Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic image enhancement based on multi-scale image decomposition

In image processing and computational photography, automatic image enhancement is one of the long-range objectives. Recently the automatic image enhancement methods not only take account of the globe semantics, like correct color hue and brightness imbalances, but also the local content of the image, such as human face and sky of landscape. In this paper we describe a new scheme for automatic image enhancement that considers both global semantics and local content of image. Our automatic image enhancement method employs the multi-scale edge-aware image decomposition approach to detect the underexposure regions and enhance the detail of the salient content. The experiment results demonstrate the effectiveness of our approach compared to existing automatic enhancement methods.
Compression of Medical Images Using Wavelet Transforms

Medical image compression is necessary for huge database storage in Medical Centres and medical data transfer for the purpose of diagnosis. Wavelet transforms present one such approach for the purpose of compression. The same has been explored in paper with respect to wide variety of medical images. In this approach, the redundancy of the medical image and DWT coefficients are reduced through thresholding and further through Huffman encoding. This paper presents a lossy image compression technique which works well over most of the medical images.
The role of serotonin in brain development and 3,4- methylenedioxymethamphetamine-induced cognitive deficits

Serotonin (5-hydroxytryptamine, 5-HT) is thought to be important during brain development and is one of the first neurotransmitters to appear. It appears to act as a neurotrophic factor supporting the growth and maturation of both serotonergic and nonserotonergic cells during the pre and early postnatal periods prior to its role as a neurotransmitter. Disruption of 5-HT functioning during human development is thought to be associated with autism and schizophrenia. Early developmental exposure to stress or drugs of abuse disrupt 5-HT development and produces altered cognitive ability. To better understand the relationship between the developing serotonergic system and longterm cognitive function we employed the use of a genetic model in which 5-HT levels are depleted ~80% throughout life and the use of a model of 3,4methylenedioxymethamphetamine (MDMA) exposure in which administration from postnatal day (P)11-20 is used; a period analogous to the second half of human gestation. Pet-1 is a transcription factor that is restricted in the brain to 5-HT neurons and has been shown to be important for their development and function. A loss of Pet-1 results in an 80% reduction of the number of 5-HT neurons as well as 5-HT tissue content. Pet-1 knockouts were tested as adults in both the Cincinnati and Morris water mazes that assess path integration and spatial learning, respectively. A reduction in cognitive ability was not observed in Pet-1 knockout mice, although they displayed decreased locomotor activity, increased marble burying, and increased startle reactivity. To better assess the role 5-HT plays during development for circuits involved in memory formation, we used a model of MDMA exposure that has previously been reported to produce protracted path integration and spatial learning deficits that last late into adulthood. In adults, MDMA
An Optimal Sinsing Algorithm for Multiband Cognitive Radio Network

In cognitive radio networks, the biggest problem preventing optimum performance is the optimal setting of individual decision thresholds while keeping the interference on the primary network bounded to a reasonably low level. In this paper, the multiband spectrum sensing problem is formulated as a class of optimization problem in which the opportunistic aggregate throughput is maximized subject to practical constraints. In order to achieve this, the detection problem is formulated based on the eigen-decomposition technique. Analytic and simulation results have indicated that the system being proposed achieves optimal opportunistic throughput for the secondary users and guarantee an acceptable level of interference to the primary users.
Experimental analysis of motion style

For interactive 3D applications and games, the ability to edit, combine, and adapt motion capture is an important technique for reducing labor, project complexity, and storage overhead. Stylistic motion capture, in particular, has unique challenges since editing can alter the original intent of the actor -sometimes with poor consequences as viewers can pick up subtle differences in body language. Thus, this thesis aims to enhance motion capture techniques, particularly when it is important that the stylistic intent of a motion is not unintentionally altered, when we might need to quantify the stylistic nature of the motion, and when better tools can help naive users works with large stylistic motion sets. In our first experiments, we investigate how changes introduced by motion editing might alter the emotional content of a motion. We find that emotions were mostly conveyed through the upper body, that the perceived intensity of an emotion can be reduced by blending with a neutral motion, and that posture changes can alter the perceived emotion but subtle changes in dynamics only alter the intensity. In our next experiments, we investigate whether fundamental numerical differences exist between neutral and stylistic motions of a single motion category and find that neutral walks had significantly lower torques than stylistic motions. Next, we model the variation over our stylistic walks using PCA by decoupling each walking example motion into an average and stylistic part such that only the stylistic part is transformed to PCA space. Although our use of PCA for motion modeling is not novel, our decision to decouple motions is a simple enhancement that makes the use of the PCA model produce consistently good output for blending, clustering, searching, and browsing. In our final experiments, we investigate whether using a minimap, created from our PCA model, to browse stylistic motions helps nonexpert users search and edit styles. We find that 82\% of participants prefer the map to a label-based interface and that when not under time pressure, users performed equally well with the map, although using fewer mouse clicks and producing more varied results. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Norman I. Badler
Association Rule Mining: An Application Perspective

Data mining is an emerging field and it is a method to find out interesting patterns and knowledge from the large amount of sales data in the transactional and interpersonal database . The most important and extremely studied function of data mining is Association Rule Mining (ARM). We have proposed a detailed review of ARM applications and presented the various areas in which association rules are applied for extracting interesting patterns and relationships among set of items in data repositories. The main purpose of this function is to find frequent patterns, associations and relationship between various database using different Algorithms. ARM is used to improve decisions making in the applications such as Market basket analysis, Disease diagnosis , building Intelli gent transportation system(ITS), Web log data, Computer aided di agnosis system of Breast cancer , fraud detection in web, CRM of credit card business by using different Algorith ms such as Apriori , FP-Growth , Equivale nce class transformation(ECLAT) , Direct hashing and pruning, Tree Projection.
A 4-D Project Control System with Information Visualization Technology

Project control, which entails the management of data concerning cost and schedule, is the fundamental task of project managers. A capable project control system is an essential tool that enables project managers to deliver projects successfully. However, most project control systems summarize a project’s status by producing huge datasets of tables and charts with formats predetermined by system analysts. Consequently, project managers must eliminate redundant data in order to diagnose problems and make decisions; all within a very limited time. Despite the great progress that has been made in information technologies in recent years, project control researchers have failed to take advantage of emerging information technologies to improve the usability of project control systems. Among emerging information technologies, information visualization technology is the most promising. It offers better support for project managers’ ability to perceive and capture project control data by allowing them to take advantage of visual cognition so that they can readily find deviations and patterns as well as make judicious decisions effectively and efficiently. Our proposed approach is to create a project information structure (PIS) integrating the data of a project’s work breakdown structure (WBS), cost breakdown structure (CBS), and organization breakdown structure (OBS) for a specific moment during construction into a three-dimensional tree. In order to understand the trend of the overall project, the 4-D project control system provides a temporal function, which allows the project manager to view the PIS along a timeline. Many potential problems can be detected early enough to correct if the project manager has the ability to scrutinize a 3-D tree consisting of project control data along a timeline.
Preschool children dietary patterns and associated factors.

OBJECTIVES To identify dietary patterns in pre-school children and to investigate its determinants. METHODS Cross-sectional study nested in a cohort born between 2004 and 2005 in the municipality of Diamantina, state of Minas Gerais, Brazil. The instrument used to explore the dietary patterns was a frequency questionnaire for food consumption. Factor analysis was used for the identification of the dietary patterns. The study also assessed personal and socioeconomic data and anthropometric measurements of 232 preschoolers in the period between July 2009 and July 2010. The determinants of food patterns were identified by logistic regression. RESULTS Three patterns were obtained, which were called: "mixed diet," "snack," and "unhealthy. " The children of mothers with low education are more likely (odds ratio - OR = 3.80; confidence interval 95% - 95%CI = 1.90-7.60) to consume food from the "mixed diet" pattern, and less likely (OR = 0.31; 95%CI = 0.15-0.61) to consume food from the "snack" pattern, while those with higher per capita income are more likely to consume food from the "unhealthy" pattern (OR = 2.43; 95%CI = 1.13-5.24). CONCLUSION Three dietary patterns were identified in the studied population, which were determined by the level of maternal education and per capita income.
Test Suite Minimization using Hybrid Algorithm for GA generated Test Cases

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible. As the software evolves the size of test suites also grows. When the no of test cases generated are more, obviously size of the test suite will also be more. So the testing time is to be minimized by reducing the execution time of the algorithm used for test data generation and also by introducing minimization procedure for test suite reduction. Due to limited resources and timing constraints for testing, test suite minimization techniques are needed to eliminate redundant test cases as possible. By considering multiple objectives rather than the coverage alone, the test cases are being generated which satisfies the testing requirements. Most of the existing techniques are code-based. In this article we present an approach by modifying an existing heuristic for test suite minimization. Genetic algorithm has been used for random test data generation and the output of GA is given to the minimization procedure for reducing the total no of generated test cases, collectively named as Hybrid Algorithm (HA). The results are satisfactory and show significant improvements in reducing test suite size with minimum execution time. Experiments have been done for simple to medium complexity java programs taken from SIR and execution time is reduced to 5,685ms for a test set. The results are compared with existing method Mutant Gene Algorithm and size of test suite is minimized upto 13.6% using Hybrid Algorithm .
Optomechanical Cooling and Inertial Sensing at Low Frequencies

An inertial sensor design is proposed in this paper to achieve high sensitivity and large dynamic range in the sub-Hz frequency regime. High acceleration sensitivity is obtained by combining optical cavity readout systems with monolithically fabricated mechanical resonators. A high-sensitivity heterodyne interferometer simultaneously monitors the test mass with an extensive dynamic range for low-stiffness resonators. The bandwidth is tuned by optical feedback cooling to the test mass via radiation pressure interaction using an intensity-modulated laser. The transfer gain of the feedback system is analyzed to optimize system parameters towards the minimum cooling temperature that can be achieved. To practically implement the inertial sensor, we propose a cascaded cooling mechanism to improve cooling efficiency while operating at low optical power levels. The overall system layout presents an integrated design that is compact and lightweight.
Handbook of human performance technology: Improving individual and organizational performance worldwide (second edition)

Since the first edition of this handbook was published in 1992 much apparently has changed. The world is far more interconnected. Communication at very low cost is at almost everyone’s fingertips via internet and the World Wide Web. Information circles the globe at lightening speed. The economics of nations are increasingly—some would say irrevocably—interlocked. The expansion of deregulation has eliminated many barriers to international trade and cooperation. Since 1992 we have also seen unprecedented expansion business record economic growth and unnerving volatility in the marketplace. There is greater pressure to produce in ever faster cycles and with high-quality output. (excerpt)
Depresif Bozukluklarda Psikososyal Girişimler

Son on yillarda depresyonun onlenmesi ve tedavisinde etkili psikososyal girisimlerdeki gelismeler dikkat cekmektedir. Dunya Saglik Orgutu major depresyonun yetiskin, yasli ve cocuklari etkiledigini ve dunya capinda tum engelliliklerin yaklasik %12’sinin onde gelen nedeni oldugunu belirtmistir. Depresyon saglik giderleri, isgucu kaybi, intihar riski, tekrarlanma durumu goz onune alindiginda saglik calisanlari acisindan onemle ele alinmasi gereken bir konudur. Bu derlemenin amaci; dunyada gittikce yayginligi artan depresif bozukluklara yonelik psikososyal girisimler ve bu girisimlerin etkinligine dikkat cekmektir.
A Review on: Web Mining Techniques

These days, the development of World Wide Web has surpassed a considerable measure with additional desires. Vast measure of content archives, sight and sound records and pictures were accessible in the web and it is even now expanding in its structures. So with a specific end goal to give better administration along upgrading the nature of sites, it has ended up exceptionally critical for the site holder to better comprehend their clients. This is carried out by mining web. Web mining is the requisition of information mining to concentrate learning from web substance, structure, and utilization which is the gathering of web innovations. Enthusiasm toward Web mining has become quickly in its short history, both in the exploration and expert groups. The proposed paper concentrates on a short diagram of web mining procedures alongside its requisition in related territory. KeywordsWeb mining, Web content mining, Web structure mining, and Web usage mining.
Biomarkers (Procalcitonin, C Reactive Protein, and Lactate) as Predictors of Mortality in Surgical Patients with Complicated Intra-Abdominal Infection.

Abstract Background: An accurate and readily available biomarker for identifying patients with complicated intra-abdominal infection needing special attention in critical care units because of their greater risk of dying would be of value for intensivists. Methods: A multi-center, observational, retrospective study explored blood lactate, C-reactive protein (CRP), and procalcitonin (PCT) concentrations, and also Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score (SAPS II) as mortality predictors in all adult patients with complicated intra-abdominal infection (cIAI) admitted to Surgical Critical Care Units (SCCUs) for ≥48 h in four Spanish hospitals (June 2012–June 2013). Logistic regression models (step-wise procedure) were constructed using as dependent variables “intra-SCCU mortality” or “overall mortality,” and variables showing differences (p≤0.1) in bivariate analyses as independent variables. Results: One hundred twenty-one cases were included. Mortality intra-SCCU (R2.
Low-complexity 2D to 3D video conversion

3D film and 3D TV are becoming reality. More facilities and devices are now 3D capable. Compared to capture 3D video content directly, 2D to 3D video conversion is a low-cost, backward compatible alternate. There also exists a tremendous amount of monoscopic 2D video content that are of high interest to be displayed on 3D devices with noticeable immersiveness. 2D to 3D video conversion, therefore, has drawn lots of attention recently. In this paper, a low complexity 2D to 3D conversion algorithm is presented. The conversion generates stereo video pairs by 3D warping based on estimated per-pixel depth maps. The depth maps are estimated jointly by motion and color cues. Subjective tests show that the proposed algorithm achieves 3D perception with acceptable artifact.
OPTIMIZATION MODELS FOR COMMUNICATION NETWORK DESIGN

The use of both Genetic Algorithms and Linier programming to solve the general problem of communication system design is considered. The network synthesis problem is known to be NP-complete and the combinatorial nature of it lends itself to genetic algorithms rather than conventional mathematical programming approaches. Once a network topology is established, linier programming can be used to optimize network flows to satisfy specified origin-destination demands. Keywords: Network Design, Genetic Algorithms
Features selection techniques for off-line handwritten isolated Arabic characters

Offline Handwritten isolated Arabic characters’ software has become a highly demand application to the machine reading of bank and post offices. In the past few years, several approaches have been used in the development of handwritten recognition applications. However, the recognition of handwritten Arabic characters is a difficult task because of the similar appearance of some different characters.In this study, the moments: contour sequence, geometric and Zernike moments are employed on handwritten characters to select the efficient features. The classification and recognition process are applied using Neural Network technique and the results are analyzed to determine the necessity of thinning and unthinning processes. The database consists of 6885 images of characters: 75% of training and 25% of testing in the network. Matlab tool is implemented to perform the classification and recognition processes. Results obtained have shown that thinning process should be excluded as it deteriorates the recognition accuracy. The experiments resulted 97.58% in Contour Sequence moments with unthinning for classification and 95.25% for recognition process. Thus, Contour Sequence moments with unthinning process exhibited the highest recognition rate as compared to Geometric moments and Zernike moments.
Discriminative Minimum Statistics Projection Coefficient Feature for Acoustic Context Recognition

Acoustic environment recognition, which can provide the important acoustic context, has been widely used in many applications and is a considerable difficult problem in the real-life and the complex environment. This paper proposes the discriminative minimum statistics project coefficient (MSPC) feature with the information of classification by using partial least squares (PLS). With the minimum statistics (MS) tracked from the input sound, the discriminative MSPC feature is extracted by projecting the MS into lower-dimensional feature subspace learned by using PLS analysis. Based on the proposed discriminative MSPC feature, the acoustic environment recognition is implemented by using Gaussian Mixture Model (GMM) for modeling each sound class. The experimental results show that the proposed discriminative MSPC feature based on PLS outperforms the MSPC feature based on PCA for acoustic environment recognition.
[Impact of the Royal Decree-Law 16/2012 on the number of prescriptions and pharmaceutical expenditures].

BACKGROUND this research aims to understand if the consequences on drug expenditures and number of prescriptions of Royal Decree-Law 16/2012 as estimated by the Ministry of Health, Social Services and Equality (MHSSE) are similar to those found by using common statistical approaches. In addition, several models have been built to forecast the evolution of both variables for the period September 2013-December 2014. METHODS the Box-Jenkins methodology and the Box-Tiao intervention analysis were applied to data of the period 2003-13 to forecast the monthly values of the number of prescriptions and pharmaceutical expenditures. Forecasts were used in a counter-factual analysis to be compared to the actual values of prescriptions and drug expenditures. Moreover, forecasts for the period September 2013 to December 2014 were obtained to observe the impact of the policy in the future. RESULTS the counterfactual analysis estimated a decrease in the number of prescriptions of 12.18% and 12.83% in the pharmaceutical expenditure; these figures were 12,75% and 14,03% respectively, when the intervention analysis was used. CONCLUSION the estimated reduction in the number of prescriptions for the period June 2012-August 2013 was similar to the figure offered by the MHSSE, while the reduction in the drug expenditure series was smaller. The Box-Jenkins methodology generated low forecast errors (less than 3%) what makes this procedure useful to reliably anticipate future consumptions.
A LEARNING SYSTEM'S APPROACH TO CROSS-CULTURAL VIRTUAL ORGANIZATIONS

Globally linked virtual organizations (VOs) hold the promise of successfully tackling complex problems and designs which lead social, industrial and technological innovation. Yet these cross-cultural virtual organizations (CCVOs) appear to be hampered by problems of communication, conflict and productivity. One approach to mitigating these problems is to ask how such CCVOs can pattern the so-called co-located VOs or face-to-face work groups. This article proposes a different approach. A learning systems approach to CCVOs has the potential for successfully addressing such issues and does so in a manner that is continuously adaptive (organic). Further, a learning system approach can turn seeming problems of CCVOs into resources for diverse thinking and creative design. The article outlines this approach and proposes not only the strengths of such an approach but also the challenges it presents.
Tools to Simulate Realistic Censored Survival-Time Distributions

Simulation of realistic censored survival times is challenging. Most research studies use highly simplified models, such as the exponential, that do not adequately reflect the patterns of time to event and censoring seen in real datasets. In this article, I present a general method of simulating such data based on flexible parametric survival models (Royston and Parmar, 2002, Statistics in Medicine 21: 2175–2197). A key component of the approach is modeling not only the time to event but also the time to censoring. I illustrate the methods in data from clinical trials and from a prognostic study. I also describe a new Stata program, stsurvsim, that does the necessary calculations.