Miracle-dz/UniFAR-FTUs huggingface dataset:
An Explanation Model for Quality Improvement Effect of Peer Reviews

Through the analysis of Rayleigh model, an explanation model on the quality effect of peer reviews is constructed. The review activities are evaluated by the defect removal rate at each phase. We made Hypotheses on how these measurements are related to the product quality. These Hypotheses are verified through regression analysis of actual project data and concrete calculation formulae are obtained as a model. This explanation model can be used to (1) evaluate the effect of peer review, especially the upper steam review quantitatively, (2) make concrete review plan and to set objective values for review activities, (3) evaluate the effect of each peer review activity or method.
The Importance of Trend Inflation in the Search for Missing Disinflation

Some inflation-forecasting models based on the Phillips curve suggest that there should have been more disinflation since the Great Recession than has shown up in core PCE or core CPI data. One way researchers have found to make the disinflation disappear is to remove the long-term unemployed from the overall unemployment measure that is typically used in the models. This analysis shows that the disinflation arises in such models because of the way they account for the long-term trend in inflation. Under a different measurement of trend inflation, which historical forecast accuracy suggests should be preferable, the recent path of inflation can be reasonably well explained by an inflation-forecasting model that incorporates the overall unemployment rate.
EGID: an ensemble algorithm for improved genomic island detection in genomic sequences

Genomic islands (GIs) are genomic regions that are originally transferred from other organisms. The detection of genomic islands in genomes can lead to many applications in industrial, medical and environmental contexts. Existing computational tools for GI detection suffer either low recall or low precision, thus leaving the room for improvement. In this paper, we report the development of our Ensemble algorithm for Genomic Island Detection (EGID). EGID utilizes the prediction results of existing computational tools, filters and generates consensus prediction results. Performance comparisons between our ensemble algorithm and existing programs have shown that our ensemble algorithm is better than any other program. EGID was implemented in Java, and was compiled and executed on Linux operating systems. EGID is freely available at http://www5.esu. edu/cpsc/bioinfo/software/EGID.
Clinical Significance of Molecular Diagnostic Tools for Bacterial Bloodstream Infections: A Systematic Review

Bacterial bloodstream infection (bBSI) represents any form of invasiveness of the blood circulatory system caused by bacteria and can lead to death among critically ill patients. Thus, there is a need for rapid and accurate diagnosis and treatment of patients with septicemia. So far, different molecular diagnostic tools have been developed. The majority of these tools focus on amplification based techniques such as polymerase chain reaction (PCR) which allows the detection of nucleic acids (both DNA and small RNAs) that are specific to bacterial species and sequencing or nucleic acid hybridization that allows the detection of bacteria in order to reduce delay of appropriate antibiotic therapy. However, there is still a need to improve sensitivity of most molecular techniques to enhance their accuracy and allow exact and on time antibiotic therapy treatment. In this regard, we conducted a systematic review of the existing studies conducted in molecular diagnosis of bBSIs, with the main aim of reporting on clinical significance and benefits of molecular diagnosis to patients. We searched both Google Scholar and PubMed. In total, eighteen reviewed papers indicate that shift from conventional diagnostic methods to molecular tools is needed and would lead to accurate diagnosis and treatment of bBSI.
An Adoption of Semantic Web from the Perspective of Technology Innovation : A Qualitative Research Approach

What factors affect the adoption and diffusion of Semantic Web innovation? This paper answers this question using multiple case studies. The case study as a qualitative approach is appropriate to achieve this, because the Semantic Web is at the early introduction stage. Data was gathered from seven out of nine organizations adopting the Semantic Web in Korea. Semi-structured interviews were used to collect data. Three factors affecting adoption of the Semantic Web were identified. The first factor is demand pull including requirements for solving search and integration problems of the existing systems and for creating new services. Second, environmental conduciveness, potential business value, government sponsorship programs, and active roles of suppliers affect the adoption of the Semantic Web from the perspective of technology push. Finally, absorptive capacity is an important role of the adoption. Four propositions are derived from this case study.
Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm-2. For values higher than 3 g middotcm-2, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.
GINI Country Report: Growing Inequalities and their Impacts in the United States

In brief, we find that inequality is high and rising in the United States, but the great majority of the increase was captured by the top end of the distribution since the mid-1990s—the top 1 percent in any given year and the top 5 to 10 percent overall—over the past 40 years. Overall inequality increased at a slow pace in recent years owing to the better economic status of the elderly, but inequality among families and children has continued to rise in the United States. The general focus in chapter 2 is on longer-term trends in inequality and poverty going back to 1967 or 1979. Data on trends in income and consumption inequality, poverty, and wealth inequality, show considerable increases over this period but a relatively constant level of poverty using European relative poverty definitions. When we assess the proximate causes or driving factors of U.S. inequality and find that labor markets (rising inequality in wages and earnings) and income from capital drive the inequality distribution in the United States, with redistribution playing only a minor and more or less constant role. There is a huge body of research on the causes of rising inequality and the consensus view is that technological change has increased the demand for skilled workers faster than the supply of skilled workers in the United States. Goldin and Katz (2008) show that the growth of U.S. schooling slowed for people entering the labor market in the late 1970s. As the supply of skills slowed down and the demand for skills continued to rise (due to technological change and wider international markets), the premium for having an education led to a rapid rise in wages at the top of the distribution.
Bayesian Networks in Reliability : The Good , the Bad , and the Ugly

Bayesian network (BN) models gain more and more popularity a s a tool in reliability analysis. In this paper we consider some of th e properties of BNs that have made them popular, consider some of the recent developm nts, and also point to the most important remaining challenges when using BNs in rel ability.
Exploring Halo Substructure with Giant Stars. X. Extended Dark Matter or Tidal Disruption?: The Case for the Leo I Dwarf Spheroidal Galaxy

We present a wide-field (4.5 deg2) photometric and spectroscopic survey of the Leo I dwarf spheroidal (dSph) galaxy to explore its extended morphology and dynamics. To select Leo I red giant branch star candidates we exploit M, T2, and DDO51 filter photometry; this yields 100% pure Leo I stars among more than 100 M < 21.5 Leo I giant candidates having previous or new Keck spectroscopy. The two-dimensional distribution of all photometric Leo I giant candidates is well fitted by a single-component King profile of limiting radius 13.4' out to a major axis radial distance of ~10', but beyond this point the density profile shows an excess of stars along the major axis of the main body. This spatial configuration, together with a rather flat velocity dispersion profile and an asymmetric radial velocity distribution among Leo I members at large radii, supports a picture where Leo I has been tidally disrupted on one or two perigalactic passages about a massive Local Group member. We demonstrate this hypothesis using mass-follows-light, N-body simulations of satellites in a Milky Way-like potential that reproduce the observed structural and dynamical properties of Leo I remarkably well. These models include ~3 × 107 solar mass, tidally disrupting dSphs on bound orbits with rather high eccentricity (0.93-0.96) and small perigalactica (10-15 kpc). The simulations yield an observationally constrained orbit for Leo I without the measurement of its proper motion. Given the overall success of our satellite models to account for the observed properties of Leo I, we conclude that there is no need to invoke an extended dark matter halo around the satellite and that an overall modest M/L for the satellite is consistent with the available data.
Effects of EU Enlargement to the Central European Countries on Agricultural Markets

Accession of the Central and Eastern European Countries is one of the biggest political projects for the European Union. As in all previous enlargements the agricultural chapter proves to be one of the most sensitive fields of the negotiations. This book examines the effects of enlargement on agricultural markets and government expenditure in the candidate countries as well as possible effects of different choices in the politically sensitive areas of direct payments and production quotas. The ten candidate countries have quite diverse economic and agricultural characteristics. Membership in the European Union, therefore, leads to varying macroeconomic effects. Two chapters specifically deal with these aspects and their effects on agriculture as well as likely country-specific developments in Hungary and in Slovenia. The quantitative tools developed and used for this analysis have also been more widely used by governments and institutions in analyses of questions related to enlargement. The book describes and documents these quantitative tools. Contents: Agricultural Markets in Central and Eastern Europe - The CAP of 1992 - The Agenda 2000 policies - Quotas and direct payments - Land retirement - Welfare effects for producers and consumers.
A framework of 2D Fisher discriminant analysis: application to face recognition with small number of training samples

A novel framework called 2D Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in conventional one-dimensional linear discriminant analysis (1D-LDA). Different from the 1D-LDA based approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. This framework contains unilateral and bilateral 2D-FDA. It is applied to face recognition where only few training images exist for each subject. Both the unilateral and bilateral 2D-FDA achieve excellent performance on two public databases: ORL database and Yale face database B.
Realities and challenges of nextgen air traffic management: the case of ADS-B

Air traffic is continuously increasing worldwide, with both manned and unmanned aircraft looking to coexist in the same airspace in the future. Next generation air traffic management systems are crucial in successfully handling this growth and improving the safety of billions of future passengers. The Automatic Dependent Surveillance Broadcast (ADS-B) system is a core part of this future. Unlike traditional radar systems, this technology empowers aircraft to automatically broadcast their locations and intents, providing enhanced situational awareness. This article discusses important issues with the current state of ADS-B as it is being rolled out. We report from our OpenSky sensor network in Central Europe, which is able to capture about 30 percent of the European commercial air traffic. We analyze the 1090 MHz communication channel to understand the current state and its behavior under the increasing traffic load. Furthermore, the article considers important security challenges faced by ADS-B. Our insights are intended to help identify open research issues, furthering new interest and developments in this field.
Correlation of plasma osteoprotegerin (OPG) and receptor activator of the nuclear factor κB ligand (RANKL) levels with clinical risk factors in patients with advanced carotid atherosclerosis

Summary Background Osteoprotegerin (OPG) is considered to be a crucial regulatory mediator of bone metabolism by acting as a decoy receptor of the receptor activator of nuclear factor κB ligand (RANKL). OPG and RANKL have further become the subject of intense interest for their potential role in cardiovascular disease. The present study aimed to assess the clinical implication of plasma OPG and RANKL levels in patients with advanced carotid atherosclerosis Material/Methods Plasma OPG and RANKL concentrations measured by solid-phase enzyme-linked immunosorbent assay (ELISA) were correlated with medical history, risk factors and medication intake in 131 patients who underwent carotid endarterectomy for vascular repair. Results Plasma OPG concentrations were associated with patients’ age (p=0.0258), homocysteine levels (p<0.00001), eGFR (p=0.0254), history of diabetes (p=0.0324), statins therapy (p=0.0044), hyperlipidemia (p=0.0407), smoking (p=0.0226) and CAD (p=0.0377). Plasma RANKL concentrations were associated with patients’ age (p=0.0191), homocysteine levels (p<0.00001), history of smoking (p=0.0185) and statins therapy (p=0.0004). Diabetes, CAD, smoking status, statins therapy and homocysteine were identified as independent predictors of OPG concentrations (p=0.0157, p=0.0030, p=0.0249, p=0.0047 and p=0.0072, respectively), whereas smoking showed an independent effect for RANKL (p=0.0010). Conclusions The present data reinforce the clinical utility of OPG in carotid atherosclerosis, whereas the clinical implication of RANKL seems uncertain.
Emerging Preservation Techniques for Controlling Spoilage and Pathogenic Microorganisms in Fruit Juices

Fruit juices are important commodities in the global market providing vast possibilities for new value added products to meet consumer demand for convenience, nutrition, and health. Fruit juices are spoiled primarily due to proliferation of acid tolerant and osmophilic microflora. There is also risk of food borne microbial infections which is associated with the consumption of fruit juices. In order to reduce the incidence of outbreaks, fruit juices are preserved by various techniques. Thermal pasteurization is used commercially by fruit juice industries for the preservation of fruit juices but results in losses of essential nutrients and changes in physicochemical and organoleptic properties. Nonthermal pasteurization methods such as high hydrostatic pressure, pulsed electric field, and ultrasound and irradiations have also been employed in fruit juices to overcome the negative effects of thermal pasteurization. Some of these techniques have already been commercialized. Some are still in research or pilot scale. Apart from these emerging techniques, preservatives from natural sources have also shown considerable promise for use in some food products. In this review article, spoilage, pathogenic microflora, and food borne outbreaks associated with fruit juices of last two decades are given in one section. In other sections various prevention methods to control the growth of spoilage and pathogenic microflora to increase the shelf life of fruit juices are discussed.
Civique: Using Social Media to Detect Urban Emergencies

We present the Civique system for emergency detection in urban areas by monitoring micro blogs like Tweets. The system detects emergency related events, and classifies them into appropriate categories like "fire", "accident", "earthquake", etc. We demonstrate our ideas by classifying Twitter posts in real time, visualizing the ongoing event on a map interface and alerting users with options to contact relevant authorities, both online and offline. We evaluate our classifiers for both the steps, i.e., emergency detection and categorization, and obtain F-scores exceeding 70% and 90%, respectively. We demonstrate Civique using a web interface and on an Android application, in realtime, and show its use for both tweet detection and visualization.
Large-Scale Distributed Language Modeling

A novel distributed language model that has no constraints on the n-gram order and no practical constraints on vocabulary size is presented. This model is scalable and allows for an arbitrarily large corpus to be queried for statistical estimates. Our distributed model is capable of producing n-gram counts on demand. By using a novel heuristic estimate for the interpolation weights of a linearly interpolated model, it is possible to dynamically compute the language model probabilities. The distributed architecture follows the client-server paradigm and allows for each client to request an arbitrary weighted mixture of the corpus. This allows easy adaptation of the language model to particular test conditions. Experiments using the distributed LM for re-ranking N-best lists of a speech recognition system resulted in considerable improvements in word error rate (WER), while integration with a machine translation decoder resulted in significant improvements in translation quality as measured by the BLEU score.
Livestock to 2020: The Next Food Revolution

A revolution is taking place in global agriculture that has profound implications for human health, livelihoods and the environment. Population growth, urbanization and income growth in developing countries are fuelling a massive increase in demand for food of animal origin. These changes in the diets of billions of people could significantly improve the well-being of many rural poor. Governments and industry must prepare for this continuing revolution with long-term policies and investments that will satisfy consumer demand, improve nutrition, direct income growth opportunities to those who need them most, and alleviate environmental and public health stress.
The Elasticity of Partisanship in Congress: An Analysis of Legislative Bipartisanship

In recent election cycles, members of Congress distanced themselves, at least rhetorically, from an unpopular party image or president. Why should the behavior of members change in the short-term if there are no changes in ideology or membership? I argue that party popularity affects the relative benefits of pursuing individual or collective goals, and thus the extent to which members wants to distance themselves from or attach themselves to a party label. This project explores three complementary questions central to this idea. First, do members of Congress go beyond rhetoric, to actually changing their legislative behavior, in response to changes in the popularity of their party? Second, does the relationship between legislative behavior and party popularity vary depending on the closeness of an election? Third, are there groups of members who are more responsive to party popularity? By looking at the bill cosponsorship patterns of individual members from the 102nd - 109th Congresses, I examine the likelihood that members engage in bipartisan coalitions as a function of party popularity in the electorate. The evidence suggests that members do respond to the popularity of their party in the electorate, engaging in more bipartisanship when their party is less popular. However, these effects occur only when elections are close. The results of this study have implications for a number of areas of legislative politics, including legislative behavior and preferences, electoral responsiveness, and party branding.
How do experts observe movement?

Laban Movement Analysis (LMA) is an expert-based method by which Certified Movement Analysts observe and analyze movement. LMA is increasingly used in a variety of research fields, particularly when studying movement expressivity and computation where it is essential to generate an understanding of the observation process. In this paper we articulate the application of LMA as a tool for movement analysis in HCI research by using qualitative methods to deconstruct the observation process of LMA experts. We conducted a focus group in which 12 expert-participants observed and annotated videos of movement according to LMA categories. We transcribed their observation process and analyzed it using grounded theory in order to extract categories, concepts and theories that best explain and describe the process of observation in LMA. By doing so, we open research perspectives in which LMA can be integrated as a method for observation in the design of movement-based computational systems.