nils-herrmann/clean_scirepeval huggingface dataset:
Quality Control of Photosystem II: The Mechanisms for Avoidance and Tolerance of Light and Heat Stresses are Closely Linked to Membrane Fluidity of the Thylakoids
Venue: Front. Plant Sci.

When oxygenic photosynthetic organisms are exposed to excessive light and/or heat, Photosystem II is damaged and electron transport is blocked. In these events, reactive oxygen species, endogenous radicals and lipid peroxidation products generated by photochemical reaction and/or heat cause the damage. Regarding light stress, plants first dissipate excessive light energy captured by light harvesting chlorophyll protein complexes as heat to avoid the hazards, but once light stress is unavoidable, they tolerate the stress by concentrating damage in a particular protein in photosystem II, i.e. the reaction center binding D1 protein of Photosystem II. The damaged D1 is removed by specific proteases and replaced with a new copy produced through de novo synthesis (reversible photoinhibition) When light intensity becomes extremely high, irreversible aggregation of D1 occurs and thereby D1 turnover is prevented. Once the aggregated products accumulate in Photosystem II complexes, removal of them by proteases is difficult, and irreversible inhibition of Photosystem II takes place (irreversible photoinhibition) Important is that various aspects of both the reversible and irreversible photoinhibition are highly dependent on the membrane fluidity of the thylakoids. Heat stress induced inactivation of photosystem II is an irreversible process, which may be also affected by the fluidity of the thylakoid membranes. Here I describe why the membrane fluidity is a key to regulate the avoidance and tolerance of Photosystem II on environmental stresses.
Bayesian Networks for Identifying Semantic Relations in a Never Ending Learning System
Venue: ISDA

A new paradigm of Machine Learning named Never Ending Learning has been proposed through a system known as NELL (Never Ending Language Learning) The major idea of this system is to learn to read the web better each day and to store the gathered knowledge in a knowledge base (KB) continually and incrementally. This paper proposes a new method that can help NELL populating its own KB using Bayesian Networks (BN) More specifically, we use facts (knowledge) already stored in NELL's KB as input for a BN learning algorithm named VOMOS (Variable Ordering Multiple Offspring Sampling) by aiming at representing the acquired knowledge by NELL system. In addition, we propose to use the BN induced by VOMOS for identifying new semantic relations to be added to NELL's KB, expanding thus its initial ontology.
A Framework for Opinion Mining System with Design Pattern
Venue: 2016 International Computer Symposium (ICS)

Due to the sheer volume of opinion rich web resources such as discussion forum, review sites, blogs, and news corpora available in digital form, much of the current research is focusing on the area of sentiment analysis. People are intended to develop a system that can identify and classify opinion or sentiment as represented in an electronic text. An accurate method for predicting sentiments could enable us, to extract opinions from the internet and predict on line customer's preferences, which could prove valuable for economic or marketing research. In this paper we present a framework for opinion mining in Traditional Chinese called FOM (Framework of Opinion Mining) to collect unstructured articles in the popular web site and analyse the opinion and sentiment in the semi automatic way. The framework is developed by objected oriented design patterns, such as to support the flexibility and maintainability. With the FOM framework, new analysis algorithm can be easily replaced and integrated in a new application. A flood predication application based on facebook text in Taiwan will be demonstrated in this paper.
Pruning a Random Forest by Learning a Learning Algorithm
Venue: MLDM

Ensemble Learning is a popular learning paradigm and finds its application in many diverse fields. Random Forest, a decision tree based ensemble learning algorithm has received constant attention in the research community due to its ability to learn complex rules and generalize well for unknown data. Identifying the number of base classifiers (trees) required for a particular dataset is one of the key questions addressed in this paper. Statistical analyses of individual base classifiers are carried out to prune the ensemble model without compromising the classification accuracy of the model. Learning the learned model, i.e. learning the statistics of the forest in its entirety along with the information available in the dataset can reveal the optimal thresholds that should be used to prune an ensemble model. Experimental results reveal that, on an average 78% of the trees were pruned on 26 different datasets obtained from the UCI repository. The impact of pruning was positive with 22 out of 26 datasets showing equal or better classification accuracy in comparison with the Classical Random Forest algorithm.
Radicalization revisited: violence, politics and the skills of the body

For more than a decade, 'radicalization' has been a keyword in our understanding of terrorism. From the outset, radicalization was conceived of as an intellectual process through which an individual would increasingly come under a spell of extremist ideas. This ideological understanding of radicalization still prevails. In a 2015 speech on extremism, British Prime Minister David Cameron, for instance, claimed that the 'root cause of the threat we face is the extremist ideology itself' But the way we understand radicalization has specific consequences for the way we manage and fight the scourge of terrorism. Considering recent events, including the November 2015 Paris attacks, the present article sets out to reassess the above mentioned intellectualist understanding of radicalization and come up with new suggestions as to how radicalization may be understood today. Initially, the article suggests that ideology is not necessarily a precondition for violence, but that a prior experience with violence is more often a precondition for engaging an extremist ideology. Such experience with violence can be both domestic and international, obtained in Europe or Syria and other conflict zones. In the second part of the article it is argued that although radicalization is often conceived of as an individual process, pathways towards terrorism are inherently social and political. Finally, the article argues that by stressing the importance of ideology and ideological processes, concepts of radicalization have abstracted away from another factor that is pivotal for understanding pathways towards terrorist violence: the skills and capacities of the body.
Factors affecting consumer buying behavior

Consumer Buying Behaviour refers to the buying behaviour of the ultimate consumer. Many factors, specificities and characteristics influence the individual in what he is and the consumer in his decision making process, shopping habits, purchasing behavior, the brands he buys or the retailers he goes. A purchase decision is the result of each and every one of these factors. An individual and a consumer is led by his culture, his subculture, his social class, his membership groups, his family, his personality, his psychological factors, etc. and is influenced by cultural trends as well as his social and societal environment. By identifying and understanding the factors that influence their customers, brands have the opportunity to develop a strategy, a marketing message (Unique Value Proposition) and advertising campaigns more efficient and more in line with the needs and ways of thinking of their target consumers, a real asset to better meet the needs of its customers and increase sales.
Materials characterization by near field scanning microwave microscopy
Venue: 2016 Progress in Electromagnetic Research Symposium (PIERS)

Near field scanning microwave microscope (NFSMM) has potentials to achieve characterizations of materials with high spatial resolution and sensitivity. A wide range of applications have been addressed such as dielectric constant measurement, impedance analysis of metals and semiconductor materials/devices, gas and chemical sensing, etc.  In this work, we propose a home made NFSMM to perform materials nondestructive characterization. This system includes a vector network analyzer (VNA) a matching network and an evanescent microwave probe (EMP) [3, 4] The configuration of the system is given in Fig. 1. The broadband matching network is composed of a hybrid coupler and an impedance tuner. This matching circuit permits the measurements with high sensitivity and accuracy in the frequency range from 2 GHz to 18 GHz. It is well known that the evanescent wave is concentrated around the probe apex and decays rapidly with the distance away from the probe tip. In this study, an EMP with apex of 66 mm is used to achieve a very local characterization of materials with a sub wavelength resolution when the tip material separation is in the order of the apex size. The electromagnetic coupling between the probe and the material under test is also investigated using numerical simulations (ANSYS/HFSS(tm) based on finite element method (FEM) Thanks to the fact that microwave penetrates into the material, the electric properties (impedance and dielectric constant) of materials can be quantitatively determined based on the wave reflected by the material. The proposed microscopy platform thus allows a straightforward estimation of material properties rather than just topographic information. Furthermore, another advantage of the system is the nondestructive characterization possibility, which avoids injuring the sample surface.
Analysis of semiparametric regression models for the cumulative incidence functions under the two phase sampling designs

Competing risks often arise where a subject may be exposed to two or more mutually exclusive causes of failure. In the competing risks setting, the effects of covariates on the semiparametric model for cumulative incidence function can be assessed by using the direct binomial regression approach. In epidemiologic cohort studies, case cohort study designs have been widely used to evaluate the effects of covariates on failure times when the occurrence of the failure event is rare. In this dissertation, we study estimating procedures for the cumulative incidence function based on competing risks data under case cohort/two phase sampling designs. First, we introduce the semiparametric model for the cumulative incidence function with missing data. The estimation procedure is based on the direct binomial regression model, which enables us to evaluate the effects of the covariates directly when there exists competing risks. We develop an estimating equation for the missing model by using the inverse probability weighting of the complete case method. However, the IPW method loses the efficiency because it uses only complete data of subjects. To overcome this inefficiency, we also propose an estimating equation for the semiparametric model by using augmented inverse probability of complete case method. The AIPW method is doubly robust and it can improve efficiency. The asymptotic properties of the proposed IPW and AIPW estimators are established. The finite sample properties of those estimators are investigated by the simulation studies. The proposed estimating methods are applied to analyze data from the RV144 vaccine efficacy trial. Department of Mathematics, UNC Charlotte, Charlotte, NC 28223
Effective Cross Domain Collaborative Filtering using Temporal Domain A Brief Survey

Cross domain collaborative filtering (CDCF) is an evolving research topic in recommender systems. It aims to alleviate the data sparsity problem in individual domains by transferring knowledge among related domains. But it has an issue of user interest drift over time. Along with data sparsity, we should also consider the temporal domains to overcome user interest drift over time problem to predict more accurately as per the current user's interest. This paper surveys few of the pilot studies in this research line and the methods of how to add the temporal domains in the recommender systems. The paper also proposes possible extensions of using temporal domains with different contexts in current timestamp.
Mainstreaming Impact Evaluation in Nature Conservation

An important part of conservation practice is the empirical evaluation of program and policy impacts. Understanding why conservation programs succeed or fail is essential for designing cost effective initiatives and for improving the livelihoods of natural resource users. The evidence we seek can be generated with modern impact evaluation designs. Such designs measure causal effects of specific interventions by comparing outcomes with the interventions to outcomes in credible counterfactual scenarios. Good designs also identify the conditions under which the causal effect arises. Despite a critical need for empirical evidence, conservation science has been slow to adopt these impact evaluation designs. We identify reasons for the slow rate of adoption and provide suggestions for mainstreaming impact evaluation in nature conservation.
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Venue: NIPS

Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout. This grounding of dropout in approximate Bayesian inference suggests an extension of the theoretical results, offering insights into the use of dropout with RNN models. We apply this new variational inference based dropout technique in LSTM and GRU models, assessing it on language modelling and sentiment analysis tasks. The new approach outperforms existing techniques, and to the best of our knowledge improves on the single model state of the art in language modelling with the Penn Treebank (73.4 test perplexity) This extends our arsenal of variational tools in deep learning.
Overview of Advanced Nursing Practice

The field of advanced nursing practice and diverse levels of advanced nursing is a growing trend worldwide in the provision of a variety of healthcare services. This chapter introduces the concept of advanced nursing practice by offering an international definition and key characteristics of an advanced practice nurse. Assumptions that should be found wherever nursing exists are identified as the foundation for progressing to advanced levels of nursing. Topics to consider when forging a new nursing role are discussed along with country issues that shape advanced nursing practice development.
The Relationship Between Viral Marketing, Purchase Intention, and Brand Visibility: Study with Brazilian Customers

The present study aimed to evaluate the influence of viral marketing such as advertising communication strategy in purchase intent and brand visibility from the perspective of the online consumer. The literature review sought to contextualize the internet marketing; aspects of digital marketing, viral marketing and advertising communication strategy, social media in relationships between organizations and consumers, brand awareness, purchase intent, plus an overview of scientific literature on the viral marketing and constructs: consumer perception on advertising campaigns posted on the network, viral marketing, brand awareness and purchase intent. We sought to examine the positive relationship between consumer perception of advertising campaigns posted on the network, viral marketing, brand awareness, and purchase intent. In a descriptive research with quantitative variables, totaling 321 respondents through an electronic survey with the adoption of a structured questionnaire with responses in a Likert scale. The collected data were analyzed using the Partial Least Squares method, allowing the work with a small sample. We found statistically positive values for the relationship between consumer perception of social media advertising, viral marketing, brand awareness, and purchase intent. Statistical data served as the basis for the validation of the model proposed in this work.
Pressure relief sizing of reactive system using DIERS simplified methods and dynamic simulation method

Incidents involving uncontrolled chemical reactions continue to result in fatality, injury and economic loss. These incidents are often the result of inadequate pressure relief system designs due to a limited knowledge of the chemical reactivity hazard. A safe process design requires knowledge of the chemical reactivity of desired as well as undesired chemical reactions due to upset conditions. Simplified, cost effective methods to relief system sizing are presented by The Design Institute of Emergency Relief Systems (DIERS) They require multiple experiments, and sizing is only valid for the system composition and thermal inertia represented by the small scale experiments. Results are often conservative, especially for gassy systems. Detailed, dynamic computer simulation is highly accurate and can be used for iterative design and multiple scenario evaluation. In this study, an accelerating rate calorimeter (ARC (r) and a low thermal inertia calorimeter (automatic pressure tracking adiabatic calorimeter APTAC(tm) were used to collect chemical reactivity data for the dicumyl peroxide and toluene system. Results of the pressure relief system sizing using the dynamic simulation method are presented and compared with DIERS simplified methods.
Design for a fast high precision UAV power emergency relief system
Venue: 2016 4th International Conference on Applied Robotics for the Power Industry (CARPI)

Unmanned aerial vehicle (in this paper, it is referred to as UAV) is widely used in the field of electric power emergency relief. The route planning time of traditional UAV emergency relief system is too long to deliver the disaster delay, and its inaccurate image feedback lead to blind command. In this paper, a fast high precision UAV power emergency relief system has been successfully designed, mainly including route map fast planning system, automatic high precision UAV flight platform and ground monitoring command system. The route map fast planning system has been designed based on the C language, GIS Data development technology and image recognition technology. Automatic high precision UAV flight platform mainly include UAV flight platform, multiple picture Video segmentation processor and camera fixed device. UAV route planning can be finished fast, nearly 60% of the planning map time has been saved than traditional UAV system.
The study, evaluation, and improvement of university student self efficacy

In this review of 64 articles published since the year 2000, a strong association between self efficacy and student learning outcomes was apparent. Self efficacy is also related to other factors such as value, self regulation and metacognition, locus of control, intrinsic motivation, and strategy learning use. The review revealed that university student self efficacy is higher under certain conditions than others, and that it can be improved. Examples of teaching strategies that may be used to improve self efficacy are outlined. In screening articles for inclusion in the review, several conflicting definitions of self efficacy arose. Clarification on the meaning and scope of the self efficacy term is provided. The interpretation of the results of some studies reviewed was limited by design or analysis issues. Suggestions for addressing these issues in future research and evaluation work is given.
LEARNING STRATEGY IMPLEMENTATION OF GENERATIVE LEARNING ASSISTED SCIENTIST'S CARD TO IMPROVE SELF EFFICACY OF JUNIOR HIGH SCHOOL STUDENT IN CLASS VIII

In general, self efficacy of the students is still low. This study aims to determine the learning strategies implementation of generative learning assisted scientist's card in improving self efficacy and cognitive learning outcomes of the students. The study designed form O ne G roup P retest P osttest Design The improvement of self efficacy can be determined from the change in the questionnaire score before and after the learning and observations during the learning process. Cognitive learning outcomes are known from pretest and posttest scores. To determine the improvement, the data were analyzed by using the gain test. The results showed that N gain of self efficacy is 0.13 (low) and N gain of cognitive learning is 0.60 (medium) Based on the observation, students' self efficacy has increased each meeting. Cognitive learning results also achieved mastery learning as big as 72.88% It could be concluded that the learning strategy of generative learning assisted scientist's card can improve self efficacy and cognitive learning outcomes of the students. Pada umumnya, s elf efficacy yang dimiliki siswa masih rendah. Penelitian ini bertujuan untuk mengetahui penerapan strategi pembelajaran generative learning berbantuan scientist's card dalam meningkatkan self efficacy dan hasil belajar kognitif siswa. Desain penelitian berbentuk O ne G roup P retest P osttest Design Peningkatan s elf efficacy dapat diketahui dari perubahan skor angket sebelum dan sesudah pembelajaran dan hasil observasi selama pembelajaran. Hasil belajar kognitif diketahui dari skor pretest dan posttest. Untuk mengetahui peningkatannya, data yang diperoleh dianalisis menggunakan uji gain. Hasil penelitian menunjukkan bahwa peningkatan self efficacy berkatagori rendah dan peningkatan hasil belajar kognitif berkatagori sedang. Berdasarkan hasil observasi, self efficacy siswa setiap pertemuan meningkat. Hasil belajar ranah kognitif juga mencapai ketuntasan belajar .Jadi dapat disimpulkan bahwa strategi pembelajaran generative learning berbantuan scientist's card dapat meningkatkan self efficacy dan hasil belajar ranah kognitif siswa.
THE EFFECTIVENESS OF COLLABORATIVE THINK WRITE PAIR SHARE COMPARED TO PEER EDITING STRATEGY FOR TEACHING DESCRIPTIVE TEXT WRITING TO STUDENTS OF HIGH AND LOW SELF EFFICACY

The process of writing has become priority in EFL teaching and Learning and requires valuable ways to help learners gain progress. This study aimed at knowing the effectiveness of Think Write Pair Share compared to Peer editing as colaborative writing strategies used to boost students' descriptive writing achievement and seeing the interactional effect between the strategies and students' self efficacy toward their writing achievement. To realize the purpose of this study, quantitative approach was applied to two groups pretest and posttest of 2x2 factorial design. There were 28 students of the fourth semester of English Department of STKIP Soe in the academic year of 2014/2015 participated in this study. The study applied some instruments like self efficacy statements, test and observation checklist. In general, the statistical analysis using Paired Sample t test revealed that there was significant improvement on students' writing achievement after learning using TWPS and Peer editing strategy. Both high and low efficacy students experienced significant improvement in writing after learning using TWPS strategy. Meanwhile, another analysis using Independent Sample t test, the high and low self efficacy students of TWPS group got better achievement compared to those in Peer editing. Besides the results above, the analysis using ANOVA test revealed interactional effect between teaching strategies and self efficacy toward writing achievement of students' with high self efficacy. While, the students with low self efficacy were greatly influenced by the strategies. Shortly, both teaching strategies are effective to improve students' writing achievement although TWPS is better than Peer editing strategy.