Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between wild bee communities and floral resources Association between wild bee communities and floral resources in the Conservation Reserve Program in the Conservation Reserve Program

Due to the significance of the plant-pollinator relationship, the loss of pollinators may impose widespread effects on plant communities. Pollinator declines raise questions for the future of agricultural productivity, food security, and human health. Due to the large diversity of the wild bee population and their essential role as pollinators of natural land and agricultural crops, implementing conservation practices are necessary to preserve decreasing populations. Previous conservation efforts have been limited to planting native flowering forbs in unused fields or hedgerows bordering farms and roadsides in order to establish sufficient floral resources. However, restored prairies have the ability to support bee richness and abundance to the level a remnant prairie can. Therefore, small patches of restored prairies may be a sanctuary and offer refuge to the diminishing bee population. Large scale habitat restoration, such as the Conservation Reserve Program (CRP) Pollinator Initiative, could increase not only the quantity of habitat for wild bees, but also the quality of the resources the bees have access to. In this study, I examined the relationship between floral resources and the wild bee community in young CRP fields. I particularly focused on finding correlations between bees and flowering plants in the CRP program. In addition, I examined the wild bee community from year to year in response to changing floral resources. Fields planted in 2015 were selected to be surveyed in 2018, and fields planted in 2016 were selected to be surveyed in 2019. Altogether, 19 CRP sites were surveyed. For each site surveyed, bees were collected, vegetation density, and floral resource abundance was measured. No apparent relationship was exhibited between the bee community and floral resources. A positive correlation was discovered between the density of sown flowers and the density and richness of bees, which suggests further usage of seed mixes by farmers enrolling in the CRP programs. The data showed sown floral resources are supporting
The Renaissance of Wizard of Oz (WoOz) – Using the WoOz methodology to prototype automated vehicles

The strong increase in momentum behind the development of automated systems is leading to a change in paradigm with regard to the distribution of control in human-machine interaction. Therefore, in the context of automated driving, it is necessary to explore fundamental questions such as the interaction between driver and vehicle. However, the underlying automated driving functions are still under development and thus can only be used for studies to a limited extend. From a technical point of view, the introduction of automated systems results in an increased proportion of probabilistic components. Due to the resulting non-determined behaviour of the automation, it is difficult to perform studies in a systematic manner. A suitable method to study the effects of such “intelligent” probabilistic systems are Wizard of Oz (WoOz) setups, where a human simulates the behaviour of the system. The results obtained through WoOz studies are promising, but considering the system behaviour reproduced by the driving wizard researchers apply the method in different ways. Furthermore, there seems to be a lack of systematics regarding the experimental procedure, ethics and the guarantee of scientific quality. This article evaluates and systematizes published experimental approaches and proposes a specification language for the driving wizard’s behaviour.
Resistance to Erysiphe fischeri in two populations of Senecio vulgaris

The frequency and distribution of different specific phenotypes for resistance to Erysiphe fischeri was studied in two populations of the annual weed Senecio vulgaris (groundsel) one located in Glasgow, Scotland the other located about 480 km south at Wellesbourne, England. Progeny of individual plants from the two host populations were tested for their response to up to 10 different isolates of E. fischeri, five from each location; each isolate had a different specific virulence phenotype. Most plants in each sample were susceptible to all 10 isolates. The proportion of plants whose progeny were resistant to a particular isolate ranged from 1% to 10% with the exception of resistance to one isolate that occurred with a frequency of 37% at Wellesbourne. Overall, resistance to one or more of the 10 isolates appeared to be more common in the plant population sampled at Wellesbourne than at Glasgow. Of the total number of groundsel line/isolate combinations tested, 10% involving Wellesbourne plants and 2% involving Glasgow plants were incompatible, i.e. resistant/avirulent. Both groundsel populations tended to be dominated by one or two resistance phenotypes but they were nevertheless highly heterogeneous when less frequent resistance phenotypes were considered. This was particularly evident at Wellesbourne where 10 different resistance phenotypes were recorded amongst a total of 75 plants growing within an area of 1 m2.
An adaptive assistance system for subjective critical driving situations: understanding the relationship between subjective and objective complexity

Partial and conditional automated driving allows the driver to transfer responsibility to the vehicle. While assistance systems are designed to deal with aspects of the driving task, currently no assistance systems are available to predict driver behaviour for take-over when the vehicle is handling the driving task. This is important as drivers might interpret driving situations differently than an activated automation function. This can cause self-initiated take-overs leading to a reduction of trust in the system. In theory, if a prediction is robust, an assistance system could also adapt based on this prediction. A new subjective complexity model addressing these situations is introduced. The subjective complexity model learns situations in which individual drivers have previously self-initiated control of the driving task. Based on exemplary sideswipe manoeuvres, the system concept is explained and simulated with a training and test dataset. Upon introducing this system, a discussion is initated on the difference between objective and subjective situation complexity. A distinction is drawn between mathematical descriptions based on vehicular sensor data and human interpretation of the environment. The proposed system also functions as a carrier technology for further investigations between the differences of objective and subjective complexities.
Advances in imaging for lung emphysema

Lung emphysema represents a major public health burden and still accounts for five percent of all deaths worldwide. Hence, it is essential to further understand this disease in order to develop effective diagnostic and therapeutic strategies. Lung emphysema is an irreversible enlargement of the airways distal to the terminal bronchi (i.e., the alveoli) due to the destruction of the alveolar walls. The two most important causes of emphysema are (I) smoking and (II) α1-antitrypsin-deficiency. In the former lung emphysema is predominant in the upper lung parts, the latter is characterized by a predominance in the basal areas of the lungs. Since quantification and evaluation of the distribution of lung emphysema is crucial in treatment planning, imaging plays a central role. Imaging modalities in lung emphysema are manifold: computed tomography (CT) imaging is nowadays the gold standard. However, emerging imaging techniques like dynamic or functional magnetic resonance imaging (MRI), scintigraphy and lately also the implementation of radiomics and artificial intelligence are more and more diffused in the evaluation, diagnosis and quantification of lung emphysema. The aim of this review is to shortly present the different subtypes of lung emphysema, to give an overview on prediction and risk assessment in emphysematous disease and to discuss not only the traditional, but also the new imaging techniques for diagnosis, quantification and evaluation of lung emphysema.
The Design of Wireless Portable Electrocardiograph Monitoring System Based on ZigBee

The emerging trend in wireless hospitals requires the development of bio-signal acquisition devices to be easily integrated into a single clinical routine. The wired devices used in hospitals and clinics, which makes setup very inconvenient, costly, and difficult to manage patients. It is crucial to develop and design a clinical diagnostic system that takes bio-signal from the body of the patient and transmits wirelessly to the physicians/hospital. In this paper, we have designed a novel portable system for ECG signal acquisition, processing, and wireless transmission using a ZigBee module. Our portable design enhances mobility, small in size, and very cheap for long term monitoring of cardiac patients. The results show that the ECG wave successfully transmitted to the physician, and heartbeat values are visible to the patient. Furthermore, the results of the ECG wave are unclear when the position of electrodes is dislocated.
Rumor Detection System for Twitter (A Micro-Blogging Site)

Micro-blog provides a platform for the users to transfer their thoughts and information in limited words more expressively. Its concise and easy to access nature makes it popular among every age group. Inspite, of all its pros and popularity, some people use it to achieve their bad motives i.e. to misguide people and create violence. To overcome this problem a system is required that will help to detect fake tweets in a limited amount of time. In this paper, a feature based approach for rumor detection has been proposed. The proposed approach utilizes 9 features which shows author as well as readers reaction to identify rumor tweets which may differ for different users in different situations. For experimentation synthetic data and from Pheme has been utilize. A comparative study of the approach for the datasets has been done on the basis of evaluation parameters Recall, Precision and fmeasure. Satisfactory results have been obtained for Pheme data with less number of features as compare to synthetic dataset.
NOTES AND CORRESPONDENCE A Dynamical Downscaling Case Study for Typhoons in Southeast Asia Using a Regional Climate Model

This study explores the possibility of reconstructing the weather of Southeast Asia for the last decades using an atmospheric regional climate model, the Climate version of the Lokal-Modell (CLM). For this purpose global National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses data were dynamically downscaled to 50 km and in a double-nesting approach to 18-km grid distance. To prevent the regional model from deviating significantly from the reanalyses with respect to large-scale circulation and large-scale weather phenomena, a spectral nudging technique was used. The performance of this technique in dealing with Southeast Asian typhoons is now examined by considering an ensemble of one simulated typhoon case. This analysis is new insofar as it deals with simulations done in the climate mode (so that any skill of reproducing the typhoon is not related to details of initial conditions), is done in ensemble mode (the same development is described by several simulations), and is done with a spectral nudging constraint (so that the observed large-scale state is enforced in the model domain). This case indicates that tropical storms that are coarsely described by the reanalyses are correctly identified and tracked; considerably deeper core pressure and higher wind speeds are simulated compared to the driving reanalyses. When the regional atmospheric model is run without spectral nudging, significant intraensemble variability occurs; also additional, nonobserved typhoons form. Thus, the insufficiency of lateral boundary conditions alone for determining the details of the dynamic developments in the interior becomes very clear. The same lateral boundary conditions are consistent with different developments in the interior. Several sensitivity experiments were performed concerning varied grid distances, different initial starting dates of the simulations, and changed spectral nudging parameters.
Double Precision Floating Point Fft Processor using Vedic Mathematics

There should be rapid, efficient and simple process for every scenario now a day. To compute the N point DFT, Fast Fourier Transform (FFT) is a productive algorithm. It has great applications in communication, signal and image processing and instrumentation. In the implementation of FFT one of the challenges is the complex multiplications, so to make this process rapid and simple it’s necessary for a multiplier to be fast and power efficient. To tackle this problem Karatsuba sutra and Nikhilam sutra are an efficient method of multiplication in Vedic Mathematics. This paper will present a design methodology of Double Precision Floating Point Fast Fourier Transform (FFT) Processor.The execution time and complexity can be reduced by the algorithm which is there in Vedic.The main aim is to make FFT Processor process rapid and simple by designing a multiplier which is fast and power efficient by using double precision floating point and Vedic Mathematics concepts.
Supplementary Materials

These are the supplementary materials to the paper: “Learning Weighted Riemannian Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders”. Section 1 provides elements of Riemannian geometry. Section 2 presents the proof of Proposition 2. Section 3 highlights the difference between a Riemannian VAE and a VAE trained on the tangent space on the input manifold. Section 4 provides the computations for the theoretical 1D example of the paper. Sections 5 and 6 provide details on the experiments on synthetic data and real brain connectomes. 1. Elements of Riemannian Geometry 1.1. (Sub)manifolds, Weighted (Sub)manifolds We review some elements of Riemannian geometry. In the main paper, these elements enable to introduce the Riemannian variational autoencoder (rVAE) and its goodness of fit in terms of weighted submanifold learning. We refer to [4, 3] for details on differential geometry. Definition 1 (Riemannian manifold) A Riemannian manifoldM is a differentiable manifoldM equipped with a Riemannian metric g, which is a smoothly varying inner product on the tangent spaces of M . We assume the Riemannian manifolds considered are simply connected and complete [4]. As the objective of the rVAE is submanifold learning, we recall the definition of a submanifold. Definition 2 (Submanifold and embedded submanifold) A subset N of M is a submanifold of M if it is included in M and is a manifold. The submanifold N of M is called an embedded submanifold of M if there exists a smooth manifold X and a smooth embedding f such that: N = f(X). The submanifold N inherits differential and geometric structure fromM . In particular, the Riemannian inner product from M restricts to tangent spaces TxN for x ∈ N to give a Riemannian metric on N . The Riemannian metric g of M induces an infinitesimal volume element on each tangent space. Thus, a measure on the manifold M has the expression dM = √ det(g(x))dx in a local coordinate system x. As the submanifold N can be equipped with the Riemannian metric inherited from M , we can define a Riemannian measure on it similarly. Once a measure is defined, we can introduce weighted manifolds and submanifolds. Definition 3 (Weighted (sub)manifold) Given a complete d-dimensional Riemannian manifold (M, g) and a smooth probability distribution ω : M → R, the weighted manifold (M,ω) associated to M and ω is defined as the triplet:
Security in Cloud: Attacks & Prevention Techniques

- Cloud computing is considered as the future of IT organizations. In weigh against to conventional solutions in which the IT services are under proper physical logical and personnel controls, it moves all the computing resources to the centralized large data centers, so users can enjoy services in a large scale on demand. Chiefly small and medium-size organizations can manage their projects by using cloud-based services and also able to achieve productivity enhancement with limited budgets. But, apart from all of these benefits, it may not be fully trustworthy. Cloud Computing do not keep data on the user’s system, so there is a need of data security. The user pays progressively attention about data security due to this off-side storage of data on cloud computing. In this paper, all possible threats to security in cloud computing are presented
Timing and Pattern of Molt in Kuhl's Bat, Pipistrellus kuhlii, in Saudi Arabia

Kuhl's bat (Pipistrellus kuhlii) from Unizah Province, near the center of Saudi Arabia, was studied monthly from May 2005 to August 2006 to investigate the timing and patterns of molt. Adults and juveniles undergo a single molt extending annually over a five month period in summer from late April until September and following the breeding season. Few males of Kuhl's bat initiate molt before females but the majority of both sexes molt in July and terminated the molt almost simultaneously. Molting begins on the dorsum and then spreads to the ventrum once the dorsal molt is completed. Our findings provide comparable observations to previous studies on molt in bats suggesting that bats appear to exhibit a species-specific timing and sequence of molt.
Development of a Threat Detection System for Network Attacks

In today’s world the structure and dynamic interactions in the large network systems has become substantially complex. The threats and security attacks are currently spread everywhere and are tend to increase signiﬁcantly in the future with the Internet of Things (IoT). The late detection of security threats causes a signiﬁcant increase in the risk of irreparable damages, disabling any defense attempt. In this new era of security, information security professionals must deliver a very effective, real-time defense that can predict inherent threats to all the critical assets. All attacks will leave detectable traces, even though most of them will be complex and very hard to analyze. Threat monitoring systems, must have the capacity to observe activities in big data collected from networks and detect the threats. In order to provide the most secured network environment and network traffic monitoring threat detection systems must handle the real-time data. An accurate and reliable TDS will be automated that will be able to improve the traditional methods in order to fulfill the goals quickly and detect malicious activity and act accordingly. We focus on a robust classification method that includes an efficient SVM classifier will be used to handle network security concerning big network traffic.
A Review on Image Feature Detection and Description

In computer vision and image processing, feature detection and description are essential parts of many applications which require a representation for objects of interest. Applications like object recognition or motion tracking will not produce high accuracy results without good features. Due to its importance, research on image feature has attracted a significant attention and several techniques have been introduced. This paper provides a review on well-known image feature detection and description techniques. Moreover, two experiments are conducted for the purpose of evaluating the performance of mentioned techniques.
Body cavity fluid flowcytometry in the diagnosis of haematolymphoid neoplasms

Introduction: Flow cytometry provides a rapid and accurate analysis of haematopoetic tumour cells in serous fluids. Material & Methods: This is a study of body cavity fluids in which flow cytometry was used to characterize the haematopoetic cells. Period of study is from January 2012 to May 2018. Results: 39 patients were included in the study, out of which, there were 8ascitic fluid, 2 CSF (cerebrospinal fluid) and 29 pleural fluid samples. Among the asciticfluid samples, most common cases were of Burkitt lymphoma. In the 2 CSF cases, one was a rare case of leptomeningeal Diffuse Large B cell lymphoma and the other was a reactive lymphoid population. Among the pleural fluids, most common was TLymphoblastic Lymphoma. Among the pleural fluid samples there were 4 reactive lymphocytosis (3 associated with Classical Hodgkin Lymphoma & 1 with a Primary Mediastinal Large B cell Lymphoma). The 3 benign effusions associated with Classical Hodgkin Lymphoma showed an abnormal increased CD4:CD8 ratio. In addition to flow cytometry, in cases of DLBCL/ Burkitt lymphoma IHC markers like bcl2, bcl6, SIgM, TdT and MIB were done on cell block preparation to corroborate the diagnosis. Conclusion: Flow cytometry helps rapidly and accurately characterize benign and malignant haematolymphoid effusions and in conjugation with immunohistochemistry on cell block preparation obviates the need for a tissue biopsy . The increased CD4 T cell subset in pleural fluid samples of Classical Hodgkin Lymphoma probably corresponds to the increase in T regulatory cells with a CD4, CD25, CD152, and FoxP3 immunophenotype which has been identified in the background T cell population of Classical Hodgkin Lymphoma.
Illumination Estimation for Color Constancy Using Convolutional Neural Network (CNN)

: - In color constancy research, one of the well-studied problems is how to estimate and remove scene illumination chromaticity from image data. Throughout several decades, illuminant estimation methods have been developed by many researchers and institutions as evidenced in literature. These methods have yet to overcome several color distortion problems such as color leakage, luminance shift, hue shift and so forth. Accordingly, this article proposes an illuminant estimation method that combines preprocessing and CNN to achieve color constancy. The preprocessing uses an average image to normalize a given input image as a method of addressing both varying illumination and uneven background from a real-life scene image. A 3-layer CNN architecture is then used to estimate scene illumination efficiently. Given image patches as input data, the CNN works in a spatial domain, which does not require taking hand crafted features usually adopted for conventional methods. To create a more effective model for estimating scene illumination, the proposed method integrates feature learning and regression to institute one optimization process within the network structure. The experimental results prove that the proposed method delivers better performance of predicting scene illumination for color constancy over the conventional methods.