nils-herrmann/clean_scirepeval huggingface dataset:
Exploring the Improved Personal Credit Scoring Model of Ant Financial Services in its Disruptive Innovation Process

Ant Financial Services, by taking cloud computing and big data as underlying facilities and credit system as basis, opens up low end small and micro and individual customer market which is not satisfied in traditional financial market by disruptive service modes and concepts. "Sesame Credit" with unique digital credit means, aiming at accurately measuring personal credit score, is one of the key links for Ant Financial Services to carry out disruptive innovation. Sesame Credit is in open beta period, in spite of wide attention and remarkable achievement, it is still at the experimental stage with certain limitations. Thus, this paper first discloses the disruptive innovation characteristics that Ant Financial Services has, then analyses and discusses the scoring of Sesame Credit, and finally on this basis proposes an improved personal credit scoring model. Index Terms Ant financial services; Disruptive innovation; Sesame credit scoring; Improved personal credit scoring model. 1.  Internet finance market is positioned at "small and micro" level, characterized by small amount, fastness, and convenience, and it thus has outstanding advantages in the sector of small and micro finance (1) Internet finance constantly innovates credit means by using big data mining technology to greatly reduce credit and financing costs of small and micro enterprises (2) as well as information asymmetry. The key of the healthy development of Internet finance is to deepen the credit system. The improved and perfected credit system enhances the financial risk management level and the efficiency of resource allocation, thus it better provides convenient services to low end long tailed users in the financial market and achieves disruptive innovation in the financial sector. Ant Financial Services subordinate to Alibaba Group, as a typical representative of Internet financial companies, establishes Sesame Credit, the China's first personal digital credit throughout history, in the support of Internet platform, cloud computing, and big data technologies. It can serve low end micro and small and individual customer markets where cannot be satisfied in the traditional financial market. Sesame Credit is still in open beta period but has the room for improvement in the objective acquisition of data (3) as well as for improvement in the accurate scoring (4) Therefore, this paper first discloses the disruptive innovation characteristics of Ant Financial Services, then analyses and discusses Sesame Credit scoring, and finally on this basis proposes the improved personal credit scoring model to provide reference for personal credit scoring.
Production predicts comprehension: Animacy effects in Mandarin relative clause processing

This article investigates the relationship between production and comprehension of relative clauses in Mandarin Chinese. In a picture description task, we find strong head noun animacy effects on relative clause production despite the fact that Mandarin has head final relative clauses [relative clause] head noun] so that the animate/inanimate head noun is uttered late. These and other production results have implications for theories of incremental language planning. We then used corpus analyses to investigate the distribution of structure message pairings in the language that result from these animacy based production biases. Mandarin is particularly interesting from the language comprehension side, as there is an extensive literature on relative clause comprehension, with conflicting results. A gated sentence completion task reveals comprehenders' animacy linked expectations in relative clause interpretation and also shows the substantial amount of syntactic ambiguity in Mandarin relative clauses, owing to their head final structure. The completion data were reliable predictors of comprehenders' self paced reading times, but the distance between syntactically dependent elements in the sentences was not. We argue that these results argue against accounts of sentence comprehension that posit that some sentence types are inherently more difficult than others. Instead we suggest that sentence types with which comprehenders have little experience are difficult, and we link the results ultimately to producers' different production choices in different animacy configurations and consequent variation in language patterns that comprehenders experience.
Smart Cities
Venue: Informatik Spektrum

Edy Portmann: Die grundlegende Idee hinter ,Smart Cities" ist, dass Informationsund Kommunikationstechnologien fur eine nachhaltigere, sozialere und okologischere Gestaltung des stadtischen Raumes eingesetzt werden konnen. Dazu benotigt man eine effiziente Sammlung und Auswertung stadtbezogener Daten, sowie die Koordination ihrer Nutzung mittels Internetund Web basierter Services. Dies dient naturlich zum einen der Effizienzsteigerung. Es geht jedoch auch ausdrucklich darum, den Burgern Moglichkeiten zu eroffnen, ihren Lebensraum aktiv mitzugestalten. Ziel ist es, die Stadt zu einem Ort zu machen, wo das Bedurfnis des Einzelnen nach individualisierten Formen der Lebensfuhrung, welche Raum fur die personliche Entfaltung lassen, in Einklang mit den Anspruchen der Gemeinschaft gebracht werden kann. Das betrifft ganz unterschiedliche Bereiche des stadtischen Lebens. Besonders eindrucklich sind jedoch die Moglichkeiten, die mittlerweile etwa in den Bereichen Verkehr und Mobilitat sowohl im Individualverkehr, als auch mit Blick auf den offentlichen Verkehr Umweltund Klimaschutz, Abfallmanagement, ressourcenschonende Nutzung von Energie und Wasser sowie der Bereitstellung von Dienstleistungen der stadtischen Verwaltung angeboten werden konnen. Smart City zielt also darauf ab, ganz konkrete Probleme zu losen und Bedurfnisse zu befriedigen.
Generating a morphable model of ears
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

This paper describes the generation of a morphable model for external ear shapes. The aim for the morphable model is to characterize an ear shape using only a few parameters in order to assist the study of morphoacoustics. The model is derived from a statistical analysis of a population of 58 ears from the SYMARE database. It is based upon the framework of large deformation diffeomorphic metric mapping (LDDMM) and the vector space that is constructed over the space of initial momentums describing the diffeomorphic transformations. To develop a morphable model using the LDDMM framework, the initial momentums are analyzed using a kernel based principal component analysis. In this paper, we examine the ability of our morphable model to construct test ear shapes not included in the principal component analysis.
A Morphable Model to simulate rejuvenation trajectory of 3D face images: Preliminary results
Venue: 2016 International Conference on Bio engineering for Smart Technologies (BioSMART)

Facial Aging (FA) process as a natural phenomenon has been of great interest to many researchers as well as for studies in the field of gerontology and private sector firms like airports and police departments. As opposed to previous studies which focused on predicting the facial appearance changes of the individuals, this paper details the backward aging trajectory of face images in 3 dimensional environment. This model is learned from a database of three dimensional facial images which is built by five individual age groups between 3 to 35 years old. 3D facial aging modeling is a complex process since it affects both the shape and texture of the face. We propose the Statistical Rejuvenation Model (SRM) a morphable face model to transform the given adult face to its youthful appearance by changing its shape and texture simultaneously.
IL 33 regulates the IgA microbiota axis to restrain IL 1a dependent colitis and tumorigenesis.
Venue: The Journal of clinical investigation

Inflammatory bowel diseases (IBD) affect over 5 million individuals in the industrialized world, with an increasing incidence rate worldwide. IBD also predisposes affected individuals to development of colorectal cancer, which is a leading cause of cancer related deaths in adults. Mutations in genes encoding molecules in the IL 33 signaling pathway are associated with colitis and colitis associated cancer (CAC) but how IL 33 modulates gut homeostasis is unclear. Here, we have shown that Il33 deficient mice are highly susceptible to colitis and CAC. Mechanistically, we observed that IL 33 promoted IgA production from B cells, which is important for maintaining microbial homeostasis in the intestine. Il33 deficient mice developed a dysbiotic microbiota that was characterized by increased levels of mucolytic and colitogenic bacteria. In response to chemically induced colitis, this microbial landscape promoted the release of IL 1a, which acted as a critical driver of colitis and CAC. Consequently, reconstitution of symbiotic microbiota or IL 1a ablation markedly ameliorated colitis susceptibility in Il33 deficient animals. Our results demonstrate that IL 33 promotes IgA production to maintain gut microbial homoeostasis and restrain IL 1a dependent colitis and CAC. This study therefore highlights modulation of IL 33, IgA, IL 1a, and the microbiota as a potential therapeutic approach in the treatment of IBD and CAC.
Edible ginger derived nanoparticles: A novel therapeutic approach for the prevention and treatment of inflammatory bowel disease and colitis associated cancer.
Venue: Biomaterials

There is a clinical need for new, more effective treatments for chronic and debilitating inflammatory bowel disease (IBD) including Crohn's disease and ulcerative colitis. In this study, we characterized a specific population of nanoparticles derived from edible ginger (GDNPs 2) and demonstrated their efficient colon targeting following oral administration. GDNPs 2 had an average size of ~230 nm and exhibited a negative zeta potential. These nanoparticles contained high levels of lipids, a few proteins, ~125 microRNAs (miRNAs) and large amounts of ginger bioactive constituents (6 gingerol and 6 shogaol) We also demonstrated that GDNPs 2 were mainly taken up by intestinal epithelial cells (IECs) and macrophages, and were nontoxic. Using different mouse colitis models, we showed that GDNPs 2 reduced acute colitis, enhanced intestinal repair, and prevented chronic colitis and colitis associated cancer (CAC) 2D DIGE/MS analyses further identified molecular target candidates of GDNPs 2 involved in these mouse models. Oral administration of GDNPs 2 increased the survival and proliferation of IECs and reduced the pro inflammatory cytokines (TNF a, IL 6 and IL 1b) and increased the anti inflammatory cytokines (IL 10 and IL 22) in colitis models, suggesting that GDNPs 2 has the potential to attenuate damaging factors while promoting the healing effect. In conclusion, GDNPs 2, nanoparticles derived from edible ginger, represent a novel, natural delivery mechanism for improving IBD prevention and treatment with an added benefit of overcoming limitations such as potential toxicity and limited production scale that are common with synthetic nanoparticles.
The TNBS induced colitis animal model: An overview
Venue: Annals of medicine and surgery

Despite recent advances the pathogenesis of Crohn's disease remains incompletely understood. A variety of animal models have been utilized in an effort to provide further insights and develop more therapeutic options. In order to simulate, to an extent, the pathogenesis and the clinical course of the disease, TNBS induced colitis is often used. Various approaches for inducing TNBS colitis have been described in the literature.  In this review, we sought to present the animal model of TNBS induced colitis and outline the pathogenesis, pathophysiology, clinical course and pathological characteristics of the model. Furthermore, we describe the differences among those protocols regarding types of animals and colitis induction.  The MEDLINE database was thoroughly searched using the keywords: TNBS, colitis, Crohn's disease, animal model. Two investigators independently reviewed the abstracts and appropriate articles were included in this review. Additional articles were gathered and evaluated.  The aim of this study was to thoroughly present an updated review of the TNBS induced colitis protocols that are implemented by researchers.
Gender Play Girls And Boys In School

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Shaping Interactive Marketing Communication (IMC) Through Social Media Analytics and Modelling
Venue: Australasian Conference on Artificial Intelligence

Social media marketing represents a dynamic field for intense research. However, existing researches have not evidently estimated the materiality of the information circulated on Social Media regarding the benefits of business entities. In this work, "Online consumer's behavior" will be analyzed using Artificial Intelligence (AI) for classificatory modelling through AISAS model by applying the family of Bayesian Classifiers. This study would help marketers in understanding key drivers of the perception towards their impact on the benefits, and enable users of such data to identify triggers that are worth monitoring and investing in.
Group Decision Making with Dispersion in the Analytic Hierarchy Process

With group judgments in the context of the Analytic Hierarchy Process (AHP) one would hope for broad consensus among the decision makers. However, in practice this will not always be the case, and significant dispersion may exist among the judgments. Too much dispersion violates the principle of Pareto Optimality at the comparison level and/or matrix level, and if this happens, then the group may be homogenous in some comparisons and heterogeneous in others. The question then arises as to what would be an appropriate aggregation scheme when a consensus cannot be reached and the decision makers are either unwilling or unable to revise their judgments. In particular, the traditional aggregation via the geometric mean has been shown to be inappropriate in such situations. In this paper, we propose a new method for aggregating judgments when the raw geometric mean cannot be used. Our work is motivated by a supply chain problem of managing spare parts in the nuclear power generation sector and can be applied whenever the AHP is used with judgments from multiple decision makers. The method makes use of principal components analysis (PCA) to combine the judgments into one aggregated value for each pairwise comparison. We show that this approach is equivalent to using a weighted geometric mean with the weights obtained from the PCA.
A three dimensional movie of structural changes in bacteriorhodopsin
Venue: Science

Snapshots of bacteriorhodopsin Bacteriorhodopsin is a membrane protein that harvests the energy content from light to transport protons out of the cell against a transmembrane potential. Nango et al. used timeresolved serial femtosecond crystallography at an x ray free electron laser to provide 13 structural snapshots of the conformational changes that occur in the nanoseconds to milliseconds after photoactivation. These changes begin at the active site, propagate toward the extracellular side of the protein, and mediate internal protonation exchanges that achieve proton transport. Science, this issue p. 1552 Time resolved serial crystallography using an x ray free electron laser reveals structural changes in bacteriorhodopsin. Bacteriorhodopsin (bR) is a light driven proton pump and a model membrane transport protein. We used time resolved serial femtosecond crystallography at an x ray free electron laser to visualize conformational changes in bR from nanoseconds to milliseconds following photoactivation. An initially twisted retinal chromophore displaces a conserved tryptophan residue of transmembrane helix F on the cytoplasmic side of the protein while dislodging a key water molecule on the extracellular side. The resulting cascade of structural changes throughout the protein shows how motions are choreographed as bR transports protons uphill against a transmembrane concentration gradient.
Movie Description
Venue: International Journal of Computer Vision

Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total) The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
RDF 2 Vec RDF Graph Embeddings and Their Applications

Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks. However, most of the existing tools require features in propositional form, i.e. a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph sub structures, harvested by Weisfeiler Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. We evaluate our approach on three different tasks: (i) standard machine learning tasks (ii) entity and document modeling (iii) content based recommender systems. The evaluation shows that the proposed entity embeddings outperform existing techniques, and that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.
RDF2Vec: RDF Graph Embeddings for Data Mining
Venue: SEMWEB

Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e. a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph sub structures, harvested by Weisfeiler Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. Our evaluation shows that such vector representations outperform existing techniques for the propositionalization of RDF graphs on a variety of different predictive machine learning tasks, and that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.
HeteroSales: Utilizing Heterogeneous Social Networks to Identify the Next Enterprise Customer
Venue: WWW

Nowadays, a modern e commerce company may have both online sales and offline sales departments. Normally, online sales attempt to sell in small quantities to individual customers through broadcasting a large amount of emails or promotion codes, which heavily rely on the designed backend algorithms. Offline sales, on the other hand, try to sell in much larger quantities to enterprise customers through contacts initiated by sales representatives, which are more costly compared to online sales. Unlike many previous research works focusing on machine learning algorithms to support online sales, this paper introduces an approach that utilizes heterogenous social networks to improve the effectiveness of offline sales. More specifically, we propose a two phase framework, HeteroSales, which first constructs a company to company graph, a.k.a. Company Homophily Graph (CHG) from semantics based meta path learning, and then adopts label propagation on the graph to predict promising companies that we may successfully close an offline deal with. Based on the statistical analysis on the world's largest professional social network, LinkedIn, we demonstrate interesting discoveries showing that not all the social connections in a heterogeneous social network are useful in this task. In other words, some proper data preprocessing is essential to ensure the effectiveness of offline sales. Finally, through the experiments on LinkedIn social network data and third party offline sales records, we demonstrate the power of HereroSales to identify potential enterprise customers in offline sales.
Nature 2.0: Exploring and theorizing the links between new media and nature conservation
Venue: New Media Soc.

Web 2.0 and social media applications that allow people to share, co create and rate online content are crucial new ways for conservation organizations to reach audiences and for concerned individuals and organizations to be (seen as) 'green' These dynamics are rapidly changing the politics and political economy of nature conservation. By developing the concept of 'nature 2.0' and building on empirical insights, the article explores and theorizes these changes. It argues that online activities stimulate and complicate the commodification of biodiversity and help to reimagine ideas, ideals and experiences of 'pristine' nature. By exploring the implications of these arguments in relation to several key themes in new media studies, the article aims to provide building blocks for further investigations into the world of nature 2.0 and the effects of new media on human nature dynamics more broadly.