nils-herrmann/clean_scirepeval huggingface dataset:
Distributional Smoothing with Virtual Adversarial Training

Smoothness regularization is a popular method to decrease generalization error. We propose a novel regularization technique that rewards local distributional smoothness (LDS), a KLdistance based measure of the model’s robustness against perturbation. The LDS is defined in terms of the direction to which the model distribution is most sensitive in the input space. We call the training with LDS regularization virtual adversarial training (VAT). VAT resembles the adversarial training (Goodfellow et al., 2015), but distinguishes itself in that it determines the adversarial direction from the model distribution alone, and does not use the label information. The technique is therefore applicable even to semi-supervised learning. When we applied our technique to the classification task of the permutation invariant MNIST dataset, it not only eclipsed all the models that are not dependent on generative models and pre-training, but also performed well even in comparison to the state of the art method (Rasmus et al., 2015) that uses a highly advanced generative model.
A Study of Authentication in Daily Life

We report on a wearable digital diary study of 26 participants that explores people’s daily authentication behavior across a wide range of targets (phones, PCs, websites, doors, cars, etc.) using a wide range of authenticators (passwords, PINs, physical keys, ID badges, fingerprints, etc.). Our goal is to gain an understanding of how much of a burden different kinds of authentication place on people, so that we can evaluate what kinds of improvements would most benefit them. We found that on average 25% of our participants’ authentications employed physical tokens such as car keys, which suggests that token-based authentication, in addition to password authentication, is a worthy area for improvement. We also found that our participants’ authentication behavior and opinions about authentication varied greatly, so any particular solution might not please everyone. We observed a surprisingly high (3–12%) false reject rate across many types of authentication. We present the design and implementation of the study itself, since wearable digital diary studies may prove useful for others exploring similar topics of human behavior. Finally, we provide an example use of participants’ logs of authentication events as simulation workloads for investigating the possible energy consumption of a “universal authentication” device.
Character-level Convolutional Network for Text Classification Applied to Chinese Corpus

Compared with word-level and sentence-level convolutional neural networks (ConvNets), the character-level ConvNets has a better applicability for misspellings and typos input. Due to this, recent researches for text classification mainly focus on character-level ConvNets. However, while the majority of these researches employ English corpus for the character-level text classification, few researches have been done using Chinese corpus. This research hopes to bridge this gap, exploring character-level ConvNets for Chinese corpus test classification. We have constructed a large-scale Chinese dataset, and the result shows that character-level ConvNets works better on Chinese character dataset than its corresponding pinyin format dataset, which is the general solution in previous researches. This is the first time that character-level ConvNets has been applied to Chinese character dataset for text classification problem.
Comparison of topic extraction approaches and their results

This is the last paper in the Synthesis section of this special issue on ‘Same Data, Different Results’. We first provide a framework of how to describe and distinguish approaches to topic extraction from bibliographic data of scientific publications. We then compare solutions delivered by the different topic extraction approaches in this special issue, and explore where they agree and differ. This is achieved without reference to a ground truth, since we have to assume the existence of multiple, equally important, valid perspectives and want to avoid bias through the adoption of an ad-hoc yardstick. Instead, we apply different ways to quantitatively and visually compare solutions to explore their commonalities and differences and develop hypotheses about the origin of these differences. We conclude with a discussion of future work needed to develop methods for comparison and validation of topic extraction results, and express our concern about the lack of access to non-proprietary benchmark data sets to support method development in the field of scientometrics.
A Mixture Model for Learning Multi-Sense Word Embeddings

Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.
Adaptive image contrast enhancement using generalizations of histogram equalization

This paper proposes a scheme for adaptive image-contrast enhancement based on a generalization of histogram equalization (HE). HE is a useful technique for improving image contrast, but its effect is too severe for many purposes. However, dramatically different results can be obtained with relatively minor modifications. A concise description of adaptive HE is set out, and this framework is used in a discussion of past suggestions for variations on HE. A key feature of this formalism is a "cumulation function," which is used to generate a grey level mapping from the local histogram. By choosing alternative forms of cumulation function one can achieve a wide variety of effects. A specific form is proposed. Through the variation of one or two parameters, the resulting process can produce a range of degrees of contrast enhancement, at one extreme leaving the image unchanged, at another yielding full adaptive equalization.
A functional architecture for autonomous driving

As the Technology Readiness Levels (TRLs) of self-driving vehicles increase, it is necessary to investigate the Electrical/Electronic(E/E) system architectures for autonomous driving, beyond proof-of-concept prototypes. Relevant patterns and anti-patterns need to be raised into debate and documented. This paper presents the principal components needed in a functional architecture for autonomous driving, along with reasoning for how they should be distributed across the architecture. A functional architecture integrating all the concepts and reasoning is also presented.
Fast Feature Selection for Learning to Rank

An emerging research area named Learning-to-Rank (LtR) has shown that effective solutions to the ranking problem can leverage machine learning techniques applied to a large set of features capturing the relevance of a candidate document for the user query. Large-scale search systems must however answer user queries very fast, and the computation of the features for candidate documents must comply with strict back-end latency constraints. The number of features cannot thus grow beyond a given limit, and Feature Selection (FS) techniques have to be exploited to find a subset of features that both meets latency requirements and leads to high effectiveness of the trained models. In this paper, we propose three new algorithms for FS specifically designed for the LtR context where hundreds of continuous or categorical features can be involved. We present a comprehensive experimental analysis conducted on publicly available LtR datasets and we show that the proposed strategies outperform a well-known state-of-the-art competitor.
Dietary Polyunsaturated Fatty Acids and Inflammation: The Role of Phospholipid Biosynthesis

The composition of fatty acids in the diets of both human and domestic animal species can regulate inflammation through the biosynthesis of potent lipid mediators. The substrates for lipid mediator biosynthesis are derived primarily from membrane phospholipids and reflect dietary fatty acid intake. Inflammation can be exacerbated with intake of certain dietary fatty acids, such as some ω-6 polyunsaturated fatty acids (PUFA), and subsequent incorporation into membrane phospholipids. Inflammation, however, can be resolved with ingestion of other fatty acids, such as ω-3 PUFA. The influence of dietary PUFA on phospholipid composition is influenced by factors that control phospholipid biosynthesis within cellular membranes, such as preferential incorporation of some fatty acids, competition between newly ingested PUFA and fatty acids released from stores such as adipose, and the impacts of carbohydrate metabolism and physiological state. The objective of this review is to explain these factors as potential obstacles to manipulating PUFA composition of tissue phospholipids by specific dietary fatty acids. A better understanding of the factors that influence how dietary fatty acids can be incorporated into phospholipids may lead to nutritional intervention strategies that optimize health.
Understanding disentangling in β-VAE

We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modified ELBO bound in β-VAE, as training progresses. From these insights, we propose a modification to the training regime of β-VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in β-VAE, without the previous trade-off in reconstruction accuracy.
How Reading Volume Affects both Reading Fluency and Reading Achievement

Long overlooked, reading volume is actually central to the development of reading proficiencies, especially in the development of fluent reading proficiency. Generally no one in schools monitors the actual volume of reading that children engage in. We know that the commonly used commercial core reading programs provide only material that requires about 15 minutes of reading activity daily. The remaining 75 minute of reading lessons is filled with many other activities such as completing workbook pages or responding to low-level literal questions about what has been read. Studies designed to enhance the volume of reading that children do during their reading lessons demonstrate one way to enhance reading development. Repeated readings have been widely used in fostering reading fluency but wide reading options seem to work faster and more broadly in developing reading proficiencies, including oral reading fluency.
Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics

Although online consumer reviews (OCR) have helped consumers to know about the strengths and weaknesses of different products and find the ones that best suit their needs, they introduce a challenge for businesses to analyze them because of their volume, variety, velocity and veracity. This research investigates the predictors of readership and helpfulness of OCR using a sentiment mining approach for big data analytics. Our findings show that reviews with higher levels of positive sentiment in the title receive more readerships. Sentimental reviews with neutral polarity in the text are also perceived to be more helpful. The length and longevity of a review positively influence both its readership and helpfulness. Because the current methods used for sorting OCR may bias both their readership and helpfulness, the approach used in this study can be adopted by online vendors to develop scalable automated systems for sorting and classification of big OCR data which will benefit both vendors and consumers.
Deep Packet Inspection in Perspective: Tracing its lineage and surveillance Potentials

Internet Service Providers (ISPs) are responsible for transmitting and delivering their customers’ data requests, ranging from requests for data from websites, to that from filesharing applications, to that from participants in Voice over Internet Protocol (VoIP) chat sessions. Using contemporary packet inspection and capture technologies, ISPs can investigate and record the content of unencrypted digital communications data packets. This paper explains the structure of these packets, and then proceeds to describe the packet inspection technologies that monitor their movement and extract information from the packets as they flow across ISP networks. After discussing the potency of contemporary deep packet inspection devices, in relation to their earlier packet inspection predecessors, and their potential uses in improving network operators’ network management systems, I argue that they should be identified as surveillance technologies that can potentially be incredibly invasive. Drawing on Canadian examples, I argue that Canadian ISPs are using DPI technologies to implicitly ‘teach’ their customers norms about what are ‘inappropriate’ data transfer programs, and the appropriate levels of ISP manipulation of customer data traffic.   2008.  Doctoral student in the University of Victoria’s Political Science department. Thanks to Colin Bennett, Andrew Clement, Fenwick Mckelvey and Joyce Parsons for comments.
Early-onset cannabis use and cognitive deficits: what is the nature of the association?

Individuals who initiate cannabis use at an early age, when the brain is still developing, might be more vulnerable to lasting neuropsychological deficits than individuals who begin use later in life.



We analyzed neuropsychological test results from 122 long-term heavy cannabis users and 87 comparison subjects with minimal cannabis exposure, all of whom had undergone a 28-day period of abstinence from cannabis, monitored by daily or every-other-day observed urine samples. We compared early-onset cannabis users with late-onset users and with controls, using linear regression controlling for age, sex, ethnicity, and attributes of family of origin.



The 69 early-onset users (who began smoking before age 17) differed significantly from both the 53 late-onset users (who began smoking at age 17 or later) and from the 87 controls on several measures, most notably verbal IQ (VIQ). Few differences were found between late-onset users and controls on the test battery. However, when we adjusted for VIQ, virtually all differences between early-onset users and controls on test measures ceased to be significant.



Early-onset cannabis users exhibit poorer cognitive performance than late-onset users or control subjects, especially in VIQ, but the cause of this difference cannot be determined from our data. The difference may reflect (1). innate differences between groups in cognitive ability, antedating first cannabis use; (2). an actual neurotoxic effect of cannabis on the developing brain; or (3). poorer learning of conventional cognitive skills by young cannabis users who have eschewed academics and diverged from the mainstream culture.
HaarHOG: Improving the HOG Descriptor for Image Classification

The Histograms of Oriented Gradients (HOG) descriptor represents shape information by storing the local gradients in an image. The Haar wavelet transform is a simple yet powerful technique that can separately enhance the horizontal and vertical local features in an image. In this paper, we enhance the HOG descriptor by subjecting the image to the Haar wavelet transform and then computing HOG from the result in a manner that enriches the shape information encoded in the descriptor. First, we define the novel HaarHOG descriptor for grayscale images and extend this idea for color images. Second, we compare the image recognition performance of the HaarHOG descriptor with the traditional HOG descriptor in four different color spaces and grayscale. Finally, we compare the image classification performance of the HaarHOG descriptor with some popular descriptors used by other researchers on four grand challenge datasets.
An artificial neural network model for river flow forecasting a comparison between ANN and ARIMA

Careful modeling and precise river flow forecast is fundamental and is the basis for a wide range of problems related to the management of river hydrologic systems. Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables specially MLP network. The Feed forward Multi-layer perceptron (MLP) with back propagation algorithm has applied in this research. The selected ANN model were used to train and forecast the daily flows into the Silveh Dam on the Lavin River in IRAN, between years 2002–2012. In order to compare the performance of MLP, Box-Jenkins model was used. The results indicated that, in general, ANN approaches yielded reasonably good forecasts for 7-day lead times in comparison with the Auto Regressive Integrated Moving Average (ARIMA) model.  River flow forecasting; ARIMA; ANN; Feed forward multi-layer perceptron (MLP)
Graph-Based Data Mining

THE LARGE AMOUNT OF DATA collected today is quickly overwhelming researchers’ abilities to interpret the data and discover interesting patterns in it. In response to this problem, researchers have developed techniques and systems for discovering concepts in databases. 1–3Much of the collected data, however, has an explicit or implicit structural component (spatial or temporal), which few discovery systems are designed to handle. 4 So, in addition to the need to accelerate data mining of large databases, there is an urgent need to develop scalable tools for discovering concepts in structural databases. One method for discovering knowledge in structural data is the identification of common substructures within the data. Substructure discovery is the process of identifying concepts describing interesting and repetitive substructures within structural data. The discovered substructure concepts allow abstraction from the detailed data structure and provide relevant attributes for interpreting the data. The substructure discovery method is the basis of Subdue, which performs data mining on databases represented as graphs. The system performs two key data-mining techniques: unsupervised pattern discovery and supervised concept learning from examples. Our test applications have demonstrated the scalability and effectiveness of these techniques on a variety of structural databases. Unsupervised concept discovery
Challenges to Technological Advancement in Economically Weak Countries : An Assessment of the Nigerian Educational Situation T World

Nigeria is considered as one of the many countries in sub-Saharan Africa with a weak economy and gross deficiencies in technology and engineering. Available data from international monitoring and regulatory organizations show that technology is pivotal to determining the economic strengths of nations all over the world. Education is critical to technology acquisition, development, dissemination and adaptation. Thus, this paper seeks to critically assess and discuss issues and challenges facing technological advancement in Nigeria, particularly in the education sector, and also proffers solutions to resuscitate the Nigerian education system towards achieving national technological and economic sustainability such that Nigeria can compete favourably with other technologicallydriven economies of the world in the not-too-distant future. Economically weak countries, education, globalization and competition, technological advancement.
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference

Convolutional neural networks (CNNs) work well on large datasets. But labelled data is hard to collect, and in some applications larger amounts of data are not available. The problem then is how to use CNNs with small data – as CNNs overfit quickly. We present an efficient Bayesian CNN, offering better robustness to over-fitting on small data than traditional approaches. This is by placing a probability distribution over the CNN’s kernels. We approximate our model’s intractable posterior with Bernoulli variational distributions, requiring no additional model parameters. On the theoretical side, we cast dropout network training as approximate inference in Bayesian neural networks. This allows us to implement our model using existing tools in deep learning with no increase in time complexity, while highlighting a negative result in the field. We show a considerable improvement in classification accuracy compared to standard techniques and improve on published state-of-theart results for CIFAR-10.
The in fl uence of social media interactions on consumer – brand relationships : A three-country study of brand perceptions and marketing behaviors

Companies are increasingly allocating more of their marketing spending to social media programs. Yet there is little research about how social media use is associated with consumer–brand relationships. We conducted three studies to explore how individual and national differences influence the relationship between social media use and customer brand relationships. The first study surveyed customers in France, the U.K. and U.S. and compared those who engage with their favorite brands via social media with those who do not. The findings indicated that social media use was positively related with brand relationship quality and the effect was more pronounced with high anthropomorphism perceptions (the extent to which consumers' associate human characteristics with brands). Two subsequent experiments further validated these findings and confirmed that cultural differences, specifically uncertainty avoidance, moderated these results. We obtained robust and convergent results from survey and experimental data using both student and adult consumer samples and testing across three product categories (athletic shoes, notebook computers, and automobiles). The results offer cross-national support for the proposition that engaging customers via social media is associated with higher consumer–brand relationships and word of mouth communications when consumers anthropomorphize the brand and they avoid uncertainty.
End-to-End Open-Domain Question Answering with BERTserini

We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answering and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion. We report large improvements over previous results on a standard benchmark test collection, showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans.
SOCIAL ROLE THEORY OF UNETHICAL LEADERSHIP 1 The Social Role Theory of Unethical Leadership

Challenging the standard reasoning regarding leaders’ ethical failures, we argue that a potent contributor to these failures is the social role expectations of leaders. We maintain that leaders’ central role expectation of goal achievement contributes to the over-valuing of group goals and greater moral permissibility of the means used to achieve these goals. In studies 1 and 2 we demonstrated that the role of leader, relative to group member, is associated with an increased appraisal of group goals which is predicted by the leaders’ role expectations and not driven by the psychological effects of power. Next, we experimentally demonstrated the importance of both role expectations of leadership and group goal importance in leaders’ justification to engage in morally questionable behavior to achieve group goals. Finally, we supported the social role predictions in a laboratory experiment by assigning people to roles and assessing goal importance and unethical decision-making and behaviors.