Miracle-dz/UniFAR-FTUs huggingface dataset:
An Area Efficient Architecture For Error Correction Codes And Parsavel Checks

Algorithmic-Based Fault Tolerance (ABFT) technique is used to exploit the algorithmic properties to detect and correct errors. One example is Fast Fourier transforms (FFTs) that is used in spectral analysis in the communication. There are several protection schemes to detect and correct errors in FFTs. Among those, most likely the utilization of the Parseval or add of squares check is that the most generally glorious. The Parseval or sum of square check is the most widely known. It is most common to find several blocks operating in parallel in the modern communication systems. Recently, a method that exploits this truth to implement fault tolerance on parallel filters has been projected. During this temporary, this system is 1st applied to guard FFTs. Then, 2 improved protection schemes that mix the utilization of error correction codes and Parseval checks are projected and evaluated. This project outspread with FFT using Vedic multiplier. Simulation results are observed using Xilinx.
Data Quality Dimensions to Ensure Optimal Data Quality

Quality is more difficult to define for data, moreover the meaning of â€˜qualityâ€™ depends on the context in which it is applied. Paper gives a short overview of data quality dimensions which have been collected from literature research. This paper presents some results of expert survey on data quality issues carried out by the author. The examples illustrate the fact that it is not necessary to use all the various dimensions of data quality provided by researchers, but the most essential data quality dimensions can be combined for a specific application. To support further applications of this approach, this paper contains comparison of data quality requirements to be met from statisticians and data users point of view. The empiric method (analysis of texts and documents) and the method of theoretical research (analysis of the expert survey data) are applied.
Shopping Protocols - Buying online is more than just paying

Protocols such as SET have begun to standardise one part of the online shopping process: the payment part. Buying something online, however, involves more than just payment. Merchants and consumers need to be able to negotiate payment types, receipts, currencies and so forth. Protocols capable of supporting and automating this complete fulfilment of customer/merchant interaction requirementsshopping protocolsare now emerging, and they could turn out to lead to a fundamental restructuring of bank and retailer business processes.
Ionospheric Cold Ions Detected by MMS Behind Dipolarization Fronts

Traditionally, dipolarization front (DF) is a discontinuity at the leading edge of the high‐speed plasma jets, separating hot tenuous plasma from the denser ambient plasma. The particles behind the DF are usually hot population resulting from various heating and acceleration processes therein. Here, using Magnetospheric Multiscale (MMS) observations, we report that cold ions of ionospheric origin can be found behind the DFs. These cold ions move along the reconnected magnetic field lines directly from lobe during substorm, forming counter‐streaming cold ion flows behind the DFs. We find that cold ionospheric ions, as an additional population behind the DF, could increase ion density by ~50%. This indicates that the cold ions can change the gradients in the plasma density, such as the density‐driven instabilities near the DFs, and further affect the DF dynamics.
Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping

Abstract Landslide susceptibility mapping (LSM) is a major area of interest within the field of disaster risk management that involves planning and decision-making activities. Therefore, preparation of dataset, construction of predictive model and analysis of results are considered to be important stages for effective and efficient disaster management in LSM. In recent years, a large number of studies has mainly focused on the effects of using machine learning (ML) algorithms as a predictive model in LSM. Decision tree-based ensemble learning algorithms known as decision forest is one of the popular ML techniques based on a combination of several decision tree algorithms to construct an optimal prediction model. In this study, prediction performances of recently proposed decision tree-based ensemble-based algorithms namely canonical correlation forest (CCF) and rotation forest (RotFor) are tested on LSM. In order to compare their performances, popular ensemble learning algorithms including random forest (RF), AdaBoost and bagging algorithms are also considered. For this purpose, first, twelve conditioning factors are determined in the study area, Karabuk province of Turkey. Second, individual importance of the factors on LSM process is evaluated using Fischer score analysis and selected factors are used as an input dataset for the construction of landslide susceptibility prediction models of CCF, RotFor, RF, AdaBoost and bagging algorithms. For the assessment of the performances, overall accuracy (OA), success rate curves and the area under the curve (AUC) analysis are utilized. Furthermore, chi-squared-based McNemar’s test and well-known accuracy measures known as receiver operating characteristic (ROC) curves are employed to evaluate the pairwise comparison of the ensemble learning methods. Results show that CCF method outperforms the RotFor method by about 4%, and there is no statistically significant difference between CFF and other methods.
Remote Sensing to Detect Nests of the Leaf-Cutting Ant Atta sexdens (Hymenoptera: Formicidae) in Teak Plantations

Leaf-cutting ants of the genus Atta are an important insect pest in forest plantations in many countries of South America. The objective of this work was to evaluate the potential for using Landsat-8 images, with medium spatial resolution and distributed free of charge, to detect leaf-cutting ant nests in Tectona grandis plantations in Brazil, using partial least squares discriminant analysis (PLS-DA). The regression model adjusted by PLS-DA selected three principal components with a cross-validation error of 0.275 to map and predict the presence of leaf-cutting ant nests in these plantations. The most important bands and vegetation indices were selected using the main variables in the projection (VIP) and predicted pixels with the presence or absence of leaf-cutting ant nests with an accuracy of 72.3% on an independent validation data set. The study indicates that Landsat-8 OLI images have the potential to detect and map leaf-cutting ant nests in T. grandis plantations.
Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

High spatial resolution remote sensing (HSRRS) images contain complex geometrical structures and spatial patterns, and thus HSRRS scene classification has become a significant challenge in the remote sensing community. In recent years, convolutional neural network (CNN)-based methods have attracted tremendous attention and obtained excellent performance in scene classification. However, traditional CNN-based methods focus on processing original red-green-blue (RGB) image-based features or CNN-based single-layer features to achieve the scene representation, and ignore that texture images or each layer of CNNs contain discriminating information. To address the above-mentioned drawbacks, a CaffeNet-based method termed CTFCNN is proposed to effectively explore the discriminating ability of a pre-trained CNN in this paper. At first, the pretrained CNN model is employed as a feature extractor to obtain convolutional features from multiple layers, fully connected (FC) features, and local binary pattern (LBP)-based FC features. Then, a new improved bag-of-view-word (iBoVW) coding method is developed to represent the discriminating information from each convolutional layer. Finally, weighted concatenation is employed to combine different features for classification. Experiments on the UC-Merced dataset and Aerial Image Dataset (AID) demonstrate that the proposed CTFCNN method performs significantly better than some state-of-the-art methods, and the overall accuracy can reach 98.44% and 94.91%, respectively. This indicates that the proposed framework can provide a discriminating description for HSRRS images.
A Geographical Factor of Interest Recommended Strategies in Location Based Social Networks

The fast development of area based administrations (LBSNs) has extensively advanced individuals' city lives and pulled in a huge number of recent years. Area based informal organizations (LBSNs) allow clients to registration at a real region and offer step by step rules on purposes of-intrigue (POIs) with their pals each time and anyplace. Such check-in behavior can make daily real-life experiences spread rapidly via the Internet. Moreover, such check-in records in LBSNs can be totally exploited to understand the basic legal guidelines of humans’ every day motion and mobility. This paper centers on evaluating the scientific classification of client displaying for POI proposals through the information investigation of LBSNs. First, we quickly introduce the shape and records traits of LBSNs, then we current a formalization of user modeling for POI suggestions in LBSNs. Contingent upon which sort of LBSNs records used to be completely used in buyer displaying forms for POI proposals, we separate client demonstrating calculations into four classifications: pure check-in data-based consumer modeling, geographical information-based consumer modeling, spatial-temporal information-based consumer modeling, and geo-social information-based consumer modeling. At finally, condensing the current works, we bring up the future difficulties and new guidelines in five possible aspects
Application of Error-resilient Transmission of Sleep Apnea Patient Video with Sound over Mobile Network

Mobile video-audio transmission systems have delivered patient video with relevant snoring sound to quantify the severity of the sleep apnea patient over wireless networks, but few have optimized video-audio transmission in combination with transmission protocol over error-prone environments using wireless links. In this paper, the performance of the MPEG (Motion Picture Expert Group)-4 error resilient tools with UDP(User Datagram Protocol) protocol were evaluated over a wireless network to suggest the optimum combination of MPEG-4 error resilient tools and UDP packet size suitable for real-time transmission of video-audio transmission over error-prone mobile networks. Through experimentation, it was found that the packet size should correspond to IP(Internet Protocol) datagram size minus UDP and IP header for optimal video-audio quality. Also, for error resilient tool selection, the combination of resynchronization marker and data partitioning showed the best performance.
Taking a Closer Look at Flight Crew Handling of Complex Failures : Ten Case Studies

Non-normal events, in particular system failures with serious operational impact are rare in flight operations. These events are not always easy to handle by flight crews. The aim of the performed study is to determine where in this process potential issues may lie. Ten incident reports are studied using a newly developed operational issue analysis framework. The framework is used to determine whether and how the current interfaces communicate the initial functional impact and functional impact delayed in time. Additionally, results from pilot interviews are presented which identified three phases of non-normal event handling: fault detection, fault management and strategic planning. Analysis of the ten cases shows that current alert systems are mainly supporting the first two phases while the strategic planning phase, requiring higher level functional information integrated into the operational context as well as failure impact later in time, is relying almost entirely on pilot knowledge and reasoning.
Hardware control flow integrity

Control-Flow Integrity (CFI) is a promising and general defense against control-flow hijacking with formal underpinnings. A key insight from the extensive research on CFI is that its effectiveness depends on the precision and coverage of a program's Control-Flow Graph (CFG). Since precise CFG generation is highly challenging and often difficult, many CFI schemes rely on brittle heuristics and imprecise, coarse-grained CFGs. Furthermore, comprehensive, fine-grained CFI defenses implemented purely in software incur overheads that are unacceptably high. In this chapter, we first specify a CFI model that captures many known CFI techniques, including stateless and stateful approaches as well as fine-grained and coarse-grained CFI policies.We then design and implement a novel hardwareenhanced CFI. Key to this approach is a set of dedicated CFI instructions that can losslessly enforce any CFG and diverse CFI policies within our model. Moreover, we fully support multi-tasking and shared libraries, prevent various forms of codereuse attacks, and allow code protected with CFI to interoperate with unprotected legacy code. Our prototype implementation on the SPARC LEON3 is highly efficient with a performance overhead of 1.75% on average when applied to several SPECInt2006 benchmarks and 0.5% when applied to EEMBC's CoreMark benchmark.
Pop-up structures of Petrodonetska anticline and adjacent territories

Presents the results concerning a comparative analysis of paleotectonic reconstructions of the tectonic conditions dynamics for the formation of local plicative deformations and faulting in the study of the macrostructure and its local separation using the example of the Almazno-Marievskyi region. The formation conditions characteristics of the Petrodonetska anticline and adjacent territories were obtained. In the plan, they are located en echelon-like, which is associated with a shift. The structure of the latter indicates a thrusting of the sedimentary stratum layers from the center of the Donetsk trough to its northern periphery. The analysis of reconstruction results regarding the formation of the Lysychanska anticline on the border with the Severodonetskyi thrust and sections in experimental research of pop-up structures, reveals their similarity in structure and formation. With oblique convergence of blocks from the shear zone, a series of overlapping thrusts are extruded. A central graben arises within the transpressive shear zone, along its axis due to the divergent reverse faults in the arched uplift. A system of parallel thrust faults forms a ramp. The necessity of using the system modeling method (the finite element method), which improves the methods for predicting gas-containing tectonic elements in the pop-up structures, is justified.
Community-based ('citizen science') monitoring for catchment characterisation, modelling and management

iii Abstract Despite there being well-established meteorological and hydrometric monitoring methods, many smaller UK catchments remain ungauged. This leaves characterisation, modelling, forecasting and management activities a challenge when working on a local level. Many ‘citizen science’ projects are encouraging the public to participate in data collection activities and generate new knowledge across a range of environmental disciplines, but they have not been fully investigated within catchment science. This project has designed and implemented an innovative community-based monitoring scheme within the 42km Haltwhistle Burn catchment (Northumberland, UK) to explore the feasibility, reliability, value and sustainability of citizen science within the catchment management process. Like many rural UK catchments, the Haltwhistle Burn responds rapidly, experiences flash flooding, and does not benefit from any traditional monitoring networks. Various simple, lowcost and internet-based methods have enabled the public to collect and share rainfall, river level, water quality and flood-related observations successfully over a 29-month period. This generated a patchwork of heterogeneous catchment information. Although a wide range of people actively participated, 73% of the total number of observations were generated by just four dedicated individuals or households. Despite monitoring efforts being sporadic and unpredictable, rainfall and river level observations were favoured. Participation levels also intensified during high flows or flood events; web-based tools, particularly Twitter, then played an important role in sharing these real-time observations. However, spatial and temporal monitoring efforts are biased towards individual capabilities and interests, and should therefore fill data gaps rather than replace traditional monitoring schemes. Training, ongoing facilitation and feedback help to generate meaningful and good quality data. A traditional hydrometric monitoring network was installed to aid in assessing the quality and value of community-based observations. Examples presented here verify that citizen science can generate high quality data, provided that robust validation and verification measures are in place. Evidence suggests that participants were conscious of collecting consistent datasets, but this does not guarantee reliable data from every citizen scientist. The value of community-based observations have been demonstrated by using them to build and run a physically-based, spatially-distributed hydrological model. Results reveal how the local network of community-based observations, when used alongside traditional sources of hydro-
Consecutive Dimensionality Reduction by Canonical Correlation Analysis for Visualization of Convolutional Neural Networks

In this paper, we develop a visualization tool suitable for deep neural networks (DNN). Although typical dimensionality reduction methods, such as principal component analysis (PCA), are useful to visualize highdimensional data as 2 or 3 dimensional representations, most of those methods focus their attention on how to create essential subspaces based only on a given unique feature representation. On the other hand, DNN naturally have consecutive multiple feature representations corresponding to their intermediate layers. In order to understand relationships of those consecutive intermediate layers, we utilize canonical correlation analysis (CCA) to visualize them in a unified subspace. Our method (called consecutive CCA) can visualize “feature flow” which represents movement of samples between two consecutive layers of DNN. By using standard benchmark datasets, we show that our visualization results contain much information that typical visualization methods (such as PCA) do not represent.
Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality.
A Novel 3D CAD Model Retrieval Method Based on Vertices Classification and Weights Combination Optimization

3D shape retrieval is a problem of current interest in different fields, especially in the mechanical engineering domain. According to our knowledge, multifeature based techniques achieve the best performance at present. However, the practicability of those methods is badly limited due to the high computational cost. To improve the retrieval efficiency of 3D CAD model, we propose a novel 3D CAD model retrieval algorithm called VSC_WCO which consists of a new 3D shape descriptor named VSC and Weights Combination Optimization scheme WCO. VSC represents a 3D model with three distance distribution histograms based on vertices classification. The weighted sum of norm distances between corresponding distance histograms of two VSC descriptors is regarded as dissimilarity of two models. For higher retrieval accuracy on a classified 3D model database, WCO is proposed based on Particle Swarm Optimization and existing class information. Experimental results on ESB, PSB, and NTU databases show that the discriminative power of VSC is already comparable to or better than several typical shape descriptors. After WCO is employed, the performance of VSC_WCO is similar to the leading methods by all performance metrics and is much better by computational efficiency.
Hazard Assessments of Riverbank Flooding and Backward Flows in Dike-Through Drainage Ditches during Moderate Frequent Flooding Events in the Ningxia Reach of the Upper Yellow River (NRYR)

In this study, the riverbank inundation caused by moderate frequent flooding events (with recurrence periods of less than 20 years), along with the increasingly serious hazards of backward flows in dike-through drainage ditches in the Ningxia Reach of the upper Yellow River (NRYR), were investigated. Then, a comprehensive method for hazard assessment of the floodplains and backward flows in the NRYR was proposed, which fully integrated geographical information systems (GISs), remote sensing (RS), and a digital elevation model (DEM), as well as river dynamics theory. This study first established a one-dimensional unsteady hydrodynamic model for the NRYR. The historical flood hydrology observation from 2012, along with the aerial image measurement data of the study area, were used to calibrate and verify the accuracy of the model. The hazards of riverbank inundation and damages to water affected engineering facilities, as well as the backward flows of dike-through drainage ditches caused by the moderate frequent flooding events, were comprehensively analyzed. Also, this study configured the hazard map and proposed revisions to the flood hazard ranking regime definitions, and discussed the impacts and prevention and control measures of moderate frequent flood damages. The proposed method could effectively meet the hazard analysis demands of the moderate frequent flooding events in the NRYR.
Low-Resolution Mid-Infrared Reflection Analysis for Discernment of Contaminants in Seed Cotton

Citation: Jiang W, Whitelock D, Hughs SE, Rayson G (2018) Low-Resolution Mid-Infrared Reflection Analysis for Discernment of Contaminants in Seed Cotton. Int J Analyt Bioanalyt Methods 1:001 Copyright: © 2018 Jiang W, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author: Gary D Rayson, Department of Chemistry and Biochemistry, College of Arts and Sciences, New Mexico State University, Las Cruces, NM 88003-8001, USA, P.O. Box 30001, Tel: 575-646-5839, Fax: 575-646-2649