Miracle-dz/UniFAR-FTUs huggingface dataset:
The Eeects of Training Set Size on Decision Tree Complexity

This paper presents experiments with 19 datasets and 5 decision tree pruning algorithms that show that increasing training set size often results in a linear increase in tree size, even when that additional complexity results in no signiicant increase in classiication accuracy. Said diierently, removing randomly selected training instances often results in trees that are substantially smaller and just as accurate as those built on all available training instances. This implies that decreases in tree size obtained by more sophisticated data reduction techniques should be decomposed into two parts: that which is due to reduction of training set size, and the remainder, which is due to how the method selects instances to discard. We perform this decomposition for one recent data reduction technique, John's robust-c4.5 (John 1995), and show that a large percentage of its eeect on tree size is attributable to the fact that it simply reduces the size of the training set. We conclude that random data reduction is a baseline against which more sophisticated data reduction techniques should be compared. Finally, we examine one possible cause of the pathological relationship between tree size and training set size.
Sympatric Nesting Eastern Prairie Population (EPP) Canada Geese and Lesser Snow Geese on the Hudson Bay Lowlands: Nest Depredation and Spatial Distribution (2005 Annual Report)

Currently, there is concern about the influence of increasing nesting light goose populations (lesser snow goose [Chen caerulescens caerulescens] and Ross’s goose [Chen rossii]) on breeding Canada geese (Ankney 1996, Batt 1997, Walter 1999, Nack 2003, Nack and Andersen 2004, Gleason et al. 2004), and the potential interacting effects of predators and light goose abundance on Canada goose population dynamics in northern Manitoba.
Vehicle Identification and GPS Error Detection from a LIDAR Equipped Probe Vehicle

—When detecting and tracking vehicles in LIDAR data it is important to differentiate between the measurements from vehicles and the measurements from fixed objects such as road boundaries. In this paper we present our approach to classify the LIDAR targets as vehicles and non-vehicle objects, and detect GPS positioning errors from a probe vehicle equipped with LIDAR and DGPS. The classification approach is developed based on a world space density image generated by averaging over historical LIDAR measurements. A correlation approach is used to detect GPS positioning errors and prevent incorrect classifications of the targets caused by erroneous projections between the vehicle coordinate system and the world coordinate system. The classification approach is then modified accordingly and becomes robust to these GPS errors.
The Qualitative Report The Qualitative Report

Abstract In this article the authors seek to make the case that qualitative data analysis can be explained within the framework of critical thinking and incorporates within this framework the role of technology – specifically NVivo. First they discuss critical thinking from the perspectives of Bloom, Adler, and Polanyi. They then link critical thinking to the concept of a general inductive approach to qualitative analysis as described by Thomas. Finally, they illustrate connections of both critical thinking and the general inductive approach to technology using NVivo screenshots.
Genetic diversity in vector populations influences the transmission efficiency of an important plant virus

The transmission efficiency of aphid-vectored plant viruses can differ between aphid populations. Intra-species diversity (genetic variation, endosymbionts) is a key determinant of aphid phenotype; however, the extent of which intra-species diversity contributes towards variation in virus transmission efficiency is unclear. Here, we use multiple populations of two key aphid species that vector barley yellow dwarf virus (BYDV) strain PAV (BYDV-PAV), the grain aphid (Sitobion avenae) and the bird cherry-oat aphid (Rhopalosiphum padi), and examine how diversity in vector populations influences virus transmission efficiency. We use Illumina sequencing to characterise genetic and endosymbiont variation in multiple S. avenae and R. padi populations and conduct BYDV-PAV transmission experiments to identify links between intra-species diversity in the vector and virus transmission efficiency. We observe limited variation in the transmission efficiency of S. avenae, with transmission efficiency consistently low for this species. However, for R. padi we observe a range of transmission efficiency and show that BYDV transmission efficiency is influenced by genetic diversity within the vector, identifying 542 SNPs that potentially contribute towards variable transmission efficiency in R. padi. Our results represent an important advancement in our understanding of the relationship between genetic diversity, vector-virus interactions, and virus transmission efficiency.
2 MAGPIE AS A RESOURCE AGGREGATOR IN EDUCATION

Magpie is a suite of tools supporting a ‘zero-cost’ approach to semantic web browsing: it avoids the need for manual annotation by automatically associating an ontology-based semantic layer to web resources. An important aspect of Magpie, which differentiates it from superficially similar hypermedia systems, is that the association between items on a web page and semantic concepts is not merely a mechanism for dynamic linking, but it is the enabling condition for locating services and making them available to a user. These services can be manually activated by a user (pull services), or opportunistically triggered when the appropriate web entities are encountered during a browsing session (push services). In this paper we analyze Magpie from the perspective of building semantic web applications and we note that earlier implementations did not fulfill the criterion of “open as to services”, which is a key aspect of the emerging semantic web. For this reason, in the past twelve months we have carried out a radical redesign of Magpie, resulting in a novel architecture, which is open both with respect to ontologies and semantic web services. This new architecture goes beyond the idea of merely providing support for semantic web browsing and can be seen as a software framework for designing and implementing semantic web applications.
DEVELOPMENT OF A WIND TURBINE SIMULATOR FOR WIND ENERGY CONVERSION SYSTEMS – EXPERIMENTAL RESULTS

– The wind turbine simulator consists of a 5HP induction motor, which drives a doubly fed induction generator and is driven by a 19kVA inverter and real-time control software. A control program based on C language is developed that obtains wind profiles and, by using turbine characteristics and rotation speed of induction machine, calculates the theoretical shaft torque of a real wind turbine. The shaft torque of the induction machine is regulated according by controlling stator current demand of the inverter. In this way, the inverter has driven induction motor acts like a real wind turbine to the energy conversion system. The simulation and experimental results of the proposed wind turbine simulator show that scheme is viable.
Optimized Distances for Binary Code Ranking

In this paper, we explore how to utilize the video context to facilitate fashion parsing. Instead of annotating a large amount of fashion images, we present a general, aﬀordable and scalable solution, which harnesses the rich contexts in easily available fashion videos to boost any existing fashion parser. First, we crawl a large unlabelled fashion video corpus with fashion frames. Then for each fashion video, the cross-frame contexts are utilized for human pose co-estimation, and then video co-parsing to obtain satisfactory fashion parsing results for all frames. More speciﬁcally, Sift Flow and super-pixel matching are used to build correspondences across frames, and these correspondences then contextualize the pose estimations and fashion parsing in individual frames. Finally, these parsed video frames are used as the reference corpus for the non-parametric fashion parsing component of the whole solution. Extensive experiments on two benchmark fashion datasets as well as a newly collected challenging Fashion Icon (FI) dataset demonstrate the encouraging performance gain from our general pipeline for fashion parsing.
Structural Biology and Crystallization Communications Structural Classification of Proteins and Structural Genomics: New Insights into Protein Folding and Evolution

During the past decade, the Protein Structure Initiative (PSI) centres have become major contributors of new families, superfamilies and folds to the Structural Classification of Proteins (SCOP) database. The PSI results have increased the diversity of protein structural space and accelerated our understanding of it. This review article surveys a selection of protein structures determined by the Joint Center for Structural Genomics (JCSG). It presents previously undescribed-sheet architectures such as the double barrel and spiral-roll and discusses new examples of unusual topologies and peculiar structural features observed in proteins characterized by the JCSG and other Structural Genomics centres.
A New Secure Image Transmission Technique via Secret-Fragment Visible Mosaic Images by Nearly Reversible Color Transformation

A latest secure image transmission technique is proposed, which transforms automatically a given largevolume secret image into a so-called secret-fragment-visible mosaic image of the similar size. The mosaic image, which looks related to an arbitrarily selected target image as well as may be used as a camouflage of the secret image, is yielded by separating the secret image into fragments plus transforming their colour characteristics to be those of the corresponding blocks of the target image. Skilled techniques are planned to conduct the colour transformation development so that the secret image may be recovered almost losslessly. A plan of handling the overflows/underflows in the converted pixels’ colour values via recording the colour differences in the untransformed colour space is too proposed. The information essential for recovering the secret image is embedded into the formed mosaic image through a lossless data hiding scheme by a key. Good quality experimental results illustrate the feasibility of the proposed method. Key wordsColor transformation, data hiding, image encryption, mosaic image, secure image transmission.
AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction

In this paper, we consider the Click-Through-Rate (CTR) prediction problem. Factorization Machines and their variants consider pair-wise feature interactions, but normally we won’t do high-order feature interactions using FM due to high time complexity. Given the success of deep neural networks (DNNs) in many fields, researchers have proposed several DNN-based models to learn high-order feature interactions. Multi-layer perceptrons (MLP) have been widely employed to learn reliable mappings from feature embeddings to final logits. In this paper, we aim to explore more about these highorder features interactions. Inspired by the success of Densely Connected Convolutional Networks (DenseNet) in computer vision, we propose a novel model called Attentive DenseNet based Factorization Machines (AdnFM). AdnFM can extract more comprehensive deep features by using all the hidden layers from a feed-forward neural network as implicit highorder features, then selects dominant features via an attention mechanism. Also, high-order interactions in the implicit way using DNNs are more cost-efficient than in the explicit way, for example in FM. Extensive experiments on two realworld datasets show that the proposed model can effectively improve the performance of CTR prediction.
Route Choice Modelling and Runtime Optimisation for Simulation of Building Evacuation

Increasing number of visitors at large-scale events combined with the increasing complexity of modern buildings set a major challenge for planners, operators and emergency services. Examples include multi-purpose arenas, large railway stations and airports. In this dissertation the use of modern parallel hardware in combination with optimised algorithms are for the first time used on site to speed up the simulation of large crowds. The aim is to perform real-time forecasts of pedestrian traffic. For this purpose, special neighbourhood lists and a two-stage hybrid parallelisation are used. The second part of this dissertation deals with route choice in complex structures, which plays an important role in achieving realistic computer simulations of pedestrian flows. The developed route choice process is based on visibility and perception of the local environment by the simulated agents. It has as basis a navigation graph. The generation of the graph, especially in complex structures, has also been performed within the framework of this thesis. The work is closed with an empirical study in which the route choice profiles of spectators during various football games and concert performances are analysed and compared with the proposed model. The runtime optimisation strategies and route choice algorithms have been successfully tested in the ESPRIT arena in Dusseldorf (Germany), where they have been integrated in an evacuation assistant.
Aquatic carbon cycling from the headwaters to the sea in Eastern Australia

Inland aquatic systems are now considered important components of the global carbon (C) budget. However, the drivers of spatiotemporal variability in C fluxes occurring within and between inland aquatic systems are not well understood. This original fieldbased research aims to improve understanding of the factors influencing C cycling across aquatic gradients from the headwaters to estuaries. Variability was characterised in eight water catchments on the east coast of Australia at intra-ecosystem spatial and over diel-to-seasonal temporal scales, as well as during both the active (baseflow) and passive (stormflow) phases of the hydrologic cycle. The influence of land use change was also investigated. Findings reveal that ephemeral stream CO2 concentrations can vary up to 920 μM over a single diel cycle and up to 1600 μM over the duration of a single wet-dry cycle. These ranges were greater in magnitude than the spatial variability found for CO2 within all other perennial waters within the same water catchment (e.g., wetlands, streams, lakes, impoundments), as well as for the studied estuaries. While CO2 concentrations were most variable in the ephemeral watercourse, the gas transfer velocities at the water-air interface for this system had a relatively narrow range compared to the perennial waters of the same catchment. Accordingly, the variability in corresponding CO2-C flux rates were greater for the perennial systems than for the ephemeral watercourse. Land use change for urban and agricultural purposes was found to decrease estuarine CO2 concentrations due to nitrogen-fuelled uptake in impacted estuaries and high DOC in pristine systems. This study shows that the temporal variability occurring over diel cycles and during storm events can exceed that occurring at local spatial scales. Furthermore, that the relative areal contributions for each aquatic ecosystem type is a primary determinant in the importance of each system type in local and regional
Haptic experimentation on a hybrid active/passive force feedback device

This paper describes the design and implementation of a hybrid active/passive force feedback device. It has a single degree of freedom that is actuated by a motor and a brake pair. The use of a motor and a brake allows various objects to be simulated without a stability problem and related safety issues involved with using high torque motors only. The device performance is measured by its ability to simulate various test objects. Simple test objects are modeled as a benchmark test of the system's performance. The force feedback device is capable of simulating forces in a variety of virtual environments. This device demonstrates the effectiveness of a hybrid active/passive haptic actuator. An analysis of the use of hybrid active/passive actuators in the 2 degrees of freedom case finds that they are capable of simulating a rigid body in a broad range.
Homography estimation of a moving planar scene from direct point correspondence

Homographies provide a robust and reliable cue for visual servo control of robots. Some nonlinear observers have been recently developed for the estimation of temporal sequences of homographies associated with rigid-body motion of a camera observing a stationary planar scene. However, these algorithms do not model well time-varying changes in the homography velocity and tend to perform poorly when the camera or the scene moves fast. In this paper, an internal model-based observer posed on S L (3) for homography estimation is proposed allowing for dealing with complex camera-scene trajectories such as circular and sinusoidal motions of the camera and/or the scene. Rigorous proof of local asymptotic stability is established and excellent performance of the proposed observer is justified by experiments using an IMU-Camera prototype observing an oscillating planar target.
De-identifying facial images using singular value decomposition

In this paper, a method is proposed that manipulates images in a manner that hinders face recognition by automatic recognition algorithms. The purpose of this method, is to partly degrade image quality, so that humans can identify the person or persons in a scene, while common classification algorithms fail to do so. The approach used to achieve this involves the use of singular value decomposition (SVD). From experiments it can be concluded that, the method reduces the percentage of correct classification rate by over 90%. In addition, the final image is not degraded beyond recognition by humans.
AIS Electronic Library (AISeL)

To date, numerous open source projects are hosted on many online repositories, reflecting the popularity of open source projects. While some of these projects are active and thriving, some of them are either languishing or showing no development activities at all. This phenomenon thus begs the important question of what are the influential factors that affect the success of open source projects. In a quest to deepen our understanding of the evolution of open source projects, this research aims to analyze the survival of open source projects by using the theoretical lens of social network analysis. Based on extensive analyses of empirical data collected from online open source repositories, we study the impact of the communication patterns of the development teams on the outcomes of these projects, while accounting for project-specific characteristics. Using panel data analysis, we find significant impacts of communication patterns on project outcomes over the long term.
Geometric Attack Resistant Image Watermarking Scheme for Providing High

The main theme of this application is to provide an algorithm color image watermark to manage the scaling and translation. In the existing watermarki ng algorithms, those exploited robust features are more or less related to pixel position, so they cannot be more robust again st the attacks. In order to solve this problem t parameters rather than the pixel position for water marking. Two statistical features such as the histo gram shape and the mean Gaussian filtered low-frequency component of images are taken for this pr oposed application to algorithm robust to attacks and also interpolation technique is used to increase the number of bites t o be needed
Sakarya University Journal of Science

Ship detection and classification systems from satellite images are challenging tasks with their requirements of feature extracting, advanced pre-processing, a variety of parameters obtained from satellites and other types of images, and analyzing of images. The dissimilarity of results, enhanced dataset requirement, the intricacy of the problem domain, general use of Synthetic Aperture Radar (SAR) images and problems on generalizability are some topics of the issues related to ship detection. In this study, we propose a Deep Convolutional Neural Network (DCNN) model for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracy value by just using a pre-processed satellite image with a deep learning model built from scratch. The designed CNN model is constructed with a plain and easy to implement form in particular to the preferred satellite image set. Visual and graphical results show that the proposed model provides an efficient detection process with an accuracy of 99.60%.
Political autonomy and independence: Theory and experimental evidence

We use a game-theoretical model and results from laboratory experiments to study the process by which subordinated regions of a country can obtain a more favorable political status. In our theoretical model a dominant and a dominated region first interact through a political process. This process involves two referenda, one at the level of the country as a whole and one at the level of the subordinated region. If the political process succeeds, then the new autonomy level is implemented. If this process fails, then both regions engage in a costly political conflict in which both sides can spend resources to win the upper hand. We show that in the subgame-perfect equilibrium of our game the voting process leads to an intermediate arrangement acceptable for both parts so that the costly political struggle never occurs. In contrast, in our experiments we observe frequent fighting involving high material losses.
Word clustering and disambiguation based on co-occurrence data

We address the problem of clustering words (or constructing a thesaurus) based on co-occurrence data, and conducting syntactic disambiguation by using the acquired word classes. We view the clustering problem as that of estimating a class-based probability distribution specifying the joint probabilities of word pairs. We propose an efficient algorithm based on the Minimum Description Length (MDL) principle for estimating such a probability model. Our clustering method is a natural extension of that proposed in Brown, Della Pietra, deSouza, Lai and Mercer (1992). We next propose a syntactic disambiguation method which combines the use of automatically constructed word classes and that of a hand-made thesaurus. The overall disambiguation accuracy achieved by our method is 88.2%, which compares favorably against the accuracies obtained by the state-of-the-art disambiguation methods.