker's gender. This paper briefly summarizes the techniques used to perform gender recognition through speech analysis and proposes a practice to take gender into account in emotion recognition methods.
Metabolism in a groundwater-fed river system in the Australian wet/dry tropics: tight coupling of photosynthesis and respiration

Abstract The temporal pattern of river metabolism was estimated for high-order rivers (5–7th) in the Daly watershed, tropical Australia, during the dry season (May–October) when discharge was supplied predominantly by groundwater. Rates of photosynthesis (P) and respiration (R) were calculated at 4 sites using the open-channel method based on a model of the river's O2 budget and measured diurnal cycles of dissolved O2 concentrations and temperatures. The rivers were shallow (average depth = 0.8 m), clear (1–2 NTU), and had low concentrations of nutrients (≤15 µg/L soluble N and P at most sites) and generally open canopy. At the reach scale, P was limited by light with no evidence of light saturation. An increase in primary producer biomass over the dry season probably underpinned an approximate doubling of P at the 4 sites over the dry season, but increased water temperatures would have contributed, too. P (0.1–4.6 g O2 m−2 d−1) in the Daly watershed was similar to rates in a shaded tropical Puerto Rican stream and some temperate rivers but was lower than in nutrient-enriched temperate rivers. We surmise that most P resulted in production of dissolved organic C (DOC), rather than growth of primary producer biomass, which was nutrient limited. R exceeded P (P/R ≈ 0.5), and increased approximately linearly with P (r2 = 0.79–0.99) over the dry season with no statistically significant difference among sites. The similar environmental setting of the 4 sites underpinned their similar temporal pattern of metabolism. Bacterial metabolism of photosynthetically produced DOC (PDOC) could partially explain the tight coupling of R and P but could not account for the river's overall net heterotrophy. The priming effect of bacterial degradation of labile PDOC to increase the mineralization of recalcitrant DOC (e.g., humic acids) provides an explanation for the river's heterotrophy and tight coupling between P and R.
Natural Language Processing based Online Fake News Detection Challenges – A Detailed Review

Online social media plays an important role during real world events such as natural calamities, elections, social movements etc. Since the social media usage has increased, fake news has grown. The social media is often used by modifying true news or creating fake news to spread misinformation. The creation and distribution of fake news poses major threats in several respects from a national security point of view. Hence Fake news identification becomes an essential goal for enhancing the trustworthiness of the information shared on online social network. Over the period of time many researcher has used different methods, algorithms, tools and techniques to identify fake news content from online social networks. The aim of this paper is to review and examine these methodologies, different tools, browser extensions and analyze the degree of output in question. In addition, this paper discuss the general approach of fake news detection as well as taxonomy of feature extraction which plays an important role to achieve maximum accuracy with the help of different Machine Learning and Natural Language Processing algorithms.
Evaluation of the bounding box uncertainty of deep-learning object detection in HALCON software

Deep neural networks have become more and more relevant for vision systems, for object detection and classification in industrial fields, such as robot navigation, monitoring and tracking. For such applications, vision systems have to be robust to environment conditions, occlusions and very accurate, as for bin picking. In this paper, we evaluate the performances of deep learning object detection neural networks in HALCON software, by investigating the uncertainty of bounding box position for object detection and the impact of disturbances. In this study, results evidenced the increase of bounding box uncertainty and the reduction of confidence of neural networks when disturbances are introduced, as well as the increment of uncertainty, when confidence lowers. When errors are introduced in labeling, the uncertainty of the bounding box position becomes higher, but lower than the error introduced.
Surface Approximation using Uniform Mesh Coarsening with Minimal Angle Enhancement

The typical goal of surface remeshing is to produce high quality as well as high regularity surface meshes within the bounds of the geometric fidelity, while preserving the features of the mesh. Simultaneously attaining the balance of these parameters in surface approximation is a difficult and unsolved problem. Unfortunately, little attention has been given in the literature to solve this problem. In this paper, we present a surface approximation method that iterates in two steps. First, we coarsen the surface mesh using clustering to approximate Centroidal Voronoi Tessellation (CVT) by reducing the redundant vertices and improving mesh connectivity. The coarsened meshes follow uniform tessellation but may still contain minimal angles in the mesh yielding a poor quality of mesh elements. The second step enhances the mesh quality by enhancing the minimal angles of the mesh using local edge operators. Experiments show that the proposed approach is simple, efficient and robust resulting in a high-quality mesh with well-shaped triangles having low complexity, high regularity and simplified topology that preserves the features of the original mesh within a specified approximation error bound.
Accuracy Evaluation and Clinical Application of an Optimized Solution for Measuring Spatio-Temporal Gait Parameters

Monitoring spatio-temporal parameters of gait, such as Stride Length, Gait Time and Gait Speed, through inertial measurement units is a research topic of great interest in clinics. Despite the high interest in the field, the development of a high accuracy wearable solution for spatio-temporal parameters assessment in a clinical scenario, suitable for both normal and pathological gait, is still an open challenge. In this study we proposed a novel solution to estimate spatio-temporal parameters through Inertial Measurement Units and an optimized strap-down approach for drift reduction. For the accuracy evaluation three healthy subjects were enrolled. Each subject was equipped with two inertial sensors placed on feet. Subjects were asked to repeat seven walking trials, through the calibration volume of an optoelectronic system, used as a reference. As a clinical application, we used the solution to assess fatigue effects on healthy and pathological subjects considering the 6-Minute Walking Test. Results showed high accuracy in the estimation of spatio-temporal parameters for each subject. The mean relative error of the Stride Length estimation among subjects was 1.1±0.7% and 0.8±0.5% for the left and right side. The clinical application was conducted on a cohort of ten patients with Systemic Sclerosis and ten age-matched healthy adults. Statistical differences were observed between populations in the Stride Length and in the Gait Speed. Moreover, all subjects experienced fatigue effect reporting lower values of spatio-temporal parameters in the last minute, compared to the first one. The excellent results in the accuracy evaluation encourage application of this methodology in normal and in pathological gait monitoring where a low error in the estimation of spatial gait parameters is required.
An integrative framework for understanding the origin and evolution of avian migration

OF VIEW Point-of-view contains short papers (< 5 printed pages, about 4000 words) on selected topics that are intended for discussion and comment on recent issues. Authors are encouraged to be provocative and argue their own views on controversial issues. Point-of-View is also open for communication of new ideas and re-evaluations of previously published data and hypotheses. Readers are welcome to suggest suitable topics and/or potential authors at JAB@ekol.lu. se
3DOPE-DL: Accuracy Evaluation of a Deep Learning Framework for 3D Object Pose Estimation

This work investigates the uncertainty in 3D pose estimation of DenseFusion, a Deep Learning-based 3D Object Pose Estimation (3DOPE) framework. The accuracy and precision of object spatial localization is evaluated on the benchmark Yale Carnegie Berkeley-Video Dataset. The Average Euclidean Distance metric is employed as a figure of merit for the quantitative data analysis. Moreover, the Average Rotational and Translational Distances among the estimated and the ground-truth poses are analyzed. Results gathered on the YCB- Video Dataset are validated on a newly collected 3DOPE dataset, the ISS Dataset. The latter is populated by harnessing a 3D scanner, an RGB-D camera, and an anthropomorphic robot. Outcomes highlight an Average Euclidean Distance value of 1.8 mm on the best-processed item in the YCB-Video Dataset. Similarly, an Average Euclidean Distance value of 1.9 mm is achieved on the ISS Dataset. Eventually, the measurement uncertainty of the six components of the 3D poses is inspected. The latter assessment is performed on the ISS Dataset by relying on the ISO GUM [1].
HABITAT RELATIONSHIPS BETWEEN SYMPATRIC MULE DEER AND WHITE-TAILED DEER IN TEXAS

Abstract Desert mule deer (Odocoileus hemionus eremicus) and Texas white-tailed deer (O. virginianus texanus) are sympatric in west-central Texas. During winter, spring, and summer 1999 and 2000, we recorded locations of sympatric mule and white-tailed deer to determine habitat-use differences between species. We measured 379 mule deer and white-tailed deer locations for biotic and abiotic landscape parameters. Slope and percent shrub cover explained most variation in distributions of the 2 species. Locations of bedded deer were the most discriminated behavior type for all models. Density of shrubs and slope were the most important variables used to describe habitat preference by either deer species. Mean shrub cover used by mule and white-tailed deer at bed sites was 37.7% and 53.9%, respectively. Mean percent slopes used by mule and white-tailed deer for bed sites were 9.2 and 2.9, respectively. To a lesser extent, the same habitat parameters discriminated locations between deer species for moving and browsing for 1999 and 2000. If land managers want to maintain both deer species, several land management actions might be necessary. For mule deer on relatively steep slopes, managers should maintain lower percent woody cover and encourage native forb growth. For white-tailed deer in lower drainages, managers should maintain >50% woody species cover and encourage intermittent grass growth in the remaining areas.
Sparse Linear Injection Attack on Multi-Agent Consensus Control Systems

This letter investigates the problem of false data injection attack on the communication channels in a multi-agent system executing a consensus protocol. We formulate a non-convex optimisation problem for an attack strategy with minimal one-step attack energy to guarantee instability of the consensus system. We propose an algorithm based on ADMM to solve the problem efficiently in case standard solvers are not available. Numerical simulations are provided to illustrate the effectiveness of the attack strategy.
Does phylogeographical structure relate to climatic niche divergence? A test using maritime pine (Pinus pinaster Ait.)

Aim To disentangle the effects of environmental and geographical processes driving phylogenetic distances among clades of maritime pine (Pinus pinaster). To assess the implications for conservation management of combining molecular information with species distribution models (SDMs; which predict species distribution based on known occurrence records and on environmental variables). Location Western Mediterranean Basin and European Atlantic coast. Methods We undertook two cluster analyses for eight genetically defined pine clades based on climatic niche and genetic similarities. We assessed niche similarity by means of a principal component analysis and Schoener's D metric. To calculate genetic similarity, we used the unweighted pair group method with arithmetic mean based on Nei's distance using 266 single nucleotide polymorphisms. We then assessed the contribution of environmental and geographical distances to phylogenetic distance by means of Mantel regression with variance partitioning. Finally, we compared the projection obtained from SDMs fitted from the species level (SDMsp) and composed from the eight clade-level models (SDMcm). Results Genetically and environmentally defined clusters were identical. Environmental and geographical distances explained 12.6% of the phylogenetic distance variation and, overall, geographical and environmental overlap among clades was low. Large differences were detected between SDMsp and SDMcm (57.75% of disagreement in the areas predicted as suitable). Main conclusions The genetic structure within the maritime pine subspecies complex is primarily a consequence of its demographic history, as seen by the high proportion of unexplained variation in phylogenetic distances. Nevertheless, our results highlight the contribution of local environmental adaptation in shaping the lower-order, phylogeographical distribution patterns and spatial genetic structure of maritime pine: (1) genetically and environmentally defined clusters are consistent, and (2) environment, rather than geography, explained a higher proportion of variation in phylogenetic distance. SDMs, key tools in conservation management, better characterize the fundamental niche of the species when they include molecular information.
Diagnosis of Atherosclerotic Plaques in Carotid Artery using Transfer Learning

The presence of plaque in the carotid artery can help accurately predict cardiovascular infirmities like strokes and heart attacks in people despite having no history of cardiovascular disease. To avoid the use of an invasive method to determine the amount of plaque in the carotid artery to predict the heart risk, proposed to use deep learning methods such as transfer learning to perform computational image analysis on ultrasound images to accurately predict the presence of plaque in the artery, thus helping prevent a life-threatening heart attack. Feature extraction was done on the images to create the dataset. Various mathematical parameters are used in the preprocessing of the images such as skewness, kurtosis etc. The dataset and the images were loaded into the model for descriptive analysis. Two approaches have been used for developing our model for predicting stroke risks. The first approach was to design a simple CNN to understand our purpose and attain preliminary results for the classification and the second approach is to retrain a famous image classifier known as MobileNet to attune to our specific requirements and have seen the promising result of attaining a validation accuracy of 95%
A Blind and Soft Fusion Detector Using Monte Carlo and AWGN Spectrum Sensing Techniques

In this paper, a system model is proposed based on Monte Carlo and AWGN hybrid spectrum sensing technique to identify blind and soft fusion detection in full duplex spectrum communication. This errand depicts two pieces of building media correspondences i) the basic perception of MATLAB, and ii) the effect of Additive white Gaussian Noise (AWGN) and Monte Carlo strategies on the transmitted data using based-band proliferation under different estimations of sign for commotion (SNR) extent with this gadget. In this experiment, MATLAB gadgets are used to make the racket gathering, the data course of action, the two data and upheaval progression and incorporate them together, recover signal from clamorous got data and find out fuss power, figure SNR and BER and a short time later plot them with different SNR values. Finally, arrive at a derivation from the examination.
Targeting Oxidative Stress with Functional Polymer

Oxygen is a sword with double blades. On the one hand, it is needed for all the aerobic organisms to produce cellular energy: adenosine-5-triphosphate (ATP); on the other hand, reactive oxygen species (ROS) are generated as by-products when oxygen is reduced to water along the mitochondrial electron transport chain. ROS molecules, like superoxide anion (O2 ), hydrogen peroxide (H2O2) and hydroxyl radical ( OH) are highly reactive and will attack biomolecules like protein, lipid and DNA.