nils-herrmann/clean_scirepeval huggingface dataset:
The Development of Organizational Social Capital : Attributes of Family Firms *

We develop and extend social capital theory by exploring the creation of organizational social capital within a highly pervasive, yet often overlooked organizational form: family firms. We argue that family firms are unique in that, although they work as a single entity, at least two forms of social capital coexist: the family’s and the firm’s. We investigate mechanisms that link a family’s social capital to the creation of the family firm’s social capital and examine how factors underlying the family’s social capital affect this creation. Moreover, we identify contingency dimensions that affect these relationships and the potential risks associated with family social capital. Finally, we suggest these insights are generalizable to several other types of organizations with similar characteristics.
New Feature Parameters for Pronunciation Evaluation in English Presentations at International Conferences

We have previously proposed a statistical method for estimating the pronunciation proficiency and intelligibility of presentations made in English by non-native speakers. To investigate the relationship between various acoustic measures and the pronunciation score and intelligibility, we statistically analyzed the speaker’s actual utterances to find combinations of acoustic features with a high correlation between the score estimated by a linear regression model and the score perceived by native English teachers. In this paper, we examined the quality of new acoustic features that are useful when used in combination with the system’s estimates of pronunciation score and intelligibility. Results showed that the best combination of acoustic features produced correlation coefficients of 0.929 and 0.753 for pronunciation and intelligibility, respectively, using open data for speakers at the 10-sentence level.
Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment

Recognizing textual entailment is a fundamental task in a variety of text mining or natural language processing applications. This paper proposes a simple neural model for RTE problem. It first matches each word in the hypothesis with its most-similar word in the premise, producing an augmented representation of the hypothesis conditioned on the premise as a sequence of word pairs. The LSTM model is then used to model this augmented sequence, and the final output from the LSTM is fed into a softmax layer to make the prediction. Besides the base model, in order to enhance its performance, we also proposed three techniques: the integration of multiple word-embedding library, bi-way integration, and ensemble based on model averaging. Experimental results on the SNLI dataset have shown that the three techniques are effective in boosting the predicative accuracy and that our method outperforms several state-of-the-state ones.
Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR.

The PCR is used widely for the study of rRNA genes amplified from mixed microbial populations. These studies resemble quantitative applications of PCR in that the templates are mixtures of homologs and the relative abundance of amplicons is thought to provide some measure of the gene ratios in the starting mixture. Although such studies have established the presence of novel rRNA genes in many natural ecosystems, inferences about gene abundance have been limited by uncertainties about the relative efficiency of gene amplification in the PCR. To address this question, three rRNA gene standards were prepared by PCR, mixed in known proportions, and amplified a second time by using primer pairs in which one primer was labeled with a fluorescent nucleotide derivative. The PCR products were digested with restriction endonucleases, and the frequencies of genes in the products were determined by electrophoresis on an Applied Biosystems 373A automated DNA sequencer in Genescan mode. Mixtures of two templates amplified with the 519F-1406R primer pair yielded products in the predicted proportions. A second primer pair (27F-338R) resulted in strong bias towards 1:1 mixtures of genes in final products, regardless of the initial proportions of the templates. This bias was strongly dependent on the number of cycles of replication. The results fit a kinetic model in which the reannealing of genes progressively inhibits the formation of template-primer hybrids.
Emotion work and psychological well-being A review of the literature and some conceptual considerations

In this article, the state of the art of research on emotion work (emotional labor) is summarized with an emphasis on its effects on well-being. It starts with a definition of what emotional labor or emotion work is. Aspects of emotion work, such as automatic emotion regulation, surface acting, and deep acting, are discussed from an action theory point of view. Empirical studies so far show that emotion work has both positive and negative effects on health. Negative effects were found for emotional dissonance. Concepts related to the frequency of emotion expression and the requirement to be sensitive to the emotions of others had both positive and negative effects. Control and social support moderate relations between emotion work variables and burnout and job satisfaction. Moreover, there is empirical evidence that the cooccurrence of emotion work and organizational problems leads to high levels of burnout. D 2002
Scan2Mesh: From Unstructured Range Scans to 3D Meshes

We introduce Scan2Mesh, a novel data-driven generative approach which transforms an unstructured and potentially incomplete range scan into a structured 3D mesh representation. The main contribution of this work is a generative neural network architecture whose input is a range scan of a 3D object and whose output is an indexed face set conditioned on the input scan. In order to generate a 3D mesh as a set of vertices and face indices, the generative model builds on a series of proxy losses for vertices, edges, and faces. At each stage, we realize a one-to-one discrete mapping between the predicted and ground truth data points with a combination of convolutionaland graph neural network architectures. This enables our algorithm to predict a compact mesh representation similar to those created through manual artist effort using 3D modeling software. Our generated mesh results thus produce sharper, cleaner meshes with a fundamentally different structure from those generated through implicit functions, a first step in bridging the gap towards artist-created CAD models.
Attention, Learn to Solve Routing Problems!

The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, to push this idea towards practical implementation, we need better models and better ways of training. We contribute in both directions: we propose a model based on attention layers with benefits over the Pointer Network and we show how to train this model using REINFORCE with a simple baseline based on a deterministic greedy rollout, which we find is more efficient than using a value function. We significantly improve over recent learned heuristics for the Travelling Salesman Problem (TSP), getting close to optimal results for problems up to 100 nodes. With the same hyperparameters, we learn strong heuristics for two variants of the Vehicle Routing Problem (VRP), the Orienteering Problem (OP) and (a stochastic variant of) the Prize Collecting TSP (PCTSP), outperforming a wide range of baselines and getting results close to highly optimized and specialized algorithms.
Convolutional gated recurrent neural network incorporating spatial features for audio tagging

Environmental audio tagging is a newly proposed task to predict the presence or absence of a specific audio event in a chunk. Deep neural network (DNN) based methods have been successfully adopted for predicting the audio tags in the domestic audio scene. In this paper, we propose to use a convolutional neural network (CNN) to extract robust features from mel-filter banks (MFBs), spectrograms or even raw waveforms for audio tagging. Gated recurrent unit (GRU) based recurrent neural networks (RNNs) are then cascaded to model the long-term temporal structure of the audio signal. To complement the input information, an auxiliary CNN is designed to learn on the spatial features of stereo recordings. We evaluate our proposed methods on Task 4 (audio tagging) of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE 2016) challenge. Compared with our recent DNN-based method, the proposed structure can reduce the equal error rate (EER) from 0.13 to 0.11 on the development set. The spatial features can further reduce the EER to 0.10. The performance of the end-to-end learning on raw waveforms is also comparable. Finally, on the evaluation set, we get the state-of-the-art performance with 0.12 EER while the performance of the best existing system is 0.15 EER.
Fish Sedation, Anesthesia, Analgesia, and Euthanasia: Considerations, Methods, and Types of Drugs

Fish display robust neuroendocrine and physiologic stress responses to noxious stimuli. Many anesthetic, sedative, or analgesic drugs used in other vertebrates reduce stress in fi sh, decrease handling trauma, minimize movement and physiologic changes in response to nociceptive stimuli, and can be used for euthanasia. But extrapolating from limited published anesthetic and sedative data to all fi sh species is potentially harmful because of marked anatomic, physiologic, and behavioral variations; instead, a stepwise approach to anesthetizing or sedating unfamiliar species or using unproven drugs for familiar species is advisable. Additionally, knowledge of how water quality infl uences anesthesia or sedation helps limit complications. The most common method of drug administration is through immersion, a technique analogous to gaseous inhalant anesthesia in terrestrial animals, but the use of injectable anesthetic and sedative agents (primarily intramuscularly, but also intravenously) is increasing. Regardless of the route of administration, routine preprocedural preparation is appropriate, to stage both the animals and the supplies for induction, maintenance, and recovery. Anesthetic and sedation monitoring and resuscitation are similar to those for other vertebrates. Euthanasia is most commonly performed using an overdose of an immersion drug but injectable agents are also effective. Analgesia is an area in need of signifi cant research as only a few studies exist and they provide some contrasting results. However, fi sh have μ and κ opiate receptors throughout the brain, making it reasonable to expect some effect of at least opioid treatments in fi sh experiencing noxious stimuli.
Imitation Learning: A Survey of Learning Methods

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.
Causal Inference Using Potential Outcomes : Design , Modeling , Decisions

Causal effects are defined as comparisons of potential outcomes under different treatments on a common set of units. Observed values of the potential outcomes are revealed by the assignment mechanism—a probabilistic model for the treatment each unit receives as a function of covariates and potential outcomes. Fisher made tremendous contributions to causal inference through his work on the design of randomized experiments, but the potential outcomes perspective applies to other complex experiments and nonrandomized studies as well. As noted by Kempthorne in his 1976 discussion of Savage’s Fisher lecture, Fisher never bridged his work on experimental design and his work on parametric modeling, a bridge that appears nearly automatic with an appropriate view of the potential outcomes framework, where the potential outcomes and covariates are given a Bayesian distribution to complete the model specification. Also, this framework crisply separates scientific inference for causal effects and decisions based on such inference, a distinction evident in Fisher’s discussion of tests of significance versus tests in an accept/reject framework. But Fisher never used the potential outcomes framework, originally proposed by Neyman in the context of randomized experiments, and as a result he provided generally flawed advice concerning the use of the analysis of covariance to adjust for posttreatment concomitants in randomized trials.
Seismic Damage Prediction of Reinforced Concrete Buildings Using Pushover Analysis

This paper investigates the seismic damage of a sex storey reinforced concrete frame building designed according to the Moroccan seismic code RPS2000 [ building is residential and has a reinforced concrete frame structural system. In the first time a set of dynamic analysis are carried out to compute dynamic properties of the building (fundamental period, natural frequencies, deformation modes,), in the second time a pushover analysis is carried out.Performances levels are used with the pushover analysis to assess the seismic damage of the building. Three performance levels considered in the present study are immediate occupancy, life safety and collapse prevention.
Fake News Identification Characteristics Using Named Entity Recognition and Phrase Detection

Information explosion that can be generated by anyone may lead to the spread of fake news not only at the news channel, but also at social media, and so forth. Detection of fake news has become an urgent need on the society because of fake news spread of unrest in the society. Several related studies have been conducted in the news classification with the aim of providing a decision whether a news is included in fake news or original news. In the related research, a vector representation of documents is used. This vector representation is then given to the algorithm for further processing. This study aims to model vectors that can accommodate the characteristics of fake news before further processed by language algorithms using the Indonesian language. In this research, fake news and original news are represented according to the vector space model. Vector model combination of frequency term, inverse document frequency and frequency reversed with 10-fold cross validation using support vector machine algorithm classifier. Variations of phrase detection as well as name recognition entities (entity recognition names) are also used in vector representation. A vector representation that uses the term frequency shows promising performance. It can recognize news characteristics correctly 96.74% of 2516 documents across phrase detection and named entity recognition process.
AN APPLICATION OF HEURISTIC AND EVOLUTIONARY ALGORITHMS FOR SOLVING SOME PRACTICAL TWO-DIMENSIONAL BIN PACKING PROBLEMS

Bin packing problems are a class of optimization problems that have numerous applications in the industrial world, ranging from efficient cutting of material to packing various items in a larger container. We consider here only rectangular items cut off an infinite strip of material as well as off larger sheets of fixed dimensions. This problem has been around for many years and a great number of publications can be found on the subject. Nevertheless, it is often difficult to reconcile a theoretical paper and practical application of it. The present work aims to create simple but, at the same time, fast and efficient algorithms, which would allow one to write high-speed and capable software that can be used in a real-time application.
Home advantage and referee bias in European football.

Home advantage is well documented in a wide range of team sports including association football (soccer). Home team crowd support has been shown to be a likely causal factor and its influence on referee decision-making appears to play a significant role. Match data from the 2009/2010 and 2010/2011 seasons of the Union of European Football Associations (UEFA) Champions League and Europa League were used to investigate referee bias in terms of the association between match location (home vs. away) and disciplinary sanctions used by football referees. The adjusted mean number of yellow cards received by home and away teams and the ratios of these means were estimated from Poisson regression models. After controlling for within-match measures of attacking dominance referees in the Champions League and Europa League issued 25% (p<0.001) and 10% (p=0.002) more yellow cards, respectively, to away teams than to home teams. The higher level of home team bias in the Champions League appeared to be mainly due to higher crowd densities. In a combined analysis of both UEFA leagues the magnitude of referee bias increased with increasing crowd density (p<0.001). Crowd size and crowd proximity were not associated with referee bias after controlling for crowd density. These results provide further evidence that crowd support influences referee decisions. Failure to control for within-match team performance may over-estimate the extent of referee bias in terms of the number of disciplinary sanctions used.
The Effects of Information Format and Shopping Task on Consumers' Online Shopping Behavior: A Cognitive Fit Perspective

A feature central to the success of e-commerce Web sites is the design of an effective interface to present product information. However, the suitability of the prevalent information formats in supporting various online shopping tasks is not known. Using the cognitive fit theory as the theoretical framework, we developed a research model to investigate the fit between information format and shopping task, and exam150 HONG, THONG, AND TAM ine its influence on consumers’ online shopping performance and perceptions of shopping experience. The competition for attention theory from the marketing literature and the scanpath theory from vision research were employed to support the analyses. An experiment was conducted to examine the effects of two types of information formats (list versus matrix) in the context of two types of shopping tasks (searching versus browsing). The results show that when there is a match between the information format and the shopping task, consumers can search the information space more efficiently and have better recall of product information. Specifically, the list format better supports browsing tasks, and the matrix format facilitates searching tasks. However, a match between the information format and the shopping task has no effect on cognitive effort or attitude toward using the Web site. Overall, this research supports the application of the cognitive fit theory to the study of Web interface design. It also demonstrates the value in integrating findings from cognitive science and vision research to understand the processes involved. As the information format has been shown to affect consumers’ online shopping behavior, even when the information content is held constant, the practical implications for Web site designers include providing both types of information format on their Web sites and matching the appropriate information format to the individual consumer’s task.
Introduction to artificial neural systems for pattern recognition

The artificial intelligence community has seen a recent resurgence in the area of neural network study. Inspired by the workings of the brain and nervous system, neural networks have solved some persistent problems in vision and speech processing. However, the new systems may offer an alternative approach to decision-making via high level pattern recognition. This paper will describe the distinguishing features of neurally inspired systems, and present popular systems in a discrete-time, algorithmic framework. Examples of applications to decision problems will appear, and guidelines for their use in operations research will be established.
ACODS: adaptive computation offloading for drone surveillance system

Unmanned Aerial Vehicle (UAV) surveillance systems allow for highly advanced and safe surveillance of hazardous locations. Further, multi-purpose drones can be widely deployed for not only gathering information but also analyzing the situation from sensed data. However, mobile drone systems have limited computing resources and battery power which makes it a challenge to use these systems for long periods of time or in fully autonomous modes. In this paper, we propose an Adaptive Computation Offloading Drone System (ACODS) architecture with reliable communication for increasing drone operating time. We design not only the response time prediction module for mission critical task offloading decision but also task offloading management module via the Multipath TCP (MPTCP). Through performance evaluation via our prototype implementation, we show that the proposed algorithm achieves significant increase in drone operation time and significantly reduces the response time.