nils-herrmann/clean_scirepeval huggingface dataset:
CROWDFUNDING: A REVIEW AND RESEARCH AGENDA

Crowdfunding is an alternative model for project financing, whereby a large and dispersed audience participates through relatively small financial contributions, in exchange for physical, financial or social rewards. It is usually done via Internet-based platforms that act as a bridge between the crowd and the projects. Over the past few years, academics have explored this topic, both empirically and theoretically. However, the mixed findings and array of theories used have come to warrant a critical review of past works. To this end, we perform a systematic review of the literature on crowdfunding and seek to extract (1) the key management theories that have been applied in the context of crowdfunding and how these have been extended, and (2) the principal factors contributing to success for the different crowdfunding models, where success entails both fundraising and timely repayment. In the process, we offer a comprehensive definition of crowdfunding and identify avenues for future research based on the gaps and conflicting results in the literature.
Storage class memory and databases: Opportunities and challenges

Storage Class Memory (SCM) is emerging as a viable solution to lift DRAM’s scalability limits, both in capacity and energy consumption. Indeed, SCM combines the economic characteristics, non-volatility, anddensity of traditional storage media with the low latency and byteaddressability of DRAM. In this paper we survey research works on how SCM can be leveraged in databases and explore different solutions ranging from using SCM as disk replacement, to single-level storage architectures, where SCM is used as universal memory (i.e., as memory and storage at the same time), together with the challenges that stem from these opportunities. Finally, we synthesize our findings into recommendations on how to exploit the full potential of SCM in next-generation database architectures.
Question Answering in Restricted Domains: An Overview

Automated question answering has been a topic of research and development since the earliest AI applications. Computing power has increased since the first such systems were developed, and the general methodology has changed from the use of hand-encoded knowledge bases about simple domains to the use of text collections as the main knowledge source over more complex domains. Still, many research issues remain. The focus of this article is on the use of restricted domains for automated question answering. The article contains a historical perspective on question answering over restricted domains and an overview of the current methods and applications used in restricted domains. A main characteristic of question answering in restricted domains is the integration of domain-specific information that is either developed for question answering or that has been developed for other purposes. We explore the main methods developed to leverage this domain-specific information.
Accurate and compact large vocabulary speech recognition on mobile devices

In this paper we describe the development of an accurate, smallfootprint, large vocabulary speech recognizer for mobile devices. To achieve the best recognition accuracy, state-of-the-art deep neural networks (DNNs) are adopted as acoustic models. A variety of speedup techniques for DNN score computation are used to enable real-time operation on mobile devices. To reduce the memory and disk usage, on-the-fly language model (LM) rescoring is performed with a compressed n-gram LM. We were able to build an accurate and compact system that runs well below real-time on a Nexus 4 Android phone.
Discourses of Writing and Learning to Write

This paper presents a meta-analysis of theory and research about writing and writing pedagogy, identifying six discourses – configurations of beliefs and practices in relation to the teaching of writing. It introduces and explains a framework for the analysis of educational data about writing pedagogy inwhich the connections are drawn across viewsof language, viewsofwriting, views of learning towrite,approaches to the teaching of writing, and approaches to the assessment of writing. The framework can be used for identifying discourses of writing in data such as policy documents, teaching and learning materials, recordings of pedagogic practice, interviews and focus groups with teachers and learners, and media coverage of literacy education. The paper also proposes that, while there are tensions and contradictions among these discourses, a comprehensive writing pedagogy might integrate teaching approaches from all six.
Neural Network based Extreme Classification and Similarity Models for Product Matching

Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5% in term of accuracy and are capable of extremely efficient training and inference.
Fully Convolutional Adaptation Networks for Semantic Segmentation

The recent advances in deep neural networks have convincingly demonstrated high capability in learning vision models on large datasets. Nevertheless, collecting expert labeled datasets especially with pixel-level annotations is an extremely expensive process. An appealing alternative is to render synthetic data (e.g., computer games) and generate ground truth automatically. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. In this paper, we facilitate this issue from the perspectives of both visual appearance-level and representation-level domain adaptation. The former adapts source-domain images to appear as if drawn from the "style" in the target domain and the latter attempts to learn domain-invariant representations. Specifically, we present Fully Convolutional Adaptation Networks (FCAN), a novel deep architecture for semantic segmentation which combines Appearance Adaptation Networks (AAN) and Representation Adaptation Networks (RAN). AAN learns a transformation from one domain to the other in the pixel space and RAN is optimized in an adversarial learning manner to maximally fool the domain discriminator with the learnt source and target representations. Extensive experiments are conducted on the transfer from GTA5 (game videos) to Cityscapes (urban street scenes) on semantic segmentation and our proposal achieves superior results when comparing to state-of-the-art unsupervised adaptation techniques. More remarkably, we obtain a new record: mIoU of 47.5% on BDDS (drive-cam videos) in an unsupervised setting.
SPLINES : A PERFECT FIT FOR SIGNAL / IMAGE PROCESSING

1.  Finding a general mechanism for switching between the continuous and discrete signal domains is one of the fundamental issues in signal processing. It is a question that arises naturally during the acquisition process where an analog signal or an image is to be converted into a sequence of numbers (discrete representation). Conversely, the need for a continuous signal representation comes up every time one wishes to implement numerically an operator that is initially defined in the continuous domain. Typical examples in image processing are the detection of edges through the computation of gradients (spatial derivatives), and geometric transformations such as rotations and scaling (interpolation). The textbook approach to those problems is provided by Shannon's sampling theory which describes an equivalence between a bandlimited function and its equidistant samples taken at a frequency that is superior or equal to the Nyquist rate [76]. Even though this theory has had an enormous impact on the field, it has a number of problems associated with it. First, it relies on the use of ideal filters which are devices not commonly found in nature. Second, the bandlimited hypothesis is in contradiction with the idea of a finite (or finite duration) signal. Third, the bandlimiting operation tends to generate Gibbs oscillations which can be visually disturbing in images. Lastly, the underlying cardinal basis function (sinc(x)) has a very slow decay which makes computations in the signal domain very inefficient. While the first two problems can be dealt with by using approximations and introducing concepts such as an essential bandwidth and an essential time duration [78], there is no way to address the last two issues other than changing basis functions. Our purpose here will be to provide arguments in favor of an alternative approach that uses splines, which is equally justifiable on a theoretical basis, and which offers many practical advantages. To reassure the reader who may be afraid to enter new territory, we must emphasize that we are not loosing anything because we will retain the traditional theory as a particular case (i.e., a spline of infinite degree). The basic computational tools will also be familiar to a signal processing audience (filters and recursive algorithms), even though-2-their use in the present context is less conventional. In the course of the presentation, we will also bring out the connection with the multiresolution theory of the wavelet transform. Interestingly, splines are slightly older than …
Learning Simple Algorithms from Examples

We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using Q-learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by Q-learning.
Adding Noun Phrase Structure to the Penn Treebank

The Penn Treebank does not annotate within base noun phrases (NPs), committing only to flat structures that ignore the complexity of English NPs. This means that tools trained on Treebank data cannot learn the correct internal structure of NPs. This paper details the process of adding gold-standard bracketing within each noun phrase in the Penn Treebank. We then examine the consistency and reliability of our annotations. Finally, we use this resource to determine NP structure using several statistical approaches, thus demonstrating the utility of the corpus. This adds detail to the Penn Treebank that is necessary for many NLP applications.
Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs

We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowledge obtained from familiar classes to describe the unfamiliar class. In this paper, we build upon the recently introduced Graph Convolutional Network (GCN) and propose an approach that uses both semantic embeddings and the categorical relationships to predict the classifiers. Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category). After a series of graph convolutions, we predict the visual classifier for each category. During training, the visual classifiers for a few categories are given to learn the GCN parameters. At test time, these filters are used to predict the visual classifiers of unseen categories. We show that our approach is robust to noise in the KG. More importantly, our approach provides significant improvement in performance compared to the current state-of-the-art results (from 2 ~ 3% on some metrics to whopping 20% on a few).
A Large-Scale Hidden Semi-Markov Model for Anomaly Detection on User Browsing Behaviors

Many methods designed to create defenses against distributed denial of service (DDoS) attacks are focused on the IP and TCP layers instead of the high layer. They are not suitable for handling the new type of attack which is based on the application layer. In this paper, we introduce a new scheme to achieve early attack detection and filtering for the application-layer-based DDoS attack. An extended hidden semi-Markov model is proposed to describe the browsing behaviors of web surfers. In order to reduce the computational amount introduced by the model's large state space, a novel forward algorithm is derived for the online implementation of the model based on the M-algorithm. Entropy of the user's HTTP request sequence fitting to the model is used as a criterion to measure the user's normality. Finally, experiments are conducted to validate our model and algorithm.
Condition Monitoring and Fault Diagnosis of Electrical Motors&#8212;A Review

Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.
Multi-class blue noise sampling

Sampling is a core process for a variety of graphics applications. Among existing sampling methods, blue noise sampling remains popular thanks to its spatial uniformity and absence of aliasing artifacts. However, research so far has been mainly focused on blue noise sampling with a single class of samples. This could be insufficient for common natural as well as man-made phenomena requiring multiple classes of samples, such as object placement, imaging sensors, and stippling patterns.
 We extend blue noise sampling to multiple classes where each individual class as well as their unions exhibit blue noise characteristics. We propose two flavors of algorithms to generate such multi-class blue noise samples, one extended from traditional Poisson hard disk sampling for explicit control of sample spacing, and another based on our soft disk sampling for explicit control of sample count. Our algorithms support uniform and adaptive sampling, and are applicable to both discrete and continuous sample space in arbitrary dimensions. We study characteristics of samples generated by our methods, and demonstrate applications in object placement, sensor layout, and color stippling.
Theory and measurement of backscattering from RFID tags

This paper presents a method for measuring signal backscattering from RFID tags, and for calculating a tag's radar cross section (RCS). We derive a theoretical formula for the RCS of an RFID tag with a minimum-scattering antenna. We describe an experimental measurement technique, which involves using a network analyzer connected to an anechoic chamber with and without the tag. The return loss measured in this way allows us to calculate the backscattered power and to find the tag's RCS. Measurements were performed using an RFID tag operating in the UHF band. To determine whether the tag was turned on, we used an RFID tag tester. The tag's RCS was also calculated theoretically, using electromagnetic simulation software. The theoretical results were found to be in good agreement with experimental data
Survey of Convolutional Neural Network

Convolutional Neural Network (CNN) was firstly introduced in Computer Vision for image recognition by LeCun et al. in 1989. Since then, it has been widely used in image recognition and classification tasks. The recent impressive success of Krizhevsky et al. in ILSVRC 2012 competition demonstrates the significant advance of modern deep CNN on image classification task. Inspired by his work, many recent research works have been concentrating on understanding CNN and extending its application to more conventional computer vision tasks. Their successes and lessons have promoted the development of both CNN and vision science. This article makes a survey of recent progress in CNN since 2012. We will introduce the general architecture of a modern CNN and make insights into several typical CNN incarnations which have been studied extensively. We will also review the efforts to understand CNNs and review important applications of CNNs in computer vision tasks.
Determining Factors that Influence Students' Intention to Adopt Mobile Blackboard : A Case Study at Petra University, Jordan

As a newly developing academic domain, researches on Mobile learning are still in their initial stage. Meanwhile, M-blackboard comes from Mobile learning. This study attempts to discover the factors impacting the intention to adopt mobile blackboard. Eleven selected model on the Mobile learning adoption were comprehensively reviewed. From the reviewed articles, the most factors are identified. Also, from the frequency analysis, the most frequent factors in the Mobile blackboard or Mobile learning adoption studies are performance expectancy, effort expectancy, perceived playfulness, facilitating conditions, self-management, cost and past experiences. The descriptive statistic was performed to gather the respondents’ demographic information. It also shows that the respondents agreed on nearly every statement item. Pearson correlation and regression analysis were also conducted.
Augur: Mining Human Behaviors from Fiction to Power Interactive Systems

From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors. Today our systems fall short of these visions, however, because this range of behaviors is too large for designers or programmers to capture manually. In this paper, we instead demonstrate it is possible to mine a broad knowledge base of human behavior by analyzing more than one billion words of modern fiction. Our resulting knowledge base, Augur, trains vector models that can predict many thousands of user activities from surrounding objects in modern contexts: for example, whether a user may be eating food, meeting with a friend, or taking a selfie. Augur uses these predictions to identify actions that people commonly take on objects in the world and estimate a user's future activities given their current situation. We demonstrate Augur-powered, activity-based systems such as a phone that silences itself when the odds of you answering it are low, and a dynamic music player that adjusts to your present activity. A field deployment of an Augur-powered wearable camera resulted in 96% recall and 71% precision on its unsupervised predictions of common daily activities. A second evaluation where human judges rated the system's predictions over a broad set of input images found that 94% were rated sensible.
Do ketogenic diets really suppress appetite? A systematic review and meta-analysis.

Very-low-energy diets (VLEDs) and ketogenic low-carbohydrate diets (KLCDs) are two dietary strategies that have been associated with a suppression of appetite. However, the results of clinical trials investigating the effect of ketogenic diets on appetite are inconsistent. To evaluate quantitatively the effect of ketogenic diets on subjective appetite ratings, we conducted a systematic literature search and meta-analysis of studies that assessed appetite with visual analogue scales before (in energy balance) and during (while in ketosis) adherence to VLED or KLCD. Individuals were less hungry and exhibited greater fullness/satiety while adhering to VLED, and individuals adhering to KLCD were less hungry and had a reduced desire to eat. Although these absolute changes in appetite were small, they occurred within the context of energy restriction, which is known to increase appetite in obese people. Thus, the clinical benefit of a ketogenic diet is in preventing an increase in appetite, despite weight loss, although individuals may indeed feel slightly less hungry (or more full or satisfied). Ketosis appears to provide a plausible explanation for this suppression of appetite. Future studies should investigate the minimum level of ketosis required to achieve appetite suppression during ketogenic weight loss diets, as this could enable inclusion of a greater variety of healthy carbohydrate-containing foods into the diet.
Current Perspectives on Teaching World Englishes and English as a Lingua Franca

The purpose of this article is to explore recent research into World Englishes (henceforth WEs) and English as a Lingua Franca (ELF),1 focusing on its implications for TESOL, and the extent to which it is being taken into account by English language teachers, linguists, and second language acquisition researchers. After a brief introduction comparing the current situation with that of 15 years ago, I look more closely at definitions of WEs and ELF. Then follows an overview of relevant developments in WEs and ELF research during the past 15 years, along with a more detailed discussion of some key research projects and any controversies they have aroused. I then address the implications of WEs/ELF research for TESOL vis-à-vis English language standards and standard English, and the longstanding native versus nonnative teacher debate. Finally, I assess the consensus on WEs and ELF that is emerging both among researchers and between researchers and language teaching professionals. The article concludes by raising a number of questions that remain to be investigated in future research.
On Convergence and Stability of GANs

We analyze convergence of GANs through the lens of online learning and game theory, to understand what makes it hard to achieve consistent stable training in practice. We identify that the underlying game here can be ill-posed and poorly conditioned, and propose a simple regularization scheme based on local perturbations of the input data to address these issues. Currently, the methods that improve stability either impose additional computational costs or require the usage of specific architectures/modeling objectives. Further, we show that WGAN-GP, which is the state-of-the-art stable training procedure, is similar to LS-GAN, does not follow from KR-duality and can be too restrictive in general. In contrast, our proposed algorithm is fast, simple to implement and achieves competitive performance in a stable fashion across a variety of architectures and objective functions with minimal hyperparameter tuning. We show significant improvements over WGAN-GP across these conditions.