nils-herrmann/clean_scirepeval huggingface dataset:
Assessment of bruxism in the clinic.

Bruxism is a much-discussed clinical issue in dentistry. Although bruxism is not a life-threatening disorder, it can influence the quality of human life, especially through dental problems, such as tooth wear, frequent fractures of dental restorations and pain in the oro-facial region. Therefore, various clinical methods have been devised to assess bruxism over the last 70 years. This paper reviews the assessment of bruxism, provides information on various assessment methods which are available in clinical situations and discusses their effectiveness and usefulness. Currently, there is no definitive method for assessing bruxism clinically that has reasonable diagnostic and technical validity, affects therapeutic decisions and is cost effective. One future direction is to refine questionnaire items and clinical examination because they are the easiest to apply in everyday practice. Another possible direction is to establish a method that can measure actual bruxism activity directly using a device that can be applied to patients routinely. More clinical studies should examine the clinical impact of bruxism on oral structures, treatment success and the factors influencing the decision-making process in dental treatment.
Auxiliary Guided Autoregressive Variational Autoencoders

Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models combining the strengths of both models. Our contribution is to train such hybrid models using an auxiliary loss function that controls which information is captured by the latent variables and what is left to the autoregressive decoder. In contrast, prior work on such hybrid models needed to limit the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables and only rely on autoregressive modeling. Our approach results in models with meaningful latent variable representations, and which rely on powerful autoregressive decoders to model image details. Our model generates qualitatively convincing samples, and yields stateof-the-art quantitative results.
Audio Replay Attack Detection with Deep Learning Frameworks

Nowadays spoofing detection is one of the priority research areas in the field of automatic speaker verification. The success of Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) Challenge 2015 confirmed the impressive perspective in detection of unforeseen spoofing trials based on speech synthesis and voice conversion techniques. However, there is a small number of researches addressed to replay spoofing attacks which are more likely to be used by non-professional impersonators. This paper describes the Speech Technology Center (STC) anti-spoofing system submitted for ASVspoof 2017 which is focused on replay attacks detection. Here we investigate the efficiency of a deep learning approach for solution of the mentioned-above task. Experimental results obtained on the Challenge corpora demonstrate that the selected approach outperforms current state-of-the-art baseline systems in terms of spoofing detection quality. Our primary system produced an EER of 6.73% on the evaluation part of the corpora which is 72% relative improvement over the ASVspoof 2017 baseline system.
Interest cash: an application-based countermeasure against interest flooding for dynamic content in named data networking

As a design of information-centric network architecture, Named Data Networking (NDN) provides content-based security. The signature binding the name with the content is the key point of content-based security in NDN. However, signing a content will introduce a significant computation overhead, especially for dynamically generated content. Adversaries can take advantages of such computation overhead to deplete the resources of the content provider. In this paper, we propose Interest Cash, an application-based countermeasure against Interest Flooding for dynamic content. Interest Cash requires a content consumer to solve a puzzle before it sends an Interest. The content consumer should provide a solution to this puzzle as cash to get the signing service from the content provider. The experiment shows that an adversary has to use more than 300 times computation resources of the content provider to commit a successful attack when Interest Cash is used.
Wideband Magnetoelectric Dipole Antennas With Dual Polarization and Circular Polarization

A new 45 degrees dual-polarized magnetoelectric dipole antenna is proposed. The antenna is excited by two -shaped probes placed at a convenient location. The measured overlapped impedance bandwidth is 48% with standing-wave ratio (SWR) 1.5 from 1.69 to 2.76 GHz. The measured gains vary from 7.6 to 9.3 dBi and from 7.6 to 9.4 dBi for port 1 and port 2, respectively. The isolation between the two ports is larger than 30 dB. The proposed antenna achieves a low cross-polarization level of less than 21 dB and a low back radiation level of less than 29 dB over the operating frequency range. With a broadband 90 phase shifter and a power divider, the proposed antenna can radiate circularly-polarized (CP) wave and exhibit a wide impedance bandwidth (SWR 2) of 90% from 1.23 to 3.23 GHz, which covers the whole 3-dB axial-ratio (AR) bandwidth of 82% from 1.28 to 3.05 GHz. In this operation frequency band, the proposed CP antenna has a broadside gain of larger than 5 dBi above 1.45 GHz. Considering the common overlapped bandwidth limited by the impedance, AR, and gain, the proposed antenna exhibits an effective bandwidth of 71%.
Emerging trends in applications of big data in educational data mining and learning analytics

In the last couple of years, the use of various applications of Educational Data Mining and Learning Analytics has increased. Now a days, students have started using Internet to study. Due to the easy availability of online courses, student online activities have considerably increased which in turn has generated huge amount of data. These data gets wasted as conventional learning methods are not equipped for preparing it. Now, due to this, the Big Data technology has entered in the education field, to mine those huge chunks of datasets. This paper discusses the latest tools and technologies of Big Data in the field of Learning Analytics and Educational Data Mining.
Secure Autonomous Cyber-Physical Systems Through Verifiable Information Flow Control

Modern cyber-physical systems are complex networked computing systems that electronically control physical systems. Autonomous road vehicles are an important and increasingly ubiquitous instance. Unfortunately, their increasing complexity often leads to security vulnerabilities. Network connectivity exposes these vulnerable systems to remote software attacks that can result in real-world physical damage, including vehicle crashes and loss of control authority. We introduce an integrated architecture to provide provable security and safety assurance for cyber-physical systems by ensuring that safety-critical operations and control cannot be unintentionally affected by potentially malicious parts of the system. Finegrained information flow control is used to design both hardware and software, determining how low-integrity information can affect high-integrity control decisions. This security assurance is used to improve end-to-end security across the entire cyber-physical system. We demonstrate this integrated approach by developing a mobile robotic testbed modeling a self-driving system and testing it with a malicious attack.   2018, Toronto, ON, Canada. ACM, New York, NY, USA, 12 pages.
Adaptive deconvolutional networks for mid and high level feature learning

We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture image information in a variety of forms: low-level edges, mid-level edge junctions, high-level object parts and complete objects. To build our model we rely on a novel inference scheme that ensures each layer reconstructs the input, rather than just the output of the layer directly beneath, as is common with existing hierarchical approaches. This makes it possible to learn multiple layers of representation and we show models with 4 layers, trained on images from the Caltech-101 and 256 datasets. When combined with a standard classifier, features extracted from these models outperform SIFT, as well as representations from other feature learning methods.
Mental imagery: in search of a theory.

It is generally accepted that there is something special about reasoning by using mental images. The question of how it is special, however, has never been satisfactorily spelled out, despite more than thirty years of research in the post-behaviorist tradition. This article considers some of the general motivation for the assumption that entertaining mental images involves inspecting a picture-like object. It sets out a distinction between phenomena attributable to the nature of mind to what is called the cognitive architecture, and ones that are attributable to tacit knowledge used to simulate what would happen in a visual situation. With this distinction in mind, the paper then considers in detail the widely held assumption that in some important sense images are spatially displayed or are depictive, and that examining images uses the same mechanisms that are deployed in visual perception. I argue that the assumption of the spatial or depictive nature of images is only explanatory if taken literally, as a claim about how images are physically instantiated in the brain, and that the literal view fails for a number of empirical reasons--for example, because of the cognitive penetrability of the phenomena cited in its favor. Similarly, while it is arguably the case that imagery and vision involve some of the same mechanisms, this tells us very little about the nature of mental imagery and does not support claims about the pictorial nature of mental images. Finally, I consider whether recent neuroscience evidence clarifies the debate over the nature of mental images. I claim that when such questions as whether images are depictive or spatial are formulated more clearly, the evidence does not provide support for the picture-theory over a symbol-structure theory of mental imagery. Even if all the empirical claims were true, they do not warrant the conclusion that many people have drawn from them: that mental images are depictive or are displayed in some (possibly cortical) space. Such a conclusion is incompatible with what is known about how images function in thought. We are then left with the provisional counterintuitive conclusion that the available evidence does not support rejection of what I call the "null hypothesis"; namely, that reasoning with mental images involves the same form of representation and the same processes as that of reasoning in general, except that the content or subject matter of thoughts experienced as images includes information about how things would look.
Improved design of Vivaldi antenna with loading resistance for wideband applications

With the development of high-speed integrated circuit, the Ultra Wideband (UWB) communication system has been developed toward the direction of miniaturization, integration, which will necessarily promote UWB antenna also be developed toward the direction of miniaturization, integration, etc. This paper proposes an improved structure of Vivaldi antenna, which loads resistance at the bottom of exponential type antenna to improve Voltage Standing Wave Ratio (VSWR) at low frequency, and which opens three symmetrical unequal rectangular slots in the antenna radiation part to increase the gain. The improved Vivaldi antenna size is 150 mm * 150 mm, and the working frequency is 0.8-3.8 GHz (measured VSWR<;2).The experimental results show that the antenna has good directional radiation characteristics within the scope of bandwidth.
Combined vitamin C and E supplementation during pregnancy for preeclampsia prevention: a systematic review.

The effect of combined vitamin C and E supplementation during pregnancy on the prevention of preeclampsia and major adverse infant outcomes has been reviewed. We searched MEDLINE and the Central Library of Controlled Trials of the Cochrane Library through August 2006 for relevant clinical trials. Interstudy heterogeneity was evaluated using the chi(2) statistic (Q statistic) test. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated with a fixed or random-effects model as appropriate. Four trials that collectively randomized 4680 pregnant women to either the combination of vitamin C and vitamin E or placebo were included in the analysis. There were no significant differences between the vitamin and placebo groups in the risk of preeclampsia, 11% versus 11.4%, RR 0.97 (95% CI 0.82-1.13), fetal or neonatal loss, 2.6% versus 2.3%, RR 1.10 (95% CI 0.78-1.57), or small for gestational age (SGA) infant, 20.6% versus 20%, RR 0.94 (95% CI 0.74-1.19). Although there was a higher risk for preterm birth in the vitamin group, 19.5% versus 18%, RR 1.07 (95% CI 0.96-1.20), this finding was not significant. Combined vitamin C and E supplementation during pregnancy does not reduce the risk of preeclampsia, fetal or neonatal loss, small for gestational age infant, or preterm birth. Such supplementation should be discouraged unless solid supporting data from randomized trials become available.


TARGET AUDIENCE
Obstetricians & Gynecologists, Family Physicians



After completion of this article, the reader should be able to recall that many methods have been used to prevent preeclampsia, state that increased oxidative stress has been postulated and many trials have used antioxidants to prevent the disease, and explain that MEDLINE analysis of the literature questions the use of vitamin C and E supplements.
Why is test-restudy practice beneficial for memory? An evaluation of the mediator shift hypothesis.

Although the memorial benefits of testing are well established empirically, the mechanisms underlying this benefit are not well understood. The authors evaluated the mediator shift hypothesis, which states that test-restudy practice is beneficial for memory because retrieval failures during practice allow individuals to evaluate the effectiveness of mediators and to shift from less effective to more effective mediators. Across a series of experiments, participants used a keyword encoding strategy to learn word pairs with test-restudy practice or restudy only. Robust testing effects were obtained in all experiments, and results supported predictions of the mediator shift hypothesis. First, a greater proportion of keyword shifts occurred during test-restudy practice versus restudy practice. Second, a greater proportion of keyword shifts occurred after retrieval failure trials versus retrieval success trials during test-restudy practice. Third, a greater proportion of keywords were recalled on a final keyword recall test after test-restudy versus restudy practice.
Measuring with Murray: Touchscreen technology and preschoolers' STEM learning

American students rank well below international peers in the disciplines of science, technology, engineering, and mathematics (STEM). Early exposure to STEM-related concepts is critical to later academic achievement. Given the rise of tablet-computer use in early childhood education settings, interactive technology might be one particularly fruitful way of supplementing early STEM education. Using a between-subjects experimental design, we sought to determine whether preschoolers could learn a fundamental math concept (i.e., measurement with non-standard units) from educational technology, and whether interactivity is a crucial component of learning from that technology. Participants who either played an interactive tablet-based game or viewed a non-interactive video demonstrated greater transfer of knowledge than those assigned to a control condition. Interestingly, interactivity contributed to better performance on near transfer tasks, while participants in the non-interactive condition performed better on far transfer tasks. Our findings suggest that, while preschool-aged children can learn early STEM skills from educational technology, interactivity may only further support learning in certain
Construction of an impulse current generator prototype applied in electrical grounding systems

This paper presents a device that is able to reproduce atmospheric discharges in a small scale. First, there was simulated an impulse generator circuit that could meet the main characteristics of the common lightning strokes waveform. Later, four different generator circuits were developed with the selection made by a microcontroller. Finally, the output was subject to amplification circuits that increased its amplitude. The impulses generated had a very similar shape compared to the real atmospheric discharges to the international standards for impulse testing. The apparatus is meant for application in electric grounding systems and for tests in high frequency to measure the soil impedance.
Deep Learning for Anomaly Detection: A Survey

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and asess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, alongwith its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.
An Efficient Kernel Transformation Architecture for Binary- and Ternary-Weight Neural Network Inference

While deep convolutional neural networks (CNNs) have emerged as the driving force of a wide range of domains, their computationally and memory intensive natures hinder the further deployment in mobile and embedded applications. Recently, CNNs with low-precision parameters have attracted much research attention. Among them, multiplier-free binary- and ternary-weight CNNs are reported to be of comparable recognition accuracy with full-precision networks, and have been employed to improve the hardware efficiency. However, even with the weights constrained to binary and ternary values, large-scale CNNs still require billions of operations in a single forward propagation pass.
 In this paper, we introduce a novel approach to maximally eliminate redundancy in binary- and ternary-weight CNN inference, improving both the performance and energy efficiency. The initial kernels are transformed into much fewer and sparser ones, and the output feature maps are rebuilt from the immediate results. Overall, the number of total operations in convolution is reduced. To find an efficient transformation solution for each already trained network, we propose a searching algorithm, which iteratively matches and eliminates the overlap in a set of kernels. We design a specific hardware architecture to optimize the implementation of kernel transformation. Specialized dataflow and scheduling method are proposed. Tested on SVHN, AlexNet, and VGG-16, our architecture removes 43.4%--79.9% operations, and speeds up the inference by 1.48--3.01 times.
Enhanced SEA algorithm and fingerprint classification

This paper proposes the Enhanced Shrinking and Expanding Algorithm (ESEA) with a new categorization method. The ESEA overcomes anomalies in the original Shrinking and Expanding Algorithm (SEA) which fails to locate singular points (SPs) in many cases. Experimental results show that the accuracy rate of the ESEA reaches 94.7%, a 32.5% increase from the SEA. In the proposed fingerprint categorization method, each fingerprint will be assigned to a specific subclass. The search for a specific fingerprint can therefore be performed only on specific subclasses containing a small portion of a large fingerprint database, which will save enormous computational time.
Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a crosslayer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which results in superior performance compared with other bilinear pooling based approaches. Second, we propose a novel hierarchical bilinear pooling framework to integrate multiple cross-layer bilinear features to enhance their representation capability. Our formulation is intuitive, efficient and achieves state-of-the-art results on the widely used fine-grained recognition datasets.
Factor Structure of the Rosenberg Self-Esteem Scale

This study evaluated the factor structure of the Rosenberg Self-Esteem Scale (RSES) with a diverse sample of 1,248 European American, Latino, Armenian, and Iranian adolescents. Adolescents completed the 10-item RSES during school as part of a larger study on parental influences and academic outcomes. Findings suggested that method effects in the RSES are more strongly associated with negatively worded items across three diverse groups but also more pronounced among ethnic minority adolescents. Findings also suggested that accounting for method effects is necessary to avoid biased conclusions regarding cultural differences in selfesteem and how predictors are related to the RSES. Moreover, the two RSES factors (positive self-esteem and negative self-esteem) were differentially predicted by parenting behaviors and academic motivation. Substantive and methodological implications of these findings for crosscultural research on adolescent self-esteem are discussed.
code2seq: Generating Sequences from Structured Representations of Code

The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine translation (NMT), have achieved state-of-the-art performance on these tasks by treating source code as a sequence of tokens. We present CODE2SEQ: an alternative approach that leverages the syntactic structure of programming languages to better encode source code. Our model represents a code snippet as the set of compositional paths in its abstract syntax tree (AST) and uses attention to select the relevant paths while decoding. We demonstrate the effectiveness of our approach for two tasks, two programming languages, and four datasets of up to 16M examples. Our model significantly outperforms previous models that were specifically designed for programming languages, as well as state-of-the-art NMT models. An online demo of our model is available at http://code2seq.org. Our code, data and trained models are available at http://github.com/tech-srl/code2seq.