nils-herrmann/clean_scirepeval huggingface dataset:
Does nonbinding commitment promote children's cooperation in a social dilemma?
Venue: Journal of experimental child psychology
Year: 2020

Communication is a powerful tool for promoting cooperation in adults and is considered one of the most important solutions to social dilemmas. One feature that makes communication particularly useful in cooperative contexts is that it allows people to advertise their intentions to partners. Some work suggests that adults cooperate more after making nonbinding commitments to cooperate (i.e. commitments they do not need to uphold) than when they are not allowed to communicate their intentions to their partners. However, we know little about whether nonbinding commitments play a similar role in children. We addressed this gap by testing 6 to 9 year old children in a simultaneous version of the iterated prisoner's dilemma game. In the communication condition, children could communicate their intended decision prior to their actual choice, whereas in the silent condition, they could not communicate. Overall, children in the communication condition were no more likely to cooperate than children in the silent condition. However, in the communication condition, but not in the silent condition, children's behavior was contingent on their previous decision; they were more likely to cooperate or defect when they had previously cooperated or defected, respectively. In addition, they rarely reversed their intended decisions in the game. Our findings suggest that, although nonbinding commitment does not promote children's cooperation in general, it may encourage children to stick to their chosen strategy, perhaps for the sake of appearing consistent. More broadly, these results contribute to our emerging understanding of the ways in which children solve social dilemmas.
Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals
Venue: Pattern Recognit. Lett.
Year: 2016

Gaussian mixture modeling is used to fit the probability density function of heartbeats.Expectation maximization algorithm estimates the parameters of statistical model.Skewness, kurtosis and 5th moment of ECG signals express the shape parameters.RR interval information represents the time domain characteristics of ECG signals.An ensemble of decision trees are used to perform the final classification. In this paper we propose a novel method for accurate classification of cardiac arrhythmias. Morphological and statistical features of individual heartbeats are used to train a classifier. Two RR interval features as the exemplars of time domain information are utilized in this study. Gaussian mixture modeling (GMM) with an enhanced expectation maximization (EM) solution is used to fit the probability density function of heartbeats. Parameters of GMM together with shape parameters such as skewness, kurtosis and 5th moment are also included in feature vector. These features are then used to train an ensemble of decision trees. MIT BIH arrhythmia database containing various types of common arrhythmias is employed to test the algorithm. The overall accuracy of 99.70% in "class oriented" scheme and 96.15% in "subject oriented" scheme is achieved. Both cases express a significant improvement of accuracy compared to other methods.
Analysis of Food Adulterants in Selected Food Items Purchased From Local Grocery Stores
Year: 2017

Food is one among the basic needs for every living being. Food for human consumption should be in its possible purest form without adulterants and contaminants. The present study focuses on analyzing few selected food items purchased from local grocery stores located in the twin cities of Secunderabad and Hyderabad, Telangana. The extent of different adulterant present in the food items were, edible oil samples, argemone oil (30% prohibited color (15% mineral oil (20% dyes in fat (15% and castor oil (0% 20% of ghee samples tested positive for vanaspathi and were negative for mashed potatoes and paraffin wax (0% Coconut oil tested negative both for any other oil (0% and turbidity (0% None of the honey samples tested positive for water (0% All the jaggery samples tested were negative for sodium bicarbonate (0% Similar results were obtained in case of curd samples (negative for dalda (0% 55% of the common salt samples tested positive for the presence of impurities.
Cultural Transmission of Work Welfare Attitudes and the Intergenerational Correlation in Welfare Receipt
Year: 2008

This paper considers the potential for the cultural transmission of attitudes toward work, welfare, and individual responsibility to explain the intergenerational correlation in welfare receipt. Specifically, we investigate whether 18 year olds' views about social benefits and the drivers of social inequality depend on their families' welfare histories. We begin by incorporating welfare receipt into a theoretical model of the cultural transmission of work welfare attitudes across generations. Consistent with the predictions of our model, we find that young people's attitudes towards work and welfare are shaped by socialization within their families. Young people are more likely to oppose generous social benefits and adopt an internal view of social inequality if their mothers support these views, if their mothers were employed while they were growing up, and if their families never received welfare. These results are consistent with though do not definitively establish the existence of an intergenerational welfare culture.
Blended online learning versus traditional classroom learning: A comparison of mathematics content mastery for high school students of homeowners and non homeowners
Year: 2015

The purpose of this causal comparative study was to compare the methods of traditional face toface classroom instruction and blended online instruction for students from differing home environments who were repeating a Mathematics I course at the high school level. This quantitative study, conducted at three high schools in Georgia, used the theories of self regulated learning, student centered learning, Keller's ARCS (Attention, Relevance, Confidence, and Satisfaction) model of motivational design of instruction, and cognitive load theory to compare the two approaches to learning. The participants in this study consisted of 398 high school students taking a Mathematics I class for the second time in either a traditional classroom setting or a blended online setting between January of 2010 through June of 2013. Archival data was collected regarding demographic information and student outcomes on Georgia's End of Course Test (EOCT) from each school's student information system. Archival data was also collected from county tax records to verify homeownership status for the parents or guardians of student participants. A pretest/posttest causal comparative design was used. The pretest consisted of each student's previous EOCT score while the posttest consisted of each student's End of Course Test score after repeating the course. Two way Analysis of Covariance (ANCOVA) was used to analyze the archival data in the study. Results of the study indicated a statistically significant difference on the posttest when comparing the blended online setting and the traditional classroom setting, but no statistically significant difference based on a family's homeownership status and no significant interaction between the mode of instruction and parents' homeownership status.
Predictors of Firm Performance and Supply Chain: Evidence from Indonesian Pharmaceuticals Industry
Year: 2020

The recent trend in big data application has led the organizations to think about its application. Having in mind the importance of supply chain integration and learning the current study has attempted to explore the drivers of firm performance from the supply chain perspective. Data were collected from the employees working in the pharmaceuticals companies in Indonesia. Smart PLS was used for data analysis. The results of the study showed that there is strong significant positive relationship between big data analytical capability and firm performance. Further the results also revealed a significant influence of supply chain integration and learning on the firm performance. The results also revealed supply chain agility as a significant positive mediator between the relationships of supply chain integration, big data analytical capability, learning and firm performance. All the hypothesis were accepted.
Here, and Not Yet Here: A Dialogue at the Intersection of Queer, Trans, and Culture
Year: 2013

To start this dialogue, guest editor Karma R. Chavez posed a series of general and unbinding questions to participants about the meanings of queer theory and its relationship with questions of culture. The dialogue unfolded over the course of three weeks in an online forum and covered several important themes. First, participants engaged questions surrounding the meaning of queer, and its relationship to different cultural and linguistic contexts, especially with regard to diaspora, settler colonialism, and postcoloniality. Second, participants considered the interplay between queer and trans theories, which led to considerations of the body, memory, and homonormativity. Third, after the "coming out" of the U.S. actress Jodie Foster, participants had a lively discussion about the politics of visibility, responsibility, and accountability for different LGBTQ subjects. The dialogue concluded with final meditations.
Application of various creep analysis methods for estimating the time dependent behavior of cracked concrete beams
Year: 2020

A structural model is developed to predict the time dependent behavior of cracked reinforced and partially prestressed concrete beams. The model considers creep, shrinkage, cracking, prestress relaxation and temperature. Three different methods to model the effect of creep are presented and compared, which include the effective modulus method, integral type and rate type formulations. The effect of temperature on both the creep and shrinkage is considered. A smeared cracking model is adopted and an iterative procedure is implemented at each time step to determine the cracked rigidities which vary through the length of the beam. The governing equations are solved using the multiple shooting method at each time step. The validity of the proposed model is demonstrated through comparison to experimental data available in the literature. It was found that all three analysis methods provide equally reasonable prediction of the long term behavior. A numerical study is also presented to demonstrate the capability of the model in simulating the time variation of stresses and strains in cracked concrete beams.
Modified vegetation indices for estimating crop fraction of absorbed photosynthetically active radiation
Year: 2015

The fraction of absorbed photosynthetically active radiation (FPAR) is an important biophysical parameter of vegetation. It is often estimated using vegetation indices (VIs) derived from remote sensing data, such as the normalized difference VI (NDVI) Ideally a linear relationship is used for the estimation; however, most conventional VIs are affected by canopy background reflectance and their sensitivity to FPAR declines at high biomass. In this study, a multiplier, the ratio of the green to the red reflectance, was introduced to improve the linear relationship between VIs and crop FPAR. Three widely used VIs NDVI, the green normalized difference VI (GNDVI) and the renormalized difference VI (RDVI) were modified this way and were called modified NDVI (MNDVI) modified GNDVI (MGNDVI) and modified RDVI (MRDVI) respectively. A sensitivity study was applied to analyse the correlation between the three modified indices and the leaf area index (LAI) using the reflectance data simulated by the combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model (PROSAIL model) The results revealed that these new indices reduced the saturation trend at high LAI and achieved better linearity with crop LAI at low to medium biomass when compared with their corresponding original versions. This has also been validated using in situ FPAR measurements over wheat and maize crops. In particular, estimation using MNDVI achieved a coefficient of determination (R2) of 0.97 for wheat and 0.86 for maize compared to 0.90 and 0.82 for NDVI, respectively, while MGNDVI achieved 0.97 for wheat and 0.88 for maize, compared to 0.90 and 0.81 for GNDVI, respectively. Algorithms based on the VIs when applied to both wheat and maize showed that MNDVI and MGNDVI achieved a better linearity relationship with FPAR (R2 0.92) in comparison with NDVI (R2 0.85) and GNDVI (R2 0.82) The study demonstrated that applying the green to red reflectance ratio can improve the accuracy of FPAR estimation.
Towards Better Representation Learning for Personalized News Recommendation: a Multi Channel Deep Fusion Approach
Venue: IJCAI
Year: 2018

Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. In the past few decades, latent factor models has been widely used for building recommender systems (RSs) With the remarkable success of deep learning techniques especially in visual computing and natural language understanding, more and more researchers have been trying to leverage deep neural networks to learn latent representations for advanced RSs. Following mainstream deep learning based RSs, we propose a novel deep fusion model (DFM) which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re ranking. There are two key components in our DFM approach, namely an inception module and an attention mechanism. The inception module improves the plain multi layer network via leveraging of various levels of interaction simultaneously, while the attention mechanism merges latent representations learnt from different channels in a customized fashion. We conduct extensive experiments on a commercial news reading dataset, and the results demonstrate that the proposed DFM is superior to several state of the art models.
Deterministic and stochastic optimal inventory control with logistic stock dependent demand rate
Venue: Int. J. Math. Oper. Res.
Year: 2014

It has been suggested by many supply chain practitioners that in certain cases inventory can have a stimulating effect on the demand. In mathematical terms this amounts to the demand being a function of the inventory level alone. In this work we propose a logistic growth model for the inventory dependent demand rate and solve first the continuous time deterministic optimal control problem of maximising the present value of the total net profit over an infinite horizon. It is shown that under a strict condition there is a unique optimal stock level which the inventory planner should maintain in order to satisfy demand. The stochastic version of the optimal control problem is considered next. A bang bang type of optimal control problem is formulated and the associated Hamilton Jacobi Bellman equation is solved. The inventory level that signifies a switch in the ordering strategy is worked out in the stochastic case.
PARAPHRASING STRATEGIES IN HIGHER EDUCATION
Year: 2021

Paraphrasing as a method of avoiding plagiarism becomes the most important in academic writing. Responding this issue, the researchers interest to conduct the study in analyzing university students' paraphrasing seen from the strategies and the paraphrased identifications. This study used qualitative research. Data were collected from ten of sixth year students majoring in English at Universitas Islam Negeri (UIN) Antasari Banjarmasin who enrolled English for Academic Purposes subject in academic year of 2020 through documentation of Introduction section in their research proposals. The finding of this study indicated that using synonyms and condensing original were as the most frequency, followed by using varied structure, changing parts of speech and expanding phrase for clarity. On the other hand, students rarely used changing word order, separating long sentence, and combining sentences. The highest paraphrased identification was moderate revision that occurred five times or a half of participants, followed by near copy, minimal revision and substantial revision with same result that occurred once. This study also found that some students paraphrased the sentence without mentioning the main source and it is considered as plagiarism. Based on the result, it is suggested that English writing instruction at any university should provide more training on paraphrasing for the students, so it will make them aware of the paraphrase strategies and avoid plagiarism
Partial denial of pregnancy at 32 weeks in a diabetic and suicidal patient: A case report. What Are the Treatment Recommendations?
Venue: Psychiatria Danubina
Year: 2018

Denial of pregnancy is an issue that is often discovered a posteriori with sometimes dramatic complications. Denial of pregnancy is considered partial when the woman becomes aware of the pregnancy after the fifth month before delivery. The populations studied were heterogeneous, which made it impossible to establish a standard algorithm of the treatment and support of a discovery of partial denial of pregnancy.  Based on a literature review and a discussion of partial denial of pregnancy case and the consequential treatment with a five year follow up, the global management recommendations need consideration in the case of partial denial of pregnancy.  The reported case confirmed the significance of the trauma caused by the discovery of pregnancy in a patient in denial, but also showed that this trauma can extend to caregivers concerned by the treatment.  Continuous training of all caregivers for denial of pregnancy is essential even if the issue may be considered infrequent. Contraception, prevention of sexually transmitted diseases and the importance of gynecological follow up must be systematically addressed in a medical consultation. A standard algorithm for the treatment of partial denial is difficult to establish, but the rapid mobilization of a multidisciplinary team or hospitalization is recommended for the announcement of the diagnosis as well as personalized support during ultrasounds. The establishment of a relationship of trust remains the major issue.
Prospects and challenges of circulating tumor DNA in precision medicine of hepatocellular carcinoma
Venue: Clinical and Experimental Medicine
Year: 2020

The growing role of precision medicine in hepatocellular carcinoma (HCC) is expected to ameliorate the poor prognosis and high mortality of this highly malignant disease; however, it is faced with challenges such as the low frequency of tissue biopsy. Hence, attention is turning to the circulating tumor DNA (ctDNA) an important component of liquid biopsy. Obtaining molecular information about cancer from blood provides a good prospect in precision oncology including molecular diagnosis, molecular classification, targeted therapy, personalized decision making, and detection of drug resistance mutations. However, inherent constraints of HCC and ctDNA (like background chronic liver diseases (CLD) and low concentration of ctDNA) along with some technical issues should be well handled and solved before the potential of ctDNA in precision medicine of HCC can be truly realized. In this review, we will focus on the prospects and challenges of ctDNA in HCC precision medicine.
Design Space Exploration for the Profitability of a Rule Based Aggregator Business Model Within a Residential Microgrid
Venue: IEEE Transactions on Smart Grid
Year: 2015

The microgrid has been shown to be profitable, reliable, and efficient for military, commercial, and university like installations. However, until now, there has been no study to show how and when a residential microgrid may be profitable. Therefore, in this paper, we present a design space exploration methodology of the microgrid by modeling all the energy resources at the residential level and conducting numerous simulations with various parameters. Moreover, a set of rules are defined to make the stakeholders in the microgrid profitable. Also, by analyzing the number of houses in the microgrid, we observe that the number of years it takes to return the capital costs invested in the microgrid may become adequately short for a certain range of the number of houses. For instance, if the aggregator owns the renewable energy resources, e.g. solar panels, it may profit in less than five years when 500 houses participate in the microgrid where each house owns 500 sf solar panels. On the other hand, if the prosumers own the renewable energy resources, e.g. solar panels, the aggregator may profit in about a year. Typically, for an apartment block type housing area in U.S. there are more than 1000 houses, therefore the aggregator profitability may improve furthermore.
Edge Assisted Federated Learning: An Empirical Study from Software Decomposition Perspective
Venue: ICA3PP
Year: 2020

Federated learning is considered to be a privacy preserving collaborative machine learning training method. However, due to the general limitation of the computing ability of the terminal device, the training efficiency becomes an issue when training some complex deep neural network models. On the other hand, edges, the nearby stationary devices with higher computational capacity, might serve as a help. This paper presents the design of a component based federated learning framework, which facilitates the offloading of training layers to nearby edge devices while preserving the users' privacy. We conduct an empirical study on a classic convolutional neural network to validate our framework. Experiments show that this method can effectively shorten the time cost for mobile terminals to perform local training in the federated learning process.
Robust nonnegative matrix factorization with structure regularization
Venue: Neurocomputing
Year: 2020

Nonnegative matrix factorization (NMF) has attracted more and more attention due to its wide applications in computer vision, information retrieval, and machine learning. In contrast to the original NMF and its variants, this paper proposes a novel unsupervised learning framework, called robust structured nonnegative matrix factorization (RSNMF) which respects both global and local structures of the data space. Specifically, to learn a discriminative representation, RSNMF explores both the global structure via considering the data variance and the local structure via exploiting the data neighborhood. To well address the problem of noise and outliers, it imposes joint L2,1 norm minimization on both the loss function of NMF and the regularization of the basis matrix. The geometric structure and the joint L2,1 norm are formulated as an optimization model, which is solved by the proposed iterative algorithm. Finally, the convergence of RSNMF is analyzed theoretically and empirically. The experimental results on real world data sets show the effectiveness of our proposed algorithm in comparison to state of the art algorithms.