Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Self-Instruction on the Comprehension Monitoring Performance of Average and above Average Readers

Fifth-grade students, classified as either above average or average readers, were tested on their ability to detect errors in short essays prior to, immediately after, and one week after receiving self-instruction or didactic instruction. In the self-instruction condition (SI), students were taught a self-verbalization routine composed of task-specific statements. Students in the control didactic condition (CD) received equivalent practice and instruction without active self-verbalization. After training, the performance of the SI above average readers was significantly greater than that of the CD above average readers. The average readers displayed a similar although nonsignificant trend favoring the self-instruction strategy training. The results suggest that self-instruction procedures may effectively promote increased comprehension monitoring during reading. Implications of SI for reading instruction and directions for future research are discussed.
Extended Deductive Databases with Uncertain Information

The paper presents an approach for handling uncertain information in deductive databases using multivalued logics. Uncertainty means that database facts may be assigned logical values other than the conventional ones true and false. The logical values represent various degrees of truth, which may be combined and propagated by applying the database rules. A corresponding multivalued database semantics is defined. We show that it extends successful conventional semantics as the well-founded semantics, and has a polynomial time data complexity. Keywords—Reasoning under uncertainty, multivalued logics, deductive databases, logic programs, multivalued semantics.
Portfolios as Sites of Learning: Reconceptualizing the Connections to Motivation and Engagement

In this qualitative study, we examine how students' participation in the process of constructing portfolios may affect their motivation for and engagement in literacy learning. Research that is based on the “wisdom of practice” continues to emphasize the value of portfolios and portfolio-based assessment. Few studies, however, articulate an explicit theoretical frame for viewing portfolios and considering their effectiveness. This study grounds portfolio practices in 2 bodies of literature that lend theoretical support to their importance, and then extends outward to include literature that addresses the social construction of meaning and the situated nature of learning. Using vignettes of students from an urban high school, we lay out our initial understandings of portfolios and our a priori assumptions about the concepts of motivation and engagement; we articulate our emerging theory around process portfolios, and trace our processes of reading and interpreting the data; we explain how our theory has evolved and lead us to a new set of literature that broadens the focus of motivation and engagement to include not only the individual student but also other sociocultural factors; and, finally, we discuss the implications of this work for practitioners, researchers, and policy makers.
A virtual channel network-on-chip for GT and BE traffic

This paper presents an on-chip network for a runtime reconfigurable system-on-chip. The network uses packet-switching with virtual channels. It can provide guaranteed services as well as best effort services. The guaranteed services are based on virtual channel allocation, in contrast to other on-chip networks where guarantees are provided by time-division multiplexing. The network is particularly suitable for systems in which the traffic is dominated by streams. We model the data traffic in the system and simulate the behaviour of the network with this model. The results show that the network is capable of handling the system traffic and can provide the required guarantees
The Impact of Nationwide Education Program on Clinical Practice in Sepsis Care and Mortality of Severe Sepsis: A Population-Based Study in Taiwan

Objectives We investigated the effect of a nationwide educational program following surviving sepsis campaign (SSC) guidelines. Physicians’ clinical practice in sepsis care and patient mortality rate for severe sepsis were analyzed using a nationally representative cohort. Methods Hospitalizations for severe sepsis with organ failure from 1997 to 2008 were extracted from Taiwan’s National Health Insurance Research Database (NHIRD), and trends in sepsis incidence and mortality rates were analyzed. A before-and-after study design was used to evaluate changes in the utilization rates of SSC items and changes in severe sepsis mortality rates occurred after a national education program conducted by the Joint Taiwan Critical Care Medicine Committee since 2004. A total of 39,706 hospitalizations were analyzed, which consisted of a pre-intervention cohort of 14,848 individuals (2000-2003) and a post-intervention cohort of 24,858 individuals (2005-2008). Results The incidence rate of severe sepsis increased from 1.88 per 1,000 individuals in 1997 to 5.07 per 1,000 individuals in 2008. The cumulative mortality rate decreased slightly from 48.2% for the pre-intervention cohort to 45.9% for the post-intervention cohort. The utilization rates of almost all SSC items changed significantly between the pre-intervention and post-intervention cohorts. These changes of utilization rates were found to be associated with mild reduction in mortality rate. Conclusion The nationwide education program through a national professional society has a significant impact on physicians’ clinical practice and resulted in a slight but significant reduction of severe sepsis mortality rate.
Use of Body Model Constraints to Improve Accuracy of Inertial Motion Capture

Presents simulations of an algorithm for estimating linear acceleration of wireless inertial measurement units based on body model constraints. The behaviour of the proposed algorithm is compared to existing algorithms based on motion capture data from the Carnegie Mellon University motion capture corpus. The new algorithm is demonstrated to be capable of accurately estimating linear accelerations for walking and running motions. The use of linear acceleration estimation is demonstrated to improve the accuracy of inertial motion capture.
Job demands, job control, and mental health in an 11-year follow-up study: Normal and reversed relationships

Abstract Despite numerous studies of the demand-control model, there is a need for more longitudinal studies to test not only the normal, but also the reversed and reciprocal relationships between work characteristics and mental health. There is also a need for more studies of the multiplicative interaction between demands and control with respect to mental health, which is different from the simple additive effect of the two variables. This is a longitudinal study with an exceptionally long period of follow-up (11 years), with the focus on normal as well as reciprocal and reversed causality between work characteristics and mental health, as measured by reliable instruments close to the original definitions of Karasek. The study was based on a sample of 439 Norwegian employees of different occupations who had stayed in the same profession during the follow-up period. The results were in agreement with the hypothesis that low job control, and in particular low control in combination with high demands (“high strain jobs”), has a negative effect on mental health. There was a significant multiplicative interaction between demands and control, indicating a “buffering” effect of job control. Job demands alone were not significantly associated with mental health. The reversed relationship hypothesis was supported for job demands, but not for job control.
Increasing Awareness of Emotional Intelligence in a Business Curriculum

IN TODAY’S GLOBAL, “bigger is better” business environment, theneed for intrapersonal awareness and interpersonal communicationskills is greater than ever. Business schools are being asked to addressthisneed byincreasing attentiontocommunication coursework inthebusiness curricula. In fact, the Management Education Task Force ofthe Association to Advance Collegiate Schools of Business (AACSB)issuedareportinApril2002thatcalledforanincreaseofinstructionincommunication,leadership,andinterpersonalskillstomakecurriculamore relevant to “today’s global workplace” (Doria, Rozanski, &Cohen, 2003). Furthermore, as a response to feedback from employ-ers,alumni,andexecutiveadvisors,businessschoolsacrossthecountryareaddressingtheneedforanincreasedemphasison“peopleskills”byteaching business students the soft skills that set exemplary managersapart from their typical peers (Alsop, 2002, p. R11) and that enhancetheir ability to negotiate the “interpersonal dimension” of work life(Muir, 2004). Whereastheabilitytogather,interpret,analyze,andrespondtodatamaybeafunctionoftechnicalcompetency,theabilitytoreceive, inter-pret, analyze, and respond to messages, both external and internal, isregulated by one’s emotional intelligence (EI). EI is “a type of socialintelligence that involves the ability to monitor one’s own and others’
Reproductive health of bass in the Potomac, USA, drainage: Part 2. Seasonal occurrence of persistent and emerging organic contaminants

The seasonal occurrence of organic contaminants, many of which are potential endocrine disruptors, entering the Potomac River, USA, watershed was investigated using a two‐pronged approach during the fall of 2005 and spring of 2006. Passive samplers (semipermeable membrane device and polar organic chemical integrative sampler [POCIS]) were deployed in tandem at sites above and below wastewater treatment plant discharges within the watershed. Analysis of the samplers resulted in detection of 84 of 138 targeted chemicals. The agricultural pesticides atrazine and metolachlor had the greatest seasonal changes in water concentrations, with a 3.1‐ to 91‐fold increase in the spring compared with the level in the previous fall. Coinciding with the elevated concentrations of atrazine in the spring were increasing concentrations of the atrazine degradation products desethylatrazine and desisopropylatrazine in the fall following spring and summer application of the parent compound. Other targeted chemicals (organochlorine pesticides, polycyclic aromatic hydrocarbons, and organic wastewater chemicals) did not indicate seasonal changes in occurrence or concentration; however, the overall concentrations and number of chemicals present were greater at the sites downstream of wastewater treatment plant discharges. Several fragrances and flame retardants were identified in these downstream sites, which are characteristic of wastewater effluent and human activities. The bioluminescent yeast estrogen screen in vitro assay of the POCIS extracts indicated the presence of chemicals that were capable of producing an estrogenic response at all sampling sites.
Measuring User Influence in Financial Microblogs: Experiments Using StockTwits Data

In this paper, we study the effect of graph structure user influence measures in financial social media. In particular, we explore rich and recent data, composed of 1.2 million StockTwits messages, from June 2010 to March 2013. These data allow the creation of social network graphs by considering direct active interactions (retweets, shares or replies). Using such graphs and a realistic rolling windows evaluation, we analyzed four user influence measures (indegree, betweenness, page rank and posts) under two criteria: Percentage of Quality Users (PQU), as manually labeled by StockTwits; and the daily sentiment correlation between top lists of influential users and other users. The sentiment was based on a StockTwits labeled dataset and assessed in terms of three selections: overall sentiment (ALL) and filtered by two major technological companies (Apple -- AAPL and Google -- GOOG). Promising results were obtained, with several top lists presenting PQU values higher than 80% and correlations higher than 0.6. Overall, the best results were achieved by the page rank and posts measures.
Asperger's and Girls: What Teachers Need to Know.

Individuals with the pervasive developmental disorder Asperger’s syndrome (AS) are generally of average or above average intelligence and attend mainstream schools. Despite their intelligence, some of the characteristics and challenges associated with AS can impact upon the quality of life they experience at school. Although both males and females are diagnosed with AS, females have been underrepresented and unrepresented in much of the AS research. Consequently, much of what is known about the school experiences of students with AS is based on studies consisting predominately of male perspectives. This article discusses what adult females with AS and parents of females with AS currently attending school wish teachers understood about AS in females. Many of the experiences described by participants indicate there is a lack of understanding about AS, both generally and specifically, in relation to females. Implications of these findings and recommendations for educators and schools are provided, and directions for future research are outlined.
H∞ filter design of stochastic Markovian jump delay systems

The problem of H∞ filtering for stochastic systems with time delays and Markovian jumping parameters is considered in this paper. Based on Lyapunov-Krasovskii functional (LKF) theory and Generalized Finsler Lemma (GFL), a delay-dependent bounded real lemma (BRL) is established without using any model transformations, bounding techniques for cross terms or additional free matrix variables. Then an H∞ filter is designed for stochastic retarded Markovian jump systems in terms of a set of linear matrix inequalities (LMIs). The effectiveness of the method is demonstrated by a numerical example.
Invariant image descriptors and affine morphological scale-space

In this research report we propose a novel approach to build interest points and descriptors which are invariant to a subclass of affine transformations. Scale and rotation invariant interest points are usually obtained via the linear scale-space representation, and it has been shown that affine invariance can be achieved by warping the smoothing kernel to match the local image structure. Our approach is instead based on the so-called Affine Morphological Scale-Space, a non-linear filtering which has been proved to be the natural equivalent of the classic linear scale-space when affine invariance is required. Simple local image descriptors are then derived from the extracted interest points. We demonstrate the proposed approach by robust matching experiments.
Keeping it real: Recognizing expressions in real compared to schematic faces

Controversy exists over whether facial expression recognition is a holistic or feature-based process. The present research explored the extent to which processing is affected by whether stimuli are photographs of real faces or computer-generated schematic faces. Using a composite facial expression recognition task, we observed greater holistic processing for photographic faces than for schematic faces in two experiments. In Experiment 1, processing was more holistic when stimuli were photographs of real faces based on multiple identities than when they were computer-generated schematic faces based on a single identity. Experiment 2 replicated this effect using a between-subjects design and extended it to photographic faces based on a single identity: Processing was more holistic for single-identity photographs than for single-identity schematics. As such, results demonstrate that stimulus format influences the extent of holistic processing, and provide a potential explanation for discrepant findings in the literature.
Patient and physician asthma deterioration terminology: results from the 2009 Asthma Insight and Management survey.

Long-term achievement of asthma control is dependent in part on the use of mutually understandable asthma terminology in all verbal and written patient-physician communications. Using data from the Asthma Insight and Management (AIM) survey, the objective of this analysis is to provide a contemporary depiction of asthma deterioration terminology as used by current asthma patients and physicians in the United States. As part of the 2009 AIM survey, current asthma patients (≥12 years of age; weighted n = 2499) and physicians (n = 309) were queried about their recognition, understanding, and/or use of the terms "asthma attack," "asthma flare-up," and "asthma exacerbation" in telephone interviews. Nearly all patients had heard the term "asthma attack" (97%), but relatively few had heard the term "asthma exacerbation" (24%); 71% had heard "asthma flare-up. " In contrast, physicians reported using the term "asthma attack" least (65%) and the term "asthma exacerbation" most (77%) when discussing asthma with their patients; 70% reported using "asthma flare-up. " Among patients familiar with "asthma flare-up" and "asthma exacerbation" (n = 502), only 38% said that the terms mean the same thing; nearly all physicians (94%) said that the terms mean the same thing. Collectively, data from the AIM survey suggest that patients and physicians use different asthma deterioration terminology and, more importantly, that they do not necessarily understand each other's terms. Standardizing asthma deterioration terminology may help optimize asthma patient-physician communication to improve patient understanding of written asthma action plans and therefore, enhance patient outcomes.
An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis FunctionMachine

A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons). In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Radial Basis Function (RBF) is used for efficient classification. The RBF is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in retrieving the leaf type.
Efficient search algorithm for SimRank

Graphs are a fundamental data structure and have been employed to model objects as well as their relationships. The similarity of objects on the web (e.g., webpages, photos, music, micro-blogs, and social networking service users) is the key to identifying relevant objects in many recent applications. SimRank, proposed by Jeh and Widom, provides a good similarity score and has been successfully used in many applications such as web spam detection, collaborative tagging analysis, link prediction, and so on. SimRank computes similarities iteratively, and it needs O(N4T) time and O(N2) space for similarity computation where N and T are the number of nodes and iterations, respectively. Unfortunately, this iterative approach is computationally expensive. The goal of this work is to process top-k search and range search efficiently for a given node. Our solution, SimMat, is based on two ideas: (1) It computes the approximate similarity of a selected node pair efficiently in non-iterative style based on the Sylvester equation, and (2) It prunes unnecessary approximate similarity computations when searching for the high similarity nodes by exploiting estimations based on the Cauchy-Schwarz inequality. These two ideas reduce the time and space complexities of the proposed approach to O(Nn) where n is the target rank of the low-rank approximation (n ≪ N in practice). Our experiments show that our approach is much faster, by several orders of magnitude, than previous approaches in finding the high similarity nodes.
Learning Alignments from Latent Space Structures

In this paper we present a model that is capable of learning alignments between high-dimensional data by exploiting low-dimensional structures. Specifically, our method uses a Gaussian process latent variable model (GP-LVM) to learn alignments and latent representations simultaneously. The results show that our model performs alignment implicitly and improves the smoothness of the low dimensional representations.
The Prevalence of Cyclospora cayetanensis and Cryptosporidium spp. in Turkish patients infected with HIV-1

Opportunistic infections such as cryptosporidiosis and cyclosporiasis are commonly encountered in patients with acquired immunodeficiency syndrome (AIDS). We investigated the existence of opportunistic protozoans that significantly affect the quality of life in HIV-1 infected patients using conventional and molecular methods. The study group comprised 115 HIV-1 positive patients. In the identification of Cyclospora cayetanensis and Cryptosporidium, the formol-ether precipitation method was used and smears were evaluated in optical microscope by staining modified Ziehl-Neelsen (ZN). The primers and probes used for PCR were Heat shock protein 70 for C. cayetanensis and the oocysts wall protein for Cryptosporidium spp. Cyclospora and Cryptosporidium spp. oocysts were detected in one and two patients, respectively, by staining, whereas we detected C. cayetanensis in three patients out of 115 (2.6%) by PCR, and Cryptosporidium spp. in a further three patients (2.6%). C. cayetensis was detected in patients with CD4 counts of 64 cells/μm, 182 cells/μm and 287 cells/μm, respectively. Cryptosporidium spp. was detected in patients with CD4 counts of 176 cells/μm, 241 cells/μm and 669 cells/μm. As conclusion, PCR method is faster and more sensitive than microscopic methods and to screen intestinal pathogens routinely in patients infected with HIV should not be neglected in developing countries like Turkey.
Demographic socialization and reproductive behavior in a transitional context: a macro–micro perspective

This paper analyzes the impact of individuals’ socialization in a given demographic context on their later reproductive behavior in 19th century Geneva. To assess this socialization effect on the fertility of first generation immigrants, I combine family reconstitution data with Coale’s aggregate indexes of marital fertility. I then run two-level Poisson regression models predicting age-specific fertility rates as a function of individual characteristics at the lower level and as a function of province-level fertility at the higher level. The results show a strong socialization effect among couples who were already married when arriving in Geneva, whereas among immigrants who arrived as singles and who got married in Geneva the impact of demographic socialization turns out much weaker.