Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration the Low Cost Camera Images with the Google Earth Dataset to Create a 3D Model

As known Close range photogrammetry represents one of the most techniques to create precise 3D model. Metric camera, digital camera, and Laser scanning can be exploited for the photogrammetry with variety level of cost that may be high. In this study, the cost level is taken in to consideration to achieve balance between the cost and the obtained accuracy. This study aims to detect potential of low cost tools for creating 3D model in terms of obtained accuracy and details and comparing it with corresponding studies. Smart phone camera is the most available for everyone; this gave the motivation for use in this study. In addition, Google Earth was used to integrate the 3D model produced from all sides including the roof. Then, two different types of the mobile camera were used in addition to the DSLR camera (Digital Single Lens Reflex) for comparison and analysis purposes. Thus, this research gave flexibility in work and low cost resulting from replacement the metric camera with the smart camera and the unmanned aerial vehicle (UAV) with Google Earth data. Mechanism of the work can be summarized in four steps. Firstly, photogrammetry planning to determine suitable baselines from object and location of targets that measured using GPS and Total station devices. Secondly, collect images using close range photogrammetry technique. Thirdly, processing step to create the 3D model and integrated with Google Earth images using the Agi Photoscan software. Finally, Comparative and evaluation stage to derive the accuracy and quality of the model obtained from this study using statistical analysis method. Regarding this Study, University of Baghdad, central library was selected as the case study. The results of this paper show that the low cost 3D model resulted from integrating phone and Google Earth images gave suitable result with mean accuracy level reached to about less than 5 meters compared with DSLR camera result, this may be used for several applications such as culture heritage and architecture documentation.
Learning URL Embedding for Malicious Website Detection

The emergence of artificial intelligence technology has promoted the development of the Internet of Things. However, this promising cyber technology can encounter serious security problems while accessing the internet. A malicious website can disguise itself as a normal website, and obtain users’ private information. Thus, it is very important to detect malicious websites using tools such as machine learning (ML) algorithms, as these algorithms can help us to identify abnormal information hidden in the mass traffic more easily. Accordingly, many feature engineering tasks must be performed from memory, as a strong machine learning model is greatly improved with good features. In this article, we propose an unsupervised learning algorithm that learns URL embedding. We also explore some key parameters regarding a domain embedding model to obtain a good effect on domain features.
Effectiveness of the flipped classroom on the development of self-directed learning in nursing education: a meta-analysis

Abstract Objectives To examine the best practice evidence of the effectiveness of the flipped classroom (FC) as a burgeoning teaching model on the development of self-directed learning in nursing education. Data sources The relevant randomized controlled trial (RCT) and non-RCT comparative studies were searched from multiple electronic databases including PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials (CENTRAL), Wanfang Data, China National Knowledge Infrastructure (CNKI), and Chinese Science and Technology Periodical Database (VIP) from inception to June 2017. Review methods The data were independently assessed and extracted for eligibility by two reviewers. The quality of included studies was assessed by another two reviewers using a standardized form and evaluated by using the Cochrane Collaboration’s risk of bias tool. The self-directed learning scores (continuous outcomes) were analyzed by using the 95% confidence intervals (CIs) with the standard deviation average (SMD) or weighted mean difference (WMD). The heterogeneity was assessed using Cochran’s I2 statistic. Results A total of 12 studies, which encompassed 1440 nursing students (intervention group = 685, control group = 755), were eligible for inclusion in this review. Of 12 included studies, the quality level of one included study was A and of the others was B. The pooled effect size showed that compared with traditional teaching models, the FC could improve nursing students’ self-directed learning skill, as measured by the Self-Directed Learning Readiness Scale (SDLRS), Self-Directed Learning Readiness Scale for Nursing Education (SDLRSNE), Self-Regulated Learning Scale (SRL), Autonomous Learning Competencies scale (ALC), and Competencies of Autonomous Learning of Nursing Students (CALNS). Overall scores and subgroup analyses with the SRL were all in favor of the FC. Conclusions The result of this meta-analysis indicated that FCs could improve the effect of self-directed learning in nursing education. Future studies with more RCTs using the same measurement tools are needed to draw more authoritative conclusions.
A study of the microtubule associated protein family, MAP65, in Arabidopsis

Microtubules (MTs) play a key role in cell division and morphogenesis. Plant cells form four distinct MT arrays through the cell cycle; the interphase cortical array, the preprophase band, the spindle and the phragmoplast. The cortical array, the preprophase band and the phragmoplast are unique to plant cells. Microtubule Associated Proteins (MAPs) must be involved in the organisation of the MT structures. The most abundant plant MAPs are the MAP65 proteins. A database search has revealed the presence of nine MAP65 genes in the Arabidopsis genome. Homologues of MAP65 proteins are present in vertebrates and yeast. Using prediction servers, putative phosphorylation and post-translation modification sites were identified in AtMAP65 protein sequences and transcription regulatory elements in AtMAP65 promoter sequences. The temporal and spatial expression of all nine AtMAP65 proteins was examined by transforming Arabidopsis with AtMAP65 promoter: :GUS reporter gene transcriptional fusion constructs. The data revealed that the AtMAP65 isoforms were expressed differentially in Arabidopsis seedling and flower tissues. The abundance of the each of the AtMAP65 transcripts in various Arabidopsis tissues was investigated by Reverse Transcription-PCR. The full length of AtMAP65-6 coding sequence was cloned by screening cDNA libraries and subsequently, it was expressed in E.coli. The recombinant AtMAP65-6 protein was shown to bind MTs and to increase the turbidity of MT solutions. The possible protein- protein interactions between AtMAP65-l, AtMAP65-6 and AtMAP65-5 were assessed with yeast two hybrid assays and it was shown that AtMAP65-6 interacts with itself An antibody was raised against AtMAP65-6 and used for investigating the AtMAP65-6 localisation to MT structures through the cell cycle. AtMAP65-6 does not bind to cortical MTs, however localises to preprophase band, anaphase spindle and phragmoplast. The anti-AtMAP65-6 antibody recognises three isoforms on a western blot of a 2D SDS-PAGE gel of total protein extract of Arabidopsis tissue culture cells.
A Hybrid Multiple Access Scheme via Deep Learning-Based Detection

In this article, we propose an uplink hybrid multiple access scheme (HMAS) in order to support a highly overloaded multiuser system. In HMAS, for a fixed <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-orthogonal resources, there are <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-near users (NUs) adopting orthogonal frequency division multiple access and <inline-formula><tex-math notation="LaTeX">$J>K$</tex-math></inline-formula> far users (FUs) adopting sparse code multiple access for uplink transmission. To improve the performance of HMAS, we propose two deep learning-based detectors via deep neural network (DNN) models, one for NUs symbol detection, and the other for FUs symbol detection. Both DNN models are trained offline via simulated data and-then-applied for online symbol detection. Simulation results demonstrate the effectiveness of HMAS in terms of symbol error rate performance over Rayleigh fading channels. In particular, it shows that the HMAS with DNN-based detections outperforms significantly the one using conventional message passing algorithm and successive interference cancellation-based detection.
Central Control over Distributed Service Function Path

Service Function Chaining (SFC) supports services through linking an ordered list of functions. There may be multiple instances of the same function, which provides a challenge to select available instances for all the functions in an SFC and generate a specific Service Function Path (SFP). Aiming to solve the problem of SFP selection, we propose an architecture consisting of distributed SFP algorithm and central control mechanism. Nodes generate distributed routings based on the first function and destination node in each service request. Controller supervises all of the distributed routing tables and modifies paths as required. The architecture is scalable, robust and quickly reacts to failures because of distributed routings. Besides, it enables centralized and direct control of the forwarding behavior with the help of central control mechanism. Simulation results show that distributed routing tables can generate efficient SFP and the average cost is acceptable. Compared with other algorithms, our design has a good performance on average cost of paths and load balancing, and the response delay to service requests is much lower
Hosts with Any Network Connectivity Using "Bump-in-the-API"(BIA)

This document specifies a mechanism for hosts with any network connectivity (IPv4 only, IPv6 only, or dual IPv4/IPv6 connectivity) to run applications of any capability (IPv4 only, IPv6 only, or dual IPv4/IPv6) without any modification to those applications. It is a generalisation of a previous experimental protocol called "Bump-in- the-API" (BIA) [RFC3338]. New mechanism of BIA allows a changeover between the application layer and the IP communication layers from IPv4 to IPv6 and vice versa or IPv6 to IPv4 and vice versa, without requiring those applications to be converted in addressing capabilities, effectively shielding the application layer from IPv4 or IPv6 connectivity. This is considered by the authors to be one of the essential conditions for the transition to IPv6 in the Internet to be successful.
P-Cube: A New Two-Layer Topology for Data Center Networks Exploiting Dual-Port Servers

SUMMARY Connecting a large number of servers with high bandwidth links is one of the most crucial and challenging tasks that the Data Center Network(DCN)mustfulﬁll. DCNfacesalot ofdiﬃcultiesliketheeﬀective exploitation of DC components that, if highlighted, can aid in constructing high performance, scalable, reliable, and cost-eﬀective DCN. In this paper, we investigate the server-centric structure. We observe that current DCs use servers that mostly come with dual ports. Eﬀective exploitation of the ports ofinterestforbuildingthetopologystructurecanhelpinrealizingthepoten-tialitiesofreducingexpensivetopology.Ournewnetworktopology,named“ParallelCubes”(PCube),isaduplicatedeﬁnedstructurethatutilizesthe portsintheserversandmini-switchestoformahighlyeﬀective,scalable,andeﬃcientnetworkstructure. P-Cubeprovideshighperformanceinnet-worklatencyandthroughputandfaulttolerance.Additionally,P-Cubeishighlyscalabletoencompasshundredsofthousandsofserverswithalow stablediameterandhighbisectionwidth.WedesignaroutingalgorithmforP-CubenetworkthatutilizestheP-Cubestructuretostrikeabalance amongthenumerouslinksinthenetwork.Finally,numericalresultsareprovidedtoshowthatourproposedtopologyisapromisingstructureas itoutperformsothertopologiesanditissuperiortoFat-tree,BCubeandDCellbyapproximately24%,16%,8%respectivelyintermsofnetwork throughputandlatency.Moreover,P-CubeextremelyoutperformsFat-tree,andBCubestructuresintermsoftotalcost,complexityofcablingandpower consumption.
Investigating usefulness and usability of medical content-based image retrieval tool for skin lesion images

Content-Based Image Retrieval (CBIR) is an application of computer vision techniques for searching an existing database for visually similar entries to a specific query image. One application of CBIR in the dermatology domain is displaying a set of visually similar images with a pathology-confirmed diagnosis for a given query image of a skin lesion. Recently, CBIR algorithms using machine learning techniques have gained more attention; however, we lack insights into how interactive CBIR decision support tools are actually perceived by end users. We present the design and evaluation of a CBIR user interface for dermoscopic skin images, and investigate users’ classification accuracy, and users’ confidence, trust, timing, and the system’s educational value. Our study with 34 non-medical users indicates that CBIR enables users to make significantly more accurate and confident classifications on new skin lesion images from four categories commonly observed in clinical practice: nevus, seborrheic keratosis, basal cell carcinoma, and malignant melanoma.
CRSP for Teaching

Most finance professors, at least in the North America, are quite familiar with the CRSP database, which contains historical trading data for all American listed stocks. However, few use it in their classrooms for three reasons: it is difficult to retrieve data; time consuming to explain and does not have recently available data, i.e. for yesterday or last month. In this paper, I present an effective way to solve those shortcomings. Two words could be used to describe my method: “trivial” and “fast”. “Trivial” means that the method is so simple that it needs just a one-page long instruction. “Fast” means that loading a data set takes just a few seconds. For example, it takes just 5 seconds to load CRSP monthly data (which contains 32,676 stocks over 92 years). Another advantage of my approach is that we could retrieve IBM’s data from 1926 up to yesterday by combining CRSP with Yahoo! Finance. Over the last 5 years, more than 50 schools have launched Business Analytics/Data Analytics programs. For those programs, CRSP is an ideal database they should use for machine learning, data visualization, cluster analysis, portfolio optimization and bankruptcy prediction.
A checklist of the Aphelenchoidea (Nematoda : Tylenchina)

An updated checklist and systematic scheme of the aphelench nematodes is given. Following recent molecular advances, the former Order Aphelenchida and Suborder Aphelenchina are rejected, these nematodes falling under the Tylenchina in the superfamily Aphelenchoidea. The Aphelenchoidoidea is also rejected in this scheme, thereby leaving all aphelenchs under one superfamily. In the checklist the 28 valid genera are arranged according to the outline systematic scheme. In addition to species and accepted synonyms, lists are appended for species inquirendae, nomina nuda, etc. The Principle of Coordination is applied to authorities for changes in rank and there is a comprehensive bibliography. The following new combination is proposed: Bursaphelenchus bicoloris (Devdariani, 1973) n. comb. (syn. Parasitaphelenchus bicoloris Devdariani, 1973).
Patent Foramen Ovale Closure for Recurrent Stroke Prevention: A Network Meta-Analysis of Randomized Controlled Trials

* Corresponding Author: George S. Mina, MD Department of Cardiology Louisiana State University Health Sciences Center Shreveport 1501 Kings Hwy, Shreveport, LA 71103, USA Tel. : +1 318 675 5941; Fax: +1 318 626 3753; E-Mail: gmina@lsuhsc.edu Fax +1 203 785 3346 E-Mail: jshd@scienceinternational.org http://structuralheartdisease.org/ © 2018 Journal of Structural Heart Disease Published by Science International Corp. ISSN 2326-4004
Mitochondrial DNA control region as a tool for species identification and distinction between wolves and dogs from Croatia.

Dogs worldwide share a single genetic origin from Asian wolves. While dog and wolf lineages are diffi cult to separate in terms of nuclear genes, mitochondrial lineages are clearly distinguishable for the two species, offering a good opportunity to evaluate the differences between them. Species identifi cation from DNA is an important tool for wolf conservation in Croatia, and wolf dog differentiation is necessary for forensic cases, wildlife management and scientifi c research. The goal of this paper was to provide a data set on Croatian dog and wolf mitochondrial DNA control region sequences, and to research if these sequences can be used as a reference for species identifi cation. We analyzed 281 base pair sequences of the mitochondrial DNA (mtDNA) control region of 20 mixed breed dog blood samples, 91 grey wolf muscle samples and two muscle samples of wolf-like animals. We identifi ed 12 dog and 4 wolf mtDNA control region haplotypes. None of the haplotypes were shared, confi rming that mtDNA control region haplotypes can be used to discriminate between Croatian wolves and dogs, and to confi rm the maternal ancestry of putative hybrids. The sequences of the two wolf-like animals clearly grouped into a dog cluster.
Reliable exponential stabilisation for fractional-order semilinear parabolic distributed parameter systems: an LMI approach

ABSTRACT This paper investigates the problem of exponential stabilisation for fractional-order semilinear parabolic distributed parameter systems with actuator faults, and a reliable state feedback controller is proposed. First, the considered nonlinear fractional-order distributed parameter systems are reconstructed by Takagi-Sugeno (T-S) fuzzy partial differential equation (PDE) model, where a finite number of actuators are active only at some specified points of the spatial domain. Then, based on the obtained fractional-order T-S fuzzy PDE model, a fractional-order Lyapunov technique is used to analyse the closed-loop exponential stability. By using the vector-valued Wirtinger’s inequality, a reliable state feedback controller that can guarantee locally exponential stabilisation of the fractional-order semilinear PDE systems is presented in terms of linear matrix inequalities. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed method.
Multi-User Adaptive Video Delivery Over Wireless Networks: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach

In this paper, we investigate the adaptive video delivery for multiple users over time-varying and mutually interfering multi-cell wireless networks. The key research challenge is to jointly design the physical-layer resource allocation scheme and application-layer rate adaptation logic, such that the users’ long-term fair quality of experience (QoE) can be maximized. Due to the timescale mismatch between these two layers and the asynchrony of user requests, however, it is difficult to directly model the cross-layer stochastic control problem by using a reinforcement learning framework. To address this difficulty, we propose a novel two-level decision framework where an optimization-based beamforming scheme (performed at the base stations) and a deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals) are, respectively, developed, such that a highly complex long-term multi-user QoE fairness problem is decomposed into some relatively simple problems and solved effectively. Our strategy represents a significant departure from the existing schemes with consideration of either a short-term multi-user QoE maximization or a long-term single-user point-to-point QoE maximization. Extensive simulations demonstrate that the proposed cross-layer design is effective and promising.
Predatory and parasitic Lepidoptera: carnivores living on plants.

Moths and butterflies whose larvae do not feed on plants represent a decided minority slice of lepidopteran diversity, yet offer insights into the ecology and evolution of feeding habits. This paper summarizes the life histories of the known pred atory and parasitic lepidopteran taxa, focusing in detail on current research in the butterfly family Lycaenidae, a group disproportionately rich in aphytophagous feeders and myr mecophilous habits.
Grape juice consumption and/or exercise training-induced neuroplasticity and memory improvement in healthy elderly women

Background: Emerging evidences have been pointed out that exercise in combination with dietary factors is able to affect molecular events related to synaptic plasticity and cognitive improvement. Therefore, this study sought to verify the impact of grape juice consumption and/or exercise in plasma neurotrophic factor (BDNF) levels and memory performance in elderly women. Methods: Nineteen healthy elderly women were randomly distributed in two groups: Grape Juice Group (GJG, n=9) and Grape Juice + Exercise Group (GJEG, n=10). The GJG was instructed to drunk daily the grape juice (400 ml) for 30 days, while the GJEG was also submitted to a physical exercise intervention (twice a week, 60 min/session). Memory function was assessed via the MoCA (Montreal Cognitive Assessment) and blood sampling was taken for BDNF analysis pre/post intervention in both groups. Results: It was observed a significant increase on BDNF levels in GJG (p=0.000) and GJEG (p=0.041) after intervention, while the memory improvement was verified only in GJEG (p=0.024). No differences between groups were found in the variables. Conclusion: In conclusion, the grape juice consumption per se is capable to modulates/increases BDNF levels, response that was not potentiated by exercise practice. On the other hand, the memory performance was improved only when grape juice consumption is combined to exercise intervention in healthy elderly women.
Validation of a Vibrio parahaemolyticus predictive model in different species of Australian oysters

was enumerated using a 3-tube Most Probable Number (MPN) technique (FDA, 2004) with slight modification. Ten oysters were opened aseptically, meat and liquor placed in a filter stomacher bag with an equal weight of Peptone Salt Solution (PSS; 0.1% bacteriological peptone, 3% NaCl, pH 7.4), and stomached at high setting for 2 min. A 20-g sample of the homogenate was mixed with 80 g of modified Alkaline Peptone Water (mAPW; 1% bacteriological peptone, 3% NaCl, pH 8.4) and diluted in 10-fold increments with PSS. Ten and one milliliter from the initial dilution and 1ml of all other dilution were individually added to three tubes containing 10 ml mAPW (modified to 3%NaCl instead of 1%NaCl). Inoculated mAPW tubes were incubated at 37
Improvement of Text Segmentation TextTiling Algorithm

Text segmentation has a wide range of applications in the fields of information extraction and abstract generation. The method uses local information to determine a specific segmentation, and cannot fully consider the information of the remaining paragraphs. In response to this problem, this paper improves the segmentation point selection of the algorithm. After the similarity values of the respective interval points are obtained by the similarity calculation, the sliding window is used to slide on the interval points, and the similarity value of the interval points at the center of the window is replaced with the average of all the similarity values in the window. The experimental results show that the text segmentation based on the improved method can suppress small local changes on the curve, thus highlighting the large similarity value changes, and effectively improving the performance of text segmentation.