nils-herrmann/clean_scirepeval huggingface dataset:
Gaussian Process Prior Variational Autoencoders

Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many important datasets, such as time-series of images, this assumption is too strong: accounting for covariances between samples, such as those in time, can yield to a more appropriate model specification and improve performance in downstream tasks. In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue. The GPPVAE aims to combine the power of VAEs with the ability to model correlations afforded by GP priors. To achieve efficient inference in this new class of models, we leverage structure in the covariance matrix, and introduce a new stochastic backpropagation strategy that allows for computing stochastic gradients in a distributed and low-memory fashion. We show that our method outperforms conditional VAEs (CVAEs) and an adaptation of standard VAEs in two image data applications.
The Korean Wave : The Seoul of Asia

Over the past decade, Korean popular culture has spread infectiously throughout the world. The term, “Korean wave,” has been used to describe this rising popularity of Korean popular culture. The Korean wave exploded in the media across the world generating a ripple effect. The Korean government took full advantage of this national phenomenon and began aiding Korean media industries in exporting Korean pop culture. This global expansion has contributed to enhancing South Korea’s national image and its economy and has been seen as a tool for public diplomacy. This paper analyzed the Korean wave and its implications for cultural influence on neighboring countries. Furthermore, this study explored how national identity impacts framing processes related to media coverage and public response.
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation.
Anthropometric 3D Face Recognition

We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.
TRADE POLICY AND ECONOMIC GROWTH : A SKEPTIC ' S GUIDE TO THE CROSS-NATIONAL EVIDENCE

Do countries with lower policy-induced barriers to international trade grow faster, once other relevant country characteristics are controlled for? There exists a large empirical literature providing an affirmative answer to this question. We argue that methodological problems with the empirical strategies employed in this literature leave the results open to diverse interpretations. In many cases, the indicators of "openness" used by researchers are poor measures of trade barriers or are highly correlated with other sources of bad economic performance. In other cases, the methods used to ascertain the link between trade policy and growth have serious shortcomings. Papers that we review include Dollar (1992), Ben-David (1993), Sachs and Warner (1995), and Edwards (1998). We find little evidence that open trade policies--in the sense of lower tariff and non-tariff barriers to trade--are significantly associated with economic growth. Francisco Rodríguez Dani R odrik Department of Economics John F. Kennedy School of Government University of Maryland Harvard University College Park, MD 20742 79 Kennedy Street Cambridge, MA 02138 Phone: (301) 405-3480 Phone: (617) 495-9454 Fax: (301) 405-3542 Fax: (617) 496-5747 TRADE POLICY AND ECONOMIC GROWTH: A SKEPTIC'S GUIDE TO THE CROSS-NATIONAL EVIDENCE "It isn't what we don't know that kills us. It's what we know that ain't so." -Mark Twain
Chapter 2 Genetic Aspects of Gender Identity Development and Gender Dysphoria

B.P.C. Kreukels et al. (eds.), Gender Dysphoria and Disorders of Sex Development: Progress in Care and Knowledge, Focus on Sexuality Research, DOI 10.1007/978-1-4614-7441-8_2,   The development of gender identity, its variance, and gender dysphoria is thought to be a complex process involving biological and psychosocial factors. Heritability studies have demonstrated a genetic factor for the development of gender dysphoria. The brain is regarded as the anatomical substrate of gender identity, and sex differences of the brain are studied to elucidate the process of gender identity development. Many sex differences have been attributed to hormonal action, and the fi rst genetic studies in transsexuals were focused on sex-steroid-related genes. To this day, a convincing candidate gene has not been identifi ed, and it is now known that sex chromosomes have a direct effect on sex differentiation and that they may play a role in gender identity development. For future studies of the genetic base of gender dysphoria, new techniques, such as genome-wide studies, have become available. In addition, epigenetic studies may provide for a different association perspective of the genetics of gender dysphoria. Chapter 2 Genetic Aspects of Gender Identity Development and Gender Dysphoria
The Cartesian Product Algorithm: Simple and Precise Type Inference Of Parametric Polymorphism

Concrete types and abstract types are different and serve different purposes. Concrete types, the focus of this paper, are essential to support compilation, application delivery, and debugging in object-oriented environments. Concrete types should not be obtained from explicit type declarations because their presence limits polymorphism unacceptably. This leaves us with type inference. Unfortunately, while polymorphism demands the use of type inference, it has also been the hardest challenge for type inference. We review previous type inference algorithms that analyze code with parametric polymorphism and then present a new one: the cartesian product algorithm. It improves precision and efficiency over previous algorithms and deals directly with inheritance, rather than relying on a preprocessor to expand it away. Last, but not least, it is conceptually simple. The cartesian product algorithm has been used in the Self system since late 1993. We present measurements to document its performance and compare it against several previous algorithms.
E-government adoption: architecture and barriers

To provide an integrated architecture framework for e-government that represents the alignment of IT infrastructure with business process management in public sector organisations and classify the barriers that might complicate the implementation of the proposed architecture framework. The study will help IT practitioners in the public sector learn how to use and manage information technologies to revitalise business processes, improve decision-making, and gain a competitive advantage from the adoption of e-government. The proposed architecture framework for e-government adoption will reduce confusion surrounding e-government infrastructure in the public sector through understanding the implementation processes, identifying requirements of information and communications technology tools, highlighting the importance of the organisational management resources and the impact of barriers.  A range of earlier studies have been critically examined and analysed to provide an integrated architecture framework for e-government adoption that can address and identify the standards, infrastructure components, applications, and technologies for e-government. The authors have divided the architecture framework into four layers; access layer, e-government layer, e-business layer, and infrastructure layer; each layer addresses a particular aspect of e-government architecture. This paper then presents a critical analysis of barriers experienced in public sector organisations, which prevent the successful adoption of e-government; such barriers being presented in a taxonomy.  Defined organisational and technological requirements that will be necessary for the adoption of e-government in public sector organisations through construct an integrated architecture framework for e-government. The difficulties and barriers that have been experienced in public sector organisations which complicate the implementation process of e-government have been analysed and then identified and presented in a taxonomy.  The paper provides architecture framework for e-government adoption that can help to guide IT managers recognise the technological and organisational requirements for e-government adoption in public sector organisations. The framework can also help the decision makers to set a vision statement and strategic action plan for future direction in the information technology age through identifying key elements and stages for action. The authors also identify and classify the perceived barriers that might complicate the implementation process of e-government projects. The awareness of these barriers is important for any e-government project since they will alert the e-government project team with any problems or challenges might be existed during the implementation process so they will be ready to overcome them.
Jointly Embedding Knowledge Graphs and Logical Rules

Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest. Most existing methods perform the embedding task using only fact triples. Logical rules, although containing rich background information, have not been well studied in this task. This paper proposes a novel method of jointly embedding knowledge graphs and logical rules. The key idea is to represent and model triples and rules in a unified framework. Specifically, triples are represented as atomic formulae and modeled by the translation assumption, while rules represented as complex formulae and modeled by t-norm fuzzy logics. Embedding then amounts to minimizing a global loss over both atomic and complex formulae. In this manner, we learn embeddings compatible not only with triples but also with rules, which will certainly be more predictive for knowledge acquisition and inference. We evaluate our method with link prediction and triple classification tasks. Experimental results show that joint embedding brings significant and consistent improvements over stateof-the-art methods. Particularly, it enhances the prediction of new facts which cannot even be directly inferred by pure logical inference, demonstrating the capability of our method to learn more predictive embeddings.
The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic review.

Insufficient sleep, poor sleep quality and sleepiness are common problems in children and adolescents being related to learning, memory and school performance. The associations between sleep quality (k=16 studies, N=13,631), sleep duration (k=17 studies, N=15,199), sleepiness (k=17, N=19,530) and school performance were examined in three separate meta-analyses including influential factors (e.g., gender, age, parameter assessment) as moderators. All three sleep variables were significantly but modestly related to school performance. Sleepiness showed the strongest relation to school performance (r=-0.133), followed by sleep quality (r=0.096) and sleep duration (r=0.069). Effect sizes were larger for studies including younger participants which can be explained by dramatic prefrontal cortex changes during (early) adolescence. Concerning the relationship between sleep duration and school performance age effects were even larger in studies that included more boys than in studies that included more girls, demonstrating the importance of differential pubertal development of boys and girls. Longitudinal and experimental studies are recommended in order to gain more insight into the different relationships and to develop programs that can improve school performance by changing individuals' sleep patterns.
Evasion Attacks against Machine Learning at Test Time

In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradientbased approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks. Following a recently proposed framework for security evaluation, we simulate attack scenarios that exhibit different risk levels for the classifier by increasing the attacker’s knowledge of the system and her ability to manipulate attack samples. This gives the classifier designer a better picture of the classifier performance under evasion attacks, and allows him to perform a more informed model selection (or parameter setting). We evaluate our approach on the relevant security task of malware detection in PDF files, and show that such systems can be easily evaded. We also sketch some countermeasures suggested by our analysis.
Malware detection based on deep learning algorithm

In this study we represent malware as opcode sequences and detect it using a deep belief network (DBN). Compared with traditional shallow neural networks, DBNs can use unlabeled data to pretrain a multi-layer generative model, which can better represent the characteristics of data samples. We compare the performance of DBNs with that of three baseline malware detection models, which use support vector machines, decision trees, and the k-nearest neighbor algorithm as classifiers. The experiments demonstrate that the DBN model provides more accurate detection than the baseline models. When additional unlabeled data are used for DBN pretraining, the DBNs perform better than the other detection models. We also use the DBNs as an autoencoder to extract the feature vectors of executables. The experiments indicate that the autoencoder can effectively model the underlying structure of input data and significantly reduce the dimensions of feature vectors.
Orchid diversity: an evolutionary consequence of deception?

The Orchidaceae are one of the most species-rich plant families and their floral diversity and pollination biology have long intrigued evolutionary biologists. About one-third of the estimated 18,500 species are thought to be pollinated by deceit. To date, the focus has been on how such pollination evolved, how the different types of deception work, and how it is maintained, but little progress has been made in understanding its evolutionary consequences. To address this issue, we discuss here how deception affects orchid mating systems, the evolution of reproductive isolation, speciation processes and neutral genetic divergence among species. We argue that pollination by deceit is one of the keys to orchid floral and species diversity. A better understanding of its evolutionary consequences could help evolutionary biologists to unravel the reasons for the evolutionary success of orchids.
Fast Decoding in Sequence Models using Discrete Latent Variables

Autoregressive sequence models based on deep neural networks, such as RNNs, Wavenet and the Transformer attain state-of-the-art results on many tasks. However, they are difficult to parallelize and are thus slow at processing long sequences. RNNs lack parallelism both during training and decoding, while architectures like WaveNet and Transformer are much more parallelizable during training, yet still operate sequentially during decoding. We present a method to extend sequence models using discrete latent variables that makes decoding much more parallelizable. We first autoencode the target sequence into a shorter sequence of discrete latent variables, which at inference time is generated autoregressively, and finally decode the output sequence from this shorter latent sequence in parallel. To this end, we introduce a novel method for constructing a sequence of discrete latent variables and compare it with previously introduced methods. Finally, we evaluate our model end-to-end on the task of neural machine translation, where it is an order of magnitude faster at decoding than comparable autoregressive models. While lower in BLEU than purely autoregressive models, our model achieves higher scores than previously proposed non-autoregressive translation models.
Aligned Cluster Analysis for temporal segmentation of human motion

Temporal segmentation of human motion into actions is a crucial step for understanding and building computational models of human motion. Several issues contribute to the challenge of this task. These include the large variability in the temporal scale and periodicity of human actions, as well as the exponential nature of all possible movement combinations. We formulate the temporal segmentation problem as an extension of standard clustering algorithms. In particular, this paper proposes aligned cluster analysis (ACA), a robust method to temporally segment streams of motion capture data into actions. ACA extends standard kernel k-means clustering in two ways: (1) the cluster means contain a variable number of features, and (2) a dynamic time warping (DTW) kernel is used to achieve temporal invariance. Experimental results, reported on synthetic data and the Carnegie Mellon Motion Capture database, demonstrate its effectiveness.
Competitive Data Trading in Wireless-Powered Internet of Things (IoT) Crowdsensing Systems with Blockchain

With the explosive growth of smart IoT devices at the edge of the Internet, embedding sensors on mobile devices for massive data collection and collective environment sensing has been envisioned as a cost-effective solution for IoT applications. However, existing IoT platforms and framework rely on dedicated middleware for (semi-) centralized task dispatching, data storage and incentive provision. Consequently, they are usually expensive to deploy, have limited adaptability to diverse requirements, and face a series of data security and privacy issues. In this paper, we employ permissionless blockchains to construct a purely decentralized platform for data storage and trading in a wirelesspowered IoT crowdsensing system. In the system, IoT sensors use power wirelessly transferred from RF-energy beacons for data sensing and transmission to an access point. The data is then forwarded to the blockchain for distributed ledger services, i.e., data/transaction verification, recording, and maintenance. Due to coupled interference of wireless transmission and transaction fee incurred from blockchain’s distributed ledger services, rational sensors have to decide on their transmission rates to maximize individual utility. Thus, we formulate a noncooperative game model to analyze this competitive situation among the sensors. We provide the analytical condition for the existence of the Nash equilibrium as well as a series of insightful numerical results about the equilibrium strategies in the game.
A comparative study of traditional and newly proposed features for recognition of speech under stress

It is well known that the performance of speech recognition algorithms degrade in the presence of adverse environments where a speaker is under stress, emotion, or Lombard effect. This study evaluates the effectiveness of traditional features in recognition of speech under stress and formulates new features which are shown to improve stressed speech recognition. The focus is on formulating robust features which are less dependent on the speaking conditions rather than applying compensation or adaptation techniques. The stressed speaking styles considered are simulated angry and loud, Lombard effect speech, and noisy actual stressed speech from the SUSAS database which is available on CD-ROM through the NATO IST/TG-01 research group and LDC1 . In addition, this study investigates the immunity of linear prediction power spectrum and fast Fourier transform power spectrum to the presence of stress. Our results show that unlike fast Fourier transform’s (FFT) immunity to noise, the linear prediction power spectrum is more immune than FFT to stress as well as to a combination of a noisy and stressful environment. Finally, the effect of various parameter processing such as fixed versus variable preemphasis, liftering, and fixed versus cepstral mean normalization are studied. Two alternative frequency partitioning methods are proposed and compared with traditional mel-frequency cepstral coefficients (MFCC) features for stressed speech recognition. It is shown that the alternate filterbank frequency partitions are more effective for recognition of speech under both simulated and actual stressed conditions.
Empirical Assessment of Non-Linear Seismic Demand of Mainshock–Aftershock Ground-Motion Sequences for Japanese Earthquakes

This study investigates the effects of earthquake types, magnitudes, and hysteretic behavior on the peak and residual ductility demands of inelastic single-degree-of-freedom systems and evaluates the effects of major aftershocks on the non-linear structural responses. An extensive dataset of real mainshock–aftershock sequences for Japanese earthquakes is developed. The constructed dataset is large, compared with previous datasets of similar kinds, and includes numerous sequences from the 2011 Tohoku earthquake, facilitating an investigation of spatial aspects of the aftershock effects. The empirical assessment of peak and residual ductility demands of numerous inelastic systems having different vibration periods, yield strengths, and hysteretic characteristics indicates that the increase in seismic demand measures due to aftershocks occurs rarely but can be significant. For a large mega-thrust subduction earthquake, a critical factor for major aftershock damage is the spatial occurrence process of aftershocks.