nils-herrmann/clean_scirepeval huggingface dataset:
How Human Resource Management Practices Translate Into Sustainable Organizational Performance: The Mediating Role Of Product, Process And Knowledge Innovation
Venue: Psychology research and behavior management
Year: 2019

This study examined how human resource management (HRM) can directly and indirectly influence sustainable organizational performance (SOP) with organizational innovation (OI) as a mediator.  For this quantitative study, a 31 item questionnaire was used to collect data on HRM and SOP from 20 Shanghai branches of five Chinese banks. HRM was defined as multidimensional, consisting of employee staffing, staff development, performance management, and compensation and benefits. Partial least squares structural equation modeling (PLS SEM 3.2) was used to estimate the effects.  The HRM practices of performance management and compensation and benefits showed a direct and positive influence on SOP. Looking at indirect relationships, all four dimensions of HRM practices were positively related to OI (product, process, and knowledge innovation) which, in turn, was positively related to SOP. Organizational innovation was thus shown to play a mediating role between HRM and SOP.  The study emphasizes that the banking sector of China consists of many employees who maintain old styles of working, alongside some who attempt to take on the new innovative working mechanisms and engage with staff development programs. This latter group of personnel make a valuable contribution to SOP. Moreover, the effect on organizational dynamics of implementing HRM practices aids in bringing about innovations in processes, products, and knowledge.
Corruption and National Competitiveness in Different Stages of Country Development
Year: 2013

The aim of this study is to analyse if competitiveness is influenced by corruption and to show the nature of this influence. Analysing the national competitiveness data from The Global Competitiveness Report 2012 2013 and the data about corruption from Corruption Perceptions Index 2012, the results are expected to reveal the existence of a strong positive connection between these two indexes, corruption significantly influencing the competitiveness of a country. Also, the relation between these variables is analysed distinctively related to different stages of country development. The results of every group are revealed and discussed separately.
PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs
Venue: TOPC
Year: 2019

Natural graphs with skewed distributions raise unique challenges to distributed graph computation and partitioning. Existing graph parallel systems usually use a "one size fits all" design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high degree vertices (e.g. Pregel and GraphLab) or incur high communication cost and memory consumption even for low degree vertices (e.g. PowerGraph and GraphX) In this article, we argue that skewed distributions in natural graphs also necessitate differentiated processing on high degree and low degree vertices. We then introduce PowerLyra, a new distributed graph processing system that embraces the best of both worlds of existing graph parallel systems. Specifically, PowerLyra uses centralized computation for low degree vertices to avoid frequent communications and distributes the computation for high degree vertices to balance workloads. PowerLyra further provides an efficient hybrid graph partitioning algorithm (i.e. hybrid cut) that combines edge cut (for low degree vertices) and vertex cut (for high degree vertices) with heuristics. To improve cache locality of inter node graph accesses, PowerLyra further provides a locality conscious data layout optimization. PowerLyra is implemented based on the latest GraphLab and can seamlessly support various graph algorithms running in both synchronous and asynchronous execution modes. A detailed evaluation on three clusters using various graph analytics and MLDM (Machine Learning and Data Mining) applications shows that PowerLyra outperforms PowerGraph by up to 5.53X (from 1.24X) and 3.26X (from 1.49X) for real world and synthetic graphs, respectively, and is much faster than other systems like GraphX and Giraph, yet with much less memory consumption. A porting of hybrid cut to GraphX further confirms the efficiency and generality of PowerLyra.
Working Memory, Thought, and Action
Year: 2007

1. Introduction and overview 2. Why do we need a phonological loop? 3. The phonological loop: challenges and growing points 4. Visuospatial short term memory 5. Imagery and the visuospatial sketchpad 6. Recency retrieval and the constant ratio rule 7. Fractionating the central executive 8. Long term memory and the episodic buffer 9. Exploring the episodic buffer 10. Individual differences in working memory 11. What limits working memory span 12. Neuroimaging working memory 13. Working memory and social behaviour 14. Working memory and emotion I: fear and craving 15. Working memory and emotion II: depression and the well springs of action 16. Working memory and consciousness 17. Multilevel control of action 18. Working memory in context: life, the universe and everything
Impact of Organizational Innovation, Learning Orientation and Entrepreneurship on SME Performance: The Moderating Role of Market Turbulence and ICT
Year: 2018

The purpose of this study was to determine the effect of organizational innovation (OI) learning orientation (LO) and entrepreneurship on small and medium sized enterprise (SME) performance considering the moderating role of market turbulence and ICT. The statistical population was all active SMEs in Rasht industrial park (193 companies) SPSS Sample Power software was used to calculate the optimum sample size and the lowest optimum sample size was 85. Simple random sampling was carried out using SPSS 22. Field data was collected by questionnaire. A five point Likert scale was used for the responses to the questionnaire. The reliability and validity was confirmed. Before testing the hypotheses, outlier data and FIMIX were analyzed to ensure the homogeneity of community level variance. To assess the measurement and structural models and test the hypothesis, PLS SEM in SmartPLS3 was used. The results show that both OI and LO have a positive effect on entrepreneurship, but have no effect on SME performance considering the mediating role of entrepreneurship. The findings illustrate that an increase in market turbulence decreased the impact of innovation, LO and entrepreneurship on SME performance. On the other hand, an increase in ICT was found to increase the effect of innovation, LO and entrepreneurship on SME performance.
Influencia del riego y radiacion solar sobre el contenido de fitoquimicos en la piel de frutos de aguacate 'Hass'
Year: 2016

La actividad fotosintetica y la concentracion de fitoquimicos en las plantas son muy influenciadas por la radiacion solar incidente y su disminucion provoca importantes cambios en la concentracion tanto de clorofilas como de compuestos producidos en las rutas del metabolismo secundario. Aunado a las condiciones de iluminacion, la disponibilidad de agua y la temperatura causan cambios bioquimicos y fisiologicos que afectan el metabolismo, desarrollo y rendimiento de un cultivo. El cambio climatico es determinante para que estreses bioticos y/o abioticos se presenten durante el desarrollo de las plantas por lo que el conocimiento de sus efectos podria resultar en un manejo agronomico mas adecuado. El objetivo de este estudio fue cuantificar el contenido de clorofila y compuestos fenolicos totales en la piel de frutos de aguacate 'Hass' cultivados bajo diferente manejo de agua (con y sin riego) y cobertura del fruto (cubiertos y descubiertos) El manejo de agua no modifico el contenido de fitoquimicos en la piel de los frutos. Respecto al grado de exposicion a la radiacion solar, los frutos descubiertos presentaron una concentracion mayor de compuestos fenolicos totales y clorofila, comparados con los frutos cubiertos (estresados por falta de luz)
Locality Sensitive Hashing for Finding Nearest Neighbors in Probability Distributions
Venue: SMP
Year: 2017

In the past ten years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Approximate Nearest Neighbors search (ANN) To find the nearest neighbors in probability distribution type data, the existing Locality Sensitive Hashing (LSH) algorithms for vector type data can be directly used to solve it. However, these methods do not consider the special properties of probability distributions. In this paper, based on the special properties of probability distributions, we present a novel LSH scheme adapted to angular distance for ANN search in high dimensional probability distributions. We define the specific hashing functions, and prove their local sensitivity. Also, we propose a Sequential Interleaving algorithm based on the "Unbalance Effect" of Euclidean and angular metrics for probability distributions. Finally, we compare, through experiments, our methods with the state of the art LSH algorithms in the context of ANN on six public image databases. The results prove the proposed algorithms can provide far better accuracy in the context of ANN than baselines.
Training K 12 teachers in STEM education: A multi disciplinary approach
Venue: 2013 IEEE Integrated STEM Education Conference (ISEC)
Year: 2013

Currently, the US ranks 25th in math and 17th in science among our international peers. U.S. colleges and universities will need to train 25,000 new science, technology, engineering, and math (STEM) teachers each year to meet President Obama's challenge to train 100,000 STEM graduates over the next decade. Improving STEM education is critical for the economic stability of the nation. The nation's changing demographics and continued need to remain globally competitive make it clear that colleges and universities must increase the number of teachers trained in STEM New York Institute of Technology School of Education and School of Engineering and Computing Sciences is addressing this urgent need to improve STEM education through a multi disciplinary approach to train teachers to become effective leaders in STEM education. One hundred and fifty students in the Advanced Certificate in STEM program were recruited to participate in the study. A Questionnaire Design was utilized to collect data on students' perceptions of improved knowledge and skills in teaching STEM subjects after completion of the certificate program.
Biofilm mediated decontamination of pollutants from the environment
Year: 2016

In this review, we highlight beneficial use of microbial biofilms in remediation of environmental pollutants by bioremediation. Bioremediation is an environment friendly, cost effective, sustainable technology that utilizes microbes to decontaminate and degrade a wide variety of pollutants into less harmful products. Relative to free floating planktonic cells, microbes existing in biofilm mode are advantageous for bioremediation because of greater tolerance to pollutants, environmental stress and ability to degrade varied harsh pollutants via diverse catabolic pathways. In biofilm mode, microbes are immobilized in a self synthesized matrix which offers protection from stress, contaminants and predatory protozoa. Contaminants ranging from heavy metals, petroleum, explosives, pesticides have been remediated using microbial consortia of biofilms. In the industry, biofilm based bioremediation is used to decontaminate polluted soil and groundwater. Here we discuss conventional and newer strategies utilizing biofilms in environmental remediation.
Acid base assessment of post parturient German Holstein dairy cows from jugular venous blood and urine: A comparison of the strong ion approach and traditional blood gas analysis
Venue: PloS one
Year: 2019

Evaluating acid base status is important for monitoring dairy herd health. In a field study, we aimed to compare the acid base status measured by net acid base excretion (NABE) in urine with results of venous blood analysis in clinically healthy, but possibly metabolically burdened cows in their transition period. For this, we sampled blood from the jugular vein and urine from 145 German Holstein cows within 1 to 76 days post partum. In blood, the metabolic parameters non esterified fatty acids (NEFA) and b hydroxybutyrate (BHB) as well as numerous parameters of the acid base status were measured. The traditional approach, based on bicarbonate concentration, base excess (BE) and anion gap (AG) was compared to the strong ion approach variables, e.g. acid total (Atot) measured strong ion difference (SIDm) strong ion gap (SIG) and unmeasured anions (XA) respectively. Results of both approaches were set against the outcome of urine analysis, i.e. the NABE, base acid ratio and pH of urine, in a cluster analysis, which provided 7 moderately stable clusters. Evaluating and interpreting these 7 clusters offered novel insights into the pathophysiology of the acid base equilibrium in fresh post partum dairy cows. Especially in case of subclinical acid base disorders, the parameters of the strong ion difference theory, particularly SIDm, Atot and SIG or XA, provided more in depth information about acid base status than the traditional parameters BE, bicarbonate or AG in blood. The acid base status of fresh cows with protein aberrations in blood could be differentiated in a much better way using the strong ion approach than by traditional blood gas analysis or by the measurement of urinary excretion. Therefore, the strong ion approach seems to be a suitable supplement for monitoring acid base balance in dairy cattle.
Phishing Attacks Detection using Machine Learning Approach
Venue: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT)
Year: 2020

Evolving digital transformation has exacerbated cybersecurity threats globally. Digitization expands the doors wider to cybercriminals. Initially cyberthreats approach in the form of phishing to steal the confidential user credentials. Usually, Hackers will influence the users through phishing in order to gain access to the organizatlou's digital assets and networks. With security breaches, cybercriminals execute ransomware attack, get unauthorized access, and shut down systems and even demand a ransom for releasing the access. Anti phishing software and techniques are circumvented by the phishers for dodging tactics. Though threat intelligence and behavioural analytics systems support organizations to spot the unusual traffic patterns, still the best practice to prevent phishing attacks is defended in depth. In this perspective, the proposed research work has developed a model to detect the phishing attacks using machine learning (ML) algorithms like random forest (RF) and decision tree (DT) A standard legitimate dataset of phishing attacks from Kaggle was aided for ML processing. To analyze the attributes of the dataset, the proposed model has used feature selection algorithms like principal component analysis (PCA) Finally, a maximum accuracy of 97% was achieved through the random forest algorithm.
Forecasting UK GDP growth and inflation under structural change. A comparison of models with time varying parameters
Year: 2014

Evidence from a large and growing body of empirical literature strongly suggests that there have been changes in the inflation and output dynamics in the United Kingdom. The majority of these papers base their results on a class of econometric models that allows for time variation in the coefficients and volatilities of shocks. While these models have been used extensively for studying evolving dynamics and for structural analysis, there has been little evidence that they are useful for forecasting UK output growth and inflation. This paper attempts to fill this gap by comparing the performances of a wide range of time varying parameter models in forecasting output growth and inflation. We find that allowing for time varying parameters can lead to large and statistically significant gains in forecast accuracy.
Monitoring Health Care Workers at Risk for COVID 19 Using Wearable Sensors and Smartphone Technology: Protocol for an Observational mHealth Study
Venue: JMIR research protocols
Year: 2021

Health care workers (HCWs) have been working on the front lines of the COVID 19 pandemic with high risks of viral exposure, infection, and transmission. Standard COVID 19 testing is insufficient to protect HCWs from these risks and prevent the spread of disease. Continuous monitoring of physiological data with wearable sensors, self monitoring of symptoms, and asymptomatic COVID 19 testing may aid in the early detection of COVID 19 in HCWs and may help reduce further transmission among HCWs, patients, and families.  By using wearable sensors, smartphone based symptom logging, and biospecimens, this project aims to assist HCWs in self monitoring COVID 19.  We conducted a prospective, longitudinal study of HCWs at a single institution. The study duration was 1 year, wherein participants were instructed on the continuous use of two wearable sensors (Fitbit Charge 3 smartwatch and TempTraq temperature patches) for up to 30 days. Participants consented to provide biospecimens (ie, nasal swabs, saliva swabs, and blood) for up to 1 year from study entry. Using a smartphone app called Roadmap 2.0, participants entered a daily mood score, submitted daily COVID 19 symptoms, and completed demographic and health related quality of life surveys at study entry and 30 days later. Semistructured qualitative interviews were also conducted at the end of the 30 day period, following completion of daily mood and symptoms reporting as well as continuous wearable sensor use.  A total of 226 HCWs were enrolled between April 28 and December 7, 2020. The last participant completed the 30 day study procedures on January 16, 2021. Data collection will continue through January 2023, and data analyses are ongoing.  Using wearable sensors, smartphone based symptom logging and survey completion, and biospecimen collections, this study will potentially provide data on the prevalence of COVID 19 infection among HCWs at a single institution. The study will also assess the feasibility of leveraging wearable sensors and self monitoring of symptoms in an HCW population.
Online learning in higher education: exploring advantages and disadvantages for engagement
Venue: J. Comput. High. Educ.
Year: 2018

As the popularity of online education continues to rise, many colleges and universities are interested in how to best deliver course content for online learners. This study explores the ways in which taking courses through an online medium impacts student engagement, utilizing data from the National Survey of Student Engagement. Data was analyzed using a series of ordinary least squares regression models, also controlling for relevant student and institutional characteristics. The results indicated numerous significant relationships between taking online courses and student engagement for both first year students and seniors. Those students taking greater numbers of online courses were more likely to engage in quantitative reasoning. However, they were less likely to engage in collaborative learning, student faculty interactions, and discussions with diverse others, compared to their more traditional classroom counterparts. The students with greater numbers of online courses also reported less exposure to effective teaching practices and lower quality of interactions. The relationship between these engagement indicators and the percentage of classes taken online suggests that an online environment might benefit certain types of engagement, but may also be somewhat of a deterrent to others. Institutions should consider these findings when designing online course content, and encourage faculty to contemplate ways of encouraging student engagement across a variety of delivery types.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer
Venue: Science Translational Medicine
Year: 2016

Detection of circulating tumor DNA in patients with resected stage II colon cancer provides evidence of residual disease. Footprints of persistent cancer Stage II colon cancer, which has spread through the wall of the colon but has not metastasized to the lymph nodes, can present a therapeutic dilemma. On one hand, these tumors can usually be completely removed by surgery, and the majority does not recur even without chemotherapy. On the other hand, it is difficult to determine which of these tumors will recur and to identify patients who would benefit from adjuvant chemotherapy after surgery. Tie et al. show that the presence of circulating tumor DNA in a patient's blood after surgery is a sign of persistent tumor and a greatly increased risk of relapse, suggesting that this group of patients may require chemotherapy to prevent recurrence. Detection of circulating tumor DNA (ctDNA) after resection of stage II colon cancer may identify patients at the highest risk of recurrence and help inform adjuvant treatment decisions. We used massively parallel sequencing based assays to evaluate the ability of ctDNA to detect minimal residual disease in 1046 plasma samples from a prospective cohort of 230 patients with resected stage II colon cancer. In patients not treated with adjuvant chemotherapy, ctDNA was detected postoperatively in 14 of 178 (7.9% patients, 11 (79% of whom had recurred at a median follow up of 27 months; recurrence occurred in only 16 (9.8 of 164 patients with negative ctDNA [hazard ratio (HR) 18; 95% confidence interval (CI) 7.9 to 40; P 0.001] In patients treated with chemotherapy, the presence of ctDNA after completion of chemotherapy was also associated with an inferior recurrence free survival (HR, 11; 95% CI, 1.8 to 68; P 0.001) ctDNA detection after stage II colon cancer resection provides direct evidence of residual disease and identifies patients at very high risk of recurrence.
Clinically Significant Bleeding With Ticagrelor Versus Clopidogrel in Korean Patients With Acute Coronary Syndromes Intended for Invasive Management: A Randomized Clinical Trial.
Venue: Circulation
Year: 2019

Owing to the differential propensity for bleeding and ischemic events with response to antiplatelet therapy, the safety and effectiveness of potent P2Y12 inhibitor ticagrelor in East As.
Argumentative texts and clause types
Venue: ArgMining@ACL
Year: 2016

Argumentative texts have been thoroughly analyzed for their argumentative structure, and recent efforts aim at their automatic classification. This work investigates linguistic properties of argumentative texts and text passages in terms of their semantic clause types. We annotate argumentative texts with Situation Entity (SE) classes, which combine notions from lexical aspect (states, events) with genericity and habituality of clauses. We analyse the correlation of SE classes with argumentative text genres, components of argument structures, and some functions of those components. Our analysis reveals interesting relations between the distribution of SE types and the argumentative text genre, compared to other genres like fiction or report. We also see tendencies in the correlations between argument components (such as premises and conclusions) and SE types, as well as between argumentative functions (such as support and rebuttal) and SE types. The observed tendencies can be deployed for automatic recognition and fine grained classification of argumentative text passages.