nils-herrmann/clean_scirepeval huggingface dataset:
Functional explanation and the function of explanation

Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted--for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3-5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.
Deep Semantic Embedding

We introduce Deep Semantic Embedding (DSE), a supervised learning algorithm which computes semantic representation for text documents by respecting their similarity to a given query. Unlike other methods that use singlelayer learning machines, DSE maps word inputs into a lowdimensional semantic space with deep neural network, and achieves a highly nonlinear embedding to model the human perception of text semantics. Through discriminative finetuning of the deep neural network, DSE is able to encode the relative similarity between relevant/irrelevant document pairs in training data, and hence learn a reliable ranking score for a query-document pair. We present test results on datasets including scientific publications and user-generated knowledge base.
An overview of lactic acid bacteria.

Lactic acid bacteria (LAB) are renowned for the potential of producing antimicrobial compound and other value added products. Undeniable to concern these probiotic has contributed to the importance of human life. Deserving an attention for its capabilities, this paper will discuss on the general description of lactic acid bacteria, genetics, metabolism and its application to the industries.  Khalisanni Khalid  typhloids@gmail.com International Journal of Biosciences (IJB) ISSN: 2220-6655 (Print) 2222-5234 (Online) Vol. 1, No. 3, p. 1-13, 2011 http://www.innspub.net
Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction

Matrix factorization of knowledge bases in universal schema has facilitated accurate distantlysupervised relation extraction. This factorization encodes dependencies between textual patterns and structured relations using lowdimensional vectors defined for each entity pair; although these factors are effective at combining evidence for an entity pair, they are inaccurate on rare pairs, or for relations that depend crucially on the entity types. On the other hand, tensor factorization is able to overcome these shortcomings when applied to link prediction by maintaining entity-wise factors. However these models have been unsuitable for universal schema. In this paper we first present an illustration on synthetic data that explains the unsuitability of tensor factorization to relation extraction with universal schemas. Since the benefits of tensor and matrix factorization are complementary, we then investigate two hybrid methods that combine the benefits of the two paradigms. We show that the combination can be fruitful: we handle ambiguously phrased relations, achieve gains in accuracy on real-world relations, and demonstrate that entity embeddings encode entity types.
INVENTORY MANAGEMENT , SERVICE LEVEL AND SAFETY STOCK

There are many studies that emphasize as a first objective of inventory management to minimize the value invested in inventory because it has a direct impact on return on assets. This approach is not fully correct. The actual objective is to determine the value and the mix of inventory that support a high service level for customers and that maximizing the companies’ financial performance. Many companies look at their own demand fluctuations and assume that there are too many variables to predict demand variability. Service level is used in inventory management to measure the performance of inventory policies and represents the probability of not being stock-out and not losing sales. Safety stock is inventory that is carried to prevent stock outs. Safety stock determinations are not intended to eliminate all stock outs, just majority of them. Companies choose to keep safety stock level high as a buffer against demand variability resulting in inefficiencies and high working capital requirements. Safety stock optimization enables companies to achieve savings and increase inventory turns.
Review of Three-Phase PWM AC–AC Converter Topologies

This paper presents first an overview of the well-known voltage and current dc-link converter topologies used to implement a three-phase PWM ac-ac converter system. Starting from the voltage source inverter and the current source rectifier, the basics of space vector modulation are summarized. Based on that, the topology of the indirect matrix converter (IMC) and its modulation are gradually developed from a voltage dc-link back-to-back converter by omitting the dc-link capacitor. In the next step, the topology of the conventional (direct) matrix converter (CMC) is introduced, and the relationship between the IMC and the CMCs is discussed in a figurative manner by investigating the switching states. Subsequently, three-phase ac-ac buck-type chopper circuits are considered as a special case of matrix converters (MCs), and a summary of extended MC topologies is provided, including three-level and hybrid MCs. Therewith, a common knowledge basis of the individual converter topologies is established.
Deep Learning of Vortex Induced Vibrations

Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics (CFD) methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible NavierStokes equations coupled with the structure’s dynamic motion equation. In the first case, given scattered data in space-time on the velocity field and the structure’s motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure’s dynamic motion. In the second case, given scattered data in space-time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.
The Effect of Consumer Privacy Empowerment on Trust and Privacy Concerns in E-Commerce

Privacy concerns and a lack of trust have been shown to reduce consumer’s willingness to transact with an online vendor. Understandably, firms are searching for methods to reduce consumer privacy concerns and increase trust. In this study, we introduce a new construct – consumer privacy empowerment. We then propose and test a theoretical model that examines the relationship between consumer privacy empowerment, familiarity, privacy concern and trust. Results indicate support for the model and suggest that perceived privacy empowerment has a strong influence on both privacy concern and trust in e-
Approximate Note Transcription for the Improved Identification of Difficult Chords

The automatic detection and transcription of musical chords from audio is an established music computing task. The choice of chord profiles and higher-level time-series modelling have received a lot of attention, resulting in methods with an overall performance of more than 70% in the MIREX Chord Detection task 2009. Research on the front end of chord transcription algorithms has often concentrated on finding good chord templates to fit the chroma features. In this paper we reverse this approach and seek to find chroma features that are more suitable for usage in a musically-motivated model. We do so by performing a prior approximate transcription using an existing technique to solve non-negative least squares problems (NNLS). The resulting NNLS chroma features are tested by using them as an input to an existing state-of-the-art high-level model for chord transcription. We achieve very good results of 80% accuracy using the song collection and metric of the 2009 MIREX Chord Detection tasks. This is a significant increase over the top result (74%) in MIREX 2009. The nature of some chords makes their identification particularly susceptible to confusion between fundamental frequency and partials. We show that the recognition of these diffcult chords in particular is substantially improved by the prior approximate transcription using NNLS.
Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation

Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to replicate some of these abilities with a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which the agent can move and interact with objects it sees, the agent learns a world model predicting the dynamic consequences of its actions. Simultaneously, the agent learns to take actions that adversarially challenge the developing world model, pushing the agent to explore novel and informative interactions with its environment. We demonstrate that this policy leads to the self-supervised emergence of a spectrum of complex behaviors, including ego motion prediction, object attention, and object gathering. Moreover, the world model that the agent learns supports improved performance on object dynamics prediction and localization tasks. Our results are a proof-of-principle that computational models of intrinsic motivation might account for key features of developmental visuomotor learning in infants.
Unemployment and labour market mismatch in the European Union Countries

The purpose of this paper is to investigate some of the main drivers of high unemployment rates in the European Union countries starting from two sources highlighted in the economic literature: the shortfall of the aggregate demand and the increasing labour market mismatches. Our analysis is based on a panel database and focuses on two objectives: to measure the long and short-term impact of GDP growth on unemployment over recent years for different categories of labour market participants (young, older and low educated workers) and to evaluate the relationship between mismatches related to skills (educational and occupational) and unemployment. One of the main conclusions is that unemployment rates of young and low educated workers are more responsive to economic growth variations both in the long and short run, while unemployment rates of older workers show a greater capacity of adjustment. In addition, occupational mismatches seem to have a significant long-term impact on the changes in unemployment of all categories of unemployed, whereas the short run effect is rather mixed, varying across countries. One explanation is the fact that during crisis, economy’s structure tends to change more rapidly than labour market and educational system can adapt.  22-02-2017; accepted: 31-05-2017 1 The research is supported by the Czech Science Foundation, project P402/12/G097 “DYME – Dynamic Models in Economics”. 2 Assistant Professor, Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest Academy of Economic Studies, Piata Romana Square 6, 1st district, Bucharest, 010374 Romania.  labour market imbalances, international comparisons, regional competitiveness, panel data models.  21 319 19 00.  21 319 18 99. E-mail: gina.dimian@csie.ase.ro (corresponding author). 3 Full Professor, Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest Academy of Economic Studies, Piata Romana Square 6, 1st district, Bucharest, 010374 Romania.  macroeconomics, economic convergence, international relationships, international statistics.  21 319 19 00.  21 319 18 99. E-mail: liviu.begu@csie.ase.ro. 4 Full Professor, Faculty of Informatics and Statistics, University of Economics. W. Churchill Sq. 4, 130 67 Prague 3, Czech Republic.  efficiency analysis, multiple-criteria analysis, data mining methods.   . Personal website: http://webhosting.vse.cz/jablon. Gina Cristina Dimian, Liviu Stelian Begu, Josef Jablonsky • Unemployment and labour... 14 Zb. rad. Ekon. fak.  • 2017
Cumulative fatigue damage and life prediction theories : a survey of the state of the art for homogeneous materials

Fatigue damage increases with applied load cycles in a cumulative manner. Cumulative fatigue damage analysis plays a key role in life prediction of components and structures subjected to field load histories. Since the introduction of damage accumulation concept by Palmgren about 70 years ago and ‘linear damage rule’ by Miner about 50 years ago, the treatment of cumulative fatigue damage has received increasingly more attention. As a result, many damage models have been developed. Even though early theories on cumulative fatigue damage have been reviewed by several researchers, no comprehensive report has appeared recently to review the considerable efforts made since the late 1970s. This article provides a comprehensive review of cumulative fatigue damage theories for metals and their alloys. emphasizing the approaches developed between the early 1970s to the early 1990s. These theories are grouped into six categories: linear damage rules; nonlinear damage curve and two-stage linearization approaches; life curve modification methods; approaches based on crack growth concepts: continuum damage mechanics models: and energy-based theories.
Characterization and antimicrobial susceptibility of motile aeromonads isolated from freshwater ornamental fish showing signs of septicaemia.

A total of 74 phenotypically identified presumptive motile Aeromonas isolates recovered from septicaemic freshwater ornamental fish in Sri Lanka were genetically characterized by sequencing of rpoD and gyrB genes. rpoD/gyrB phylogeny confirmed only 53 isolates as Aeromonas, among which A. veronii was the predominant species (79.2%), followed by A. hydrophila (7.5%), A. caviae (5.7%), A. jandaei (1.9%), A. dhakensis (3.8%) and A. entero pelogenes (1.9%). The aeromonads confirmed by sequencing were further subjected to 16S rDNA PCR-RFLP which substantiated sequencing results for 83% of isolates. Fingerprinting of A. enteropelogenes (n = 42) using ERIC-PCR revealed no dominant clones, and the majority were genetically distinct. All isolates were screened by PCR for 7 virulence determinant genes (aer, act, ast, alt, fla, ser, exu) and 2 integrase encoding genes (intI1, intI2). Each isolate contained ≥3 of the virulence genes tested for, with a heterogeneous distribution. Of the isolates, 77% harboured the intI1 gene, while none had intI2. In vitro antimicrobial susceptibility testing showed highest resistances towards tetracycline (58.5%) and erythromycin (54.7%). Our results indicate the diverse range of aeromonads that could potentially be associated with motile aeromonad septicaemia in ornamental fish. This is the first isolation of A. dhakensis from a septicaemic ornamental fish since its original description from the same host.
Design and analysis of a social botnet

Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of today’s Web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for eight weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80%, (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of today’s underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security.
Flat2Sphere: Learning Spherical Convolution for Fast Features from 360° Imagery

While 360° cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield “flat" filters, yet 360° images cannot be projected to a single plane without significant distortion. A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems. We propose to learn a spherical convolutional network that translates a planar CNN to process 360° imagery directly in its equirectangular projection. Our approach learns to reproduce the flat filter outputs on 360° data, sensitive to the varying distortion effects across the viewing sphere. The key benefits are 1) efficient feature extraction for 360° images and video, and 2) the ability to leverage powerful pre-trained networks researchers have carefully honed (together with massive labeled image training sets) for perspective images. We validate our approach compared to several alternative methods in terms of both raw CNN output accuracy as well as applying a state-of-the-art “flat" object detector to 360° data. Our method yields the most accurate results while saving orders of magnitude in computation versus the existing exact reprojection solution.
Causal mediation analysis

Estimating the mechanisms that connect explanatory variables with the explained variable, also known as “mediation analysis,” is central to a variety of social-science fields, especially psychology, and increasingly to fields like epidemiology. Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We implement in Stata computational approaches based on recent developments in the statistical methodology of mediation analysis. In particular, we provide functions for the correct calculation of causal mediation effects using several different types of parametric models, as well as the calculation of sensitivity analyses for violations to the key identifying assumption required for interpreting mediation results causally.
On the Usability Evaluation of E-Learning Applications

Despite the advances of the electronic technologies in e-learning, a consolidated evaluation methodology for e-learning applications does not yet exist. The goal of e-learning is to offer the users the possibility to become skillful and acquire knowledge on a new domain. The evaluation of educational software must consider its pedagogic effectiveness as well as its usability. The design of its interface should take into account the way students learn and also provide good usability so that student's interactions with the software are as natural and intuitive as possible. In this paper, we present the results obtained from a first phase of observation and analysis of the interactions of people with e-learning applications. The aim is to provide a methodology for evaluating such applications.
Hierarchical Bayesian Inference and Recursive Regularization for Large-Scale Classification

In this article, we address open challenges in large-scale classification, focusing on how to effectively leverage the dependency structures (hierarchical or graphical) among class labels, and how to make the inference scalable in jointly optimizing all model parameters. We propose two main approaches, namely the hierarchical Bayesian inference framework and the recursive regularization scheme. The key idea in both approaches is to reinforce the similarity among parameter across the nodes in a hierarchy or network based on the proximity and connectivity of the nodes. For scalability, we develop hierarchical variational inference algorithms and fast dual coordinate descent training procedures with parallelization. In our experiments for classification problems with hundreds of thousands of classes and millions of training instances with terabytes of parameters, the proposed methods show consistent and statistically significant improvements over other competing approaches, and the best results on multiple benchmark datasets for large-scale classification.