Miracle-dz/UniFAR-FTUs huggingface dataset:
Young Women's Career and Educational Development through Extracurricular Activity Participation: A Qualitative Study.

The effects of participation in extracurricular activities on young women's career and educational development were examined. A constant comparative method was used to analyze the open-ended responses to questionnaires completed by 156 high school girls and the transcripts from semi-structured interviews with 5 female college freshmen and 2 young adult women who were in the work force. All seven women volunteered to be interviewed because of the positive nature of their high school extracurricular experiences. The analysis established that girls participate in extracurricular activities primarily for personal, social, physical, and emotional reasons. Relatively few of the high school girls believed that their educational and career development was being directly affected by participation in extracurricular activities. The most common reasons given by the high school girls for participation in extracurricular activities were "need for college applications" and "need for scholarships. " The young women interviewed credited participation in extracurricular activities with increasing their motivation for school and enhancing their awareness of career options. It was therefore recommended that parents and teachers encourage girls to become involved in extracurricular activities, particularly sports, at an early age. (The bibliography lists nine references. The interview protocols used for adult women participants and first-year undergraduates are appended.) (MN) Reproductions supplied by EDRS are the best that can be made from the original document. U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDU ATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. 0 Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) Young Women's Career and Educational Development through Extracurricular Activity Participation: A Qualitative Study Annette H. Parkerson Arizona State University
'Time' for Cloud? Design and Implementation of a Time-Based Cloud Resource Management System

The current pay-per-use model adopted by public cloud service providers has influenced the perception on how a cloud should provide its resources to end-users, i.e. on-demand and access to an unlimited amount of resources. However, not all clouds are equal. While such provisioning models work for well-endowed public clouds, they may not always work well in private clouds with limited budget and resources such as research and education clouds. Private clouds also stand to be impacted greatly by issues such as user resource hogging and the misuse of resources for nefarious activities. These problems are usually caused by challenges such as (1) limited physical servers/ budget, (2) growing number of users and (3) the inability to gracefully and automatically relinquish resources from inactive users. Currently, cloud resource management frameworks used for private cloud setups, such as OpenStack and CloudStack, only uses the pay-per-use model as the basis when provisioning resources to users. In this paper, we propose OpenStack Café, a novel methodology adopting the concepts of 'time' and booking systems' to manage resources of private clouds. By allowing users to book resources over specific time-slots, our proposed solution can efficiently and automatically help administrators manage users' access to resource, addressing the issue of resource hogging and gracefully relinquish resources back to the pool in resource-constrained private cloud setups. Work is currently in progress to adopt Café into OpenStack as a feature, and results of our prototype show promises. We also present some insights to lessons learnt during the design and implementation of our proposed methodology in this paper.
Two-integral distribution functions for axisymmetric stellar systems with separable densities

We show different expressions of distribution functions (DFs) which depend only on the two classical integrals of the energy and the magnitude of the angular momentum with respect to the axis of symmetry for stellar systems with known axisymmetric densities. The density of the system is required to be a product of functions separable in the potential and the radial coordinate, where the functions of the radial coordinate are powers of a sum of a square of the radial coordinate and its unit scale. The even part of the two-integral DF corresponding to this type of density is in turn a sum or an infinite series of products of functions of the energy and of the magnitude of the angular momentum about the axis of symmetry. A similar expression of its odd part can be also obtained under the assumption of the rotation laws. It can be further shown that these expressions are in fact equivalent to those obtained by using Hunter and Qian’s contour integral formulae for the system. This method is generally computationally preferable to the contour integral method. Two examples are given to obtain the even and odd parts of their two-integral DFs. One is for the prolate Jaffe model and the other for the prolate Plummer model. It can be also found that the Hunter-Qian contour integral formulae of the twointegral even DF for axisymmetric systems can be recovered by use of the LaplaceMellin integral transformation originally developed by Dejonghe.
Converting Continuous-Space Language Models into N-gram Language Models with Efficient Bilingual Pruning for Statistical Machine Translation

The Language Model (LM) is an essential component of Statistical Machine Translation (SMT). In this article, we focus on developing efficient methods for LM construction. Our main contribution is that we propose a Natural N-grams based Converting (NNGC) method for transforming a Continuous-Space Language Model (CSLM) to a Back-off N-gram Language Model (BNLM). Furthermore, a Bilingual LM Pruning (BLMP) approach is developed for enhancing LMs in SMT decoding and speeding up CSLM converting. The proposed pruning and converting methods can convert a large LM efficiently by working jointly. That is, a LM can be effectively pruned before it is converted from CSLM without sacrificing performance, and further improved if an additional corpus contains out-of-domain information. For different SMT tasks, our experimental results indicate that the proposed NNGC and BLMP methods outperform the existing counterpart approaches significantly in BLEU and computational cost.
Language policy as ‘frozen’ ideology: exploring the administrative function in Swedish higher education

ABSTRACT This article examines Language Policy documents within higher education institutions in Sweden. Its main focus is on how national language policies and policies for internationalization of the higher education are reinterpreted as local language policy. The analysis of ideologies surrounding the prescribed language(s) in meetings of decision making bodies in fifteen local Language Policy documents reveals how a monolingual national language policy is negotiated to accommodate an internationalized multilingual workplace. When the multilingual context of the internationalized university intrudes on the nationally oriented context of public administration, which favors Swedish as a default language, there are two solutions to be found in the language policy documents. (1) a temporary or permanent shift towards an internationally oriented monolingual context by allowing the use of another language, favoring English, or (2) a temporary or permanent shift towards parallel language-informed practices. Restrictive measures are found to construct situations where participation is made possible for non-regular non-Swedish-speaking members, while permissive measures provide inclusion for regular non-Swedish-speaking members, too.
Moderating role of job autonomy and work place spirituality between organizational based self -esteem and organizational learning culture

In this study impact of organizational based sel-esteem has been explained on organizational learning culture. Workplace spirituality and job autonomy have also been incorporated as two moderators. A model is proposed to scientifically explore the moderating effect of these two variables between organizational based self-esteem and organizational learning culture. PROCESS MACRO 3 executed in SPSS and linear regression analysis are employed to calculate the results whereas, data is collected from employees of private security companies operating in Pakistan. Main reason for selecting private security companies of Pakistan as prime sector for this study is ever increasing and vital role of these companies in overall security apparatus of Pakistan and minimal research in this industry which is disproportionate to its actual importance. This study proposed five hypotheses and results indicate that organizational based sel-esteem has positive and significant impact on organizational learning culture. Similarly, Workplace Spirituality and job autonomy are also found to have positive and significant impact on organizational learning culture. No moderation is established by work place spirituality between organizational based sel-esteem and organizational learning culture, whereas job autonomy figured out as moderator with significant results.
Bayesian Estimation of a Dynamic Game with Endogenous, Partially Observed, Serially Correlated State

We consider dynamic games that can have state variables that are partially observed, serially correlated, endogenous, and heterogeneous. We propose a Bayesian method that uses a particle filter to compute an unbiased estimate of the likelihood within a Metropolis chain. Unbiasedness guarantees that the stationary density of the chain is the exact posterior, not an approximation. The number of particles required is easily determined. The regularity conditions are weak. Results are verified by simulation from two dynamic oligopolistic games with endogenous state. One is an entry game with feedback to costs based on past entry and the other a model of an industry with a large number of heterogeneous firms that compete on product quality.
Carbon-use efficiency in green sinks is increased when a blend of apoplastic fructose and glucose is available for uptake

Understanding how green sink strength is regulated in planta poses a difficult problem because non-structural carbohydrate (NSC) levels can have integrated, simultaneous feedback effects on photosynthesis, sugar uptake, and respiration that depend on specific NSC moieties. Photosynthetic gametophytes of the fern Ceratopteris richardii provide a simple land plant model to assess how different NSCs imported from the apoplast of intact plants affect green sink strength. Sink strength was quantified as the amount of exogenous sugar that plants grown in low light depleted from their liquid media, and the relative contributions of carbon assimilation by photosynthesis and sugar uptake was estimated from stable isotope analysis of plant dry mass. Gametophytes absorbed fructose and glucose with equal affinity when cultured on either hexose alone, or in the presence of an equimolar blend of both sugars. Plants also depleted sucrose from the surrounding media, although a portion of this disaccharide that was hydrolysed into fructose and glucose by putative cell wall invertase activity remained in the media. The δ13C in plant dry masses harvested from sugar treatments were all close to –18‰, indicating that 25–39% of total plant carbon was from C3 photosynthesis (δ13C=–29‰) and 61–75% was from uptake of exogenous sugars (δ13C=–11‰). Carbon-use efficiency (i.e. carbon accumulated/carbon depleted) was significantly improved when plants had a blend of exogenous sugars available compared with plants grown in a single hexose alone. Plants avoided complete down-regulation of photosynthesis even though a large excess of exogenous carbon fluxed through their cells.
KERP Keele Economics Research Papers The Keele Economics

We study the technological pre-conditions for a cost-minimizing choice of technique in the presence of government emission targets on by-products of production. Whether a by-product is a desirable commodity or an undesirable pollutant is determined endogeneously as part of the price-quantity equilibrium solution. Non-trivial counterexamples highlight the potential risk of over-ambitious pollution targets. We show that pollution targets can be supported by the appropriate taxes providing that technology allows for a certain type of labour-intensive pollution abatement activities. Our proof is constructive: the tax equilibria we posit can be computed by the Lemke Complementary Pivoting Algorithm.
The missing link: augmenting biology laboratory notebooks

Using a participatory design process, we created three prototype augmented laboratory notebooks that provide the missing link between paper, physical artifacts and on-line data. The final a-book combines a graphics tablet and a PDA. The tablet captures writing on the paper notebook and the PDA acts as an "interaction lens" or window between physical and electronic documents. Our approach is document-centered, with a software architecture based on layers of physical and electronic information.
Noise-robust automatic speech recognition with exemplar-based sparse representations using multiple length adaptive dictionaries

In this work, we apply our recently proposed sparse representations based speech recognition system on the small vocabulary track of the 2 ‘CHiME’ Speech Separation and Recognition Challenge. This system uses exemplars of different length to approximate noisy speech segments as a linear combination of the speech and noise exemplars with sparse weights. The exemplars are labeled speech segments extracted from the training data, each representing half words and they are organized in multiple dictionaries based on their class and length. A reconstruction error-based decoding is adopted to find the best matching class sequence. After the initial experiments on AURORA-2, we further apply our system on the CHIME data which is a more challenging task addressing not only non-stationary noise but also reverberation. Moreover, the structure of the CHIME data allows speaker-dependent acoustic modeling and sampling noise segments from the immediate acoustic context of the target utterances. Using speaker-dependent dictionary sets, several recognition experiments are conducted on the development and test sets to evaluate the system performance with different kinds of noise dictionaries. These experiments show that combined noise dictionaries containing noise exemplars extracted from both the immediate acoustic context of the test utterances and noise-only segments in the training data provide better recognition accuracies compared to fixed and adaptive dictionaries.
Student performance analysis using clustering algorithm

University and technical organizations are facing high competition and their challenge is in analyzing their performance. The major challenges are in admission, student placement and in the curriculum. The two most important process during which data's are collected and analyzed are admission and placement. The ranking of the university depends on academic performance and placement of the student. Apart from academic performance there are various other factors which help in understanding the overall performance of the student. In this research work, the data mining technique is used to understand the performance of student and group the students under various categories as a student need to consistently improve to compete in today's world.
Intelligent Frozen Shoulder Self-Home Rehabilitation Monitoring System

Upper limb function rehabilitation exercises can assist in improving shoulder pain, activity, and maintain muscle strength. In addition to the regular supervision of professional rehabilitation staff, comply with the self-home rehabilitation can highlight the effectiveness. In this paper, smart phone is served as the platform which integrates an accelerometer-based senor network to facilitate the monitoring of self-home exercise rehabilitation for frozen shoulder patients. The acceleration-based sensor network consists of two developed accelerometer sensors and the smart phone build-in accelerometer, which communicated by Bluetooth protocol. The activities of upper limb exercise is recognized by the Support Vector Machine algorithms, and recorded in smart phone. The records can be used to remind the patients what he/she has done. On the other hands, the records can be uploaded to the server in hospital for helping physicians to monitoring the exercise effectiveness. The proposed approach is low cost and easy to upgrade for further different monitoring target by installing a new Android APP.
Multi-Document Summarization from First Principles

We present SumBasic+, a powerful multi-document summarization system built from first principles. SumBasic+ is designed as a baseline system to gauge the level of summarization results we could obtain using simple statistical techniques. Our extractive summarization system is based on word frequency statistics similar to the SumBasic method. Nevertheless, we were able to considerably improve its summarization performance by tuning the amount and type of redundancy removal performed, adding a simple query-focused summarization component, and by employing a number of pre- and post-processing compression techniques. The resulting system, SumBasic+, is a strong baseline system that is ideal for comparing with new summarization approaches, as it principally uses existing techniques and per- forms surprisingly well. Of 43 competing systems in the TAC 2010 summarization track, our system achieved fourth and third place in R-2 and R-SU4 ROUGE scores respectively, and second overall in the manual average pyramid evaluation for the initial summaries.
COPING WITH HIGH CORN PRICES : LOW STARCH DIETS AND LACTATION PERFORMANCE BY DAIRY COWS

Lactating dairy cow diets with more than 24% starch (DM basis) are common, but recent high corn prices have fueled a desire to feed lower-starch diets. The potential for using digestible neutral detergent fiber or pectin from byproduct feeds or sugars to partially replace starch from corn grain in diets fed to lactating dairy cows was reviewed. Potential modifying effects of corn silage, starch digestibility and ionophore on lactation performance by dairy cows fed low starch diets was also evaluated. Lactation performance was reduced for 18% and 20% starch diets (DM basis) formulated using beet pulp and citrus pulp, respectively, to partially replace corn grain. Lactation performance was not reduced for 16%-17% starch diets (DM basis) formulated using soyhulls to partially replace corn grain. Diets containing 21% starch (DM basis) appear to be acceptable when high-fiber, moderate protein byproduct feeds are used to partially replace corn grain and protein supplement. Feeding corn silage at a higher percentage of the forage, high starch corn silage, sugars and ionophore can reduce the percentage of corn needed in highstarch diets or increase the “corn equivalency” of low-starch diets. The cost of using digestible NDF or pectin from byproduct feeds or sugars to partially replace starch from corn grain needs to be evaluated relative to corn and protein supplement prices. The starch in low-starch diets should be highly digestible.
Relationship between biomarker responses and contaminant concentration in selected tissues of flounder (Platichthys flesus) from the Polish coastal area of the Baltic Sea

Previous studies in the Gulf of Gdansk discussed the responses of selected enzymatic biomarkers to the contaminant gradient in fish and mussels. In the present study, flounder muscle and liver tissues were analyzed for polychlorinated biphenyls (PCB congeners: 28, 52, 101, 118, 138, 153 and 180), organochlorine pesticides (HCHs, HCB and DDTs), and trace metals (Pb, Cd, Zn, Cu, Hg, Cr). An attempt was made to identify the relationship between the measured enzymatic biomarker responses (cholinesterases, malic enzyme, isocitrate dehydrogenase, glutathione S-transferase) and contaminant concentrations in selected flounder tissues. The observed differences in enzymatic biomarker levels suggest that chronic exposure to low-concentration mixtures of contaminants may be occurring in the studied area. However, no conclusive evidence was found of a clear link between the biomarker responses and contaminant concentrations in flounder tissues.
Non Inflammatory Boronate Based Glucose-Responsive Insulin Delivery Systems

Boronic acids, known to bind diols, were screened to identify non-inflammatory cross-linkers for the preparation of glucose sensitive and insulin releasing agglomerates of liposomes (Agglomerated Vesicle Technology-AVT). This was done in order to select a suitable replacement for the previously used cross-linker, ConcanavalinA (ConA), a lectin known to have both toxic and inflammatory effects in vivo. Lead-compounds were selected from screens that involved testing for inflammatory potential, cytotoxicity and glucose-binding. These were then conjugated to insulin-encapsulating nanoparticles and agglomerated via sugar-boronate ester linkages to form AVTs. In vitro, the particles demonstrated triggered release of insulin upon exposure to physiologically relevant concentrations of glucose (10 mmoles/L–40 mmoles/L). The agglomerates were also shown to be responsive to multiple spikes in glucose levels over several hours, releasing insulin at a rate defined by the concentration of the glucose trigger.
Large margin AR model for time series classification

In this paper we propose a new method for time series pattern classification. It is based on the generative modeling using Autoregressive(AR) model and optimizing the boundaries between these models using the large margin concepts. The developed model captures the correlations in the time series data. Multi-class classification can be performed directly without performing binary classification. The optimization is performed using genetic algorithm for obtaining global optimal parameters. The developed method is applied on simulated and ECG data and found to perform better than the methods which utilize the AR coefficients as the features for the classification.
An optimization of disjunctive queries: union-pushdown

Most previous works on query optimization techniques deal with conjunctive queries only because the queries with disjunctive predicates are complex to optimize. Hence, for disjunctive queries, query optimizers based on these techniques generate plans using rather simple methods such as CNF- and DNF-based optimization. However, the plans generated by these methods perform extremely poorly for certain types of queries. The authors propose new query optimization method, union-pushdown, for disjunctive queries. This method is composed of four phases, and each phase utilizes some advantageous techniques of CNF- and DNF-based methods. They analyze the performance of the union-pushdown plan against those of conventional plans and show that union-pushdown can be applied to various disjunctive query types without performance degradation.
Corpus Portal for Search in Monolingual Corpora

A simple and flexible schema for storing and presenting monolingual language resources is proposed. In this format, data for 18 different languages is already available in various sizes. The data is provided free of charge for online use and download. The main target is to ease the application of algorithms for monolingual and interlingual studies.