Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving Object Detection Using Recursive Algorithm

This paper deals with a real time visual surveillance system for detecting moving semantic objects of a certain class (such as humans or cars) from digital videos even in low-level illumination. Several existing systems used colour cues which is a major disadvantage in case of low-level illuminations. And in some existing systems results were not very accurate. In this method the prior identification of object is not needed whereas some existing system requires it. Here recursive algorithm is used to overcome such drawbacks. The main advantage of the proposed method is that shape analysis is used to detect objects without much noise. Both recursive algorithm and shape analysis produce very accurate results. This paper finds major application in case of security purposes. image resolution (typically lower for IR sensors than video sensors) and the number of people in its field of view. In the long run, moving object detection will be extended with models to recognize the actions of the people it tracks. People are interested in interactions between human beings and objects - e.g., people exchanging objects, leaving objects in the scene, taking objects from the scene. The descriptions of people - their global motions and the motions of their parts - developed by Moving object detection are designed to support such activity recognition. Moving object detection currently operates on video taken from a fixed camera, and many of its image analysis algorithms would not generalize easily to images taken from a moving camera. At this point, the surveillance system might stop and invoke a system like Moving object detection to verify the presence of people and recognize their actions. In Moving objects detection, foreground regions are detected in every frame by a combination of background analysis and simple low level processing of the resulting binary image. The background scene is statically modelled by the minimum and maximum intensity values and maximal temporal derivative for each pixel recorded over some period, and is updated periodically. Each foreground region is matched to the current set of objects using a combination of shape analysis and tracking. These include simple spatial occupancy overlap tests between the predicted locations of objects and the locations of detected foreground regions, and ―dynamic" template matching algorithms that correlate evolving appearance models of objects with foreground regions. Second-order motion models, which combine robust techniques for region detection and matching of silhouette edges with recursive least square estimation, are used to predict the locations of objects in future frames. II. SYSTEM DESCRIPTION
A Network for Semantic Segmentation of Point Cloud reorganized by Sectoral Cylindrical Partition

The existing deep learning-based algorithms for point cloud semantic segmentation has achieved an unprecedented precision in accuracy and robustness under some classical situation. However, the uneven density and large scale of point clouds make it difficult for segmentation networks to handle these hard scenes. Therefore, effective downsampling and uniformization of the point cloud spatial distribution are meaningful research directions. Along the way, this paper firstly proposes a new sectoral cylindrical partitioning (SCP) strategy to reorganized large point clouds, and secondly designs a SCP based semantic segmentation network (SCP-SSNet), which achieves high-precision segmentation of large-scale non-uniformly distributed point clouds. SCP-SSNet consists of three modules: PC-Embedding, SCP-Homogenization, and Transf. -Fusing. Specially, the point cloud embedding (PC-Embedding) module is designed to provide long-range contextual information for the subsequent segmentation network. The point cloud feature sectoral cylindrical partitioning-based point cloud homogenization (SCP-Homogenization) module is proposed to effectively improve the problem of uneven feature distribution. The Transformer based feature fusing (Transf.-Fusing) module is introduced to fuse contextual association and position encoding, achieving high-precision semantic segmentation of point clouds. The experimental results on some public datasets demonstrate that SCP-SSNet can efficiently predict a more accurate segmentation results of point cloud and outperform the state of-the-art methods.
Online Human Touch (OHT) Instruction and Programming: A Conceptual Framework to Increase Student Engagement and Retention in Online Education, Part 1

The long-term sustainability of online degree programs is highly dependent upon student enrollment and retention. While national growth in online education has increased approximately 10% between 2005 and 2006 to 3.5 million students, student attrition in online programs remains higher than on-campus traditional programs (Allen & Seaman, 2007). To proactively address student attrition, the Master of Science in Higher Education Program at Drexel University has developed and implemented the concept of Online Human Touch (OHT) instruction and programming. This interactive and personalized approach to online education has resulted in high student retention rates and high levels of student satisfaction. This article is the first of a two-part series that focuses on OHT in online education.
A new model to predict weak-lensing peak counts III. Filtering technique comparisons

This is the third in a series of papers that develop a new and flexible model to predict weak-lensing (WL) peak counts, which have been shown to be a very valuable non-Gaussian probe of cosmology. In this paper, we compare the cosmological information extracted from WL peak counts using different filtering techniques of the galaxy shear data, including linear filtering with a Gaussian and two compensated filters (the starlet wavelet and the aperture mass), and the nonlinear filtering method MRLens. We present improvements to our model that account for realistic survey conditions, which are masks, shear-to-convergence transformations, and non-constant noise. We create simulated peak counts from our stochastic model, from which we obtain constraints on the matter density $\Omega_\mathrm{m}$, the power spectrum normalisation $\sigma_8$, and the dark-energy parameter $w_0$. We use two methods for parameter inference, a copula likelihood, and approximate Bayesian computation (ABC). We measure the contour width in the $\Omega_\mathrm{m}$-$\sigma_8$ degeneracy direction and the figure of merit to compare parameter constraints from different filtering techniques. We find that starlet filtering outperforms the Gaussian kernel, and that including peak counts from different smoothing scales helps to lift parameter degeneracies. Peak counts from different smoothing scales with a compensated filter show very little cross-correlation, and adding information from different scales can therefore strongly enhance the available information. Measuring peak counts separately from different scales yields tighter constraints than using a combined peak histogram from a single map that includes multiscale information. Our results suggest that a compensated filter function with counts included separately from different smoothing scales yields the tightest constraints on cosmological parameters from WL peaks.
Scholarly and Creative Work from DePauw University Scholarly and Creative Work from DePauw University

Despite a wealth of knowledge on the importance of resource availability and reward processing for emotional regulation, surprisingly little is known about the extent to which these two mechanisms interact. Indeed, while research largely supports a positive association between reward processing and recovering from a negative emotional experience, the research does not make a clear prediction regarding the effect of resource availability on this relationship. In two experiments, we explored the extent to which resource availability impacts the efficacy of reward processing to reduce the aversive emotional experience of anxiety. We manipulated participants’ mental resource availability, induced anxiety, and varied exposure to either a rewarding or non-rewarding stimulus. The findings consistently demonstrate an interaction between resource availability and reward processing; specifically, the combination of high resource availability and reward processing facilitated the greatest levels of anxiety reduction. Moreover, this interaction was shown to amplify with the intensity of participants’ exposure to the reward stimulus. We discuss the practical contributions of these findings and their generative nature for further clarifying the processes underlying emotional regulation.
Scholarly and Creative Work from DePauw University Scholarly and Creative Work from DePauw University

Several research questions from information systems (IS) are based on textual data, such as product reviews and fake news. In this paper, we investigate in which areas NeuroIS is best suited to better understand human processing of text and subsequent human behavior or decision making. To evaluate this question, we propose a taxonomy to distinguish these research questions dep ending on how users’ corresponding response is formed. We first review all publications about textual data in the IS basket journals from 2010 – 2020. Then, we distinguish text-based research questions along two dimensions, namely, if a user’s response is in fluenced by subjectivity and if additional information is required to make an objective assessment. We find that NeuroIS research on textual data is still in its infancy. Existing NeuroIS studies focus on texts, where users’ responses are subject to a high er need for additional data, which is not part of the text.
Analysis of fifty year Gas Flaring Emissions from oil/gas companies in Africa

Flaring is a process during which waste gases are burned in an open atmosphere. The quantification of gas flaring emissions represents a major scientific concern due to its magnitude and related uncertainties. In global/regional emission inventories, this source, though releasing large amounts of pollutants in the atmosphere, is still poorly quantified if not missing. It can represent the main emission source of gaseous compounds and particles in some areas, as observed during the AMMA project in the Gulf of Guinea. Our study focuses on Africa, and includes Nigeria, which is one of the largest natural oil and gas reserve in the world. Africa is an important gas flaring area, since technologies for the exploitation of this energy source and the reduction of flaring activities have been only recently implemented. We have developed an emission inventory for gases and particles from flaring in Africa. We have first compiled the few published available dataset of fuel consumption from flaring. The spatial distribution of CO2 and black carbon (BC) emissions from gas flaring in 2011 is estimated using a methodology based on field reports and remote sensing (DMSP satellite data). Our results point out to the importance of flaring activities into the regional anthropogenic emissions in Africa over the period 1960-2011. Finally, the contribution of flaring to total anthropogenic emission can be large and needs to be accurately quantified.
Description of the male of Tityus kuryi Lourenço, 1997 and notes about males of Tityus stigmurus (Thorell, 1877) and Tityus serrulatus Lutz & Mello, 1922 (Scorpiones, Buthidae)

Abstract The male of Tityus kuryi Lourenço, 1997 is described for the first time. Despite being very similar to the female, the male presents more robust metasomal segments. Additionally, the distribution of the sexual populations of another two species of the T. stigmurus complex is reported herein: T. serrulatus Lutz & Mello, 1922 and T. stigmurus (Thorell, 1877). Males of T. serrulatus were, until now, restricted to the Minas Gerais State (Southwestern region of Brazil), and with new records reported here, its known distribution now encompasses the Northeastern region of Brazil. Males of T. stigmurus were previously recorded only for two municipalities in the State of Bahia, and here we present eight new records for Bahia State and one for Pernambuco State. We present a key to related species of the T. stigmurus complex based on morphology and coloration pattern.
Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence

Traditional techniques for estimating the weight of clusters in a winery, generally consist of manually counting the variety of clusters per vine, and scaling by means of the entire variety of vines. This method can be arduous, costly, and its accuracy is dependent on the scale of the sample. To overcome these problems, hybrid approaches of Computer Vision (CV), Deep Learning (DL) and Machine Learning (ML) based vineyard yield prediction systems are proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. DL-based approach for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image is employed using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally a prediction model is proposed to estimate the yield of the entire vineyard. The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system with grape cluster segmentation pixel accuracy upto 94.81% and yield prediction accuracy upto 99.58%.
Robust Feature Selection Method Based on Joint L2,1 Norm Minimization for Sparse Regression

Feature selection methods are widely used in machine learning tasks to reduce the dimensionality and improve the performance of the models. However, traditional feature selection methods based on regression often suffer from a lack of robustness and generalization ability and are easily affected by outliers in the data. To address this problem, we propose a robust feature selection method based on sparse regression. This method uses a non-square form of the L2,1 norm as both the loss function and regularization term, which can effectively enhance the model’s resistance to outliers and achieve feature selection simultaneously. Furthermore, to improve the model’s robustness and prevent overfitting, we add an elastic variable to the loss function. We design two efficient convergent iterative processes to solve the optimization problem based on the L2,1 norm and propose a robust joint sparse regression algorithm. Extensive experimental results on three public datasets show that our feature selection method outperforms other comparison methods.
On Effect of Communication Delay and Packet Reordering on Estimation and Control

Design of communication protocols for real time estimation and control is an important problem in cyberphysical systems. Control performance is dependent on both the throughput and the delay characteristics of the communication network. We consider the problem of estimation and control of a linear time-invariant process across a communication channel that stochastically introduces both a time-varying delay and an erasure event for the sensor data at every time step. Thus, the data may arrive at the receiver delayed and out of order, or may simply be lost. There are two contributions of this work. We prove that even if the delay distribution has an infinite support and the delays are correlated across time, the necessary and sufficient condition for stability depends merely on the erasure probability. We also show that performance of the plant can not be characterized through a few moments of the delay distribution. Thus, conjectures such as a delay distribution with lesser variance always yields better performance, as compared to a delay distribution with the same mean but larger variance, are incorrect.
Vegetation Regrowth Following Wildfires in the Santa Cruz Mountains of Northern California Monitored Using Landsat Satellite Image Analysis

The Santa Cruz Mountain range in northern California is a coastal landscape with a history of extensive forest logging and frequent large wildfires that have recently destroyed numerous residential structures at the wildland interface. Results from Landsat satellite image time-series analysis since 1984 of the study area within the Los Gatos Creek and Corralitos Creek watersheds showed that none of the severe drought periods since the 1980s have notably inhibited rapid tree and shrub regrowth rates on steep hill slopes burned recently by the 1985 Lexington Fire and the 2008 Summit Fire. In high burn severity areas of both fires, post-fire vegetation types showed a marked increase in shrub cover, mainly at the expense of evergreen tree cover. Most of these low (·ha-1 of standing woody biomass on the burned areas. This study is the first of its kind to utilize a full 30-year record of Landsat vegetation index data to monitor tree and shrub regrowth after stand-replacing wildfires in California.
Psychotria Viridis - A Botanical Source of Dimethyltryptamine (DMT)

Dimethyltryptamine was identified by GC/MS in a sample of dried leafy material that was subsequently identified as Psychotria viridis (Rubiaceae), a tropical shrub native to Central and South America that has ethnobotanical use as a hallucinogen by many indigenous peoples of tropical South America. The botanical characteristics of Psychotria viridis are illustrated and described.
THE RELATIONSHIP OF EPIPHYLLOUS LIVERWORTS WITH LEAF CHARACTERISTICS AND LIGHT IN MONTE VERDE, COSTA RICA

In a study of the ecology of epiphyllous liverworts in a Tropical lower montane et forest, it was found that the degree of epiphyllic cover and herbivory are generally higher in larger leaves, vhich indicates that both behave as functions of area. The epiphyllic growth and area consumed by herbivores increase more rapidly than leaf area, and there is no statistical relationship between epiphyllv, and herbivory and leaf shape. Absolute and relative epiphyllic cover are higher in the forest clearing than in the understory, perhaps as a result of high atmospheric humidity and occurrence of heliophilic species. This quantitative survey approach is convenient for two reasons: it provides a defined view of actual field conditions and serves as a guide to posterior experimental corroboration. RESUME - Letude de lecologie des hepatiques epiphylles en foret tropicale humide de basse altitude permet de mettre en evidence que Ic recouvrement par les Cpiphylles et l'action des herbivores soot generalement plus elevEs sur les feuilles larges; ils sont donc fonction de laire foliaire. Le developpement des epiphylles et laire attaquee par les herbivores augmentent plus rapidement que Ia surface foliaire. II ny a pas de relation statistique entre I epiphyllie, et les herbivores et Ia forme de Ia feuille. Les recouvrements absolu et relatif par les epiphylles sont plus importants dans Ia foret clairsemee que daris Ia forCt dense, peut-etre a cause dune humidite atmospheriquc plus elevee et de Ia presence d'especes heliophiles. Cette approche globale quantitative est utile pour deux raisoris: elle permet de definir les conditions actuelles in situ, et elle pourra servir de guide a une confirmation experimentale ulterieure.
Carbonate Sequence Stratigraphy and Petroleum Geology of the Jurassic Deep Panuke Field, Offshore Nova Scotia, Canada

PanCanadian Petroleum (now EnCana Corporation) discovered the deep Panuke field in 1998 with the drilling of the PP-3C well. The well was drilled in 90 m (295 ft) of water, 250 km (155 mi) southeast of Halifax, Canada. Subsequent delineation and development drilling has proven a significant gas accumulation. The gas is trapped, by a combined structural-stratigraphic configuration, in the Upper Jurassic reefal and oolitic limestones and dolomites of the Abenaki Formation. The Jurassic carbonate platform on the Scotian Shelf was attached to a metamorphic hinterland, so that the sediments contain varying amounts of siliciclastics. Abundant secondary porosity was encountered, ranging from leached matrix and intercrystalline to vuggy and/or cavernous. Textural, petrographic, and isotopic evidences suggest that deep burial and hydrothermal diagenetic processes caused the porosity. The gas is believed to have been sourced from adjacent Verrill Canyon Formation shales, whereas small amounts of hydrogen sulfide have been isotopically linked to synrift evaporites underlying the Abenaki. The Abenaki is divided into seven third-order depositional sequences, the Abenaki V being the primary gas zone. These sequences have been regionally correlated using geology and a grid of two-dimensional seismic data. Three-dimensional seismic data was used in delineation drilling and reservoir characterization. Deep Panuke is the first, and remains at writing, the only significant hydrocarbon discovery in the Mesozoic carbonates of the continental shelf of eastern North America.
The Behaviour of Particulate Matter and Carbon Dioxide Concentrations in Harare

As African countries industrialize, the emission of various pollutants is likely to increase. Air pollution can have adverse impacts on human and animal health and ecosystems. There has been very limited air pollution monitoring activities in Southern Africa. This paper presents results of a study of the temporal behaviour of particulate matter (PM2.5), total dust precipitation (TDP) and carbon dioxide (CO2), determination of the effects of meteorological parameters on the concentrations of the PM2.5 and CO2 and the spatial distribution of TDP as well as trace elements in residential areas of Harare, Zimbabwe. The (PM2.5) concentrations were determined as scattering coefficients using a nephelometer and CO2 measurements were carried out using an infra-red gas analyser. The TDP were collected using plastic pots and samples were analysed for the trace elements (iron, lead and nickel) using an atomic absorption spectrometer. CO2 mixing ratios and PM2.5 concentration followed a similar diurnal trend, with maximum peaks observed at around 2300 and troughs around midday. High wind speeds were associated with low concentrations of both CO2 and PM2.5 and low wind speeds with high concentrations of both CO2 and PM2.5. Wind from the city centre and the industrial areas advected towards the sampling site. The concentration of (PM2.5) decreased significantly during rainfall. CO2 mixing ratios were not greatly affected by rainfall. The southern residential areas of Harare which are close to the dumping sites, busy highways, busy bus terminus and industrial areas recorded significant amounts of the trace elements and TDP than the northern residential areas which are far away from the main sources of pollution. The northern residential areas are upwind of the main sources of air pollution whilst the southern residential areas are located downwind of the main air stream of Harare and are highly polluted.