nils-herrmann/clean_scirepeval huggingface dataset:
Corralling a Band of Bandit Algorithms

We study the problem of combining multiple bandit algorithms (that is, online learning algorithms with partial feedback) with the goal of creating a master algorithm that performs almost as well as the best base algorithm if it were to be run on its own. The main challenge is that when run with a master, base algorithms unavoidably receive much less feedback and it is thus critical that the master not starve a base algorithm that might perform uncompetitively initially but would eventually outperform others if given enough feedback. We address this difficulty by devising a version of Online Mirror Descent with a special mirror map together with a sophisticated learning rate scheme. We show that this approach manages to achieve a more delicate balance between exploiting and exploring base algorithms than previous works yielding superior regret bounds. Our results are applicable to many settings, such as multi-armed bandits, contextual bandits, and convex bandits. As examples, we present two main applications. The first is to create an algorithm that enjoys worst-case robustness while at the same time performing much better when the environment is relatively easy. The second is to create an algorithm that works simultaneously under different assumptions of the environment, such as different priors or different loss structures.
A Survey of Model Compression and Acceleration for Deep Neural Networks

Deep convolutional neural networks (CNNs) have recently achieved great success in many visual recognition tasks. However, existing deep convolutional neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resources or in applications with strict latency requirements. Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance. During the past few years, tremendous progresses have been made in this area. In this paper, we survey the recent advanced techniques for compacting and accelerating CNNs model developed. These techniques are roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transfered/compact convolutional filters and knowledge distillation. Methods of parameter pruning and sharing will be described at the beginning, after that the other techniques will be introduced. For each scheme, we provide insightful analysis regarding the performance, related applications, advantages and drawbacks etc. Then we will go through a few very recent additional successful methods, for example, dynamic networks and stochastic depths networks. After that, we survey the evaluation matrix, main datasets used for evaluating the model performance and recent benchmarking efforts. Finally we conclude this paper, discuss remaining challenges and possible directions in this topic.
Deep Computational Phenotyping

We apply deep learning to the problem of discovery and detection of characteristic patterns of physiology in clinical time series data. We propose two novel modifications to standard neural net training that address challenges and exploit properties that are peculiar, if not exclusive, to medical data. First, we examine a general framework for using prior knowledge to regularize parameters in the topmost layers. This framework can leverage priors of any form, ranging from formal ontologies (e.g., ICD9 codes) to data-derived similarity. Second, we describe a scalable procedure for training a collection of neural networks of different sizes but with partially shared architectures. Both of these innovations are well-suited to medical applications, where available data are not yet Internet scale and have many sparse outputs (e.g., rare diagnoses) but which have exploitable structure (e.g., temporal order and relationships between labels). However, both techniques are sufficiently general to be applied to other problems and domains. We demonstrate the empirical efficacy of both techniques on two real-world hospital data sets and show that the resulting neural nets learn interpretable and clinically relevant features.
A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.
Personality and Social Psychology Be yourself , believe in yourself , and be happy : Self-efficacy as a mediator between personality factors and subjective well-being

Research has shown close connections between personality and subjective well-being (SWB), suggesting that personality traits predispose individuals to experience different levels of SWB. Moreover, numerous studies have shown that self-efficacy is related to both personality factors and SWB. Extending previous research, we show that general self-efficacy functionally connects personality factors and two components of SWB (life satisfaction and subjective happiness). Our results demonstrate the mediating role of self-efficacy in linking personality factors and SWB. Consistent with our expectations, the influence of neuroticism, extraversion, openness, and conscientiousness on life satisfaction was mediated by self-efficacy. Furthermore, self-efficacy mediated the influence of openness and conscientiousness, but not that of neuroticism and extraversion, on subjective happiness. Results highlight the importance of cognitive beliefs in functionally linking personality traits and SWB.
Customer Brand Engagement on Online Social Media Platforms : A Conceptual Model and Empirical Analysis

The aim of this paper is to contribute to the knowledge of a newly emerged concept of customer engagement with brands in the context of online social media platforms. Drawing on the overview of currently available academic literature and the results of a quantitative consumer study, the paper delivers a thorough investigation of the concept and proposes a conceptual model of customer brand engagement on online social media platforms. The study suggests that customer brand relationship related factors will influence the level of customer engagement, which in turn will affect the levels of behavioral loyalty and intention to recommend the brand. Hence, this paper is an important contribution to the academic marketing literature in the field of customer engagement, which also provides useful managerial insights for marketing practitioners.
Automatic Generation of Natural Language Explanations

An interesting challenge for explainable recommender systems is to provide successful interpretation of recommendations using structured sentences. It is well known that user-generated reviews, have strong influence on the users' decision. Recent techniques exploit user reviews to generate natural language explanations. In this paper, we propose a character-level attention-enhanced long short-term memory model to generate natural language explanations. We empirically evaluated this network using two real-world review datasets. The generated text present readable and similar to a real user's writing, due to the ability of reproducing negation, misspellings, and domain-specific vocabulary.
Vector Design for Improved DNA Vaccine Efficacy, Safety and Production

DNA vaccination is a disruptive technology that offers the promise of a new rapidly deployed vaccination platform to treat human and animal disease with gene-based materials. Innovations such as electroporation, needle free jet delivery and lipid-based carriers increase transgene expression and immunogenicity through more effective gene delivery. This review summarizes complementary vector design innovations that, when combined with leading delivery platforms, further enhance DNA vaccine performance. These next generation vectors also address potential safety issues such as antibiotic selection, and increase plasmid manufacturing quality and yield in exemplary fermentation production processes. Application of optimized constructs in combination with improved delivery platforms tangibly improves the prospect of successful application of DNA vaccination as prophylactic vaccines for diverse human infectious disease targets or as therapeutic vaccines for cancer and allergy.
Natural Language Processing in Serious Games: A state of the art

In the last decades, Natural Language Processing (NLP) has obtained a high level of success. Interactions between NLP and Serious Games have started and some of them already include NLP techniques. The objectives of this paper are twofold: on the one hand, providing a simple framework to enable analysis of potential uses of NLP in Serious Games and, on the other hand, applying the NLP framework to existing Serious Games and giving an overview of the use of NLP in pedagogical Serious Games. In this paper we present 11 serious games exploiting NLP techniques. We present them systematically, according to the following structure: first, we highlight possible uses of NLP techniques in Serious Games, second, we describe the type of NLP implemented in the each specific Serious Game and, third, we provide a link to possible purposes of use for the different actors interacting in the Serious Game.
Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan

The efficiency of decision processes which can be divided into two stages has been measured for the whole process as well as for each stage independently by using the conventional data envelopment analysis (DEA) methodology in order to identify the causes of inefficiency. This paper modifies the conventional DEA model by taking into account the series relationship of the two sub-processes within the whole process. Under this framework, the efficiency of the whole process can be decomposed into the product of the efficiencies of the two sub-processes. In addition to this sound mathematical property, the case of Taiwanese non-life insurance companies shows that some unusual results which have appeared in the independent model do not exist in the relational model. In other words, the relational model developed in this paper is more reliable in measuring the efficiencies and consequently is capable of identifying the causes of inefficiency more accurately. Based on the structure of the model, the idea of efficiency decomposition can be extended to systems composed of multiple stages connected in series.
The 2018 PIRM Challenge on Perceptual Image Super-Resolution

This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018. In contrast to previous SR challenges, our evaluation methodology jointly quantifies accuracy and perceptual quality, therefore enabling perceptualdriven methods to compete alongside algorithms that target PSNR maximization. Twenty-one participating teams introduced algorithms which well-improved upon the existing state-of-the-art methods in perceptual SR, as confirmed by a human opinion study. We also analyze popular image quality measures and draw conclusions regarding which of them correlates best with human opinion scores. We conclude with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.
Zirconia in dentistry: Part 1. Discovering the nature of an upcoming bioceramic.

Advanced ceramic materials such as zirconia have great potential as substitutes for traditional materials in many biomedical applications. Since the end of the 1990s, the form of partially stabilized zirconia has been promoted as suitable for dental use due to its excellent strength and superior fracture resistance as result of an inherent transformation toughening mechanism. In addition, zirconia bioceramic presents enhanced biocompatibility, low radioactivity, and interesting optical properties. The introduction of computer-aided design/computer-aided manufacturing (CAD/CAM) techniques has increased the general acceptance of zirconia in dentistry. However, some fabrication procedures such as grinding, polishing, sandblasting, heat treatment, and veneering of the fine-grained metastable zirconia microstructures may affect the long-term stability and success of the material by influencing its aging sensitivity. The purpose of this review is to address the evolution of zirconia as a biomaterial; to explore the material's physical, chemical, biological, and optical properties; to describe strengthening procedures; and finally to examine aging, processing, and core/veneer interfacial effects.
Information Selection in Noisy Environments with Large Action Spaces

A critical aspect of human cognition is the ability to effectively query the environment for information. The ‘real’ world is large and noisy, and therefore designing effective queries involves prioritizing both scope – the range of hypotheses addressed by the query – and reliability – the likelihood of obtaining a correct answer. Here we designed a simple information-search game in which participants had to select an informative query from a large set of queries, trading off scope and reliability. We find that adults are effective information-searchers even in large, noisy environments, and that their information search is best explained by a model that balances scope and reliability by selecting queries proportionately to their expected information gain.
Distinct magnetic field dependence of Néel skyrmion sizes in ultrathin nanodots

We investigate the dependence of the Néel skyrmion size and stability on perpendicular magnetic field in ultrathin circular magnetic dots with out-of-plane anisotropy and interfacial Dzyaloshinskii-Moriya exchange interaction. Our results show the existence of two distinct dependencies of the skyrmion radius on the applied field and dot size. In the case of skyrmions stable at zero field, their radius strongly increases with the field applied parallel to the skyrmion core until skyrmion reaches the metastability region and this dependence slows down. More common metastable skyrmions demonstrate a weaker increase of their size as a function of the field until some critical field value at which these skyrmions drastically increase in size showing a hysteretic behavior with coexistence of small and large radius skyrmions and small energy barriers between them. The first case is also characterized by a strong dependence of the skyrmion radius on the dot diameter, while in the second case this dependence is very weak.
The critical success factors of business process management

Although business process management ( ̳BPM‘) is a popular concept, it has not yet been properly theoretically grounded. This leads to problems in identifying both generic and case specific critical success factors of BPM programs. The paper proposes an underlying theoretical framework with the utilization of three theories: contingency, dynamic capabilities and task technology fit. The main premise is that primarily the fit between the business environment and business processes is needed. Then both continuous improvement and the proper fit between business process tasks and information systems must exist. The underlying theory is used to identify critical success factors on a case study from the banking sector.
Historical Roots of Agile Methods: Where Did "Agile Thinking" Come From?

The appearance of Agile methods has been the most noticeable change to software process thinking in the last fifteen years [16], but in fact many of the “Agile ideas” have been around since 70’s or even before. Many studies and reviews have been conducted about Agile methods which ascribe their emergence as a reaction against traditional methods. In this paper, we argue that although Agile methods are new as a whole, they have strong roots in the history of software engineering. In addition to the iterative and incremental approaches that have been in use since 1957 [21], people who criticised the traditional methods suggested alternative approaches which were actually Agile ideas such as the response to change, customer involvement, and working software over documentation. The authors of this paper believe that education about the history of Agile thinking will help to develop better understanding as well as promoting the use of Agile methods. We therefore present and discuss the reasons behind the development and introduction of Agile methods, as a reaction to traditional methods, as a result of people's experience, and in particular focusing on reusing ideas from history.
Abusive Text Detection Using Neural Networks

Neural network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architectures has recently attracted a high level of attention amongst researchers. In this paper, we focus on how neural network models have been applied in text classification. Secondly, we extend our previous work [4, 3] using a neural network strategy for the task of abusive text detection. We compare word embedding features to the traditional feature representations such as n-grams and handcrafted features. In addition, we use an off-the-shelf neural network classifier, FastText[16]. Based on our results, the conclusions are: (1) Extracting selected manual features can increase abusive content detection over using basic ngrams; (2) Although averaging pre-trained word embeddings is a naive method, the distributed feature representation has better performance to ngrams in most of our datasets; (3) While the FastText classifier works efficiently with fast performance, the results are not remarkable as it is a shallow neural network with only one hidden layer; (4) Using pre-trained word embeddings does not guarantee better performance in the FastText classifier.
A Large-Scale Analysis of the Security of Embedded Firmwares

As embedded systems are more than ever present in our society, their security is becoming an increasingly important issue. However, based on the results of many recent analyses of individual firmware images, embedded systems acquired a reputation of being insecure. Despite these facts, we still lack a global understanding of embedded systems’ security as well as the tools and techniques needed to support such general claims. In this paper we present the first public, large-scale analysis of firmware images. In particular, we unpacked 32 thousand firmware images into 1.7 million individual files, which we then statically analyzed. We leverage this large-scale analysis to bring new insights on the security of embedded devices and to underline and detail several important challenges that need to be addressed in future research. We also show the main benefits of looking at many different devices at the same time and of linking our results with other large-scale datasets such as the ZMap’s HTTPS survey. In summary, without performing sophisticated static analysis, we discovered a total of 38 previously unknown vulnerabilities in over 693 firmware images. Moreover, by correlating similar files inside apparently unrelated firmware images, we were able to extend some of those vulnerabilities to over 123 different products. We also confirmed that some of these vulnerabilities altogether are affecting at least 140K devices accessible over the Internet. It would not have been possible to achieve these results without an analysis at such wide scale. We believe that this project, which we plan to provide as a firmware unpacking and analysis web service, will help shed some light on the security of embedded devices.
Determinants of FDI in BRICS Countries : A panel analysis

This study examines the factors determining FDI inflows of BRICS countries using annual dataset from the period 1975 to 2007 (for Russia required data set is available from 1990 onwards). The study employs Panel data analysis and finds that the selected variables Market size, Labour cost, Infrastructure, Currency value and Gross Capital formation as the potential determinants of FDI inflows of BRICS countries. The Economic Stability and Growth prospects (measured by inflation rate and Industrial production respectively), Trade openness (measured by the ratio of total trade to GDP) are seems to be the insignificant determinant of FDI inflows of the BRICS countries. The empirical results are robust in general for alternative variables determining FDI flows.
Recognizing Human Actions as the Evolution of Pose Estimation Maps

Most video-based action recognition approaches choose to extract features from the whole video to recognize actions. The cluttered background and non-action motions limit the performances of these methods, since they lack the explicit modeling of human body movements. With recent advances of human pose estimation, this work presents a novel method to recognize human action as the evolution of pose estimation maps. Instead of relying on the inaccurate human poses estimated from videos, we observe that pose estimation maps, the byproduct of pose estimation, preserve richer cues of human body to benefit action recognition. Specifically, the evolution of pose estimation maps can be decomposed as an evolution of heatmaps, e.g., probabilistic maps, and an evolution of estimated 2D human poses, which denote the changes of body shape and body pose, respectively. Considering the sparse property of heatmap, we develop spatial rank pooling to aggregate the evolution of heatmaps as a body shape evolution image. As body shape evolution image does not differentiate body parts, we design body guided sampling to aggregate the evolution of poses as a body pose evolution image. The complementary properties between both types of images are explored by deep convolutional neural networks to predict action label. Experiments on NTU RGB+D, UTD-MHAD and PennAction datasets verify the effectiveness of our method, which outperforms most state-of-the-art methods.