nils-herrmann/clean_scirepeval huggingface dataset:
Quality management on Amazon Mechanical Turk

Crowdsourcing services, such as Amazon Mechanical Turk, allow for easy distribution of small tasks to a large number of workers. Unfortunately, since manually verifying the quality of the submitted results is hard, malicious workers often take advantage of the verification difficulty and submit answers of low quality. Currently, most requesters rely on redundancy to identify the correct answers. However, redundancy is not a panacea. Massive redundancy is expensive, increasing significantly the cost of crowdsourced solutions. Therefore, we need techniques that will accurately estimate the quality of the workers, allowing for the rejection and blocking of the low-performing workers and spammers.
 However, existing techniques cannot separate the true (unrecoverable) error rate from the (recoverable) biases that some workers exhibit. This lack of separation leads to incorrect assessments of a worker's quality. We present algorithms that improve the existing state-of-the-art techniques, enabling the separation of bias and error. Our algorithm generates a scalar score representing the inherent quality of each worker. We illustrate how to incorporate cost-sensitive classification errors in the overall framework and how to seamlessly integrate unsupervised and supervised techniques for inferring the quality of the workers. We present experimental results demonstrating the performance of the proposed algorithm under a variety of settings.
An Examination of Contemporary Celebrity Endorsement in Fashion

This paper examines the contemporary relationship between fashion brands and celebrities. Noting the historic role of celebrities in fashion and their current prevalence in the industry, the paper moves beyond discussion of the motives and effectiveness of celebrity endorsement, and instead explores its nature and practice in the fashion sector. The paper proposes a new definition of celebrity endorsement in fashion, offers a classification of celebrities involved in fashion brand endorsement, and presents a typology examining the contemporary means by which a fashion brand may collaborate with celebrities. The typology is defined in context of the nature, length and cost to the brand of the relationship between it and the celebrity. The methodology uses secondary sources and qualitative primary research in an exploratory agenda in order to propose conclusions and suggest ideas for further research.
Principles and clinical implications of the brain–gut–enteric microbiota axis

While bidirectional brain–gut interactions are well known mechanisms for the regulation of gut function in both healthy and diseased states, a role of the enteric flora—including both commensal and pathogenic organisms—in these interactions has only been recognized in the past few years. The brain can influence commensal organisms (enteric microbiota) indirectly, via changes in gastrointestinal motility and secretion, and intestinal permeability, or directly, via signaling molecules released into the gut lumen from cells in the lamina propria (enterochromaffin cells, neurons, immune cells). Communication from enteric microbiota to the host can occur via multiple mechanisms, including epithelial-cell, receptor-mediated signaling and, when intestinal permeability is increased, through direct stimulation of host cells in the lamina propria. Enterochromaffin cells are important bidirectional transducers that regulate communication between the gut lumen and the nervous system. Vagal, afferent innervation of enterochromaffin cells provides a direct pathway for enterochromaffin-cell signaling to neuronal circuits, which may have an important role in pain and immune-response modulation, control of background emotions and other homeostatic functions. Disruption of the bidirectional interactions between the enteric microbiota and the nervous system may be involved in the pathophysiology of acute and chronic gastrointestinal disease states, including functional and inflammatory bowel disorders.
Efficient Markov Logic Inference for Natural Language Semantics

Using Markov logic to integrate logical and distributional information in natural-language semantics results in complex inference problems involving long, complicated formulae. Current inference methods for Markov logic are ineffective on such problems. To address this problem, we propose a new inference algorithm based on SampleSearch that computes probabilities of complete formulae rather than ground atoms. We also introduce a modified closed-world assumption that significantly reduces the size of the ground network, thereby making inference feasible. Our approach is evaluated on the recognizing textual entailment task, and experiments demonstrate its dramatic impact on the efficiency
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling

Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available. To address this fundamental obstacle, we introduce the MultiDomain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. At a size of 10k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora. The contribution of this work apart from the open-sourced dataset labelled with dialogue belief states and dialogue actions is two-fold: firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided. The proposed data-collection pipeline is entirely based on crowd-sourcing without the need of hiring professional annotators; secondly, a set of benchmark results of belief tracking, dialogue act and response generation is reported, which shows the usability of the data and sets a baseline for future studies.
Remembering through lifelogging: A survey of human memory augmentation

Human memory is unquestionably a vital cognitive ability but one that can often be unreliable. External memory aids such as diaries, photos, alarms and calendars are often employed to assist in remembering important events in our past and future. The recent trend for lifelogging, continuously documenting ones life through wearable sensors and cameras, presents a clear opportunity to augment human memory beyond simple reminders and actually improve its capacity to remember. This article surveys work from the fields of computer science and psychology to understand the potential for such augmentation, the technologies necessary for realising this opportunity and to investigate what the possible benefits and ethical pitfalls of using such technology might be.
Organizational Commitment , Job Redesign , Employee Empowerment and Intent to Quit Among Survivors of Restructuring and Downsizing

This study is designed to determine the relationship between job redesign, employee empowerment and intent to quit measured by affective organizational commitment among survivors of organizational restructuring and downsizing. It focused on middle level managers and employees in supervisory positions because survivors of this group are often called upon to assume expanded roles, functions and responsibilities in a post restructuring and downsizing environment. The results show statistically significant positive relationships between job redesign, empowerment and affective commitment. It therefore, provides empirical data to support theoretical models for managing and mitigating survivors’ intent to quit and subsequent voluntary turnover among survivors of organizational restructuring and downsizing. The implications of these findings, which suggest expanded roles for job redesign and employee empowerment, are discussed.
Codon-substitution models for heterogeneous selection pressure at amino acid sites.

Comparison of relative fixation rates of synonymous (silent) and nonsynonymous (amino acid-altering) mutations provides a means for understanding the mechanisms of molecular sequence evolution. The nonsynonymous/synonymous rate ratio (omega = d(N)d(S)) is an important indicator of selective pressure at the protein level, with omega = 1 meaning neutral mutations, omega < 1 purifying selection, and omega > 1 diversifying positive selection. Amino acid sites in a protein are expected to be under different selective pressures and have different underlying omega ratios. We develop models that account for heterogeneous omega ratios among amino acid sites and apply them to phylogenetic analyses of protein-coding DNA sequences. These models are useful for testing for adaptive molecular evolution and identifying amino acid sites under diversifying selection. Ten data sets of genes from nuclear, mitochondrial, and viral genomes are analyzed to estimate the distributions of omega among sites. In all data sets analyzed, the selective pressure indicated by the omega ratio is found to be highly heterogeneous among sites. Previously unsuspected Darwinian selection is detected in several genes in which the average omega ratio across sites is <1, but in which some sites are clearly under diversifying selection with omega > 1. Genes undergoing positive selection include the beta-globin gene from vertebrates, mitochondrial protein-coding genes from hominoids, the hemagglutinin (HA) gene from human influenza virus A, and HIV-1 env, vif, and pol genes. Tests for the presence of positively selected sites and their subsequent identification appear quite robust to the specific distributional form assumed for omega and can be achieved using any of several models we implement. However, we encountered difficulties in estimating the precise distribution of omega among sites from real data sets.
Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems

Stochastic programming can effectively describe many deci sion making problems in uncertain environments. Unfortunately, such programs are often computationally demanding to solve. In addition, their solution can be misleading when there is ambiguity in the choice of a distribution for the ran dom parameters. In this paper, we propose a model that describes uncertainty in both the distribution form (discr ete, Gaussian, exponential, etc.) and moments (mean and cov ariance matrix). We demonstrate that for a wide range of cost fun ctio s the associated distributionally robust (or min-max ) stochastic program can be solved efficiently. Furthermore, by deriving a new confidence region for the mean and the covariance matrix of a random vector, we provide probabilis tic arguments for using our model in problems that rely heavily on historical data. These arguments are confirmed in a pra ctical example of portfolio selection, where our framework leads to better performing policies on the “true” distribut on underlying the daily returns of financial assets.
Industry 4.0 and Lean Management - Synergy or Contradiction? - A Systematic Interaction Approach to Determine the Compatibility of Industry 4.0 and Lean Management in Manufacturing Environment

Considering the ongoing trend of digitalization of the manufacturing industry to Industry 4.0, this paper assists in the transformation. The research work is focused on studying the possible impacts of Industry 4.0 on lean management (LM) tools which play a vital role to foster quality and reliability of products and services that are delivered to the customers. The LM tools which are impacted by the advent of Industry 4.0 and assisting in successful implementation of future smart factory will be investigated in particular focus. An interaction plot matrix is established to quantify the influence of LM tools on Industry 4.0. Interaction between these Industry 4.0 design principles and LM tools reveal several opportunities for achieving synergies thus leading to successful implementation of future interconnected smart factories. Overall, the research work serve as a guideline for industries that are under the transformation phase towards future smart factory and offers space for further scientific discussion.
Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL

Internet evolution has affected all industrial and commercial activity and accelerated e-learning growth. Due to cost, time, or flexibility for designer courses and learners, e-learning has been adopted by corporations as an alternative training method. E-learning effectiveness evaluation is vital, and evaluation criteria are diverse. A large effort has been made regarding e-learning effectiveness evaluation; however, a generalized quantitative evaluation model, which considers both the interaffected relation between criteria and the fuzziness of subjective perception concurrently, is lacking. In this paper, the proposed new novel hybrid MCDM model addresses the independent relations of evaluation criteria with the aid of factor analysis and the dependent relations of evaluation criteria with the aid of DEMATEL. The AHP and the fuzzy integral methods are used for synthetic utility in accordance with subjective perception environment. Empirical experimental results show the proposed model is capable of producing effective evaluation of e-learning programs with adequate criteria that fit with respondent’s perception patterns, especially when the evaluation criteria are numerous and intertwined.
Boosted Wrapper Induction

Recent work in machine learning for information extraction has focused on two distinct sub-problems: the conventional problem of filling template slots from natural language text, and the problem of wrapper induction, learning simple extraction procedures (“wrappers”) for highly structured text such as Web pages produced by CGI scripts. For suitably regular domains, existing wrapper induction algorithms can efficiently learn wrappers that are simple and highly accurate, but the regularity bias of these algorithms makes them unsuitable for most conventional information extraction tasks. Boosting is a technique for improving the performance of a simple machine learning algorithm by repeatedly applying it to the training set with different example weightings. We describe an algorithm that learns simple, low-coverage wrapper-like extraction patterns, which we then apply to conventional information extraction problems using boosting. The result is BWI, a trainable information extraction system with a strong precision bias and F1 performance better than state-of-the-art techniques in many domains.
Framing the Frameworks: A Review of IT Governance Research

With the passage of the Sarbanes-Oxley Act in the United States in 2002, and an ever-increasing corporate focus on ensuring prudent returns on technology investments, the notion of IT governance became a major issue for both business practitioners and academics. Although the term “IT governance” is a relatively new addition to the syntax of academic research, significant previous work is reported on IT decisions rights and IT loci of control, notions that are synonymous with the current understanding of IT governance. This paper presents a literature review for existing research in IT governance. A framework, named the Conceptual Framework For IT Governance Research is proposed to provide a logical structure for existing research results. Using this framework, we classify the previous literature on governance into two separate streams that follow parallel paths of advancement. A popular contemporary notion of IT governance is then presented, together with the argument that this new notion, by implicitly extending both streams of research, represents an initial amalgamation of the two paths of literature. We conclude that even with the consideration of contemporary structures, academicians and practitioners alike continue to explore the concept of IT governance in an attempt to find appropriate mechanisms to govern corporate IT decisions.
Object Localization and Motion Transfer learning with Capsules

Inspired by CapsNet’s routing-by-agreement mechanism, with its ability to learn object properties, and by center-of-mass calculations from physics, we propose a CapsNet architecture with object coordinate atoms and an LSTM network for evaluation. The first is based on CapsNet but uses a new routing algorithm to find the objects’ approximate positions in the image coordinate system, and the second is a parameterized affine transformation network that can predict future positions from past positions by learning the translation transformation from 2D object coordinates generated from the first network. We demonstrate the learned translation transformation is transferable to another dataset without the need to train the transformation network again. Only the CapsNet needs training on the new dataset. As a result, our work shows that object recognition and motion prediction can be separated, and that motion prediction can be transferred to another dataset with different object types.
The Operation of the Cross-Border e-commerce Logistics in China

According to the cross-border e-commerce background, the article is analyzed its operation on the cross-border e-commerce logistics in china. Firstly, this paper illustrates the operation characteristics of cross-border e-commerce logistics, then analyzes some aspects of the cross-border e-commerce logistics, like operations, logistics cost management and so on. Secondly, this paper analyzes existing problems in cross-border e-commerce logistics from the development of electronic commerce logistics cross-border in China. Finally, some suggestions were put forward on cross-border e-commerce logistics operation from the two aspects of macro level of cross-border e-commerce and micro level of cross-border e-commerce enterprise.
Relation of threatened egotism to violence and aggression: the dark side of high self-esteem.

Conventional wisdom has regarded low self-esteem as an important cause of violence, but the opposite view is theoretically viable. An interdisciplinary review of evidence about aggression, crime, and violence contradicted the view that low self-esteem is an important cause. Instead, violence appears to be most commonly a result of threatened egotism--that is, highly favorable views of self that are disputed by some person or circumstance. Inflated, unstable, or tentative beliefs in the self's superiority may be most prone to encountering threats and hence to causing violence. The mediating process may involve directing anger outward as a way of avoiding a downward revision of the self-concept.
Link Prediction via Matrix Completion

Ratha Pech, Hao Dong1,2,∗, Liming Pan, Hong Cheng, Zhou Tao1,2,∗ 1 CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China 2 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China and 3 Center for Robotics, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
A cloud-scale acceleration architecture

Hyperscale datacenter providers have struggled to balance the growing need for specialized hardware (efficiency) with the economic benefits of homogeneity (manageability). In this paper we propose a new cloud architecture that uses reconfigurable logic to accelerate both network plane functions and applications. This Configurable Cloud architecture places a layer of reconfigurable logic (FPGAs) between the network switches and the servers, enabling network flows to be programmably transformed at line rate, enabling acceleration of local applications running on the server, and enabling the FPGAs to communicate directly, at datacenter scale, to harvest remote FPGAs unused by their local servers. We deployed this design over a production server bed, and show how it can be used for both service acceleration (Web search ranking) and network acceleration (encryption of data in transit at high-speeds). This architecture is much more scalable than prior work which used secondary rack-scale networks for inter-FPGA communication. By coupling to the network plane, direct FPGA-to-FPGA messages can be achieved at comparable latency to previous work, without the secondary network. Additionally, the scale of direct inter-FPGA messaging is much larger. The average round-trip latencies observed in our measurements among 24, 1000, and 250,000 machines are under 3, 9, and 20 microseconds, respectively. The Configurable Cloud architecture has been deployed at hyperscale in Microsoft's production datacenters worldwide.
CQArank: jointly model topics and expertise in community question answering

Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. Experiments carried out on Stack Overflow data, the largest CQA focused on computer programming, show that our method achieves significant improvement over existing methods on multiple metrics.
Effects of increased seawater pCO2 on early development of the oyster Crassostrea gigas

This study demonstrated that the increased partial pressure of CO2 (pCO2) in seawater and the attendant acidification that are projected to occur by the year 2300 will severely impact the early development of the oyster Crassostrea gigas. Eggs of the oyster were artificially fertilized and incubated for 48 h in seawater acidified to pH 7.4 by equilibrating it with CO2-enriched air (CO2 group), and the larval morphology and degree of shell mineralization were compared with the control treatment (air-equilibrated seawater). Only 5% of the CO2 group developed into normal ‘Dshaped’ veliger larvae as compared with 68% in the control group, although no difference was observed between the groups up to the trochophore stage. Thus, during embryogenesis, the calcification process appears to be particularly affected by low pH and/or the low CaCO3 saturation state of high-CO2 seawater. Veliger larvae with fully mineralized shells accounted for 30% of the CO2-group larvae, compared with 72% in the control (p < 0.005). Shell mineralization was completely inhibited in 45% of the CO2-group larvae, but only in 16% of the control (p < 0.05). Normal D-shaped veligers of the control group exhibited increased shell length and height between 24 and 48 h after fertilization, while the few D-shaped veligers of the CO2 group showed no shell growth during the same period. Our results suggest that future ocean acidification will have deleterious impacts on the early development of marine benthic calcifying organisms.