Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Factors Influencing Peasant Workers’ Mental Health

Base on the survey data collected in Xiamen in 2008, this study analyzed peasant workers’ health status and the influencing social factors. The SCL-90 scale was applied to measure peasant workers’ mental health, which showed that peasant workers’ mental health did not measure up with that of other groups in our society. Using the SCL-90 score as the dependent variable, and three factors of individual SES (sex, age, income, and relative socioeconomic status), migrant pressure and social capital (trust, network) as predictors, a regression model was constructed, which yielded the following outcomes:1) gender, age and marriage influenced mental health significantly, with male peasant workers better than their female counterparts, elder peasant workers better than their younger counterparts, and those married or divorced better than singles; 2) individuals’ absolute SES did not have significant impact on mental health but their relative SES did; 3) the factor of migrant pressure significantly influenced mental health, and it was a strong predictor – peasant workers as a group migrating to cities for employment turned out to be a main cause for their low mental health as compared with other groups as they might have suffered from the negative impact of all kinds of pressures they had to face in the migration process such as exclusion and unfair treatments from local groups; and 4) the influences of different factors in social capital on mental health varied, some positive, some negative. Network density that indicated interaction with colleagues and neighbors had positive influences on mental health, i.e., the higher the frequency, the lower score of their SCL-90 measure and fewer neurotic symptoms. Although not reaching the significance level, the impact of trust on this group’s mental health was in the positive direction.
Landmark based shape representation scheme for recognition of two dimensional shapes

This paper presents a novel fuzzy-symbolic approach for the recognition of two-dimensional shapes. The proposed method represents a binary shape using symbolic (multi interval valued) features. The representation scheme is based on the landmark points (dominant points) of the shape and the concept of fuzzy equilateral triangle membership function. A similarity measure to estimate the degree of similarity between two shapes is proposed. The proposed method is shown to be invariant to rotation, translation and scale.
Term Sense Disambiguation for Ontology Learning

An important issue in the construction of domain ontologies is the task of identifying terms and their corresponding definitions. Though many methods exist for automatic extraction of terminology from plain text, the semantic interpretation of these terms is either manual or semi-automatic. In this paper we present an unsupervised method for automatic term sense disambiguation (TSD), based on the identification of relevant contextual information from the Web. The proposed TSD can be applied to ontology learning (OL)
Knowledge reuse of software architecture design decisions and rationale within the enterprise

In enterprise application development the architectural decisions and rationale are rarely captured with the intention of reuse across projects. This implies that for new projects, the process of collecting requirements, user scenarios, high-level architecture, low-level design and the implementation proposal will be repeated with little reuse of such details from similar projects enterprise-wide. Rather, it would be much better if there is a structured, organized way to compile the knowledge and the rationale behind the decisions that were taken by the architect, hence paving the way to enable architecture knowledge reuse within in an organization. In this paper we introduce a method and system for documenting the architectural knowledge in a structured, unobtrusive, simple and efficient manner using office automation and Web application technologies to enable automated architectural decision and rationale reuse through enterprise knowledge repository. We have also developed a prototype and presented snapshots of the same in this paper to demonstrate the effectiveness of our proposed approach.
Adaptive Filters

The term adaptive filter implies changing the characteristic of a filter in some automated fashion to obtain the best possible signal quality in spite of changing signal/system conditions. Adaptive filters are usually associated with the broader topic of statistical signal processing. The operation of signal filtering by definition implies extracting something desired from a signal containing both desired and undesired components. With linear FIR and IIR filters the filter output is obtained as a linear function of the observation (signal applied) to the input. An optimum linear filter in the minimum mean square sense can be designed to extract a signal from noise by minimizing the error signal formed by subtracting the filtered signal from the desired signal. For noisy signals with time varying statistics, this minimization process is often done using an adaptive filter.
Making Smart Moves to Untangle Programs

We present a novel algorithm to improve the design of programs by removing circular dependencies between packages without completely collapsing the package structure. This is achieved by moving classes between packages. The algorithm is based on a scoring function that is used to select the classes to be moved. The algorithm is validated against several open source case studies. The results show that our algorithm improves the program structure and removes inter-package cycles.
Multichannel adaptive filtering using an efficient matching pursuit-like algorithm for removal of CPR artifacts in ECG signals

In this contribution we present a novel algorithm for multichannel adaptive filtering. The algorithm is based on selective update of filter coefficients using a matching pursuit-like strategy operating on a time varying “dictionary” given by the current multichannel data matrix. The algorithm has low computational complexity and is robust in rank deficient situations. We present the adaptive filter in an application oriented setting in which the objective is to remove artifacts in electrocardiograms (ECG) resulting from cardiopulmonary resuscitation (CPR). We present simulation results on realistic data showing the superior performance of the algorithm.
Adaptation for Protein Synthesis Efficiency in a Naturally Occurring Self-Regulating Operon

The korAB operon in RK2 plasmids is a beautiful natural example of a negatively and cooperatively self-regulating operon. It has been particularly well characterized both experimentally and with mathematical models. We have carried out a detailed investigation of the role of the regulatory mechanism using a biologically grounded mechanistic multi-scale stochastic model that includes plasmid gene regulation and replication in the context of host growth and cell division. We use the model to compare four hypotheses for the action of the regulatory mechanism: increased robustness to extrinsic factors, decreased protein fluctuations, faster response-time of the operon and reduced host burden through improved efficiency of protein production. We find that the strongest impact of all elements of the regulatory architecture is on improving the efficiency of protein synthesis by reduction in the number of mRNA molecules needed to be produced, leading to a greater than ten-fold reduction in host energy required to express these plasmid proteins. A smaller but still significant role is seen for speeding response times, but this is not materially improved by the cooperativity. The self-regulating mechanisms have the least impact on protein fluctuations and robustness. While reduction of host burden is evident in a plasmid context, negative self-regulation is a widely seen motif for chromosomal genes. We propose that an important evolutionary driver for negatively self-regulated genes is to improve the efficiency of protein synthesis.
In Vitro Cytotoxic Potential of Afghanistan Sand Extract

Abstract : U.S. troops deployed to the Middle East are often exposed to dust storms with particulate matter levels above the military exposure guideline which can result in an increased incidence of pulmonary diseases. The present study evaluated the effects of the soluble components of Afghanistan sand on rat dopaminergic neuronal cells. Sterilized sand samples were extracted overnight in serum free media and exposed at various concentrations (0-500 mg/ml) to the neuronal cells. Following 24 hr cytotoxicity was assessed estimating cellular release of LDH and MTT. Sand extract exposed cells showed increased LDH release dose-dependently with significant increase at higher concentrations, and decreased MTT metabolism. In addition, extract exposure resulted in significant reduction in antioxidant capacity with an elevation of reactive oxygen species. However, N-acetyl cysteine pretreated cells showed decreased ROS and cytotoxicity. This indicated that Afghanistan sand extracts produced cytotoxicity through an oxidative stress mechanism. Moreover, the toxicity was associated with mitochondrial dysfunction followed by release of cytochrome C and increased caspase-3 activity. All these activated apoptotic components were significantly reduced in the presence of cyclosporine A, NAC or z-VAD. Taken together, results indicate that soluble components of Afghanistan sands can be toxic to neuronal cells in culture by enhancing ROS and impairing mitochondrial function.
OLSR based peer to peer instant messaging for Ad-hoc networks

Instant messaging is one of the most popular applications on the Internet. Furthermore, we assist to the emerging of Peer to Peer systems based on a decentralized architecture. These systems share similarities with spontaneous Ad-hoc networks. The crossing of the Ad hoc networks and the Peer to Peer systems opens a field of possibilities for new spontaneous collaborative applications such as the instant messaging. In this work, we propose a Peer to Peer instant messaging system for Ad-hoc networks based on the IETF work in progress. This will guarantee the opening and the viability of the system.
Conditional simulation for efficient global optimization

A classic Kriging or Gaussian process (GP) metamodel estimates the variance of its predictor by plugging-in the estimated GP (hyper)parameters; namely, the mean, variance, and covariances. The problem is that this predictor variance is biased. To solve this problem for deterministic simulations, we propose “conditional simulation” (CS), which gives predictions at an old point that in all bootstrap samples equal the observed value. CS accounts for the randomness of the estimated GP parameters. We use the CS predictor variance in the “expected improvement” criterion of “efficient global optimization” (EGO). To quantify the resulting small-sample performance, we experiment with multi-modal test functions. Our main conclusion is that EGO with classic Kriging seems quite robust; EGO with CS only tends to perform better in expensive simulation with small samples.
An Algorithm for Stochastic Multiple-Choice Knapsack Problem and Keywords Bidding

We model budget-constrained keyword bidding in sponsored search auctions as a stochastic multiple-choice knapsack prob- lem (S-MCKP) and propose a new algorithm to solve S- MCKP and the corresponding bidding optimization prob- lem. Our algorithm selects items online based on a thresh- old function which can be built/updated using historical data. Our algorithm achieved about 99% performance com- pared to the offline optimum when applied to a real bidding dataset. With synthetic dataset and iid item sets, its per- formance ratio against the offline optimum converges to one empirically with increasing number of periods.
Joint Impact of Quantization and Clipping on Single- and Multi-Carrier Block Transmission Systems

This work investigates the joint impact of quantization and clipping, caused by analog-to-digital converters (ADCs) with low bit resolutions, on single- and multi-carrier block transmission systems in wireless multipath environments. We consider single carrier block transmission with frequency domain equalization (SC-FDE) and the multi-carrier techniques OFDM and MC-CDMA. By approximating the ADC input as Gaussian distributed, the effective signal-to-noise ratio in the received signal and decision variables are derived and analyzed. The bit-error rate (BER) performance is simulated and compared for various constellations under different multipath conditions. The results reveal that frequency diversity is an effective measure to combat the joint impact of quantization and clipping. For the ADCs with moderate resolution bits, i.e., R = 5 and 4 bits, SC-FDE is shown to achieve the same performance as MC-CDMA. It is noted that due to the applied Gaussian approximation for the received signals, the derived SC-FDE results provide a lower bound on the performance of the systems under the influence of the ADC nonlinearity. This bound is tight, however, in case of rich multipath environments.
Recognition of facial images with subspace projection and dissimilarity representation

In this work, we present a framework for face recognition, combining face detection algorithm, dimensionality reduction method and a dissimilarity-based classifier. The face detection algorithm is intended to detect and extract faces in complex scenes, prior to face recognition. The Spectral Regression method, in sparse setting, is used for dimensionality reduction. The classification problem is solved by the Proximity Index ”Shape Coefficient” with SVM decision rules and Prototype Selection based classification. The results with real world experiments encourage us to propose this framework as good alternative to other face recognition methods.
A Novel FLDA Formulation for Numerical Stability Analysis

Fisher Linear Discriminant Analysis (FLDA) is one of the most popular techniques used in classification applying dimensional reduction. The numerical scheme involves the inversion of the within-class scatter matrix, which makes FLDA potentially ill-conditioned when it becomes singular. In this paper we present a novel explicit formulation of FLDA in terms of the eccentricity ratio and eigenvector orientations of the within-class scatter matrix. An analysis of this function will characterize those situations where FLDA response is not reliable because of numerical instability. This can solve common situations of poor classification performance in computer vision.
Extraction of indicative summary sentences from imaged documents

A system for selecting sentences from an imaged document for presentation as part of a document summary is presented. The extracts are identified without the use of optical character recognition. The sentences are selected based on a set of discrete features characterizing the words within a sentence and the location of the sentence within the imaged document. Each sentence is scored based on the values of the discrete features using a statistically based classifier. The imaged document is processed to identify the word locations, the reading order of words, and the location of sentence and paragraph boundaries in the text. The words are grouped into equivalence classes to mimic the terms in a text document. A sample extract for a technical document is shown, and evaluation against a set of abstracts created by a professional abstracting company is given. These results are compared with text-based abstracts.
The code of silence and female police officers in Slovenia

Purpose – For some time now, research conducted in the field of human behavior and criminology has pertained to the contemporary question as to whether there are any relevant differences between the genders regarding their integrity and opinions held and, if so, which of these lead to different behaviors. The purpose of this paper is to determine whether there are any gender differences in willingness to report police misconduct and if so, what is the nature of these differences. Design/methodology/approach – In spring 2011, the study was conducted on a representative sample of 408 frontline Slovenian police officers (87.3 percent were male and 12.7 percent were female). The assessment of the code of silence was conducted using the method developed by Klockars and Kutnjak Ivkovic (2004), and consisted of 14 hypothetical scenarios describing a range of various forms of police misconduct, from those that merely give the appearance of a conflict of interest, to incidents of bribery and theft. One of the ques.
Shapes of RNA Pseudoknot Structures

In this article, we study abstract shapes of k-noncrossing, σ-canonical RNA pseudoknot structures. We consider lv1k- and lv5k-shapes, which represent a generalization of the abstract π'- and π-shapes of RNA secondary structures introduced by Giegerich et al. Using a novel approach, we compute the generating functions of lv1k- and lv5k-shapes as well as the generating functions of all lv1k- and lv5k-shapes induced by all k-noncrossing, σ-canonical RNA structures for fixed n. By means of singularity analysis of the generating functions, we derive explicit asymptotic expressions For online Supplementary Material, see www.liebertonline. com.
Content based Image Retrieval based on the Integration of Color Histogram, Color Moment and Gabor Texture

Based Image Retrieval (CBIR) systems help users to retrieve relevant images based on their contents such as color and texture. In this paper, a new approach is proposed in which color histogram, color moment and Gabor texture descriptors are integrated. The color histogram has the advantages of rotation and translation invariance. The HSV (16, 4, 4) quantization scheme has been adopted for color histogram and an image is represented by a vector of 256- dimension. The color histogram has the disadvantages of lack of spatial information and to improve the discriminating power of color indexing techniques, a minimal amount of spatial information is encoded in the color index by dividing the image horizontally into three equal non-overlapping regions and extracts the three moments (mean, variance and skewness) from each region, for all the color channels. Thus, for a HSV color space, 27 floating point numbers per image are used for indexing. As its texture feature, Gabor texture descriptors are adopted. Weights are assigned to each feature respectively and calculate the similarity with combined features of color histogram, color moment and Gabor texture using Histogram intersection distance and Canberra distance as similarity measures. Experimental results show that the proposed method has higher retrieval accuracy in terms of precision than other conventional methods combining color histogram, color moment and Gabor texture based on global features approach.
Elision Based Text Zooming

Scrolling through text documents is a cumbersome and ineffective means of getting context and overview. I present a superior technique that augments scrolling by using elision to simulate zooming. This allows smooth, rapid transitions between overview and detail and effectively supports the task of recovering lost context when reading or navigating through a document. I describe an implementation of the technique for a popular web browser. CR
Minimum local distance density estimation

ABSTRACT We present a local density estimator based on first-order statistics. To estimate the density at a point, x, the original sample is divided into subsets and the average minimum sample distance to x over all such subsets is used to define the density estimate at x. The tuning parameter is thus the number of subsets instead of the typical bandwidth of kernel or histogram-based density estimators. The proposed method is similar to nearest-neighbor density estimators but it provides smoother estimates. We derive the asymptotic distribution of this minimum sample distance statistic to study globally optimal values for the number and size of the subsets. Simulations are used to illustrate and compare the convergence properties of the estimator. The results show that the method provides good estimates of a wide variety of densities without changes of the tuning parameter, and that it offers competitive convergence performance.
ROSAT coronal temperatures of young late type stars

We analyze ROSAT X-ray spectra of a sample of young late type stars by tting them with a continuous emis- sion measure model with power-law temperature dependence up to a maximum temperature. This model gives successful ts to all spectra of our sample. While most of the spectra can also tted equally successful with the frequently used two tempera- ture model, we argue and provide evidence that the continuous temperature distribution model probably describes the coronal temperature structure in a more meaningful way. From our tting results we nd a very good correlation be- tween the X-ray surface flux and the maximum coronal temper- ature with the functional formFX / T (2:20:2) max . This correlation extends over several orders of magnitude and is valid for the very young T Tauri stars as well as for the much older young main sequence stars in our sample. Since the X-ray surface flux is a good measure of the stellar activity and shows a clear de- pendenceonthestellarrotationalvelocity,thecorrelationmeans that the coronal temperature is related to the X-ray activity and ultimately determined by stellar rotation. Finally, we interpret our results with a simple analytic loop model and nd that the coronae of the active young stars are probably dominated by loops with generally higher pressure as compared to typical solar active region loops.