Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Fusion Algorithm Implementation on Microchip PIC Microcontroller

The paper describes an implementation of a Brooks-Iyengar algorithm on the Microchip PIC18F4550 platform. The circuit is considered as a testing platform to check the algorithm concept simulated in MATLAB before the final implementation as ASIC IP core. The results confirm the correctness of the proposed approach, which will be used in the final IP Core design.
IoT-enabled autonomous system collaboration for disaster-area management

Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical, but also very challenging and risky. Despite first responders putting their lives at risk in saving others, human-physical limits cause delays in response time, resulting in fatality and property damage. In this paper, we proposed and implemented a framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster. The framework consists of unmanned aerial vehicles ( UAVs ) , unmanned ground vehicles ( UGVs ) , a cloud-based remote control station ( RCS ) . A light-weight message queuing telemetry transport ( MQTT ) based communication is adopted for facilitating collaboration between autonomous systems. To effectively work under unfavorable disaster conditions, antenna tracker is developed as a tool to extend network coverage to distant areas, and mobile charging points for the UAVs are also implemented. The proposed frameworkʼ s performance is evaluated in terms of end-to-end delay and analyzed using architectural analysis and design language ( AADL ) . Experimental measurements and simulation results show that the adopted communication protocol performs more efficiently than other conventional communication protocols, and the implemented UAV control mechanisms are functioning properly. Several scenarios are implemented to validate the overall effectiveness of the proposed framework and demonstrate possible use cases.
A recurrent attention and interaction model for pedestrian trajectory prediction

The movement of pedestrians involves temporal continuity, spatial interactivity, and random diversity. As a result, pedestrian trajectory prediction is rather challenging. Most existing trajectory prediction methods tend to focus on just one aspect of these challenges, ignoring the temporal information of the trajectory and making too many assumptions. In this paper, we propose a recurrent attention and interaction ( RAI ) model to predict pedestrian trajectories. The RAI model consists of a temporal attention module, spatial pooling module, and randomness modeling module. The temporal attention module is proposed to assign different weights to the input sequence of a target, and reduce the speed deviation of different pedestrians. The spatial pooling module is proposed to model not only the social information of neighbors in historical frames, but also the intention of neighbors in the current time. The randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random noise. We conduct extensive experiments on several public datasets. The results demonstrate that our method outperforms many that are state-of-the-art.
Special Session - Challenges and Opportunities for Assessment in XR

This Special Session will examine the opportunities and challenges associated with developing and implementing authentic assessments in XR to inform teachers, faculty, researchers, and industry trainers interested in advancements in this field. Methods for conducting real-time assessments in XR environments will be demonstrated and include audience participation. A brief introduction regarding the state of assessment in XR will be presented by moderator Dr. Cindy Ziker, followed by four presenters and an interactive discussion on assessment in XR that will include topics posed by the audience. Evelien Ydo will present on performance-based assessment (PBA) and simulation-based assessment (SBA) using virtual reality. Dr. Diego Zapata-Rivera’s presentation will focus on conducting adaptive, embedded assessments that leverage the virtual presence of experts, teachers, and observers, and interactions with virtual agents and virtual objects. Dr. Mathew Hillier will present on the "Transforming Assessment Project", that includes the use of unique software tools and augmented reality. Dr. Michael Casale will present on innovations in work-based assessment and industry training.
Discovering Density-Based Clustering Structures Using Neighborhood Distance Entropy Consistency

Traditional clustering algorithms model the clustering problem as an optimization task, in which the objective is defined based on minimizing specific metrics. These algorithms are limited to find clusters with convex polytopes. In contrast, density-based clustering algorithms aim at overcoming this limitation and try to partition data objects into meaningful groups that have relatively high density separated by low-density regions. This work describes and evaluates a new density-based clustering algorithm, called neighborhood distance entropy consistency (NDEC), which is able to not only detect clusters of arbitrary size, shape, and density, but also identify outliers. To this end, both local and global densities are considered simultaneously to accurately discover the intrinsic clustering structure. In addition, the consistency of neighborhood distance entropy is used as an important criterion to merge potential subclusters. Experiments on synthetic and real benchmark clustering data sets have demonstrated the efficiency and effectiveness of the NDEC method. Comparisons with ${k}$ -means, DBSCAN, OPTICS, and density peaks clustering algorithms further show that NDEC can successfully discover natural clusters. Additionally, the utility of NDEC is demonstrated with its application on two real-world problems including brain white matter tracts segmentation using diffusion tensor imaging and characterizing motor unit potential trains extracted from electromyographic signals.
Ten-Year Assessment of IASI Radiance and Temperature

The Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 (IASI-B), and 2018 (IASI-C). They measure infrared radiance from the Earth and atmosphere system, from which the atmospheric composition and temperature can be retrieved using dedicated algorithms, forming the Level 2 (L2) product. The operational near real-time processing of IASI data is conducted by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT). It has improved over time, but due to IASI’s large data flow, the whole dataset has not yet been reprocessed backwards. A necessary step that must be completed before initiating this reprocessing is to uniformize the IASI radiance record (Level 1C), which has also changed with time due to various instrumental and software modifications. In 2019, EUMETSAT released a reprocessed IASI-A 2007–2017 radiance dataset that is consistent with both the L1C product generated after 2017 and with IASI-B. First, this study aimed to assess the changes in radiance associated with this update by comparing the operational and reprocessed datasets. The differences in the brightness temperature ranged from 0.02 K at 700 cm−1 to 0.1 K at 2200 cm−1. Additionally, two major updates in 2010 and 2013 were seen to have the largest impact. Then, we investigated the effects on the retrieved temperatures due to successive upgrades to the Level 2 processing chain. We compared IASI L2 with ERA5 reanalysis temperatures. We found differences of ~5–10 K at the surface and between 1 and 5 K in the atmosphere. These differences decreased abruptly after the release of the IASI L2 processor version 6 in 2014. These results suggest that it is not recommended to use the IASI inhomogeneous temperature products for trend analysis, both for temperature and trace gas trends.
SmartBench

This paper proposes SmartBench, a benchmark focusing on queries resulting from (near) real-time applications and longer-term analysis of IoT data. SmartBench, derived from a deployed smart building monitoring system, is comprised of: 1) An extensible schema that captures the fundamentals of an IoT smart space; 2) A set of representative queries focusing on analytical tasks; and 3) A data generation tool that generates large amounts of synthetic sensor and semantic data based on seed data collected from a real system. We present an evaluation of seven representative database systems and highlight some interesting findings that can be considered when deciding what database technologies to use under different types of IoT query workloads.
RAINBOW: An Operational Oriented Combined IR-Algorithm

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring.
Individual Tree Detection from UAV Imagery Using Hölder Exponent

This article explores the application of Hölder exponent analysis for the identification and delineation of single tree crowns from very high-resolution (VHR) imagery captured by unmanned aerial vehicles (UAV). Most of the present individual tree crown detection (ITD) methods are based on canopy height models (CHM) and are very effective as far as an accurate digital terrain model (DTM) is available. This prerequisite is hard to accomplish in some environments, such as alpine forests, because of the high tree density and the irregular topography. Indeed, in such conditions, the photogrammetrically derived DTM can be inaccurate. A novel image processing method supports the segmentation of crowns based only on the parameter related to the multifractality description of the image. In particular, the multifractality is related to the deviation from a strict self-similarity and can be treated as the information about the level of inhomogeneity of considered data. The multifractals, even if well established in image processing and recognized by the scientific community, represent a relatively new application in VHR aerial imagery. In this work, the Hölder exponent (one of the parameters related to multifractal description) is applied to the study of a coniferous forest in the Western Alps. The infrared dataset with 10 cm pixels is captured by a UAV-mounted optical sensor. Then, the tree crowns are detected by a basic workflow. This consists of the thresholding of the image on the basis of the Hölder exponent. Then, the single crowns are segmented through a multiresolution segmentation approach. The ITD segmentation was validated through a two-level validation analysis that included a visual evaluation and the computing of quantitative measures based on 200 reference crowns. The results were checked against the ITD performed in the same area but using only spectral, textural, and elevation information. Specifically, the visual assessment included the estimation of the producer’s and user’s accuracies and the F1 score. The quantitative measures considered are the root mean square error (RMSE) (for the area, the perimeter, and the distance between centroids) and the over-segmentation and under-segmentation indices, the Jaccard index, and the completeness index. The F1 score indicates positive results (over 73%) as well as the completeness index that does not exceed 0.23 on a scale of 0 to 1, taking 0 as the best result possible. The RMSE of the extension of crowns is 3 m2, which represents only 14% of the average extension of reference crowns. The performance of the segmentation based on the Hölder exponent outclasses those based on spectral, textural, and elevation information. Despite the good results of the segmentation, the method tends to under-segment rather than over-segment, especially in areas with sloping. This study lays the groundwork for future research into ITD from VHR optical imagery using multifractals.
Wishart-Based Adaptive Temporal Filtering of Polarimetric SAR Imagery

Temporal filtering for speckle reduction of polarimetric SARimages is described. The method is based on a sequential complex Wishart-based change detection algorithm which is applied to polarized SAR imagery, including the dual-polarization intensity data archived on the Google Earth Engine (GEE). Software for convenient application and analysis is presented. Results compare favorably with, and improve upon, standard spatial and temporal filters for speckle reduction.
Deterioration progression monitoring in concrete bridge decks using periodical NDE surveys

Reinforced concrete decks are in most cases the fastest deteriorating component of a bridge due to a multitude of influencing factors. The study concentrates on the condition assessment of bridge decks using complementary nondestructive evaluation (NDE) techniques. The assessment consists of three main components: corrosive environment and corrosion processes, concrete degradation assessment, and assessment with respect to the deck delamination. Five NDE techniques: impact echo (IE), ground penetrating radar (GPR), half-cell potential (HCP), ultrasonic surface waves (USW) and electrical resistivity (ER), are utilized. A brief overview of the NDE techniques and their complementary use is illustrated by the results from bridge deck testing. Deterioration progression from periodical NDE surveys is illustrated by condition maps and condition ratings for the whole bridge deck and bridge deck segments. Different condition rating schemes, guided by different objectives of their usage, are presented. Those include: 1) condition rating comparisons of bridges on the network level for condition monitoring and rehabilitation, and 2) segmentation and rating on the project level to identify areas of the deck that should have higher priority in rehabilitation. In addition, samples of correlations between different NDE technology results are presented as one of the means to improve the confidence level of the condition assessment.
International Journal of Research in Arts and Social Sciences

This study isa pragmatic analysis of rural discourse of AmandimOlo village meetings. It explores how pragmatic inferences and contexts help in the maximization of point of relevance in communicative events, usingSperber and Wilson‟s (1986) Relevance Theoretical Approach.Data are retrieved from audio recorded minutes of the discourse which is transcribed into English and analyzed qualitatively according to RT procedures. The findings reveal that complex(long) sentences, proverbs, elliptical statements, ostensive cues and deixis take longer processing efforts; but familiar words, and simple (short) sentences are easily processed. Thus, the researchers conclude that pragmatic inferences and contexts assist the hearer to interpret thespeaker‟s communicative intentions in oral rural discourse of Igbo village meetings.
Timeless Timing Attacks: Exploiting Concurrency to Leak Secrets over Remote Connections

To perform successful remote timing attacks, an adversary typically collects a series of network timing measurements and subsequently performs statistical analysis to reveal a difference in execution time. The number of measurements that must be obtained largely depends on the amount of jitter that the requests and responses are subjected to. In remote timing attacks, a signiﬁcant source of jitter is the network path between the adversary and the targeted server, making it practically infeasible to successfully exploit timing side-channels that exhibit only a small difference in execution time. In this paper, we introduce a conceptually novel type of timing attack that leverages the coalescing of packets by network protocols and concurrent handling of requests by applications. These concurrency-based timing attacks infer a relative timing difference by analyzing the order in which responses are returned, and thus do not rely on any absolute timing information. We show how these attacks result in a 100-fold improvement over typical timing attacks performed over the Internet, and can accurately detect timing differences as small as 100ns, similar to attacks launched on a local system. We describe how these timing attacks can be successfully deployed against HTTP/2 webservers, Tor onion services, and EAP-pwd, a popular Wi-Fi authentication method.
Predominance of Staphylococcus and Staphylococcus aureus Susceptibility to Antibiotics in Men with Urethral Discharge in Benin

DOI: 10.21276/sjpm.2017. 2.8. 3 Abstract: Antimicrobial resistance became a growing public health problem in the world and sexually transmitted infections (STIs) are one of the five types of pathologies which special care should be given. Staphylococcus aureus appear the most prevalent bacteria encountered causing infections in male urethral discharge (MUD) at Cotonou in Benin. It prevalence and it resistance to antibiotics were evaluated. This paper aims to describe the bacterial profile and antimicrobial susceptibility of S aureus strains isolated from MUD specimens at the National Laboratory (NL) of Heath Ministry. A retrospective study included 81 men at Cotonou suspected MUD during 1 st January 2004 and 31 st December 2015. MUD swabs were collected and analyzed to identify the bacteria by standard biochemical reactions, screening of trichomonads and detection of intracellular Gram-negative diplococci within phagocytes. The three main pathogens encountered among 81 MUD were Staphylococcus aureus (30/81; 37.04%), Staphylococcus spp (19/81; 23.46%) and Staphylococcus dore (12/81; 14.81%). S aureus susceptibility ranged from 6080% for minocyclin, lincomycin, chloramphenicol and gentamycin. But resistance was observed to ofloxacin, kanamycin, amoxicillin, amoxicillin-clavulanic acid, oxacillin, ceftriaxon, tetracyclin, trimethoprim+Sulfonamides and ampicillin between 59-100%. Conclusion: This study highlights the importance of routine susceptibility tests to antibiotics in the treatment of S aureus responsible of MUD in Benin.
Automatic Radar Waveform Recognition using the Wigner-Ville distribution and AlexNet-SVM

—In this paper, we propose a radar signal modulation algorithm to recognize three different radar signals amidst other wireless communication waveforms, including Barker, linear frequency modulation, and rectangular codes. First we extract the features of the original signal by computing its smoothed pseudo Wigner-Ville distribution. Second, we construct a transfer learning-based convolutional neural network over AlexNet to further extract features from the time-frequency images. Finally, a support vector machine classiﬁer is applied for the signal classiﬁcation. We also perform a similar analysis with models which use AlexNet for both feature extraction, and classiﬁcation tasks. Results show that the proposed model which incorporates the linear classiﬁer, achieves the highest recognition accuracy of 97.8%.
Fast Image Dehazing Method Based On Homomorphism Filtering and Contrast Enhancement

- Images captured via camera in the bad weather conditions like hazy or foggy ones would result in degradation of images. This is mainly due to the scattering of atmospheric light. This could lead to the deterioration of the image due to the scattering of atmospheric particles and due to the presence of haze. In this paper, a fast algorithm for single image dehazing is proposed using the homomorphism filtering and then using the contrast enhancement technique. Firstly, to accurately measure the atmospheric light an additional channel method is proposed based on quad-tree subdivision. Here the average grays and gradients are used as assessment criteria. Secondly, after finding the atmospheric light portion in the input image filtering is being applied. For this, homomorphism filtering technique is used. This is a generalized technique for both signal and image processing, involving a nonlinear mapping to a different mapping to a different domain in which linear filter techniques are applied, followed by mapping back to the original domain . At last the haze-free image is obtained as the result of atmospheric scattering model. Numbers of both subjective and objective evaluations are being done with the aid of experiments which shows that this algorithm can clearly and naturally recover the image, with at most clarity at the edges where there is sudden changes in the depth of field. Here the algorithmic time complexity is the linear function of the image size. This has advantages in the running time my maintaining balance between the running times and processing .
Is the Whole World Watching? Building a Typology of Protest Coverage on Social Media From Around the World

ABSTRACT Previous research suggests that mainstream media coverage around the world follows a “protest paradigm” that demonizes protesters and marginalizes their causes. Given the recent increase in global protest activity and the growing importance of social media for activism, this paper content analyzes 1,438 protest-related English and Spanish news stories from around the world that were shared on social media, examining framing, sourcing, and marginalizing devices across media outlet type, region, language, and social media platform in order to create a typology of how the protest paradigm operates in an international and social media context. Results showed type of protest, location of protest, and type of media outlet were significantly related to whether news stories adhered to the protest paradigm. Social media shares were predicted by region of media outlet, English-language media, and type of protest.