Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Adverse Events Classification for Domain Knowledge Extraction

In the viewpoint of sharing knowledge, users need to input keywords before the agent retrievals the related information from the Internet. At the same time, the traditional method ignored the true meaning of the terms. However, the semantic web has been created to improve the disadvantage. Ontology is the fundamental element of the semantic web that is a kind of knowledge presentation and can present the variety of property for concept.According to the regulation of Department of Health, Executive Yuan, R.O. C, the hospital has implemented the new Hospital Accreditation from 2005. Furthermore, the Hospital Accreditation evolves the variety of items of accreditation. Medical adverse event reporting is a kind of the Hospital Accreditation. The system which can deal with the medical adverse event reporting is operated by human at present. None the less, the system caused some questions, such as inconsistent knowledge and human negligence. The research proposed a new framework which can replace the human automatically. We get concepts through extraction of information and analyze sentences through classification of events. After that we analyze those classes which can construct variable and dynamic ontology in medical adverse event reporting.
MUTLU , YILMAZ 49 SIMULATION AND PERFORMANCE ANALYSIS OF DISTRIBUTED COOPERATIVE TRUST BASED INTRUSION DETECTION FRAMEWORK FOR MANETs

Mobile Ad Hoc Network (MANET) is a collection of nodes, which form an infastructureless topology. There is no central access point or centralized management. Intrusion detection in MANETs, however, is challenging for a number of reasons. This paper introduces intrusion detection architecture for MANETs, based on trust relationship and cooperation. In our proposed framework, intrusion detection system relies on local and global determination of attacks within network and intrusion detection is carried out in a distributed fashion. Reputation mechanism is used for trust assessment, which is obtained by watching the neighbor nodes behaviors. IDS alert messages are used to disseminate evidences of an intrusion attempt. A distributed IDS engine is the focal point of the architecture and we aim to utilize a cooperative trust based intrusion detection system to cope with the disadvantages drawn from mobility of nodes. In this paper, we present the feasibility of the proposed architecture by a detailed performance analyses according to the results obtained from simulations.
Analysis of A Predator-Prey Population Model

In this paper, we have introduced a fractional-ordered predator-prey population model which has been successfully solved with the help of two powerful analytical methods, namely, Homotopy Perturbation Method (HPM) and Variational Iteration Method (VIM). The fractional derivatives are described in the Caputo sense. Using initial values, we have derived the explicit solutions of predator-prey populations for different particular cases by using HPM and VIM. This paper represents an analytical as well as numerical comparison between these two methods for solving our fractional-ordered predator-prey population model for different fractional orders. Our analytical and numerical results show that these two techniques require less computational work and provide the approximate solutions with easily computable components. These methods are extremely efficient for obtaining quantitatively reliable results.
Meta-Analysis: Effects of Probiotic Supplementation on Lipid Profiles in Normal to Mildly Hypercholesterolemic Individuals

Introduction Recent experimental and clinical studies have suggested that probiotic supplementation has beneficial effects on serum lipid profiles. However, there are conflicting results on the efficacy of probiotic preparations in reducing serum cholesterol. Objective To evaluate the effects of probiotics on human serum lipid levels, we conducted a meta-analysis of interventional studies. Methods Eligible reports were obtained by searches of electronic databases. We included randomized, controlled clinical trials comparing probiotic supplementation with placebo or no treatment (control). Statistical analysis was performed with Review Manager 5.3. 3. Subanalyses were also performed. Results Eleven of 33 randomized clinical trials retrieved were eligible for inclusion in the meta-analysis. No participant had received any cholesterol-lowering agent. Probiotic interventions (including fermented milk products and probiotics) produced changes in total cholesterol (TC) (mean difference –0.17 mmol/L, 95% CI: –0.27 to –0.07 mmol/L) and low-density lipoprotein cholesterol (LDL-C) (mean difference –0.22 mmol/L, 95% CI: –0.30 to –0.13 mmol/L). High-density lipoprotein cholesterol and triglyceride levels did not differ significantly between probiotic and control groups. In subanalysis, long-term (>4-week) probiotic intervention was statistically more effective in decreasing TC and LDL-C than short-term (≤4-week) intervention. The decreases in TC and LDL-C levels with probiotic intervention were greater in mildly hypercholesterolemic than in normocholesterolemic individuals. Both fermented milk product and probiotic preparations decreased TC and LDL-C levels. Gaio and the Lactobacillus acidophilus strain reduced TC and LDL-C levels to a greater extent than other bacterial strains. Conclusions In conclusion, this meta-analysis showed that probiotic supplementation could be useful in the primary prevention of hypercholesterolemia and may lead to reductions in risk factors for cardiovascular disease.
Recent Developments in High Performance FFT Algorithms and Architectures: A Learning Module for Undergraduate Algorithms and Architecture Courses

The Discrete Fourier Transform (DFT) has a large number of signal processing applications such as computational medicine, communications, instrumentation, biomedical engineering, sonics and acoustics, numerical methods, applied mechanics and engineering. DFTs are not computed directly, but instead depend on Fast Fourier Transform (FFT). FFT algorithms efficiently compute the Fourier Transform by speeding up the number of computations as compared to DFT. Many of these algorithms generally adopt parallel approaches of FFT computation to improve performance. Introducing FFT algorithms to Computer Science and Engineering students is not an easy task. Therefore, we attempted to develop an active learning module to help students understand recent developments in FFT algorithms and architectures. This paper will focus on both sequential and parallel FFT algorithms and recent advances in hardware architectures.
Cross-Validation of Multivariate

In recent years, the focus of study in smoothing parameter selection for kernel density estimation has been on the univariate case, while multivariate kernel density estimation has been largely neglected. In part, this may be due to the perception that calibrating multivariate densities is substantially more diicult. In this paper, we explicitly derive and compare multivariate versions of the bootstrap method of Taylor (1989), the least-squares cross-validation method developed by Bowman (1984) and Rudemo (1982), and a biased cross-validation method similar to that of Scott and Terrell (1987) for mul-tivariate kernel estimation using the product kernel estimator. The theoretical behavior of these cross-validation algorithms is shown to improve (surprisingly) as the dimension increases, approaching the best rate of O(n ? 1=2). Simulation studies suggest that the new biased cross-validation method performs quite well and with reasonable variability as compared to the other two methods. Bivariate examples with heart disease and ozone data are given to illustrate the behavior of these algorithms.
Improving the face recognition system by hybrid image preprocessing

In this paper we present a framework for improving face recognition system that have several stages. Some improvements of every stage are very important to the recognition results. Driven by this intuition, we proposed a novel scheme that gives the system a better performance. The scheme including dataset augment for learning, especially for big data requirement of deep learning. Enhancing the image contrast ratio and rotate the image for several angles that can improve the detection accuracy. Then, cropping the face in appropriate area for feature extraction and getting the optimal feature vector for face recognition at last.
The paradox of embedded agency from a strong structuration perspective

PurposeThis paper aims to use Stones’ strong structuration theory (SST) that combines Giddens’ duality and Archer’s analytical dualism to deal with the paradox of embedded agency, focussing on resistance, in the budgeting literature. It also applies this framework to an illustrative case study that examines a failed attempt to implement performance-based budgeting (PBB) in the Egyptian Sales Tax Department (ESTD). Design/methodology/approachThe authors have used SST as an analytical framework. Longitudinal case study data were collected from interviews, observations, discussions and documentary analysis and from publicly available reports and other media issued by the World Bank.FindingsThe SST framework identifies the circumstances in which middle managers as embedded agency have limited possibilities to change their dispositions to act and identify opportunities for emancipation in the wider social context in which they are embedded. The official explanation for the failure to implement PBB in Egypt was obstruction by middle managers. The findings of this study provide an alternative explanation to that published by the World Bank for the failure to institutionalise PBB in Egypt. It was found that the middle managers were the real supporters of PBB. Other parties and existing laws and regulations contributed to the failure of PBB.Research limitations/implicationsAs a practical implication of the study, the analysis presented here offers an alternative interpretation of the failure of the Egyptian project for monitoring and evaluation to that published by the World Bank. This case and similar cases may enhance the understanding of how and when monitoring and evaluation technologies should be introduced at the global level to manage conflicts of interest between agencies and beneficiaries.Originality/valueThis paper contributes to the extant management accounting literature on the use of ST in addressing the paradox of embedded agency in making or resisting structural change. It uses SST to integrate Giddens’ ST with critical realist theory, incorporating duality and dualism in a stronger model of structuration. The SST framework offers a means of analysing case studies that result from interactions and conjunctures between different groups of actors at different ontological levels. The paper also examines the issue of embedded agency in budgeting research using an illustrative case study from a developing country, Egypt.
Nonparametric weighted feature extraction for classification

In this paper, a new nonparametric feature extraction method is proposed for high-dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L-1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for nonnormal datasets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy.
A simple route to fluids with photo-switchable viscosities based on a reversible transition between vesicles and wormlike micelles

Recently, there has been much interest in photorheological (PR) fluids, i.e., fluids whose rheological properties can be tuned by light. In particular, there is a need for simple, low-cost PR fluids that can be easily created using inexpensive, commercially available ingredients and that show substantial, reversible changes in rheology upon exposure to different wavelengths of light. Towards this end, we report a class of photoreversible PR fluids prepared by combining the azobenzene derivative 4-azobenzene carboxylic acid (ACA) (in its salt form) with the cationic surfactant erucyl bis(2-hydroxyethyl)methyl ammonium chloride (EHAC). We show that certain aqueous mixtures of EHAC and ACA, which are low-viscosity solutions at the outset, undergo nearly a million-fold increase in viscosity when irradiated with UV light. The same solutions revert to their initial viscosity when subsequently exposed to visible light. Using an array of techniques including UV-vis and NMR spectroscopies, small-angle neutron scattering (SANS) and cryo-transmission electron microscopy (cryo-TEM), we have comprehensively characterized these PR fluids at the molecular, nanostructural, and macroscopic scales. Initially, EHAC–ACA are self-assembled into unilamellar vesicles, which are discrete container structures and give the sample a low viscosity. Upon exposure to UV light, ACA undergoes a trans to cis photoisomerization, which alters the geometry of the EHAC–ACA complex. In turn, the molecules self-assemble into a different structure, viz. wormlike micelles, which are long, entangled chains and impart a high viscosity to the sample. The above changes in viscosity are repeatable, and the sample can be reversibly cycled back and forth between low and high viscosity states. Our photoreversible PR fluids can be easily replicated in any industrial or academic lab, and it is hoped that these “smart” fluids will eventually find a host of applications.
An automatic noun compound extraction from Arabic corpus

The identification of noun compound as multi-word lexical units is very important task in natural language processing applications that require some degree of semantic interpretation such as, machine translation, information retrieval and text summarization. In this paper, we used the hybrid method for extracting the noun compound from Arabic corpus that is based on linguistic knowledge and statistical measures. For the candidate identification, we have used some linguistic analysis tools such as lemmatization and POS in order to filter the candidates and determine the variations. The association measures have been computed for each candidate to rank the candidates. After that, we have evaluated the association measures by using the n-best evaluation method. We reported the precision values for each association measure in each n-best list. The experimental results showed that the log-likelihood ratio is the best association measure that achieved highest precision.
AN INTRODUCTION TO THE DIAGNOSTIC EVALUATION OF SWITCHBOARD-CORPUS AUTOMATIC SPEECH RECOGNITION SYSTEMS

A diagnostic evaluation of eight Switchboard-corpus recognition (and six forced-alignment) systems was conducted in order to ascertain whether the associated error patterns can be traced to a specific set of factors. Each recognition system’s output was converted to a common format and scored relative to a reference transcript derived from phonetically hand-labeled data (comprising fifty-four minutes of material from several hundred speakers). This reference material was analyzed with respect to several dozen acoustic, linguistic and speaker characteristics, which in turn, were correlated with the recognition-error patterns via a decision-tree analysis. The decision trees indicate that the most consistent factors associated with superior recognition performance pertain to accurate classification of phonetic segments and features. These results suggest that future-generation recognition systems would benefit from improving the acoustic models used for phonetic classification, as well as the pronunciation models involved in lexical matching.
Unsupervised Ontology-based Semantic Tagging for Knowledge Markup

A promising approach to automating knowledge markup for the Semantic Web is the application of information extraction technology, which may be used to instantiate classes and their attributes directly from textual data. An important prerequisite for information extraction is the identification and classification of linguistic entities (single words, complex terms, names, etc.) according to concepts in a given ontology. Classification can be handled by standard machine learning approaches, in which concept classifiers are generated by the collection of context models from a training set. Here we describe an unsupervised approach to concept tagging for ontology-based knowledge markup. We discuss the architecture of this system, and our strategy for and results of performance evaluation.
Effective object tracking by matching object and background models using active contours

In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background. This maximization is implemented using an EM-like algorithm, which evolves the object contour exactly to its boundaries, and adapts the parameters of the object and background distributions.
Improving Question Retrieval in Community Question Answering Using World Knowledge

Community question answering (cQA), which provides a platform for people with diverse background to share information and knowledge, has become an increasingly popular research topic. In this paper, we focus on the task of question retrieval. The key problem of question retrieval is to measure the similarity between the queried questions and the historical questions which have been solved by other users. The traditional methods measure the similarity based on the bag-of-words (BOWs) representation. This representation neither captures dependencies between related words, nor handles synonyms or polysemous words. In this work, we first propose a way to build a concept thesaurus based on the semantic relations extracted from the world knowledge of Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance the question similarity in the concept space. Experiments conducted on a real cQA data set show that with the help of Wikipedia thesaurus, the performance of question retrieval is improved as compared to the traditional methods.
Using CRISP to model global characteristics of fixation durations in scene viewing and reading with a common mechanism

Fixation durations vary when we read text or inspect a natural scene. Past studies suggest that this variability is controlled by the visual input available within the current fixation. The present study directly compared the control of fixation durations in reading and scene viewing in a common experimental paradigm, and attempted to account for the control of these durations within a common modelling framework using the CRISP architecture (Nuthmann, Smith, Engbert, & Henderson, 2010). In the experimental paradigm, a stimulus onset delay paradigm was used. A visual mask was presented at the beginning of critical fixations, which delayed the onset of the text or scene, and the length of the delay was varied. Irrespective of task, two populations of fixation durations were observed. One population of fixations was under the direct control of the current stimulus, increasing in duration as delay increased. A second population of fixation durations was relatively constant across delay. Additional task-specific quantitative differences in the adjustment of fixation durations were found. The pattern of mixed control of fixation durations obtained for scene viewing has been previously simulated with the CRISP model of fixation durations. In the present work, the model's generality was tested by applying its architecture to the text reading data, with task-specific influences realized by different parameter settings. The results of the numerical simulations suggest that global characteristics of fixation durations in scene viewing and reading can be explained by a common mechanism.
Why the going-concern accounting anomaly: gambling in the market?

The market fails to incorporate the adverse information conveyed by the going-concern (GC) opinion in a timely manner. This paper seeks to explain this market-pricing paradox. In particular, we argue that the lottery-like features of GC stocks attract a predominantly retail clientele who use such stocks to gamble in the market. Such trading behavior leads to underreaction to the GC event itself followed by a continuing fall in prices of almost 20% over the next 12 months. Using first time GC firms from 1993 to 2007 we show that GC stocks have extreme lottery-type characteristics. We further demonstrate that retail investors have a proclivity to be net-buyers of these stocks both at the GC event and subsequently, and such contrarian behavior is directly related to the lottery-like nature of GC firms. We test, and rule out, a range of alternative explanations for why retail investors are net-buyers of GC stocks, and conclude that it is their gambling-type behavior that appears to be driving the short-term market reaction to, and the longer-term market response following, the going-concern audit opinion.
One way to cluster documents by meaning using semantic-ball

Semantic technology dealing with meaning is difficult to process meaning as intended by people due to it is flexibility, instability, and even uncertainty. Moreover, there is no explicit definition of how to sense meaning and expression by computer. To overcome these barriers, this paper suggests Semantic-ball, which is a co-occurrence of different word-set, and based on assumptions that Semantic-ball has emergence self-organization attribution, group relationship not pair relationship, and specificity. Therefore this paper focuses on finding Semantic-ball to solve semantic problems. Approach to find Semantic-ball is based on conventional statistical ways, the FP Growth algorithm(Frequency Pattern Growth algorithm)[1], and the bottom-up method. While the previous researches have been based on the definition which used to pin down meaning to solve semantic problems, this paper does not bring any definition. Moreover, Semantic-ball is made only using computing ability automatically. Thus, Semantic-ball helps to sense word and cluster documents by meaning.