nils-herrmann/clean_scirepeval huggingface dataset:
Commonsense Reasoning about Causality: Deriving Behavior from Structure

This paper presents a qualitative-reasoning method for predicting the behavior of mechanisms characterized by continuous, time-varying parameters. The structure of a mechanism is described in terms of a set of parameters and the constraints that hold among them : essentially a 'qualitative differential equation'. The qualitative-behavior description consists of a discrete set of time-points, at which the values of the parameters are described in terms of ordinal relations and directions of change. The behavioral description, or envisionment, is derived by two sets of rules: propagation rules which elaborate the description of the current time-point, and prediction rules which determine what is known about the next qualitatively distinct state of the mechanism. A detailed example shows how the envisionment method can detect a previously unsuspected landmark point at which the system is in stable equilibrium.
Learning Texture Manifolds with the Periodic Spatial GAN

This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has several novel abilities which surpass the current state of the art in texture synthesis. First, we can learn multiple textures from datasets of one or more complex large images. Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smoothly interpolate between samples in the structured noise space and generate novel samples, which lie perceptually between the textures of the original dataset. In addition, we can also accurately learn periodical textures. We make multiple experiments which show that PSGANs can flexibly handle diverse texture and image data sources. Our method is highly scalable and it can generate output images of arbitrary large size.
From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach

Training machine leaning algorithms on augmented data from different related sources is a challenging task. This problem arises in several applications, such as the Internet of Things (IoT), where data may be collected from devices with different settings. The learned model on such datasets can generalize poorly due to distribution bias. In this paper we consider the problem of classifying unseen datasets, given several labeled training samples drawn from similar distributions. We exploit the intrinsic structure of samples in a latent subspace and identify landmarks, a subset of training instances from different sources that should be similar. Incorporating subspace learning and landmark selection enhances generalization by alleviating the impact of noise and outliers, as well as improving efficiency by reducing the size of the data. However, since addressing the two issues simultaneously results in an intractable problem, we relax the objective function by leveraging the theory of nonlinear projection and solve a tractable convex optimisation. Through comprehensive analysis, we show that our proposed approach outperforms stateof-the-art results on several benchmark datasets, while keeping the computational complexity low.
Comparing Population Means Under Local Differential Privacy: With Significance and Power

A statistical hypothesis test determines whether a hypothesis should be rejected based on samples from populations. In particular, randomized controlled experiments (or A/B testing) that compare population means using, e.g., t-tests, have been widely deployed in technology companies to aid in making data-driven decisions. Samples used in these tests are collected from users and may contain sensitive information. Both the data collection and the testing process may compromise individuals’ privacy. In this paper, we study how to conduct hypothesis tests to compare population means while preserving privacy. We use the notation of local differential privacy (LDP), which has recently emerged as the main tool to ensure each individual’s privacy without the need of a trusted data collector. We propose LDP tests that inject noise into every user’s data in the samples before collecting them (so users do not need to trust the data collector), and draw conclusions with bounded type-I (significance level) and type-II errors (1− power). Our approaches can be extended to the scenario where some users require LDP while some are willing to provide exact data. We report experimental results on real-world datasets to verify the effectiveness of our approaches.
The measurement of observer agreement for categorical data.

This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.
Automatic genre classification of music content: a survey

This paper reviews the state-of-the-art in automatic genre classification of music collections through three main paradigms: expert systems, unsupervised classification, and supervised classification. The paper discusses the importance of music genres with their definitions and hierarchies. It also presents techniques to extract meaningful information from audio data to characterize musical excerpts. The paper also presents the results of new emerging research fields and techniques that investigate the proximity of music genres
GENERATIVE MATCHING NETWORKS

Despite recent advances, the remaining bottlenecks in deep generative models are necessity of extensive training and difficulties with generalization from small number of training examples. Both problems may be addressed by conditional generative models that are trained to adapt the generative distribution to additional input data. So far this idea was explored only under certain limitations such as restricting the input data to be a single object or multiple objects representing the same concept. In this work we develop a new class of deep generative model called generative matching networks which is inspired by the recently proposed matching networks for one-shot learning in discriminative tasks and the ideas from meta-learning. By conditioning on the additional input dataset, generative matching networks may instantly learn new concepts that were not available during the training but conform to a similar generative process, without explicit limitations on the number of additional input objects or the number of concepts they represent. Our experiments on the Omniglot dataset demonstrate that generative matching networks can significantly improve predictive performance on the fly as more additional data is available to the model and also adapt the latent space which is beneficial in the context of feature extraction.
Translating emotion theory and research into preventive interventions.

Scientific advances in the field of emotions suggest a framework for conceptualizing the emotion-related aspects of prevention programs that aim to enhance children's socioemotional competence and prevent the emergence of behavior problems and psychopathology. A conception of emotions as inherently adaptive and motivational and the related empirical evidence from several disciplines and specialities suggest 7 principles for developing preventive interventions: the utilization of positive and negative emotions, emotion modulation as a mediator of emotion utilization, emotion patterns in states and traits, different processes of emotion activation, emotion communication in early life, and the development of connections for the modular and relatively independent emotions and cognitive systems. Each principle's practical implications and application in current prevention programs are discussed.
Analysis, Design, and Implementation of Virtual Impedance for Power Electronics Interfaced Distributed Generation

This paper presents a virtual impedance design and implementation approach for power electronics interfaced distributed generation (DG) units. To improve system stability and prevent power couplings, the virtual impedances can be placed between interfacing converter outputs and the main grid. However, optimal design of the impedance value, robust implementation of the virtual impedance, and proper utilization of the virtual impedance for DG performance enhancement are key for the virtual impedance concept. In this paper, flexible small-signal models of microgrids in different operation modes are developed first. Based on the developed microgrid models, the desired DG impedance range is determined considering the stability, transient response, and power flow performance of DG units. A robust virtual impedance implementation method is also presented, which can alleviate voltage distortion problems caused by harmonic loads compared to the effects of physical impedances. Furthermore, an adaptive impedance concept is proposed to further improve power control performances during the transient and grid faults. Simulation and experimental results are provided to validate the impedance design approach, the virtual impedance implementation method, and the proposed adaptive transient impedance control strategies.
Human-body effects on the design of card-type UHF RFID tag antennas

This paper presents design considerations of a UHF-band card-type tag placed on the packet near the chest of a human body for our University-campus applications. Using FEKO simulation tool, we designed four kinds of highly platform-tolerant tag antennas and evaluated the influences of a simple human model on the power transmission coefficient, radiation pattern, and realized gain. Also, the input impedances of the tag antennas are measured by a test fixture and a two-port network analyzer to verify the simulation results and confirm the availability of the simulation tool. Finally, a better tag antenna is chosen a candidate for student-card RFID applications.
Life and Understanding: The Origins of “Understanding” in Self-Organizing Nervous Systems

This article is motivated by a formulation of biotic self-organization in Friston (2013), where the emergence of "life" in coupled material entities (e.g., macromolecules) was predicated on bounded subsets that maintain a degree of statistical independence from the rest of the network. Boundary elements in such systems constitute a Markov blanket; separating the internal states of a system from its surrounding states. In this article, we ask whether Markov blankets operate in the nervous system and underlie the development of intelligence, enabling a progression from the ability to sense the environment to the ability to understand it. Markov blankets have been previously hypothesized to form in neuronal networks as a result of phase transitions that cause network subsets to fold into bounded assemblies, or packets (Yufik and Sheridan, 1997; Yufik, 1998a). The ensuing neuronal packets hypothesis builds on the notion of neuronal assemblies (Hebb, 1949, 1980), treating such assemblies as flexible but stable biophysical structures capable of withstanding entropic erosion. In other words, structures that maintain their integrity under changing conditions. In this treatment, neuronal packets give rise to perception of "objects"; i.e., quasi-stable (stimulus bound) feature groupings that are conserved over multiple presentations (e.g., the experience of perceiving "apple" can be interrupted and resumed many times). Monitoring the variations in such groups enables the apprehension of behavior; i.e., attributing to objects the ability to undergo changes without loss of self-identity. Ultimately, "understanding" involves self-directed composition and manipulation of the ensuing "mental models" that are constituted by neuronal packets, whose dynamics capture relationships among objects: that is, dependencies in the behavior of objects under varying conditions. For example, movement is known to involve rotation of population vectors in the motor cortex (Georgopoulos et al., 1988, 1993). The neuronal packet hypothesis associates "understanding" with the ability to detect and generate coordinated rotation of population vectors-in neuronal packets-in associative cortex and other regions in the brain. The ability to coordinate vector representations in this way is assumed to have developed in conjunction with the ability to postpone overt motor expression of implicit movement, thus creating a mechanism for prediction and behavioral optimization via mental modeling that is unique to higher species. This article advances the notion that Markov blankets-necessary for the emergence of life-have been subsequently exploited by evolution and thus ground the ways that living organisms adapt to their environment, culminating in their ability to understand it.
Do students like the flipped classroom? An investigation of student reaction to a flipped undergraduate IT course

The flipped classroom pedagogy has achieved significant mention in academic circles in recent years. "Flipping" involves the reinvention of a traditional course so that students engage with learning materials via recorded lectures and interactive exercises prior to attending class and then use class time for more interactive activities. Proper implementation of a flipped classroom is difficult to gauge, but combines successful techniques for distance education with constructivist learning theory in the classroom. While flipped classrooms are not a novel concept, technological advances and increased comfort with distance learning have made the tools to produce and consume course materials more pervasive. Flipped classroom experiments have had both positive and less-positive results and are generally measured by a significant improvement in learning outcomes. This study, however, analyzes the opinions of students in a flipped sophomore-level information technology course by using a combination of surveys and reflective statements. The author demonstrates that at the outset students are new - and somewhat receptive - to the concept of the flipped classroom. By the conclusion of the course satisfaction with the pedagogy is significant. Finally, student feedback is provided in an effort to inform instructors in the development of their own flipped classrooms.
Behavioral Analysis of obfuscated Code

Classically, the procedure for reverse engineering binary code is to use a disassembler and to manually reconstruct the logic of the original program. Unfortunately, this is not always practical as obfuscation can make the binary extremely large by overcomplicating the program logic or adding bogus code. We present a novel approach, based on extracting semantic information by analyzing the behavior of the execution of a program. As obfuscation consists in manipulating the program while keeping its functionality, we argue that there are some characteristics of the execution that are strictly correlated with the underlying logic of the code and are invariant after applying obfuscation. We aim at highlighting these patterns, by introducing different techniques for processing memory and execution traces. Our goal is to identify interesting portions of the traces by finding patterns that depend on the original semantics of the program. Using this approach the high-level information about the business logic is revealed and the amount of binary code to be analyze is considerable reduced. For testing and simulations we used obfuscated code of cryptographic algorithms, as our focus are DRM system and mobile banking applications. We argue however that the methods presented in this work are generic and apply to other domains were obfuscated code is used.
Recent Advances in Recommender Systems: Matrices, Bandits, and Blenders

Recent years have witnessed an explosion in methods applied to solve the recommendation problem. Modern recommender systems have become increasingly more complex compared to their early content-based and collaborative filtering versions. In this tutorial, we will cover recent advances in recommendation methods, focusing on matrix factorization, multi-armed bandits, and methods for blending recommendations. We will also describe evaluation techniques, and outline open issues and challenges. The ultimate goal of this tutorial is to present a toolkit of new recommendation methods in perspective to data-related problems, and highlight opportunities and new research paths for researchers and practitioners that work on problems in the intersection of recommender systems and databases.
Cycling to work in 90 large American cities : new evidence on the role of bike paths and lanes

This article analyzes the variation in bike commuting in large American cities, with a focus on assessing the influence of bike paths and lanes, which have been the main approach to increasing cycling in the USA. To examine the role of cycling facilities, we used a newly assembled dataset on the length of bike lanes and paths in 2008 collected directly from 90 of the 100 largest U.S. cities. Pearson’s correlation, bivariate quartile analysis, and two different types of regressions were used to measure the relationship between cycling levels and bikeways, as well as other explanatory and control variables. Ordinary Least Squares and Binary Logit Proportions regressions confirm that cities with a greater supply of bike paths and lanes have significantly higher bike commute rates—even when controlling for land use, climate, socioeconomic factors, gasoline prices, public transport supply, and cycling safety. Standard tests indicate that the models are a good fit, with R ranging between 0.60 and 0.65. Computed coefficients have the expected signs for all variables in the various regression models, but not all are statistically significant. Estimated elasticities indicate that both off-street paths and on-street lanes have a similar positive association with bike commute rates in U.S. cities. Our results are consistent with previous research on the importance of separate cycling facilities and provide additional information about the potentially different role of paths vs. lanes. Our analysis also revealed that cities with safer cycling, lower auto ownership, more students, less sprawl, and higher gasoline prices had more cycling to work. By comparison, annual precipitation, the number of cold and hot days, and public transport supply were not statistically significant predictors of bike commuting in large cities. R. Buehler (&) School of Public and International Affairs, Virginia Tech, Alexandria Center, 1021 Prince Street, Suite 200, Alexandria, VA 22314, USA e-mail: ralphbu@vt.edu J. Pucher Bloustein School of Planning and Public Policy, Rutgers University, 33 Livingston Avenue, Room 363, New Brunswick, NJ 08901, USA e-mail: pucher@rutgers.edu 123 Transportation (2012) 39:409–432 DOI 10.1007/s11116-011-9355-8
Radar Cross Section Reduction of a Microstrip Antenna Based on Polarization Conversion Metamaterial

A novel method aimed at reducing radar cross section (RCS) under incident waves with both x- and y-polarizations, with the radiation characteristics of the antenna preserved, is presented and investigated. The goal is accomplished by the implementation of the polarization conversion metamaterial (PCM) and the principle of passive cancellation. As a test case, a microstrip patch antenna is simulated and experimentally measured to demonstrate the proposed strategy for dramatic radar cross section reduction (RCSR). Results exhibit that in-band RCSR is as much as 16 dB compared to the reference antenna. In addition, the PCM has a contribution to a maximum RCSR value of 14 dB out of the operating band. With significant RCSR and unobvious effect on the radiation performance of the antenna, the proposed method has a wide application for the design of other antennas with a requirement of RCS control.
Steganalysis via a Convolutional Neural Network using Large Convolution Filters

For the past few years, in the race between image steganography and steganalysis, deep learning has emerged as a very promising alternative to steganalyzer approaches based on rich image models combined with ensemble classifiers. A key knowledge of image steganalyzer, which combines relevant image features and innovative classification procedures, can be deduced by a deep learning approach called Convolutional Neural Networks (CNN). These kind of deep learning networks is so well-suited for classification tasks based on the detection of variations in 2D shapes that it is the state-of-the-art in many image recognition problems. In this article, we design a CNN-based steganalyzer for images obtained by applying steganography with a unique embedding key. This one is quite different from the previous study of Qian et al. and its successor, namely Pibre et al. The proposed architecture embeds less convolutions, with much larger filters in the final convolutional layer, and is more general: it is able to deal with larger images and lower payloads. For the “same embedding key” scenario, our proposal outperforms all other steganalyzers, in particular the existing CNN-based ones, and defeats many state-of-the-art image steganography schemes. ∗This is a corrected version of the eprint J.-F. Couchot, R. Couturier, C. Guyeux, and M. Salomon (2016): Steganalysis via a Convolutional Neural Network using Large Convolution Filters.  eprint . Publication Date: 05/2016. In this version, we used improperly prepared stego images with the same stego key, resulting in embedding changes pretty much in the same places. This error has been pointed out by a reviewer after submission to  AND team ‡raphael.couturier@univ-fcomte.fr 1 ar X iv :1 60 5. 07 94 6v 2 [ cs .M M ] 2 7 Ju l 2 01 6
Factors associated with desistence and persistence of childhood gender dysphoria: a quantitative follow-up study.

To examine the factors associated with the persistence of childhood gender dysphoria (GD), and to assess the feelings of GD, body image, and sexual orientation in adolescence.



The sample consisted of 127 adolescents (79 boys, 48 girls), who were referred for GD in childhood (<12 years of age) and followed up in adolescence. We examined childhood differences among persisters and desisters in demographics, psychological functioning, quality of peer relations and childhood GD, and adolescent reports of GD, body image, and sexual orientation. We examined contributions of childhood factors on the probability of persistence of GD into adolescence.



We found a link between the intensity of GD in childhood and persistence of GD, as well as a higher probability of persistence among natal girls. Psychological functioning and the quality of peer relations did not predict the persistence of childhood GD. Formerly nonsignificant (age at childhood assessment) and unstudied factors (a cognitive and/or affective cross-gender identification and a social role transition) were associated with the persistence of childhood GD, and varied among natal boys and girls.



Intensity of early GD appears to be an important predictor of persistence of GD. Clinical recommendations for the support of children with GD may need to be developed independently for natal boys and for girls, as the presentation of boys and girls with GD is different, and different factors are predictive for the persistence of GD.