nils-herrmann/clean_scirepeval huggingface dataset:
Are men really more ‘ oriented ’ toward short-term mating than women ?

According to Sexual Strategies Theory (D.M. Buss and D.P. Schmitt 1993), both men and women possess psychological adaptations for short-term mating. However, men may possess three adaptations that make it seem as though they are generally more ‘oriented’ toward short-term mating than women: (1) Men possess greater desire for short-term sexual relationships than women; (2) Men prefer larger numbers of sexual partners over time than women; and (3) Men require less time before consenting to sex than women. We review a wide body of psychological theory and evidence that corroborates the presence of these adaptations in men’s short-term sexual psychology. We also correct some recurring misinterpretations of Sexual Strategies Theory, such as the mistaken notion that women are designed solely for long-term mating. Finally, we document how the observed sex differences in short-term mating complement some feminist theories and refute competing evolutionary theories of human sexuality. Psychology, Evolution & Gender 3.3 December 2001 pp. 211–239 Psychology, Evolution & Gender ISSN 1461-6661 print/ISSN 1470-1073 online
Hesitant fuzzy sets

Several extensions and generalizations of fuzzy sets have been introduced in the literature, for example, Atanassov’s intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets. In this paper, we propose hesitant fuzzy sets. Although from a formal point of view, they can be seen as fuzzy multisets, we will show that their interpretation differs from the two existing approaches for fuzzy multisets. Because of this, together with their definition, we also introduce some basic operations. In addition, we also study their relationship with intuitionistic fuzzy sets. We prove that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set. We prove also that the operations we propose are consistent with the ones of intuitionistic fuzzy sets when applied to the envelope of the hesitant fuzzy sets.
Effectiveness of Credit Management System on Loan Performance : Empirical Evidence from Micro Finance Sector in Kenya

Microfinance institutions in Kenya experience high levels of non-performing loans. This trend threatens viability and sustainability of MFIs and hinders the achievement of their goals. This study was aimed at assessing the effectiveness of credit management systems on loan performance of microfinance institutions. Specifically we sought to establish the effect of credit terms, client appraisal, credit risk control measures and credit collection policies on loan performance. We adopted a descriptive research design. The respondents were the credit officers of the MFIs in Meru town. Collection policy was found to have a higher effect on loan repayment with =12.74, P=0.000 at 5% significance level. Further research is recommended on the effectiveness of credit referencing on loan performance of MFIs. This study is informative in terms of public policy adjustments and firm level competences required for better operation of MFIs and it also contributes to credit management literature.
How to Enhance Cloud Architectures to Enable Cross-Federation: Towards Interoperable Storage Providers

Small/medium cloud storage providers can hardly compete with the biggest cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated cloud ecosystem they belong to. According to such an approach, the challenge consists in creating federated cloud ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.
TANDEM-bottleneck feature combination using hierarchical Deep Neural Networks

To improve speech recognition performance, a combination between TANDEM and bottleneck Deep Neural Networks (DNN) is investigated. In particular, exploiting a feature combination performed by means of a multi-stream hierarchical processing, we show a performance improvement by combining the same input features processed by different neural networks. The experiments are based on the spontaneous telephone recordings of the Cantonese IARPA Babel corpus using both standard MFCCs and Gabor as input features.
An Empirical Study on Using the National Vulnerability Database to Predict Software Vulnerabilities

Software vulnerabilities represent a major cause of cybersecurity problems. The National Vulnerability Database (NVD) is a public data source that maintains standardized information about reported software vulnerabilities. Since its inception in 1997, NVD has published information about more than 43,000 software vulnerabilities affecting more than 17,000 software applications. This information is potentially valuable in understanding trends and patterns in software vulnerabilities, so that one can better manage the security of computer systems that are pestered by the ubiquitous software security flaws. In particular, one would like to be able to predict the likelihood that a piece of software contains a yet-to-be-discovered vulnerability, which must be taken into account in security management due to the increasing trend in zero-day attacks. We conducted an empirical study on applying data-mining techniques on NVD data with the objective of predicting the time to next vulnerability for a given software application. We experimented with various features constructed using the information available in NVD, and applied various machine learning algorithms to examine the predictive power of the data. Our results show that the data in NVD generally have poor prediction capability, with the exception of a few vendors and software applications. By doing a large number of experiments and observing the data, we suggest several reasons for why the NVD data have not produced a reasonable prediction model for time to next vulnerability with our current approach.
Fast Point Feature Histograms (FPFH) for 3D registration

In our recent work , , we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we present several optimizations that reduce their computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features for realtime applications. To validate our results we demonstrate their efficiency for 3D registration and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local non-linear optimizer: SAC-IA (SAmple Consensus Initial Alignment).
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables

Evaluating whether machines improve on human performance is one of the central questions of machine learning. However, there are many domains where the data is selectively labeled, in the sense that the observed outcomes are themselves a consequence of the existing choices of the human decision-makers. For instance, in the context of judicial bail decisions, we observe the outcome of whether a defendant fails to return for their court appearance only if the human judge decides to release the defendant on bail. This selective labeling makes it harder to evaluate predictive models as the instances for which outcomes are observed do not represent a random sample of the population. Here we propose a novel framework for evaluating the performance of predictive models on selectively labeled data. We develop an approach called contraction which allows us to compare the performance of predictive models and human decision-makers without resorting to counterfactual inference. Our methodology harnesses the heterogeneity of human decision-makers and facilitates effective evaluation of predictive models even in the presence of unmeasured confounders (unobservables) which influence both human decisions and the resulting outcomes. Experimental results on real world datasets spanning diverse domains such as health care, insurance, and criminal justice demonstrate the utility of our evaluation metric in comparing human decisions and machine predictions.
Multimodal communication on tumblr: "i have so many feels!"

We manually analyzed a corpus of Tumblr posts for sentiment, looking at images, text, and their combination. A dataset was constructed of posts with both text and images, as well as a dataset of posts containing only text, along with a codebook for classifying and counting the content in each. This paper reports on the construction of the overall corpus and the codebook, and presents the results of a preliminary analysis that focuses on emotion. Posts containing images expressed more emotion, more intense emotion, and were more positive in valence than posts containing only text. The study contributes a micro-level analysis of multimodal communication in a social media platform, as well as a gold standard corpus that can be used to train learning algorithms to identify sentiment in multimodal Tumblr data.
The Parasocial Contact Hypothesis

We propose a communication analogue to Allport’s (1954) Contact Hypothesis called the Parasocial Contact Hypothesis (PCH). If people process mass-mediated parasocial interaction in a manner similar to interpersonal interaction, then the socially beneficial functions of intergroup contact may result from parasocial contact. We describe and test the PCH with respect to majority group members’ level of prejudice in three studies, two involving parasocial contact with gay men (Six Feet Under and Queer Eye for the Straight Guy) and one involving parasocial contact with comedian and male transvestite Eddie Izzard. In all three studies, parasocial contact was associated with lower levels of prejudice. Moreover, tests of the underlying mechanisms of PCH were generally supported, suggesting that parasocial contact facilitates positive parasocial responses and changes in beliefs about the attributes of minority group categories.
Organizing Books and Authors by Multilayer SOM

This paper introduces a new framework for the organization of electronic books (e-books) and their corresponding authors using a multilayer self-organizing map (MLSOM). An author is modeled by a rich tree-structured representation, and an MLSOM-based system is used as an efficient solution to the organizational problem of structured data. The tree-structured representation formulates author features in a hierarchy of author biography, books, pages, and paragraphs. To efficiently tackle the tree-structured representation, we used an MLSOM algorithm that serves as a clustering technique to handle e-books and their corresponding authors. A book and author recommender system is then implemented using the proposed framework. The effectiveness of our approach was examined in a large-scale data set containing 3868 authors along with the 10500 e-books that they wrote. We also provided visualization results of MLSOM for revealing the relevance patterns hidden from presented author clusters. The experimental results corroborate that the proposed method outperforms other content-based models (e.g., rate adapting poisson, latent Dirichlet allocation, probabilistic latent semantic indexing, and so on) and offers a promising solution to book recommendation, author recommendation, and visualization.
Breakthrough in improving the skin sagging with focusing on the subcutaneous tissue structure , retinacula cutis

Skin sagging is one of the most prominent aging signs and a concerning issue for people over middle age. Although many cosmetic products are challenging the reduction of the skin sagging by improving the dermal elasticity, which decreases with age, the effects are insufficient for giving drastic changes to the facial morphology. This study focused on subcutaneous tissue for investigating a skin sagging mechanism. Subcutaneous tissue consists of predominantly adipose tissue with fibrous network structures, called retinacula cutis (RC), which is reported to have a possibility to maintain the soft tissue structure and morphology. This study investigated the effect of subcutaneous tissue physical-property alteration due to RC-deterioration on the skin sagging. For evaluating RC structure noninvasively, the tomographic images of faces were obtained by magnetic resonance (MR) imaging. Subcutaneous tissue network structures observed by MR imaging were indicated to be RC by comparing MR images and the histological specimens of human skin. The density of RC was measured by image analysis. For evaluating sagging degree and physical properties of the skin, sagging scoring and the measurement of elasticity of deeper skin layers were performed. The density of RC was correlated with the elasticity data of deeper skin layers, and the sagging scores tended to increase with decreasing the density. These results suggested that the sparse RC structure gave a decrease in the elasticity of subcutaneous tissue layer, which consequently would be the cause of facial sagging. This study would be a pathfinder for the complete elimination of skin sagging.
Knowledge Engineering: Principles and Methods

This paper gives an overview about the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in the last years we describe three modeling frameworks: CommonKADS, MIKE, and PROTÉGÉ-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods, and ontologies. We conclude with outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.
Konsequenzen aus der Altruismusforschung für die Ausbildung in Erster Hilfe

Innerhalb der psychologischen Altruismusforschung konnten Faktoren identifiziert werden, die sich förderlich bzw. hemmend auf die Hilfsbereitschaft von Menschen auswirken. Angewandt auf den Bereich akuter Notfälle lassen sich aus diesen Erkenntnissen Konsequenzen für die Gestaltung der Erste-Hilfe-Ausbildung ableiten. Inhaltlich sollten in den Kursen anstelle komplexer Algorithmen einfach anzuwendende, konkrete und vor allem relevante Maßnahmen vermittelt werden. Den Teilnehmern muss die Angst genommen werden, etwas falsch zu machen.  Weiteren sind die Kurse um psychologische Lerninhalte zu erweitern. Dabei sollte die Ausbildung bereits bei Kindern beginnen und in regelmäßigen Abständen fortgesetzt werden. Zudem sind spezielle Kursformate sowohl für Frauen als auch für Senioren zu entwickeln. In Ergänzung hierzu sollten Massenmedien genutzt werden, um einen Wandel des Images von Erster Hilfe herbeizuführen. Factors promoting and retarding the willing to help somebody are identified within the psychological altruism research. This scientific insight is able to deduce consequences towards the specification of first-aid-course-formats. Instead of teaching complex algorithm-procedures the concepts should concentrate on easy reproducible, precise and foremost relevant measurements. The anxiety of the trainees potentially doing harming mistakes as a bystander has to be reduced sustainable. Adding psychological aspects to the formats is indispensable. The First-Aid-Training should already be initiated for kids in school and refresher-training is mandatory. Also adapted course-formats for women and senior-citizens are indicated. Mass media activities could be an effective transmitter to change the old image of “first-aid”.
Hierarchical diffusion curves for accurate automatic image vectorization

Diffusion curve primitives are a compact and powerful representation for vector images. While several vector image authoring tools leverage these representations, automatically and accurately vectorizing arbitrary raster images using diffusion curves remains a difficult problem. We automatically generate sparse diffusion curve vectorizations of raster images by fitting curves in the Laplacian domain. Our approach is fast, combines Laplacian and bilaplacian diffusion curve representations, and generates a hierarchical representation that accurately reconstructs both vector art and natural images. The key idea of our method is to trace curves in the Laplacian domain, which captures both sharp and smooth image features, across scales, more robustly than previous image- and gradient-domain fitting strategies. The sparse set of curves generated by our method accurately reconstructs images and often closely matches tediously hand-authored curve data. Also, our hierarchical curves are readily usable in all existing editing frameworks. We validate our method on a broad class of images, including natural images, synthesized images with turbulent multi-scale details, and traditional vector-art, as well as illustrating simple multi-scale abstraction and color editing results.
Fast and Scalable Learning of Neuro-Symbolic Representations of Biomedical Knowledge

In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. Based on a recently published comprehensive biological knowledge graph (Alshahrani, 2017) that was used for demonstrating neurosymbolic representation learning, we show how to train fast (under 1 minute) log-linear neural embeddings of the entities. We utilize these representations as inputs for machine learning classifiers to enable important tasks such as biological link prediction. Classifiers are trained by concatenating learned entity embeddings to represent entity relations, and training classifiers on the concatenated embeddings to discern true relations from automatically generated negative examples. Our simple embedding methodology greatly improves on classification error compared to previously published state-of-the-art results, yielding a maximum increase of +0.28 F-measure and +0.22 ROC AUC scores for the most difficult biological link prediction problem. Finally, our embedding approach is orders of magnitude faster to train (≤ 1 minute vs. hours), much more economical in terms of embedding dimensions (d = 50 vs. d = 512), and naturally encodes the directionality of the asymmetric biological relations, that can be controlled by the order with which we concatenate the embeddings.
The Mutational Landscape of Lethal Castrate Resistant Prostate Cancer

Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains and losses, including ETS gene family fusions, PTEN loss and androgen receptor (AR) amplification, which drive prostate cancer development and progression to lethal, metastatic castration-resistant prostate cancer (CRPC). However, less is known about the role of mutations. Here we sequenced the exomes of 50 lethal, heavily pre-treated metastatic CRPCs obtained at rapid autopsy (including three different foci from the same patient) and 11 treatment-naive, high-grade localized prostate cancers. We identified low overall mutation rates even in heavily treated CRPCs (2.00 per megabase) and confirmed the monoclonal origin of lethal CRPC. Integrating exome copy number analysis identified disruptions of CHD1 that define a subtype of ETS gene family fusion-negative prostate cancer. Similarly, we demonstrate that ETS2, which is deleted in approximately one-third of CRPCs (commonly through TMPRSS2:ERG fusions), is also deregulated through mutation. Furthermore, we identified recurrent mutations in multiple chromatin- and histone-modifying genes, including MLL2 (mutated in 8.6% of prostate cancers), and demonstrate interaction of the MLL complex with the AR, which is required for AR-mediated signalling. We also identified novel recurrent mutations in the AR collaborating factor FOXA1, which is mutated in 5 of 147 (3.4%) prostate cancers (both untreated localized prostate cancer and CRPC), and showed that mutated FOXA1 represses androgen signalling and increases tumour growth. Proteins that physically interact with the AR, such as the ERG gene fusion product, FOXA1, MLL2, UTX (also known as KDM6A) and ASXL1 were found to be mutated in CRPC. In summary, we describe the mutational landscape of a heavily treated metastatic cancer, identify novel mechanisms of AR signalling deregulated in prostate cancer, and prioritize candidates for future study.
Media Richness Theory and New Electronic Communication Media: A Study of Voice Mail and Electronic Mail

In situations requiring the exchange of information to resolve equivocality or reduce uncertainty, can Media Richness Theory account for differences in individuals' preferences for electronic mail and voice mail relative to one another? The results of this study indicate that, as predicted, electronic mail was preferred over voice mail for the exchange of information to reduce uncertainty. However, contrary to the predictions of Media Richness Theory, voice mail was not preferred over electronic mail for the resolution of equivocality. These results suggest that Media Richness Theory in its current formulation may not be applicable to the study of the new media. Future research should redefine and extend the concept of richness and its elements to account for the nature and functionality of the new media and investigate alternative social dimensions to individuals' preferences for and usage of the new media