nils-herrmann/clean_scirepeval huggingface dataset:
Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?

Responses in task-oriented dialogue systems often realize multiple propositions whose ultimate form depends on the use of sentence planning and discourse structuring operations. For example a recommendation may consist of an explicitly evaluative utterance e.g. Chanpen Thai is the best option, along with content related by the justification discourse relation, e.g. It has great food and service, that combines multiple propositions into a single phrase. While neural generation methods integrate sentence planning and surface realization in one endto-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training. We systematically create large training corpora that exhibit particular sentence planning operations and then test neural models to see what they learn. We compare models without explicit latent variables for sentence planning with ones that provide explicit supervision during training. We show that only the models with additional supervision can reproduce sentence planning and discourse operations and generalize to situations unseen in training.
Inductive theory generation : A grounded approach to business inquiry

Grounded theory has frequently been referred to, but infrequently applied in business research. This article addresses such a deficiency by advancing two focal aims. Firstly, it seeks to de-mystify the methodology known as grounded theory by applying this established research practice within the comparatively new context of business research. Secondly, in so doing, it integrates naturalistic examples drawn from the author’s business research, hence explicating the efficacy of grounded theory methodology in gaining deeper understanding of business bounded phenomena. It is from such a socially focused methodology that key questions of what is happening and why leads to the generation of substantive theories and underpinning
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging

Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. However, the previous neural models cannot extract the complicated feature compositions as the traditional methods with discrete features. In this work, we propose a featureenriched neural model for joint Chinese word segmentation and part-of-speech tagging task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional features with convolutional and pooling layer, and then utilize long distance dependency information with recurrent layer. Experimental results on five different datasets show the effectiveness of our proposed model.
A Constrained Backpropagation Approach for the Adaptive Solution of Partial Differential Equations

This paper presents a constrained backpropagation (CPROP) methodology for solving nonlinear elliptic and parabolic partial differential equations (PDEs) adaptively, subject to changes in the PDE parameters or external forcing. Unlike existing methods based on penalty functions or Lagrange multipliers, CPROP solves the constrained optimization problem associated with training a neural network to approximate the PDE solution by means of direct elimination. As a result, CPROP reduces the dimensionality of the optimization problem, while satisfying the equality constraints associated with the boundary and initial conditions exactly, at every iteration of the algorithm. The effectiveness of this method is demonstrated through several examples, including nonlinear elliptic and parabolic PDEs with changing parameters and nonhomogeneous terms.
Flow-based benchmark data sets for intrusion detection

Anomaly based intrusion detection systems suffer from a lack of appropriate evaluation data sets. Often, existing data sets may not be published due to privacy concerns or do not reflect actual and current attack scenarios. In order to overcome these problems, we identify characteristics of good data sets and develop an appropriate concept for the generation of labelled flow-based data sets that satisfy these criteria. The concept is implemented based on OpenStack, thus demonstrating the suitability of virtual environments. Virtual environments offer advantages compared to static data sets by easily creating up-to-date data sets with recent trends in user behaviour and new attack scenarios. In particular, we emulate a small business environment which includes several clients and typical servers. Network traffic is generated by scripts which emulate typical user activities like surfing the web, writing emails, or printing documents on the clients. These scripts follow some guidelines to ensure that the user behaviour is as realistic as possible, also with respect to working hours and lunch breaks. The generated network traffic is recorded in unidirectional NetFlow format. For generating malicious traffic, attacks like Denial of Service, Brute Force, and Port Scans are executed within the network. Since origins, targets, and timestamps of executed attacks are known, labelling of recorded NetFlow data is easily possible. For inclusion of actual traffic, which has its origin outside the OpenStack environment, an external server with two services is deployed. This server has a public IP address and is exposed to real and up-to-date attacks from the internet. We captured approximately 32 million flows over a period of four weeks and categorized them into five classes. Further, the chronological sequence of the flows is analysed and the distribution of normal and malicious traffic is discussed in detail. The main contribution of this paper is the demonstration of a novel approach to use OpenStack as a basis for generating realistic data sets that can be used for the evaluation of network intrusion detection systems.
Internet Banking in Europe : a comparative analysis

A key strategic issue for banks is the implementation of internet banking. The ‘click and mortar’ model that complements classical branch banking with online facilities is competing with pure internet banks. The objective of this paper is to compare the performance of these two models across countries, so as to examine the role of differences in the banking system and technological progress. A fuzzy cluster analysis on the performance of banks in Finland, Spain, Italy and the UK shows that internet banks are hard to distinguish from banks that follow a click and mortar strategy; country borders are more important. We therefore explain bank performance by a group of selected bank features, country-specific economic and IT indicators over the period 1995-2004. We find that the strategy of banking groups to incorporate internet banks reflects some competitive edge that these banks have in their business models. Extensive technological innovation boosts internet banking.
An Enhanced Least Significant Bit Steganography Technique Mohit

Message transmission through internet as medium, is becoming increasingly popular. Hence issues like information security are becoming more relevant than earlier. This necessitates for a secure communication method to transmit messages via internet. Steganography is the science of communicating secret data in several multimedia carriers like audio, text, video or image. A modified technique to enhance the security of secret information over the network is presented in this paper. In this technique, we generate stegnokey with the help of slave image. Proposed technique provides multi-level secured message transmission. Experimental results show that the proposed technique is robust and maintains image quality. Index Terms – Steganography, Least-significant-bit (LSB) substitution, XORing pixel bits, master-slave image.
The Network Paradigm in Organizational Research : A Review and Typology

In this paper, we review and analyze the emerging network paradigm in organizational research. We begin with a conventional review of recent research organized around recognized research streams. Next, we analyze this research, developing a set of dimensions along which network studies vary, including direction of causality, levels of analysis, explanatory goals, and explanatory mechanisms. We use the latter two dimensions to construct a 2-by-2 table cross-classifying studies of network consequences into four canonical types: structural social capital, social access to resources, contagion, and environmental shaping. We note the rise in popularity of studies with a greater sense of agency than was traditional in network research.
Employees Motivation in Organizations : An integrative literature review

The purpose of this paper is to present findings of an integrative literature review related to employees’ motivational practices in organizations.  A broad search of computerized databases focusing on articles published in English during 1999– 2010 was completed. Extensive screening sought to determine current literature themes and empirical research evidence completed in employees’ focused specifically on motivation in organization.  40 articles are included in this integrative literature review. The literature focuses on how job characteristics, employee characteristic, management practices and broader environmental factors influence employees’ motivation. Research that links employee’s motivation is both based on qualitative and quantitative studies.  This literature reveals widespread support of motivation concepts in organizations. Theoretical and editorial literature confirms motivation concepts are central to employees. Job characteristics, management practices, employee characteristics and broader environmental factors are the key variables influence employees’ motivation in organization.
The dynamics of consumer behavior : A goal systemic perspective

Like most behavior, consumer behavior too is goal driven. In turn, goals constitute cognitive constructs that can be chronically active as well as primed by features of the environment. Goal systems theory outlines the principles that characterize the dynamics of goal pursuit and explores their implications for consumer behavior. In this vein, we discuss from a common, goal systemic, perspective a variety of well known phenomena in the realm of consumer behavior including brand loyalty, variety seeking, impulsive buying, preferences, choices and regret. The goal systemic perspective affords guidelines for subsequent research on the dynamic aspects of consummatory behavior as well as offering insights into practical matters in the area of marketing.
Misesian Praxeology : an illustration froM the field of sociology of delinquency

The two main principles of the praxeological system elaborated by Mises are his concept of action and his epistemological apriorism. This paper illustrates these principles in the field of the sociology of delinquency. It first shows that the Misesian concept of action is very helpful in order to (1) understand why a praxeological turn occurred in the 1950s with the critique of the culturalist approach to criminality, and (2) analyze some of the main theoretical developments that took place afterwards, such as the social control theory, the low self-control theory, and the routine activity theory. The paper then shows that, behind their positivist façade, Renaud Fillieule (renaud.fillieule@univ-lille1.fr) is Professor at the Université Lille Nord de France and member of the CLERSÉ research unit. The author wishes to thank Joe Salerno for the invitation to deliver this lecture, the Mises Institute for a flawless organization of the conference, and Guido Hülsmann for his support. VoL. 15 | N 4 | 410–431 WINTER 2012 The
A Belief Rule Based Flood Risk Assessment Expert System using Real Time Sensor Data Streaming

Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. The integrated BRBES produces reliable results in comparison to other data-driven approaches. Data for the expert system has been collected by considering different case study areas of Bangladesh to validate the system.
Differential effects of 30- vs. 60-second static muscle stretching on vertical jump performance.

It has been proposed that pre-exercise static stretching may reduce muscle force and power. Recent systematic and meta-analytical reviews have proposed a threshold regarding the effect of short (<45 seconds) and moderate (≥60 seconds) stretching durations on subsequent performance in a multi-joint task (e.g., jump performance), although its effect on power output remains less clear. Furthermore, no single experimental study has explicitly compared the effect of short (e.g., 30 seconds) and moderate (60 seconds) durations of continuous static stretching on multi-joint performance. Therefore, the aim of the present study was determine the effect of acute short- and moderate-duration continuous stretching interventions on vertical jump performance and power output. Sixteen physically active men (21.0 ± 1.9 years; 1.7 ± 0.1 m; 78.4 ± 12.1 kg) volunteered for the study. After familiarization, subjects attended the laboratory for 3 testing sessions. In the nonstretching (NS) condition, subjects performed a countermovement jump (CMJ) test without a preceding stretching bout. In the other 2 conditions, subjects performed 30-second (30SS; 4 minutes) or 60-second (60SS; 8 minutes) static stretching bouts in calf muscles, hamstrings, gluteus maximus, and quadriceps, respectively, followed by the CMJ test. Results were compared by repeated-measures analysis of variance. In comparison with NS, 60SS resulted in a lower CMJ height (-3.4%, p ≤ 0.05) and average (-2.7%, p ≤ 0.05) and peak power output (-2.0%, p ≤ 0.05), but no difference was observed between 30SS and the other conditions (p > 0.05). These data suggest a dose-dependent effect of stretching on muscular performance, which is in accordance with previous studies. The present results suggest a threshold of continuous static stretching in which muscular power output in a multi-joint task may be impaired immediately following moderate-duration (60 seconds; 8 minutes) static stretching while short-duration (30 seconds; 4 minutes) stretching has a negligible influence.
The blood-brain barrier in brain homeostasis and neurological diseases.

Brain endothelial cells are unique among endothelial cells in that they express apical junctional complexes, including tight junctions, which quite resemble epithelial tight junctions both structurally and functionally. They form the blood-brain-barrier (BBB) which strictly controls the exchanges between the blood and the brain compartments by limiting passive diffusion of blood-borne solutes while actively transporting nutrients to the brain. Accumulating experimental and clinical evidence indicate that BBB dysfunctions are associated with a number of serious CNS diseases with important social impacts, such as multiple sclerosis, stroke, brain tumors, epilepsy or Alzheimer's disease. This review will focus on the implication of brain endothelial tight junctions in BBB architecture and physiology, will discuss the consequences of BBB dysfunction in these CNS diseases and will present some therapeutic strategies for drug delivery to the brain across the BBB.
Torque Ripple Minimization of PMSM Based on Robust ILC Via Adaptive Sliding Mode Control

Torque ripples due to cogging torque, current measurement errors, and flux harmonics restrict the application of the permanent magnet synchronous motor (PMSM) that has a high-precision requirement. The torque pulsation varies periodically along with the rotor position, and it results in speed ripples, which further degrade the performance of the PMSM servo system. Iterative learning control (ILC), in parallel with the classical proportional integral (PI) controller (i.e., PI-ILC), is a conventional method to suppress the torque ripples. However, it is sensitive to the system uncertainties and external disturbances, i.e., it is paralyzed to nonperiodic disturbances. Therefore, this paper proposes a robust ILC scheme achieved by an adaptive sliding mode control (SMC) technique to further reduce the torque ripples and improve the antidisturbance ability of the servo system. ILC is employed to reduce the periodic torque ripples and the SMC is used to guarantee fast response and strong robustness. An adaptive algorithm is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. The estimated value is utilized to compensate the robust ILC speed controller in order to eliminate the effects of the disturbance, and it can suppress the sliding mode chattering phenomenon simultaneously. Experiments were carried out on a digital signal processor-field programmable gate array based platform. The obtained experimental results demonstrate that the robust ILC scheme has an improved performance with minimized torque ripples and it exhibits a satisfactory antidisturbance performance compared to the PI-ILC method.
Effects of Instructional Guidance and Sequencing of Manipulatives and Written Symbols on Second Graders’ Numeration Knowledge

Concrete objects used to illustrate mathematical ideas are commonly known as manipulatives. Manipulatives are ubiquitous in North American elementary classrooms in the early years, and although they can be beneficial, they do not guarantee learning. In the present study, the authors examined two factors hypothesized to impact second-graders’ learning of place value and regrouping with manipulatives: (a) the sequencing of concrete (base-ten blocks) and abstract (written symbols) representations of the standard addition algorithm; and (b) the level of instructional guidance on the structural relations between the representations. Results from a classroom experiment with second-grade students (N = 87) indicated that place value knowledge increased from pre-test to post-test when the base-ten blocks were presented before the symbols, but only when no instructional guidance was offered. When guidance was given, only students in the symbols-first condition improved their place value knowledge. Students who received instruction increased their understanding of regrouping, irrespective of representational sequence. No effects were found for iterative sequencing of concrete and abstract representations. Practical implications for teaching mathematics with manipulatives are considered.
Profile-guided memory optimization for deep neural networks

Recent years have seen deep neural networks (DNNs) becoming wider and deeper to achieve better performance in many applications of AI. Such DNNs however require huge amounts of memory to store weights and intermediate results (e.g., activations, feature maps, etc.) in propagation. This requirement makes it difficult to run the DNNs on devices with limited, hard-to-extend memory, degrades the running time performance, and restricts the design of network models. We address this challenge by developing a novel profile-guided memory optimization to efficiently and quickly allocate memory blocks during the propagation in DNNs. The optimization utilizes a simple and fast heuristic algorithm based on the two-dimensional rectangle packing problem. Experimenting with well-known neural network models, we confirm that our method not only reduces the memory consumption by up to 49.5% but also accelerates training and inference by up to a factor of four thanks to the rapidity of the memory allocation and the ability to use larger mini-batch sizes.
A Review on Green Purchase Behaviour Trend of Malaysian Consumers

The emergence of environmental problems and the increased awareness towards green purchase behaviour have received many responses by the stakeholders’ worldwide like from the government bodies, researchers, businesses, consumers and so on. Government’s bodies, for example, have responded by developing and introducing their own environmentally-linked policies to be implemented in their countries, which are intended to conserve and preserve the environment. Researchers, on the other hand, are continuously conducting extensive studies and publishing their findings on the issues to inform the public, while businesses that promote the selling of green products (or environmentally friendly products) in the marketplace have been increasing in number. Segments of green consumers have been observed to emerge and grow in size worldwide including Malaysia. This may be due to the increased number of green products introduced to consumers in the marketplace. Moreover, scholars from Malaysia also argued that, this trend is experiencing tremendous growth. Although there are responses from these stakeholders, especially consumers, who have had a positive impact on the environment, the trend of the green purchase behaviour by Malaysian consumers remains unobserved. Therefore, the authors aim to answer the questions concerning whether a trend can be observed in the green purchase behaviour of Malaysian consumers. The ability to observe the green purchase behaviour trend is useful, particularly for marketers and businesses that are selling or intending to sell green products within the country.
The effect of store image and service quality on brand image and purchase intention for private label brands

This study aims to investigate the direct effects of store image and service quality on brand image and purchase intention for a private label brand (PLB). This study also investigates the indirect effects mediated by perceived risk and price consciousness on these relationships. The sample in this study consisted of three hundred and sixty (360) customers of the Watsons and Cosmed chain of drugstores. The pre-test results identified ‘‘Watsons’’ and ‘‘My Beauty Diary’’ as the research brands of the PLB for the two stores, respectively. This study uses LISREL to examine the hypothesized relationships. This study reveals that (1) store image has a direct and positive effect on the purchase intention of the PLB; (2) service quality has a direct and positive effect on the PLB image; (3) the perceived risk of PLB products has a mediating effect on the relationship between the brand image and the consumers purchase intention of the PLB. 2010 Australian and New Zealand Marketing Academy. All rights reserved.