nils-herrmann/clean_scirepeval huggingface dataset:
Impact of Counterfeit Products on Consumer Buying Behavior: Empirical Investigation Form Karachiites

The study aims to investigate the factors which affect the buying behaviour of the consumers and their attitudes towards counterfeit products. The study is also set out to examine the relationship of consumers' buying behavior towards counterfeit product with purchase intention. The authors used primary data which was collected through questionnaire consisting of 12 questions. A survey of 100 respondents was conducted in the educational institutes and offices of Karachi. Regression model was used to test the hypothesis postulated and research questions. It was discovered that there is no impact of counterfeit products on consumer buying behaviour. By having a better understanding of the consumers' behavioral intentions of buying counterfeit products, the manufacturers and marketers of the genuine brand products can make better marketing strategies to attract the consumer to buy the original product and not the counterfeit version. Theoretical contribution of this study is an extension of knowledge of consumers' buying behaviour with regards to counterfeit products in the fashion industry of Karachi.
An Examination of Income Effect on Consumers' Ethical Evaluation of Counterfeit Drugs Buying Behaviour: A Cross Sectional Study in Qatar and Sudan.
Venue: Journal of clinical and diagnostic research JCDR

There are limited studies on consumer behaviour toward counterfeit products and the determining factors that motivate willingness to purchase counterfeit items.  This study aimed to fill this literature gap through studying differences in individual ethical evaluations of counterfeit drug purchase and whether that ethical evaluation affected by difference in income. It is hypothesized that individuals with lower/higher income make a more/less permissive evaluation of ethical responsibility regarding counterfeit drug purchase.  To empirically test the research assumption, a comparison was made between people who live in the low income country Sudan and people who live in the high income country Qatar. The study employed a face to face structured interview survey methodology to collect data from 1,170 subjects and the Sudanese and Qatari samples were compared using independent t test at alpha level of 0.05 employing SPSS version 22.0.  Sudanese and Qatari individuals were significantly different on all items. Sudanese individuals scored below 3 for all Awareness of Societal Consequences (ASC) items indicating that they make more permissive evaluation of ethical responsibility regarding counterfeit drug purchase. Both groups shared a basic positive moral agreement regarding subjective norm indicating that influence of income is not evident.  Findings indicate that low income individuals make more permissive evaluation of ethical responsibility regarding counterfeit drugs purchase when highlighting awareness of societal consequences used as a deterrent tool, while both low and high income individuals share a basic positive moral agreement when subjective norm dimension is exploited to discourage unethical buying behaviour.
Human gut microbes impact host serum metabolome and insulin sensitivity
Venue: Nature

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non diabetic Danish individuals. The serum metabolome of insulin resistant individuals is characterized by increased levels of branched chain amino acids (BCAAs) which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.
Alzheimer's disease and gut microbiota
Venue: Science China Life Sciences

Alzheimer's disease (AD) is a most common neurodegenerative disorder, which associates with impaired cognition. Gut microbiota can modulate host brain function and behavior via microbiota gut brain axis, including cognitive behavior. Germ free animals, antibiotics, probiotics intervention and diet can induce alterations of gut microbiota and gut physiology and also host cognitive behavior, increasing or decreasing risks of AD. The increased permeability of intestine and blood brain barrier induced by gut microbiota disturbance will increase the incidence of neurodegeneration disorders. Gut microbial metabolites and their effects on host neurochemical changes may increase or decrease the risk of AD. Pathogenic microbes infection will also increase the risk of AD, and meanwhile, the onset of AD support the "hygiene hypothesis" All the results suggest that AD may begin in the gut, and is closely related to the imbalance of gut microbiota. Modulation of gut microbiota through personalized diet or beneficial microbiota intervention will probably become a new treatment for AD.
The Problem Faced by English Department Students in Speaking English.

Speaking has always been seen as an important skill in language learning. As the purpose of speaking is conveying meaning, fluency always become more essential and required more attention. However, many English Students, especially in Indonesia, faced disfluency. There are 4 types of disfluency; Hesitation, Interjection, Repetition and Revision. In exploring English students disfluency in speaking English, a study entitle "The problem faced by English Department Students in speaking English" was arranged into two research questions that are: What is the dominant types of disfluency occurred among English Department student of UIN Ar Raniry when they speak in English and What strategies are students used in coping with disfluency problem in speaking English. It is expected that this research contributes some significant progress namely as an access to understand the process of English Speech Production and teachers of English subject can predict the students' types of speech disfluency and its strategy. Thirty three participants of this qualitative study were chosen purposively; who already took speech and communication management class. Data for this study were gathered from Questionnaires and Interview. The finding showed that students experienced 60% more frequent on revision type of disfluency in speaking English. It was because speaking requires limit time and the quick process of thinking. Students applied gesture to assist their speaking. Moreover, using simple words would also a good strategy to decrease the disfluent in speaking.
Remote Sensing And Image Interpretation

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Comparative Analysis of Satellite DNA in the Drosophila melanogaster Species Complex
Venue: G3: Genes, Genomes, Genetics

Satellite DNAs are highly repetitive sequences that account for the majority of constitutive heterochromatin in many eukaryotic genomes. It is widely recognized that sequences and locations of satellite DNAs are highly divergent even in closely related species, contributing to the hypothesis that satellite DNA differences may underlie speciation. However, due to its repetitive nature, the mapping of satellite DNAs has been mostly left out of recent genomics analyses, hampering the use of molecular genetics techniques to better understand their role in speciation and evolution. Satellite DNAs are most extensively and comprehensively mapped in Drosophila melanogaster, a species that is also an excellent model system with which to study speciation. Yet the lack of comprehensive knowledge regarding satellite DNA identity and location in its sibling species (D. simulans, D. mauritiana, and D. sechellia) has prevented the full utilization of D. melanogaster in studying speciation. To overcome this problem, we initiated the mapping of satellite DNAs on the genomes of the D. melanogaster species complex (D. melanogaster, D. simulans, D. mauritiana, and D. sechellia) using multi color fluorescent in situ hybridization (FISH) probes. Our study confirms a striking divergence of satellite DNAs in the D. melanogaster species complex, even among the closely related species of the D. simulans clade (D. simulans, D. mauritiana, and D. sechellia) and suggests the presence of unidentified satellite sequences in these species.
Efficient identification of node importance in social networks
Venue: Inf. Process. Manag.

Based on the theories of comprehensive decision making, a multiattribute ranking method is presented.The proposed method is superior to other methods through contrast experiments.The theories of comprehensive decision making are utilized to detect important nodes in social networks.This method can be used in different types and different scales of networks. In social networks, identifying influential nodes is essential to control the social networks. Identifying influential nodes has been among one of the most intensively studies of analyzing the structure of networks. There are a multitude of evaluation indicators of node importance in social networks, such as degree, betweenness and cumulative nomination and so on. But most of the indicators only reveal one characteristic of the node. In fact, in social networks, node importance is not affected by a single factor, but is affected by a number of factors. Therefore, the paper puts forward a relatively comprehensive and effective method of evaluation node importance in social networks by using the multi objective decision method. Firstly, we select several different representative indicators given a certain weight. We regard each node as a solution and different indicators of each node as the solution properties. Then through calculating the closeness degree of each node to the ideal solution, we obtain evaluation indicator of node importance in social networks. Finally, we verify the effectiveness of the proposed method experimentally on a few actual social networks.
Efficient Maximum Closeness Centrality Group Identification
Venue: ADC

As a key concept in the social networks, closeness centrality is widely adopted to measure the importance of a node. Many efficient algorithms are developed in the literature to find the top k closeness centrality nodes. In most of the previous work, nodes are treated as irrelevant individuals for a top k ranking. However, in many applications, it is required to find a set of nodes that is the most important as a group. In this paper, we extend the concept of closeness centrality to a set of nodes. We aim to find a set of k nodes that has the largest closeness centrality as a whole. We show that the problem is NP hard, and prove that the objective function is monotonic and submodular. Therefore, the greedy algorithm can return a result with (1 1/e\ approximation ratio. In order to handle large graphs, we propose a baseline sampling algorithm (BSA) We further improve the sampling approach by considering the order of samples and reducing the marginal gain update cost, which leads to our order based sampling algorithm (OSA) Finally, extensive experiments on four real world social networks demonstrate the efficiency and effectiveness of the proposed methods.
Super mediator A new centrality measure of node importance for information diffusion over social network
Venue: Inf. Sci.

We propose an efficient method to discover a new type of influential nodes in a social network, which we name "super mediators" i.e. those nodes which, if removed, decrease information spread. It is formulated mathematically as a problem of difference maximization of the average influence degree with respect to removal of a node, i.e. a node that contributes to making the difference large is influential. This definition requires use of information diffusion model for their identification and thus is "model driven" The other definition which is more empirical is that super mediators are those nodes that appear frequently in long diffusion sequences but much less frequently in short diffusion sequences. This definition does not require any model but does require abundant information diffusion data and thus is "data driven" We attempt to characterize the property of super mediators from various angles: how the resulting super mediators are different between these two definitions and which is more reasonable, how super mediators are compared with nodes identified by other centralities, e.g. betweenness, degree, closeness, etc. how super mediators are different from the solution of well studied influence maximization problem, i.e. nodes capable of widely spreading information to other recipient nodes, and the solution of reverse influence maximization problem, i.e. nodes capable of widely receiving information from other information source nodes. We conducted extensive experiments using three real world social networks. The major findings are (1) model driven super mediator degree has the best discrimination capability, while influence degree, reverse influence degree, and data driven super mediator degree are much less discriminative (all flat for high ranked nodes) (2) model driven super mediators have high scores for either influence degree or reverse influence degree, while data driven super mediators have high scores for both, and (3) model driven super mediators are closely correlated with betweenness centrality, but the strength of the correlation depends on the value of diffusion probability.
A New K Shell Decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks
Venue: ArXiv

An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k shell decomposition method, out leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.
Fast Community Detection in Large Weighted Networks Using GraphX in the Cloud
Venue: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)

Identification of different communities in large weighted networks is of crucial importance since it helps to uncover priori unknown functional modules such as topics in information networks or cyber communities in social networks. However, the typical size of networks, such as social network services or World Wide Web, now counts in millions of nodes and is computationally complex. This urgently demands feasible methods and available computing platforms to retrieve their structure efficiently. To address this problem, we propose an algorithm Fast Community Detection (FastCD) based on modularity optimization. Furthermore, FastCD easily supports parallel computation. We implement FastCD with GraphX, which is an embedded graph processing framework built on top of Apache Spark. After carrying out comprehensive experiments in a 16 nodes cluster (32 vCPU) on Amazon EC2, the results indicate that FastCD not only outperforms the state of the art algorithms in terms of computation time, but also guarantees the accuracy of the solutions under different real world networks commonly used for efficiency comparison.
MIMIC III, a freely accessible critical care database
Venue: Scientific data

III 'Medical Information Mart for Intensive Care' is a large, single center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Venue: IEEE Signal Processing Letters

Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse to fine manner. In addition, we propose a new online hard sample mining strategy that further improves the performance in practice. Our method achieves superior accuracy over the state of the art techniques on the challenging face detection dataset and benchmark and WIDER FACE benchmarks for face detection, and annotated facial landmarks in the wild benchmark for face alignment, while keeps real time performance.
Face Alignment Across Large Poses: A 3D Solution
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45) lacking the ability to align faces in large poses up to 90. The challenges are three fold: Firstly, the commonly used landmark based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA) in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN) We also propose a method to synthesize large scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state of the art methods.
From face to face to blended learning using ICT
Venue: 2016 IEEE Global Engineering Education Conference (EDUCON)

This study examines the development of the education model created in connection with the Master Studies in Mathematical Information Technology. The model has developed from the first stage, where there was only face to face teaching supported with Learning Management System, to a stage where studying is possible also fully in online and students may choose themselves how much to take advantage of technology in their studies. The examination of the development of the education model is made from the viewpoints of accessibility, increased role of technology and interaction. In earlier studies, the education model has been evaluated for example from the viewpoints of changes in the participation rate and learning results, and the results have been positive. These viewpoints, in addition to describing the development path, will be highlighted in this study.
Disturbed flow mediated modulation of shear forces on endothelial plane: A proposed model for studying endothelium around atherosclerotic plaques
Venue: Scientific reports

Disturbed fluid flow or modulated shear stress is associated with vascular conditions such as atherosclerosis, thrombosis, and aneurysm. In vitro simulation of the fluid flow around the plaque micro environment remains a challenging approach. Currently available models have limitations such as complications in protocols, high cost, incompetence of co culture and not being suitable for massive expression studies. Hence, the present study aimed to develop a simple, versatile model based on Computational Fluid Dynamics (CFD) simulation. Current observations of CFD have shown the regions of modulated shear stress by the disturbed fluid flow. To execute and validate the model in real sense, cell morphology, cytoskeletal arrangement, cell death, reactive oxygen species (ROS) profile, nitric oxide production and disturbed flow markers under the above condition were assessed. Endothelium at disturbed flow region which had been exposed to low shear stress and swirling flow pattern showed morphological and expression similarities with the pathological disturbed flow environment reported previously. Altogether, the proposed model can serve as a platform to simulate the real time micro environment of disturbed flow associated with eccentric plaque shapes and the possibilities of studying its downstream events.
Quality assessment for Linked Data: A Survey
Venue: Semantic Web

The development and standardization of semantic web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD) However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD. We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD.