Miracle-dz/UniFAR-FTUs huggingface dataset:
Image retagging

Online social media repositories such as Flickr and Zooomr allow users to manually annotate their images with freely-chosen tags, which are then used as indexing keywords to facilitate image search and other applications. However, these tags are frequently imprecise and incomplete, though they are provided by human beings, and many of them are almost only meaningful for the image owners (such as the name of a dog). Thus there is still a gap between these tags and the actual content of the images, and this significantly limits tag-based applications, such as search and browsing. To tackle this issue, this paper proposes a social image "retagging" scheme that aims at assigning images with better content descriptors. The refining process, including denoising and enriching, is formulated as an optimization framework based on the consistency between "visual similarity" and "semantic similarity" in social images, that is, the visually similar images tend to have similar semantic descriptors, and vice versa. An effective iterative bound optimization algorithm is applied to learn the improved tag assignment. In addition, as many tags are intrinsically not closely-related to the visual content of the images, we employ knowledge based method to differentiate visual content related tags from unrelated ones and then constrain the tagging vocabulary of our automatic algorithm within the content related tags. Finally, to improve the coverage of the tags, we further enrich the tag set with appropriate synonyms and hypernyms based on an external knowledge base. Experimental results on a Flickr image collection demonstrate the effectiveness of this approach. We will also show the remarkable performance improvements brought by retagging via two applications, i.e., tag-based search and automatic annotation.
Analysis of Handover Failures in Heterogeneous Networks With Fading

The handover process is one of the most critical functions in a cellular network and is in charge of maintaining seamless connectivity of user equipments across multiple cells. The handover process is driven by signal measurements from the neighboring base stations (BSs), and it is adversely affected by the time and frequency selectivity of the radio propagation channel. In this paper, we introduce a new model for analyzing handover performance in heterogeneous networks (HetNets) as a function of vehicular user velocity, cell size, and mobility management parameters. In order to investigate the impact of shadowing and fading on handover performance, we extract relevant statistics obtained from a Third-Generation Partnership Project (3GPP)-compliant HetNet simulator, and subsequently, we integrate these statistics into our analytical model to analyze both handover failure and ping-pong probabilities under fluctuating channel conditions. Computer simulations validate the analytical findings, which show that fading can significantly degrade the handover performance in HetNets with vehicular users.
Analytic antialiasing with prism splines

The theory of the multivariate polyhedral splines is applied to analytic antialiasing: a triangular simplex spline is used to represent surface intensity, while a box spline is used as a ﬁlter. Their continuous convolution is a prism spline that can be evaluated exactly via recurrence. Evaluation performance can be maximized by exploiting the properties of the prism spline and its relationship to the sampling grid. After sampling, digital signal processing can be used to evaluate exactly and efﬁciently the sampled result of any analytic spline ﬁlter in the span of the box spline basis used as the original analytic ﬁlter.
Semantic Annotation for Learning Material

This paper aims at providing the specification for semantic annotation tools for e-learning. From the specific requirements of annotating learning material, we categorize and evaluate the existing annotation tools, mainly general purpose ones. We illustrate two research prototypes of annotation tools we developed, and evaluate to what extend the specific requirements of annotating learning material are reached by these research prototypes.
Applicability of the Global Lung Initiative 2012 Reference Values for Spirometry for Longitudinal Data of Elderly Women

Background and Objectives Lung function depends nonlinearly on age and height, so that the use of age and height specific reference values is required. The widely used age and height specific GLI (Global Lung Initiative) z-scores derived from cross-sectional data, however, have not been proven for validity in an elderly population or for longitudinal data. Therefore, we aimed to test their validity in a population of elderly women followed prospectively for more than 20 years. Methods We used spirometric data (forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) and FEV1/FVC) from the SALIA cohort of German women (baseline: 1985–1994 (aged 55 years), follow-up: 2008/2009 and 2012/2013). We calculated GLI-z-scores for baseline and follow-up examination separately (cross-sectional evaluation) and individual differences in z-scores between baseline and follow-up (longitudinal evaluation) for healthy never-smoking women. Results GLI reference values for FEV1, FVC and FEV1/FVC were cross-sectionally and longitudinally equivalent with our SALIA data. The mean change in z-scores between baseline and follow-up was 0.33 for FEV1, 0.38 for FVC and -0.10 for FEV1/FVC. Conclusions In conclusion, GLI z-scores fit cross-sectionally and longitudinally with FEV1, FVC and FEV1/FVC measured in women from Germany which indicates that they can be used in longitudinal association analyses.
Measuring Local Topological Anonymity in Social Networks

Service providers of social network based services release their sanitized graph structure for third parties (e.g., business partners) from time to time. However, as these releases contain valuable information additionally to what is publicly available in the network, these may be targeted by re-identification attacks, i.e., where an attacker tries to recover the identities of the nodes that were removed during the sanitization process. One powerful type of these, called structural re-identification attacks consider only structural properties, and work according to a specific strategy: first they re-identify some nodes by their globally unique properties, and then in an optional second phase, nodes related to these are re-identified by their locally unique properties. Global re-identifiability or global node anonymity is a well studied concept, however, node anonymity for local re-identification has not yet been analyzed. Therefore in this paper, after discussing the related literature on anonymity and re-identification, we introduce the novel term of Local Topological Anonymity (LTA), which describes the resistant power of a node against local re-identification attacks, or, in other words, indicates how well the node is structurally hidden in her neighborhood. Regarding these attacks in the literature, we propose three measure variants of LTA based on structural similarity measures, and evaluate them by visual inspection and simulation in multiple networks. We show that one of the proposed measures provides good prediction on local node re-identifiability as there is correlation between the LTA values and the re-identification statistics provided by the state-of-the-art algorithm.
Detecting Election Fraud from Irregularities in Vote-Share Distributions

I develop a novel method to detect election fraud from irregular patterns in the distribution of vote-shares. I build on a widely discussed observation that in some elections where fraud allegations abound, suspiciously many polling stations return coarse vote-shares (e.g., 0.50, 0.60, 0.75) for the ruling party, which seems highly implausible in large electorates. Using analytical results and simulations, I show that sheer frequency of such coarse vote-shares is entirely plausible due to simple numeric laws and does not by itself constitute evidence of fraud. To avoid false positive errors in fraud detection, I propose a resampled kernel density method (RKD) to measure whether the coarse vote-shares occur too frequently to raise a statistically qualified suspicion of fraud. I illustrate the method on election data from Russia and Canada as well as simulated data. A software package is provided for an easy implementation of the method.
Oesophageal ulceration: more than meets the naked eye?

Background: Hepatic steatosis is associated with obesity and type II diabetes. Proton magnetic resonance spectroscopy (1H MRS) is a non-invasive method for measurement of tissue fat content, including intrahepatocellular lipids (IHCL) and intramyocellular lipids (IMCL). Patients and methods: We used 1H MRS and whole body magnetic resonance imaging (MRI) to assess the relationship between IHCL accumulation, total body adipose tissue (AT) content/distribution, and IMCL content in 11 subjects with biopsy proven hepatic steatosis and 23 normal volunteers. Results: IHCL signals were detectable in all subjects but were significantly greater in hepatic steatosis (geometric mean (GM) 11.5 (interquartile range (IQR) 7.0–39.0)) than in normal volunteers (GM 2.7 (IQR 0.7–9.3); p = 0.02). In the study group as a whole, IHCL levels were significantly greater in overweight compared with lean subjects (body mass index (BMI) >25 kg/m2 (n = 23): GM 7.7 (IQR 4.0–28.6) v BMI <25 kg/m2 (n = 11): GM 1.3 (IQR 0.3–3.6; p = 0.004)). There was a significant association between IHCL content and indices of overall obesity (expressed as a percentage of body weight) for total body fat (p = 0.001), total subcutaneous AT (p = 0.007), and central obesity (subcutaneous abdominal AT (p = 0.001) and intra-abdominal AT (p = 0.001)), after allowing for sex and age. No correlation between IHCL content and IMCL was observed. A significant correlation was observed between serum alanine aminotransferase and liver fat content (r = 0.57, p = 0.006). Conclusions: Our results suggest that hepatic steatosis appears to be closely related to body adiposity, especially central obesity. MRS may be a useful method for monitoring IHCL in future interventional studies.
Endoplasmic reticulum stress and IRE-1 signaling cause apoptosis in colon cancer cells in response to andrographolide treatment

The plant metabolite andrographolide induces cell cycle arrest and apoptosis in cancer cells. The mechanism(s) by which andrographolide induces apoptosis however, have not been elucidated. The present study was performed to determine the molecular events that promote apoptosis in andrographolide treated cells using T84, HCT116 and COLO 205 colon cancer cell lines. Andrographolide was determined to limit colony formation and Ki67 expression, alter nuclear morphology, increase cytoplasmic histone-associated-DNA-fragments, and increase cleaved caspase-3 levels. Andrographolide also induced significantly higher expression of endoplasmic reticulum (ER) stress proteins GRP-78 and IRE-1 by 48 h but not PERK or ATF6. Apoptosis signaling molecules BAX, spliced XBP-1 and CHOP were also significantly increased. Moreover, chemical inhibition of ER stress or IRE-1 depletion with siRNA in andrographolide treated cells significantly limited expression of IRE-1 and CHOP as determined by immunofluorescence staining, real time PCR, or immunobloting. This was accompanied by a decreased BAX/Bcl-2 ratio. Andrographolide significantly promotes cancer cell death compared to normal cells. These data demonstrate that andrographolide associated ER stress contributes to apoptosis through the activation of a pro-apoptotic GRP-78/IRE-1/XBP-1/CHOP signaling pathway.
Hierarchical optimization using hierarchical multi-competitive genetic algorithm and its application to multiple Vehicle Routing Problem

In this paper a new optimization technique which is effective for hierarchical optimization problem is proposed. This technique is an extension of the multiple-competitive distributed genetic algorithm (mcDGA). This method consists of two levels upper and lower. The solution space to be searched is determined at the upper level, and the optimum solution in a given solution space is determined at the lower level. The migration of the individual and competition is performed at the lower layer thereby optimal solution can be found efficiently. We apply the proposed hierarchical mcDGA to the mVRP to confirm the effectiveness. Simulation result shows the effectiveness of the proposed method.
Review of Extracting Content Structure from Web Pages using page segmentation

-The World Wide Web has several online databases and the number keeps growing every day. The data in the web pages is generally wrapped in the form of data records. Such web pages are generated dynamically. This paper focuses on extracting the data from the web pages. Till today, several techniques are proposed for retrieving information from web pages but all suffer the common problem. The problem is dependence on programming language used to design the web pages. So this paper focuses on utilizing the visual features of web page for extracting the data from the deep web pages. To make the system efficient, it can be combined with non-visual information like the symbols and tags. Approach of this paper is independent of any specific web programming language and hence it can be extended to various web pages which have different underlying architecture.
An Exploration of Capability Research

Due to the global digitalization, fast shifting business models and short technology lifecycles, modern enterprises need a strategy how to deal with those unpredictable changes in order to stay competitive. The concept of capability driven management like capability-based-planning or investment gets more and more attention by executives and scientists. In the last decade IS and management journals as well as conferences were publishing an increasing number of capability related articles, but a common understanding corresponding the identification of capabilities, their management, types or elements seems to be not existing. This work encapsulates the body of capability literature to provide an overview about capability research investigations over the last 15 years.
Amylose engineering: phosphorylase‐catalyzed polymerization of functional saccharide primers for glycobiomaterials

Interest in amylose and its hybrids has grown over many decades, and a great deal of work has been devoted to developing methods for designing functional amylose hybrids. In this context, phosphorylase‐catalyzed polymerization shows considerable promise as a tool for preparing diverse amylose hybrids. Recently, advances have been made in the chemoenzymatic synthesis and characterization of amylose‐block‐polymers, amylose‐graft‐polymers, amylose‐modified surfaces, hetero‐oligosaccharides, and cellodextrin hybrids. Many of these saccharides provide clear opportunities for advances in biomaterials because of their biocompatibility and biodegradability. Important developments in bioapplications of amylose hybrids have also been made, and such newly developed amylose hybrids will help promote the development of new generations of glyco materials. WIREs Nanomed Nanobiotechnol 2017, 9:e1423. doi: 10.1002/wnan.1423 For further resources related to this article, please visit the WIREs website.
Secondary teachers’ experiences with students with disabilities: examining the global landscape

ABSTRACT This study contributes to the international conversation about the education for students with disabilities at the secondary level by providing some international data about secondary teachers’ training and experiences with inclusive practices. Teacher leaders from 20 different countries participated in this exploratory survey research about the context in which they work, their experiences and their training as related to their efficacy to educate individuals with disabilities. We acknowledge that efficacy and teacher training for inclusion are predominated by a discourse defined through Western best practice. We learned that despite variations in the countries’ concept of disability and their implementation of inclusive educational systems, the teachers had moderately high self-efficacy for inclusive practices yet had limited training and experience with students with special needs. Results are discussed in relation to the preparation of secondary teachers for educating students with special needs.
A SIFT-LBP IMAGE RETRIEVAL MODEL BASED ON BAG-OF-FEATURES

Despite progress in image retrieval by using low-level features, such as colors, textures and shapes, the performance is still unsatisfied as there are existing gaps between low-level features and high-level semantic concepts (semantic gaps). In this research, we propose a novel image retrieval system based on bag-of-features (BoF) model by integrating scale invariant feature transform (SIFT) and local binary pattern (LBP). We show that SIFT and LBP features yield complementary and substantial improvement on image retrieval even in the case of noisy background and ambiguous objects. Two new integration models are proposed: patch-based integration and image-based integration. By using a weighted Kmeans clustering algorithm, the image-based SIFT-LBP integration achieves the superior performance on a given benchmark problem comparing to other existing algorithms.
Design of HDTS system for locating logical errors in Java programs

In the previous work, we presented HDTS for debugging logical errors in Java programs. The HDTS is a technique that combines an algorithmic program debugging a step-wise debugging, and a program slicing. It reduced the number of programmer debugging efficiently in Java programs. This paper designs HDTS system for implementing HDTS technique.
Association between the STK15 F31I Polymorphism and Cancer Susceptibility: A Meta-Analysis Involving 43,626 Subjects

The association between the Serine/threonine kinase 15 (STK15) F31I polymorphism (rs2273535) and cancer susceptibility remains controversial. To further investigate this potential relationship, we conducted a comprehensive meta-analysis of 27 published studies involving a total of 19,267 multiple cancer cases and 24,359 controls. Our results indicate statistical evidence of an association between the STK15 F31I polymorphism and the increased risk of overall cancer in four genetic models: AA vs. TA+TT, AA vs. TT, AA vs. TA, and A vs. T. In a stratified analysis by cancer type, there was an increased risk of breast cancer in four genetic models: AA vs. TA+TT, AA vs. TT, AA vs. TA, and A vs. T, as well as esophageal cancer in two genetic models: AA vs. TA+TT and AA vs. TA. In a stratified analysis by ethnicity, there was a significant increase in cancer risk among Asians, but not Caucasians, in four genetic models: AA vs. TA+TT, AA vs. TT, AA vs. TA and A vs. T. In addition, a stratified analysis by ethnicity in the breast cancer subgroup revealed a significant increase in cancer risk among Asians in two genetic models: AA vs. TA+TT and AA vs. TT, as well as among Caucasians in one genetic model: AA vs. TA. In summary, this meta-analysis demonstrates that the STK15 F31I polymorphism may be a risk factor for cancer.
The Composition and Use of Modern Mobile Phonebooks

Over the past decade, the mobile phonebook has evolved from a relatively short list of people that one calls and texts to a many-hundred person list of aggregated contacts from around the web. This is happening at a time when an increasing number of mobile applications are relying on the mobile phonebook to create one's social network in their services. Through a large-scale study of the phonebooks of 200 diverse participants, containing 65,940 contacts, we set out to understand today's mobile contact lists. Our participants reported that they did not recognize the names of 29% of their contacts and we found that the most frequently contacted five contacts represent greater than 80% of all calls and text messages with phonebook contacts. We conclude with implications for the design of mobile applications that rely on phonebook data.
Fault Diagnosis of Hydroturbine Generating Units Based on Least Squares Support Vector Machines

The algorithm of support vector machines (SVM), a novel machine learning method based on statistical learning theory, has been successfully used in pattern recognition and function estimation. The theory of least squares support vector machines (LS-SVM) is a least squares version of standard SVM, which involves equality instead of inequality constraints and works with a least squares object function. A systematic approach based on LS-SVM and wavelet decomposition for fault diagnosis of hydroturbine generating units (HGU) is proposed in this paper. The vibration signals under abnormal conditions are collected and preprocessed with the wavelet decomposition and feature information of signals is extracted as the feature vectors for training and testing the LS-SVM. To classify multiple fault modes of HGU, a multiclass classifier based on LS-SVM with minimum output codes (MOC) is constructed and used in the fault diagnosis for HGU. It's showed in the simulation result that the fault types can be identified and diagnosed by the above method. Compared with the result of a RBF neural network, more excellent identification accuracy indicates the feasibility and effectiveness of LS-SVM in the fault diagnosis of HGU.
UNIVERSAL OUTPUT FEEDBACK CONTROL OF NONLINEAR SYSTEMS WITH UNKNOWN GROWTH RATE

This paper investigates the problem of global stabilization by output feedback for a family of uncertain nonlinear systems dominated by a triangular system satisfying linear growth condition. In contrast to the previous work in the literature, the growth rate here is a positive constant but not known a priori, and therefore the problem cannot be addressed by the existing output feedback control schemes. Using the idea of universal control integrated with the recent output feedback design method that is not based on the separation principle, we construct a universal-type output feedback controller which globally regulates all the states of the uncertain systems without knowing the growth rate. We also discuss briefly how this result can be extended to a larger class of nonlinear systems with unknown parameters. Copyright c ©2005 IFAC
Some new estimation methods for weighted regression where there are possible outliers

The problem considered is the robust estimation of the variance parameter in a heteroscedastic linear model. We treat the situation in which the variance is a function of the explanatory variables. To estimate robustly the variance in this case, it is necessary to guard against the influence of outliers in the design as well as outliers in the response. By analogy with the homoscedastic regression case, we propose two estimators that do this. Their performances are evaluated on a number of data sets. We had considerable success with estimators that bound the “self-influence”—that is. the influence an observation has on its own fitted value. We conjecture that in other situations (e.g., homoscedastic regression) bounding the selfinfluence will lead to estimators with good robustness properties.