nils-herrmann/clean_scirepeval huggingface dataset:
The functional phonological unit of Japanese-English bilinguals is language dependent : Evidence from masked onset and mora priming effects 1

Speech production research has shown that Japanese monolingual speakers use mora-sized phonological units, not phoneme-sized units, when phonologically encoding Japanese words. Recent bilingual research has indicated that proficient Japanese-English bilinguals nevertheless use phoneme-sized units when phonologically encoding English words, suggesting that use of a phonological unit that is smaller than that of their L1 develops with increasing proficiency in English. The purpose of the present research was to determine whether proficient Japanese-English bilinguals also begin to use the smaller, phoneme-sized units when producing Japanese words. In a masked priming naming task, proficient Japanese-English bilinguals produced a significant masked onset priming effect for English words, confirming that they do use phoneme-sized units when phonologically encoding in English (L2). These bilinguals, however, showed only mora-based facilitation for Japanese words in an experiment involving only Japanese words. These results suggest that proficient bilinguals use different unit sizes depending on the language being produced, and that for bilinguals whose L1 and L2 have different unit sizes, the phonological encoding process is at least somewhat different in their two languages.
Improved Training of Wasserstein GANs

Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only poor samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models with continuous generators. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.
Chapter 11 Transfer Learning

Human learners appear to have inherent ways to transfer knowledge between tasks. That is, we recognize and apply relevant knowledge from previous learning experiences when we encounter new tasks. The more related a new task is to our previous experience, the more easily we can master it. Common machine learning algorithms, in contrast, traditionally address isolated tasks. Transfer learning attempts to improve on traditional machine learning by transferring knowledge learned in one or more source tasks and using it to improve learning in a related target task . Techniques that enable knowledge transfer represent progress towards making machine learning as efficient as human learning. This chapter provides an introduction to the goals, settings, and challenges of transfer learning. It surveys current research in this area, giving an overview of the state of the art and outlining the open problems.
Learning Representations for Automatic Colorization

We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturally appear according to multimodal color distributions, we train our model to predict per-pixel color histograms. This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation. On both fully and partially automatic colorization tasks, we outperform existing methods. We also explore colorization as a vehicle for self-supervised visual representation learning.  Our automatic colorization of grayscale input; more examples in Figs. 3 and 4.
PixelCNN Models with Auxiliary Variables for Natural Image Modeling

We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables. Subsequently, we describe two new generative image models that exploit different image transformations as auxiliary variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of the proposed models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models.
Learning End-to-End Goal-Oriented Dialog with Multiple Answers

In a dialog, there can be multiple valid next utterances at any point. The present end-toend neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks 1 by introducing multiple valid next utterances to the originalbAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-toend neural methods from 81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog tasks.
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is unclear if such systems can be used for tasks without large amounts of training data. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., POS tagging on Penn Treebank) is used to improve performance on a target task with fewer available annotations (e.g., POS tagging for microblogs). We examine the effects of transfer learning for deep hierarchical recurrent networks across domains, applications, and languages, and show that significant improvement can often be obtained. These improvements lead to improvements over the current state-ofthe-art on several well-studied
Autobank: a semi-automatic annotation tool for developing deep Minimalist Grammar treebanks

This paper presents Autobank, a prototype tool for constructing a widecoverage Minimalist Grammar (MG) (Stabler, 1997), and semi-automatically converting the Penn Treebank (PTB) into a deep Minimalist treebank. The front end of the tool is a graphical user interface which facilitates the rapid development of a seed set of MG trees via manual reannotation of PTB preterminals with MG lexical categories. The system then extracts various dependency mappings between the source and target trees, and uses these in concert with a non-statistical MG parser to automatically reannotate the rest of the corpus. Autobank thus enables deep treebank conversions (and subsequent modifications) without the need for complex transduction algorithms accompanied by cascades of ad hoc rules; instead, the locus of human effort falls directly on the task of grammar construction itself.
Rumor detection on twitter

Twitter is useful in a situation of disaster for communication, announcement, request for rescue and so on. On the other hand, it causes a negative by-product, spreading rumors. This paper describe how rumors have spread after a disaster of earthquake, and discuss how can we deal with them. We first investigated actual instances of rumor after the disaster. And then we attempted to disclose characteristics of those rumors. Based on the investigation we developed a system which detects candidates of rumor from twitter and then evaluated it. The result of experiment shows the proposed algorithm can find rumors with acceptable accuracy.
Smart Wheelchairs - State of the Art in an Emerging Market

Due to the shift of the age structure in today’s industrial populations, the demands of the handicapped and the elderly are more and more being recognized by politics, industry, and science. The recent development in research areas such as computer science, robotics, Artificial Intelligence, or sensor technology allows to significantly broaden the range of possible applications that support handicapped or elderly people in their daily lives. This article presents the state of the art of the most popular assistive device: the smart wheelchair.
Plastics, the environment and human health: current consensus and future trends.

Plastics have transformed everyday life; usage is increasing and annual production is likely to exceed 300 million tonnes by 2010. In this concluding paper to the Theme Issue on Plastics, the Environment and Human Health, we synthesize current understanding of the benefits and concerns surrounding the use of plastics and look to future priorities, challenges and opportunities. It is evident that plastics bring many societal benefits and offer future technological and medical advances. However, concerns about usage and disposal are diverse and include accumulation of waste in landfills and in natural habitats, physical problems for wildlife resulting from ingestion or entanglement in plastic, the leaching of chemicals from plastic products and the potential for plastics to transfer chemicals to wildlife and humans. However, perhaps the most important overriding concern, which is implicit throughout this volume, is that our current usage is not sustainable. Around 4 per cent of world oil production is used as a feedstock to make plastics and a similar amount is used as energy in the process. Yet over a third of current production is used to make items of packaging, which are then rapidly discarded. Given our declining reserves of fossil fuels, and finite capacity for disposal of waste to landfill, this linear use of hydrocarbons, via packaging and other short-lived applications of plastic, is simply not sustainable. There are solutions, including material reduction, design for end-of-life recyclability, increased recycling capacity, development of bio-based feedstocks, strategies to reduce littering, the application of green chemistry life-cycle analyses and revised risk assessment approaches. Such measures will be most effective through the combined actions of the public, industry, scientists and policymakers. There is some urgency, as the quantity of plastics produced in the first 10 years of the current century is likely to approach the quantity produced in the entire century that preceded.
Values-based service for sustainable business-Lessons from the retailers IKEA , Starbucks , H & M and Body Shop

The aim of this paper is to explore the role of values-based service for sustainable business. The two basic questions addressed are: What is ‘values-based service’? How can values create value for customers and other stakeholders? Design/ methodology/ approach This paper is based on extensive empirical studies focusing on the role of values at the corporate, country and store levels in the retail company IKEA and a comparison of the results with data from Starbucks, H&M and Body Shop. The theoretical point of departure is a business model based on the service-dominant logic (SDL) on the one hand and control through values focusing on social and environmental values forming the basis for a sustainable business.  Based on a comparative, inductive empirical analysis, five principles for a sustainable values-based service business were identified: (1) Strong company values drive customer value, (2) CSR as a strategy for sustainable service business, (3) Values-based service experience for co-creating value with customers, (4) Values-based service brand and communication for values resonance and (5) Values-based service leadership for living the values. A company built on an entrepreneurial business model often has the original entrepreneur’s values and leadership style as a model for future generations of leaders. However, the challenge for subsequent leaders is to develop these values and communicate what they mean today. Orginality/ value We suggest a new framework for managing values-based service to create a sustainable business based on values resonance.
Smartphone usage and increased risk of mobile phone addiction: A concurrent study

This study aimed to study the mobile phone addiction behavior and awareness on electromagnetic radiation (EMR) among a sample of Malaysian population.



This online study was conducted between December 2015 and 2016. The study instrument comprised eight segments, namely, informed consent form, demographic details, habituation, mobile phone fact and EMR details, mobile phone awareness education, psychomotor (anxious behavior) analysis, and health issues. Frequency of the data was calculated and summarized in the results.



Totally, 409 respondents participated in the study. The mean age of the study participants was 22.88 (standard error = 0.24) years. Most of the study participants developed dependency with smartphone usage and had awareness (level 6) on EMR. No significant changes were found on mobile phone addiction behavior between the participants having accommodation on home and hostel.



The study participants were aware about mobile phone/radiation hazards and many of them were extremely dependent on smartphones. One-fourth of the study population were found having feeling of wrist and hand pain because of smartphone use which may lead to further physiological and physiological complication.
Overview of the face recognition grand challenge

Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.
Botnet Detection Based on Anomaly and Community Detection

We introduce a novel two-stage approach for the important cybersecurity problem of detecting the presence of a botnet and identifying the compromised nodes (the bots), ideally before the botnet becomes active. The first stage detects anomalies by leveraging large deviations of an empirical distribution. We propose two approaches to create the empirical distribution: 1) a flow-based approach estimating the histogram of quantized flows and 2) a graph-based approach estimating the degree distribution of node interaction graphs, encompassing both Erdős-Rényi graphs and scale-free graphs. The second stage detects the bots using ideas from social network community detection in a graph that captures correlations of interactions among nodes over time. Community detection is performed by maximizing a modularity measure in this graph. The modularity maximization problem is nonconvex. We propose a convex relaxation, an effective randomization algorithm, and establish sharp bounds on the suboptimality gap. We apply our method to real-world botnet traffic and compare its performance with other methods.
A New View into the Regulation of Purine Metabolism: The Purinosome.

Other than serving as building blocks for DNA and RNA, purine metabolites provide a cell with the necessary energy and cofactors to promote cell survival and proliferation. A renewed interest in how purine metabolism may fuel cancer progression has uncovered a new perspective into how a cell regulates purine need. Under cellular conditions of high purine demand, the de novo purine biosynthetic enzymes cluster near mitochondria and microtubules to form dynamic multienzyme complexes referred to as 'purinosomes'. In this review, we highlight the purinosome as a novel level of metabolic organization of enzymes in cells, its consequences for regulation of purine metabolism, and the extent that purine metabolism is being targeted for the treatment of cancers.
Real-Time Gaze Estimator Based on Driver's Head Orientation for Forward Collision Warning System

This paper presents a vision-based real-time gaze zone estimator based on a driver's head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a driver's yaw. Third, we propose new features-the normalized mean and the standard deviation of the horizontal edge projection histogram-to reliably and rapidly estimate a driver's pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200 000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.
Neural networks and physical systems with emergent collective computational abilities.

Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
Formative Essay Feedback Using Predictive Scoring Models

A major component of secondary education is learning to write effectively, a skill which is bolstered by repeated practice with formative guidance. However, providing focused feedback to every student on multiple drafts of each essay throughout the school year is a challenge for even the most dedicated of teachers. This paper first establishes a new ordinal essay scoring model and its state of the art performance compared to recent results in the Automated Essay Scoring field. Extending this model, we describe a method for using prediction on realistic essay variants to give rubric-specific formative feedback to writers. This method is used in Revision Assistant, a deployed data-driven educational product that provides immediate, rubric-specific, sentence-level feedback to students to supplement teacher guidance. We present initial evaluations of this feedback generation, both offline and in deployment.
GPU-Based Spherical Light Field Rendering with Per-Fragment Depth Correction

Image-based rendering techniques are a powerful alternative to traditional polygon-based computer graphics. This paper presents a novel light field rendering technique which performs per-pixel depth correction of rays for high-quality reconstruction. Our technique stores combined RGB and depth values in a parabolic 2D texture for every light field sample acquired at discrete positions on a uniform spherical setup. Image synthesis is implemented on the GPU as a fragment program which extracts the correct image information from adjacent cameras for each fragment by applying per-pixel depth correction of rays. We show that the presented image-based rendering technique provides a significant improvement compared to previous approaches. We explain two different rendering implementations which make use of a uniform parametrisation to minimise disparity problems and ensure full six degrees of freedom for virtual view synthesis. While one rendering algorithm implements an iterative refinement approach for rendering light fields with per pixel depth correction, the other approach employs a raycaster, which provides superior rendering quality at moderate frame rates. GPU based per-fragment depth correction of rays, used in both implementations, helps reducing ghosting artifacts to a non-noticeable amount and provides a rendering technique that performs without exhaustive pre-processing for 3D object reconstruction and without real-time ray-object intersection calculations at rendering time.
Evaluating Effect, Composite, and Causal Indicators in Structural Equation Models

Although the literature on alternatives to effect indicators is growing, there has been little attention given to evaluating causal and composite (formative) indicators. This paper provides an overview of this topic by contrasting ways of assessing the validity of effect and causal indicators in structural equation models (SEMs). It also draws a distinction between composite (formative) indicators and causal indicators and argues that validity is most relevant to the latter. Sound validity assessment of indicators is dependent on having an adequate overall model fit and on the relative stability of the parameter estimates for the latent variable and indicators as they appear in different models. If the overall fit and stability of estimates are adequate, then a researcher can assess validity using the unstandardized and standardized validity coefficients and the unique validity variance estimate. With multiple causal indicators or with effect indicators influenced by multiple latent variables, collinearity diagnostics are useful. These results are illustrated with a number of correctly and incorrectly specified hypothetical models.