nils-herrmann/clean_scirepeval huggingface dataset:
Are Attractive People Rewarding? Sex Differences in the Neural Substrates of Facial Attractiveness

The current study examined the neural substrates of facial attractiveness judgments. Based on the extant behavioral literature, it was hypothesized that brain regions involved in identifying the potential reward value of a stimulus would be more active when men viewed attractive women than when women viewed attractive men. To test this hypothesis, we conducted an event-related functional magnetic resonance imaging experiment during which participants provided explicit attractiveness judgments for faces of the opposite sex. These individual ratings were subsequently used to perform analyses aimed at identifying the brain regions preferentially responsive to attractive faces for both sex groups. The results revealed that brain regions comprising the putative reward circuitry (e.g., nucleus accumbens [NAcc], orbito-frontal cortex [OFC]) showed a linear increase in activation with increased judgments of attractiveness. However, further analysis also revealed sex differences in the recruitment of OFC, which distinguished attractive and unattractive faces only for male participants.
Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder

We describe a machine learning technique for reconstructing image sequences rendered using Monte Carlo methods. Our primary focus is on reconstruction of global illumination with extremely low sampling budgets at interactive rates. Motivated by recent advances in image restoration with deep convolutional networks, we propose a variant of these networks better suited to the class of noise present in Monte Carlo rendering. We allow for much larger pixel neighborhoods to be taken into account, while also improving execution speed by an order of magnitude. Our primary contribution is the addition of recurrent connections to the network in order to drastically improve temporal stability for sequences of sparsely sampled input images. Our method also has the desirable property of automatically modeling relationships based on auxiliary per-pixel input channels, such as depth and normals. We show significantly higher quality results compared to existing methods that run at comparable speeds, and furthermore argue a clear path for making our method run at realtime rates in the near future.
Leave-one-out error and stability of learning algorithms with applications

The leave-one-out error is an important statistical estimator of the performance of a learning algorithm. Unlike the empirical error, it is almost unbiased and is frequently used for model selection. We review attempts aiming at justifying the use of leave-one-out error in Machine Learning. We especially focus on the concept of stability of a learning algorithm and show how this can be used to formally link leaveone-out error to the generalization error. Stability has also motivated recent work on averaging techniques similar to bagging which we briefly summarize in the paper. The ideas we develop are illustrated in some details in the context of kernel-based learning algorithms.
A Second-Order Class-D Audio Amplifier

Class-D audio amplifiers are particularly efficient, and this efficiency has led to their ubiquity in a wide range of modern electronic appliances. Their output takes the form of a high-frequency square wave whose duty cycle (ratio of on-time to off-time) is modulated at low frequency according to the audio signal. A mathematical model is developed here for a second-order class-D amplifier design (i.e., containing one second-order integrator) with negative feedback. We derive exact expressions for the dominant distortion terms, corresponding to a general audio input signal, and confirm these predictions with simulations. We also show how the observed phenomenon of “pulse skipping” arises from an instability of the analytical solution upon which the distortion calculations are based, and we provide predictions of the circumstances under which pulse skipping will take place, based on a stability analysis. These predictions are confirmed by simulations.
Algebraic feature extraction of image for recognition

The extraction of image features is one of the fundamental tasks in image recognition . Up until now, there have been several kinds of features to be used for the purpose of image recognition as follows: (1) visual features ; (2) statistical features of pixel ; (3) transform coefficient features . In addition, there is another kind of feature which the author believes is very useful, i.e . (4) algebraic features which represent intrinsic attributions of an image . Singular Values (SV) of image are this kind of feature . In this paper, we prove that SV feature vector has some important properties of algebraic and geometric invariance, and insensitiveness to noise . These properties are very useful for the description and recognition of images . As an example, SV feature vector is used for the problem of recognizing human facial images . In this paper, using SV feature vector samples of facial images, a normal pattern Bayes classification model based on Sammon s optimal descriminant plane is constructed . The experimental result shows that SV feature vector has good performance of class separation . Image recognition Algebraic feature extraction Singular value feature Facial image recognition Discrinlinant vector Dimensionality reduction
Data Illustrator: Augmenting Vector Design Tools with Lazy Data Binding for Expressive Visualization Authoring

Building graphical user interfaces for visualization authoring is challenging as one must reconcile the tension between flexible graphics manipulation and procedural visualization generation based on a graphical grammar or declarative languages. To better support designers' workflows and practices, we propose Data Illustrator, a novel visualization framework. In our approach, all visualizations are initially vector graphics; data binding is applied when necessary and only constrains interactive manipulation to that data bound property. The framework augments graphic design tools with new concepts and operators, and describes the structure and generation of a variety of visualizations. Based on the framework, we design and implement a visualization authoring system. The system extends interaction techniques in modern vector design tools for direct manipulation of visualization configurations and parameters. We demonstrate the expressive power of our approach through a variety of examples. A qualitative study shows that designers can use our framework to compose visualizations.
DRAW: A Recurrent Neural Network For Image Generation

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.
Flexible HALS algorithms for sparse non-negative matrix/tensor factorization

In this paper we propose a family of new algorithms for non-negative matrix/tensor factorization (NMF/NTF) and sparse nonnegative coding and representation that has many potential applications in computational neuroscience, multi-sensory, multidimensional data analysis and text mining. We have developed a class of local algorithms which are extensions of hierarchical alternating least squares (HALS) algorithms proposed by us in . For these purposes, we have performed simultaneous constrained minimization of a set of robust cost functions called alpha and beta divergences. Our algorithms are locally stable and work well for the NMF blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, new algorithms can be potentially accommodated to different noise statistics by just adjusting a single parameter. Extensive experimental results confirm the validity and high performance of the developed algorithms, especially, with usage of the multi-layer hierarchical approach .
Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News

The amount of data in the world and in our lives seems everincreasing and there’s no end to it. The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data pre-processing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up largescale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. Classification is an important data mining technique with broad applications. It classifies data of various kinds. This paper has been carried out to make a performance evaluation of REPTree, Simple Cart and RandomTree classification algorithm. The paper sets out to make comparative evaluation of classifiers REPTree, Simple Cart and RandomTree in the context of dataset of Indian news to maximize true positive rate and minimize false positive rate. For processing Weka API were used. The results in the paper on dataset of Indian news also show that the efficiency and accuracy of RandomTree is good than REPTree, and Simple Cart.  Simple Cart, RandomTree, REPTree, Weka, WWW
A dry electrode for EEG recording.

This paper describes the design, fabrication and testing of a prototype dry surface electrode for EEG signal recording. The new dry electrode has the advantages of no need for skin preparation or conductive paste, potential for reduced sensitivity to motion artifacts and an enhanced signal-to-noise ratio. The electrode's sensing element is a 3 mm stainless steel disk which has a 2000 A (200 nm) thick nitride coating deposited onto one side. The back side of the disk is attached to an impedance converting amplifier. The prototype electrode was mounted on a copper plate attached to the scalp by a Velcro strap. The performance of this prototype dry electrode was compared to commercially available wet electrodes in 3 areas of electroencephalogram (EEG) recording: (1) spontaneous EEG, (2) sensory evoked potentials, and (3) cognitive evoked potentials. In addition to the raw EEG, the power spectra of the signals from both types of electrodes were also recorded. The results suggest that the dry electrode performs comparably to conventional electrodes for all types of EEG signal analysis. This new electrode may be useful for the production of high resolution surface maps of brain activity where a large number of electrodes or prolonged recording times are required.
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees

In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summarization. We propose an innovative sentence compression method by considering every node in the constituent parse tree and deciding its status – remove or retain. Integer liner programming with discriminative training is used to solve the problem. Under this model, we incorporate various constraints to improve the linguistic quality of the compressed sentences. Then we utilize a pipeline summarization framework where sentences are first compressed by our proposed compression model to obtain top-n candidates and then a sentence selection module is used to generate the final summary. Compared with state-ofthe-art algorithms, our model has similar ROUGE-2 scores but better linguistic quality on TAC data.
Make new friends, but keep the old: recommending people on social networking sites

This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users' friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.
A 600-dpi capacitive fingerprint sensor chip and image-synthesis technique

This paper examines the possibility of a low-cost, high-resolution fingerprint sensor chip. The test chip is composed of 64 256 sensing cells (chip size: 2.7 10.8 mm2). A new detection circuit of charge sharing is proposed, which eliminates the influences of internal parasitic capacitances. Thus, the reduced sensing-capacitor size enables a high resolution of 600 dpi, even using a conventional 0.6m CMOS process. The partial fingerprint images captured are synthesized into a full fingerprint image with an image-synthesis algorithm. The problems and possibilities of this image-synthesis technique are also analyzed and discussed.
EFFICIENT CONSTRAINED PATH PLANNING VIA SEARCH IN STATE LATTICES

We propose a novel approach to constrained path planning that is based on a special search space which efficiently encodes feasible paths. The paths are encoded implicitly as connections between states, but only feasible and local connections are included. Once this search space is developed, we systematically generate a near-minimal set of spatially distinct path primitives. This set expresses the local connectivity of constrained motions and also eliminates redundancies. The set of primitives is used to define heuristic search, and thereby create a very efficient path planner at the chosen resolution. We also discuss a wide variety of space and terrestrial robotics applications where this motion planner can be especially useful.
Targetless Calibration of a Lidar - Perspective Camera Pair

A novel method is proposed for the calibration of a camera - 3D lidar pair without the use of any special calibration pattern or point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the lidar scan and a simple perspective camera is used for the 2D images. The calibration is solved as a 2D-3D registration problem using a minimum of one (for extrinsic) or two (for intrinsic-extrinsic) planar regions visible in both cameras. The registration is then traced back to the solution of a non-linear system of equations which directly provides the calibration parameters between the bases of the two sensors. The method has been tested on a large set of synthetic lidar-camera image pairs as well as on real data acquired in outdoor environment.
Architectural Data Flow Analysis

Quality properties including performance, security and compliance are crucial for a system's success but are hard to prove, especially for complex systems. Data flow analyses support this but often only consider source code and thereby introduce high costs of repair. Data flow analyses on the architectural design level use call-and-return semantics or event-based communication between components but do not define data flows as first class entities or consider important runtime or deployment configurations. We propose introducing data flows as first class entities on the architectural level. Analyses ensure that systems meet the quality requirements even after changes in e.g. runtime or deployment configurations. Having data flows modeled as first class entities allows analyzing compliance with privacy laws, requirements for external service providers, and throughput requirements in big data scenarios on architectural level. The results allow early, cost-efficient fixing of issues.
Agglomerative Information Bottleneck

We introduce a novel distributional clustering algorithm that explicitly maximizes the mutual information per cluster between the data and given categories. This algorithm can be considered as a bottom up hard version of the recently introduced “Information Bottleneck Method”. We relate the mutual information between clusters and categories to the Bayesian classification error, which provides another motivation for using the obtained clusters as features. The algorithm is compared with the top-down soft version of the information bottleneck method and a relationship between the hard and soft results is established. We demonstrate the algorithm on the 20 Newsgroups data set. For a subset of two news-groups we achieve compression by 3 orders of magnitudes loosing only 10% of the original mutual information.
Re-examining the components of transformational and transactional leadership using the Multifactor Leadership Questionnaire

A total of 3786 respondents in 14 independent samples, ranging in size from 45 to 549 in US and foreign firms and agencies, completed the latest version of the Multifactor leadership Questionnaire (MLQ Form 5X), each describing tbeir respective leader. Based on prior literature, nine models representing different factor structures were compared to determine the best fit for the MLQ survey. The models were tested in an original set of nine samples, and then in a second replication set comprised of five samples, Results indicated the factor structure for the MLQ survey was best represented by six lower order factors and three correlated higher-order factors.
Do Better ImageNet Models Transfer Better?

Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield penultimate layer features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested.
Visual Interactive Creation, Customization, and Analysis of Data Quality Metrics

During data preprocessing, analysts spend a significant part of their time and effort profiling the quality of the data along with cleansing and transforming the data for further analysis. While quality metrics—ranging from general to domain-specific measures—support assessment of the quality of a dataset, there are hardly any approaches to visually support the analyst in customizing and applying such metrics. Yet, visual approaches could facilitate users’ involvement in data quality assessment. We present MetricDoc, an interactive environment for assessing data quality that provides customizable, reusable quality metrics in combination with immediate visual feedback. Moreover, we provide an overview visualization of these quality metrics along with error visualizations that facilitate interactive navigation of the data to determine the causes of quality issues present in the data. In this article, we describe the architecture, design, and evaluation of MetricDoc, which underwent several design cycles, including heuristic evaluation and expert reviews as well as a focus group with data quality, human-computer interaction, and visual analytics experts.
Images of Success and the Preference for Luxury

This research examines the impact of media depictions of success (or failure) on consumers’ desire for luxury brands. In a pilot study and three additional studies, we demonstrate that reading a story about a similar/successful other, such as a business major from the same university, increases consumers’ expectations about their own future wealth, which in turn increases their desire for luxury brands. However, reading about a dissimilar successful other, such as a biology major, lowers consumers’preferences for luxury brands. Furthermore, we examine the role of ease of imagining oneself in the narrative as a mediator of the relation between direction of comparison, similarity, and brand preference.
Relay Attacks on Passive Keyless Entry and Start Systems in Modern Cars

We demonstrate relay attacks on Passive Keyless Entry and Start (PKES) systems used in modern cars. We build two efficient and inexpensive attack realizations, wired and wireless physical-layer relays, that allow the attacker to enter and start a car by relaying messages between the car and the smart key. Our relays are completely independent of the modulation, protocol, or presence of strong authentication and encryption. We perform an extensive evaluation on 10 car models from 8 manufacturers. Our results show that relaying the signal in one direction only (from the car to the key) is sufficient to perform the attack while the true distance between the key and car remains large (tested up to 50 meters, non line-of-sight). We also show that, with our setup, the smart key can be excited from up to 8 meters. This removes the need for the attacker to get close to the key in order to establish the relay. We further analyze and discuss critical system characteristics. Given the generality of the relay attack and the number of evaluated systems, it is likely that all PKES systems based on similar designs are also vulnerable to the same attack. Finally, we propose immediate mitigation measures that minimize the risk of relay attacks as well as recent solutions that may prevent relay attacks while preserving the convenience of use, for which PKES systems were initially introduced.
Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.