nils-herrmann/clean_scirepeval huggingface dataset:
Interviewing in Qualitative Research : A Methodological Discussion

This article summarizes findings from studies that employed electronic mail (e-mail) for conducting in-depth interviewing. It discusses the benefits of, and the challenges associated with, using e-mail interviewing in qualitative research. The article concludes that while a mixed mode interviewing strategy should be considered when possible, e-mail interviewing can be in many cases a viable alternative to face-to-face and telephone interviewing. A list of recommendations for carrying out effective e-mail interviews is presented.
Blind Steganography in Color Images by Double Wavelet Transform and Improved Arnold Transform

Steganography is a method which can put data into a media without a tangible impact on the cover media. In addition, the hidden data can be extracted with minimal differences. In this paper, twodimensional discrete wavelet transform is used for steganography in 24-bit color images. This steganography is of blind type that has no need for original images to extract the secret image. In this algorithm, by the help of a structural similarity and a two-dimensional correlation coefficient, it is tried to select part of sub-band cover image instead of embedding location. These sub-bands are obtained by 3levels of applying the DWT. Also to increase the steganography resistance against cropping or insert visible watermark, two channels of color image is used simultaneously. In order to raise the security, an encryption algorithm based on Arnold transform was also added to the steganography operation. Because diversity of chaos scenarios is limited in Arnold transform, it could be improved by its mirror in order to increase the diversity of key. Additionally, an ability is added to encryption algorithm that can still maintain its efficiency against image crop. Transparency of steganography image is measured by the peak signalto-noise ratio that indicates the adequate transparency of steganography process. Extracted image similarity is also measured by two-dimensional correlation coefficient with more than 99% similarity. Moreover, steganography resistance against increasing and decreasing brightness and contrast, lossy compression, cropping image, changing scale and adding noise is acceptable
3D Semantic Parsing of Large-Scale Indoor Spaces

In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e.g. rooms, etc) that are aligned into a canonical reference coordinate system. Second, the spaces are parsed into their structural and building elements (e.g. walls, columns, etc). Performing these with a strong notation of global 3D space is the backbone of our method. The alignment in the first step injects strong 3D priors from the canonical coordinate system into the second step for discovering elements. This allows diverse challenging scenarios as man-made indoor spaces often show recurrent geometric patterns while the appearance features can change drastically. We also argue that identification of structural elements in indoor spaces is essentially a detection problem, rather than segmentation which is commonly used. We evaluated our method on a new dataset of several buildings with a covered area of over 6, 000m2 and over 215 million points, demonstrating robust results readily useful for practical applications.
Multi-Dimensional Network Embedding with Hierarchical Structure

Information networks are ubiquitous in many applications. A popular way to facilitate the information in a network is to embed the network structure into low-dimension spaces where each node is represented as a vector. The learned representations have been proven to advance various network analysis tasks such as link prediction and node classification. The majority of existing embedding algorithms are designed for the networks with one type of nodes and one dimension of relations among nodes. However, many networks in the real-world complex systems have multiple types of nodes and multiple dimensions of relations. For example, an e-commerce network can have users and items, and items can be viewed or purchased by users, corresponding to two dimensions of relations. In addition, some types of nodes can present hierarchical structure. For example, authors in publication networks are associated to affiliations; and items in e-commerce networks belong to categories. Most of existing methods cannot be naturally applicable to these networks. In this paper, we aim to learn representations for networks with multiple dimensions and hierarchical structure. In particular, we provide an approach to capture independent information from each dimension and dependent information across dimensions and propose a framework MINES, which performs Multi-dImension Network Embedding with hierarchical Structure. Experimental results on a network from a real-world e-commerce website demonstrate the effectiveness of the proposed framework.
Brand management to protect brand equity : A conceptual model

Corporate and product brands are increasingly accepted as valuable intangible assets of organisations, evidence of which is apparent in the reported fi nancial value that strong brands fetch when traded in the mergers and acquisitions markets. However, while much attention is paid to conceptualising brand equity, less is paid to how brands should be managed and delivered in order to create and safeguard brand equity. In this article we develop a conceptual model of corporate brand management for creating and safeguarding brand equity. We argue that while legal protection of the brand is important, by itself it is insuffi cient to protect brand equity in the long term. We suggest that brand management ought to play an important role in safeguarding brand equity and propose a three-stage conceptual model for building and sustaining brand equity comprising: (1) adopting a brandorientation mindset, (2) developing internal branding capabilities, and (3) consistent delivery of the brand. We put forward propositions, which, taken together, form a theory of brand management for building and safeguarding brand equity. We illustrate the theory using 14 cases of award-winning service companies. Their use serves as a demonstration of how our model applies to brand management
A general framework for blockchain analytics

Modern cryptocurrencies exploit decentralised blockchains to record a public and unalterable history of transactions. Besides transactions, further information is stored for different, and often undisclosed, purposes, making the blockchains a rich and increasingly growing source of valuable information, in part of difficult interpretation. Many data analytics have been developed, mostly based on specifically designed and ad-hoc engineered approaches. We propose a general-purpose framework, seamlessly supporting data analytics on both Bitcoin and Ethereum --- currently the two most prominent cryptocurrencies. Such a framework allows us to integrate relevant blockchain data with data from other sources, and to organise them in a database, either SQL or NoSQL. Our framework is released as an open-source Scala library. We illustrate the distinguishing features of our approach on a set of significant use cases, which allow us to empirically compare ours to other competing proposals, and evaluate the impact of the database choice on scalability.
Human Action Generation with Generative Adversarial Networks

Inspired by the recent advances in generative models, we introduce a human action generation model in order to generate a consecutive sequence of human motions to formulate novel actions. We propose a framework of an autoencoder and a generative adversarial network (GAN) to produce multiple and consecutive human actions conditioned on the initial state and the given class label. The proposed model is trained in an end-to-end fashion, where the autoencoder is jointly trained with the GAN. The model is trained on the NTU RGB+D dataset and we show that the proposed model can generate different styles of actions. Moreover, the model can successfully generate a sequence of novel actions given different action labels as conditions. The conventional human action prediction and generation models lack those features, which are essential for practical applications.
Improvements in Tone Pronunciation Scoring for Strongly Accented Mandarin Speech

This paper discusses a tone pronunciation scoring system of Mandarin. It recognizes tones of syllables by using GMM model and uses the recognition results for tone assessment. Initially, experiment results are bad on strongly accented speech. There are two reasons: one is that the inaccurate force-alignment leads to incomplete F0 contours; the other is due to the special pattern of F0 contours. We propose several measures to the problems. The first is to make the extraction of F0 contour independent of the force-alignment. The second is to base the scoring on GMM posterior probabilities. The third is to use the same accented speech to train the GMM model. And the last is to train the fractionized bi-tone GMM models to cover tone changes in the multiplecharacter words. After these measures are taken, the tone scoring correct rate is improved from 60.2% to 83.3%.
DeepSymmetry: Joint Symmetry and Depth Estimation using Deep Neural Networks

Due to the abundance of 2D product images from the internet, developing efficient and scalable algorithms to recover the missing depth information is central to many applications. Recent works have addressed the single-view depth estimation problem by utilizing convolutional neural networks. In this paper, we show that exploring symmetry information, which is ubiquitous in man made objects, can significantly boost the quality of such depth predictions. Specifically, we propose a new convolutional neural network architecture to first estimate dense symmetric correspondences in a product image and then propose an optimization which utilizes this information explicitly to significantly improve the quality of single-view depth estimations. We have evaluated our approach extensively, and experimental results show that this approach outperforms state-of-the-art depth estimation techniques.
Recent Developments of Magnetoresistive Sensors for Industrial Applications

The research and development in the field of magnetoresistive sensors has played an important role in the last few decades. Here, the authors give an introduction to the fundamentals of the anisotropic magnetoresistive (AMR) and the giant magnetoresistive (GMR) effect as well as an overview of various types of sensors in industrial applications. In addition, the authors present their recent work in this field, ranging from sensor systems fabricated on traditional substrate materials like silicon (Si), over new fabrication techniques for magnetoresistive sensors on flexible substrates for special applications, e.g., a flexible write head for component integrated data storage, micro-stamping of sensors on arbitrary surfaces or three dimensional sensing under extreme conditions (restricted mounting space in motor air gap, high temperatures during geothermal drilling).
Word Vector Model Descriptions In Depth :

Many state of the art NLP algorithms currently use deep learning techniques. A 2014 paper by Goodfellow et al demonstrates that, at least in image classification, error rates from deep learning can be improved by adversarial training. Generating adversarial examples relies on having high­dimensional input spaces and many output labels. We present two methods of generating adversarial examples, and also introduce a new loss function for training word vectors in a CBOW model. Using QVEC as a metric for our word vectors, these techniques both improve on the vanilla CBOW model, and these methods can be used in conjunction with other generalization techniques such as dropout and early stopping.
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

This thesis investigates artificial agents learning to make strategic decisions in imperfect-information games. In particular, we introduce a novel approach to reinforcement learning from self-play. We introduce Smooth UCT, which combines the game-theoretic notion of fictitious play with Monte Carlo Tree Search (MCTS). Smooth UCT outperformed a classic MCTS method in several imperfect-information poker games and won three silver medals in the 2014 Annual Computer Poker Competition. We develop Extensive-Form Fictitious Play (XFP) that is entirely implemented in sequential strategies, thus extending this prominent game-theoretic model of learning to sequential games. XFP provides a principled foundation for self-play reinforcement learning in imperfect-information games. We introduce Fictitious Self-Play (FSP), a class of sample-based reinforcement learning algorithms that approximate XFP. We instantiate FSP with neuralnetwork function approximation and deep learning techniques, producing Neural FSP (NFSP). We demonstrate that (approximate) Nash equilibria and their representations (abstractions) can be learned using NFSP end to end, i.e. interfacing with the raw inputs and outputs of the domain. NFSP approached the performance of state-of-the-art, superhuman algorithms in Limit Texas Hold’em an imperfect-information game at the absolute limit of tractability using massive computational resources. This is the first time that any reinforcement learning algorithm, learning solely from game outcomes without prior domain knowledge, achieved such a feat.
Tracking onscreen gender and role bias over time

Film and television play an important role in popular culture. Their study, however, often requires watching and annotating video, a time-consuming process too expensive to run at scale. In this paper we study the evolution of different roles over time at a large scale by using media database cast lists. In particular, we focus on the gender distribution of those roles and how this changes over time. We compare real-life employment gender distributions to our web-mediated onscreen gender data and also investigate how gender role biases differ between film and television. We propose that these methodologies are a useful complement to traditional analysis and allow researchers to explore onscreen gender depictions using online evidence.
SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification

Many current successful Person Re-Identification(ReID) methods train a model with the softmax loss function to classify images of different persons and obtain the feature vectors at the same time. However, the underlying feature embedding space is ignored. In this paper, we use a modified softmax function, termed Sphere Softmax, to solve the classification problem and learn a hypersphere manifold embedding simultaneously. A balanced sampling strategy is also introduced. Finally, we propose a convolutional neural network called SphereReID adopting Sphere Softmax and training a single model end-to-end with a new warming-up learning rate schedule on four challenging datasets including Market-1501, DukeMTMC-reID, CHHK-03, and CUHK-SYSU. Experimental results demonstrate that this single model outperforms the state-of-the-art methods on all four datasets without fine-tuning or reranking. For example, it achieves 94.4% rank-1 accuracy on Market-1501 and 83.9% rank-1 accuracy on DukeMTMC-reID. The code and trained weights of our model will be released.
Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex.

Studies of the neurobiological processes underlying drug addiction primarily have focused on limbic subcortical structures. Here the authors evaluated the role of frontal cortical structures in drug addiction.



An integrated model of drug addiction that encompasses intoxication, bingeing, withdrawal, and craving is proposed. This model and findings from neuroimaging studies on the behavioral, cognitive, and emotional processes that are at the core of drug addiction were used to analyze the involvement of frontal structures in drug addiction.



The orbitofrontal cortex and the anterior cingulate gyrus, which are regions neuroanatomically connected with limbic structures, are the frontal cortical areas most frequently implicated in drug addiction. They are activated in addicted subjects during intoxication, craving, and bingeing, and they are deactivated during withdrawal. These regions are also involved in higher-order cognitive and motivational functions, such as the ability to track, update, and modulate the salience of a reinforcer as a function of context and expectation and the ability to control and inhibit prepotent responses.



These results imply that addiction connotes cortically regulated cognitive and emotional processes, which result in the overvaluing of drug reinforcers, the undervaluing of alternative reinforcers, and deficits in inhibitory control for drug responses. These changes in addiction, which the authors call I-RISA (impaired response inhibition and salience attribution), expand the traditional concepts of drug dependence that emphasize limbic-regulated responses to pleasure and reward.
National academy of clinical biochemistry laboratory medicine practice guidelines: recommendations for the use of laboratory tests to support poisoned patients who present to the emergency department.

Exposure to drugs and toxins is a major cause for patients' visits to the emergency department (ED).



Recommendations for the use of clinical laboratory tests were prepared by an expert panel of analytical toxicologists and ED physicians specializing in clinical toxicology. These recommendations were posted on the world wide web and presented in open forum at several clinical chemistry and clinical toxicology meetings.



A menu of important stat serum and urine toxicology tests was prepared for clinical laboratories who provide clinical toxicology services. For drugs-of-abuse intoxication, most ED physicians do not rely on results of urine drug testing for emergent management decisions. This is in part because immunoassays, although rapid, have limitations in sensitivity and specificity and chromatographic assays, which are more definitive, are more labor-intensive. Ethyl alcohol is widely tested in the ED, and breath testing is a convenient procedure. Determinations made within the ED, however, require oversight by the clinical laboratory. Testing for toxic alcohols is needed, but rapid commercial assays are not available. The laboratory must provide stat assays for acetaminophen, salicylates, co-oximetry, cholinesterase, iron, and some therapeutic drugs, such as lithium and digoxin. Exposure to other heavy metals requires laboratory support for specimen collection but not for emergent testing.



Improvements are needed for immunoassays, particularly for amphetamines, benzodiazepines, opioids, and tricyclic antidepressants. Assays for new drugs of abuse must also be developed to meet changing abuse patterns. As no clinical laboratory can provide services to meet all needs, the National Academy of Clinical Biochemistry Committee recommends establishment of regional centers for specialized toxicology testing.
Artificial Intelligence for Interstellar Travel

The large distances involved in interstellar travel require a high degree of spacecraft autonomy, realized by artificial intelligence. The breadth of tasks artificial intelligence could perform on such spacecraft involves maintenance, data collection, designing and constructing an infrastructure using in-situ resources. Despite its importance, existing publications on artificial intelligence and interstellar travel are limited to cursory descriptions where little detail is given about the nature of the artificial intelligence. This article explores the role of artificial intelligence for interstellar travel by compiling use cases, exploring capabilities, and proposing typologies, system and mission architectures. Estimations for the required intelligence level for specific types of interstellar probes are given, along with potential system and mission architectures, covering those proposed in the literature but also presenting novel ones. Finally, a generic design for an interstellar probes with an AI payload is proposed. Given current levels of increase in computational power, a spacecraft with a similar computational power as the human brain would have a mass from dozens to hundreds of tons in a 2050 – 2060 timeframe. Given that the advent of the first interstellar missions and artificial general intelligence are estimated to be by mid-21st century, a more in-depth exploration of the relationship between the two should be attempted, focusing on neglected areas such as protecting the artificial intelligence payload from radiation in interstellar space and the role of artificial intelligence in self-replication.
Mathematical Modelling of Wind Turbine in a Wind Energy Conversion System : Power Coefficient Analysis

The world is increasingly going green in its energy use. Wind power is a green renewable source of energy that can compete effectively with fossil fuel as a generator of power in the electricity market. For this effective competion, the production cost must be comparable to that of fossil fuels or other sources of energy. The initial capital investment in wind power goes to machine and the supporting infrastructure. Any factors that lead to decrease in cost of energy such as turbine design, construction and operation are key to making wind power competitive as an alternative source of energy. A mathematical model of wind turbine is essential in the understanding of the behaviour of the wind turbine over its region of operation because it allows for the development of comprehensive control algorithms that aid in optimal operation of a wind turbine. Modelling enables control of wind turbine’s performance. This paper attempts to address part or whole of these general objectives of wind turbine modelling through examination of power coefficient parameter. Model results will be beneficial to designers and researchers of new generation turbines who can utilize the information to optimize the design of turbines and minimize generation costs leading 4528 A. W. Manyonge, R. M. Ochieng, F. N. Onyango and J. M. Shichikha to decrease in cost of wind energy and hence, making it an economically viable alternative source of energy.