nils-herrmann/clean_scirepeval huggingface dataset:
Anthropometric Measurements of the External Ear in a Group of Turkish Primary School Students

Many studies have defined human body parts and their proportions to each other morphometrically [1 3,6,7,10]. These studies are important because they enable accurate definitions for morphometric properties of different body parts in different human populations. In this context, both of the ears play a vital role in producing a natural and harmonious look and an aesthetically fine facial appearance [1,2,6]. They also are helpful in defining population standards, congenital malformations, and the ergonomic design of hearing aids [6,7,9,11,12]. This study aimed to establish morphometric properties for the ears, a determining feature of the human face, in a group of Turkish children.
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate photorealistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the imageto-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors’ own photos), our model can generate high-quality facial expression videos of which about 50% are labeled as real by Amazon Mechanical Turk workers. Please see our project page1 for more demonstrations.
Pointing at 3d target projections with one-eyed and stereo cursors

We present a study of cursors for selecting 2D-projected 3D targets. We compared a stereo- and mono-rendered (one-eyed) cursor using two mouse-based and two remote pointing techniques in a 3D Fitts' law pointing experiment. The first experiment used targets at fixed depths. Results indicate that one-eyed cursors only improve screen-plane pointing techniques, and that constant target depth does not influence pointing throughput. A second experiment included pointing between targets at varying depths and used only "screen-plane" pointing techniques. Our results suggest that in the absence of stereo cue conflicts, screen-space projections of Fitts' law parameters (target size and distance) yield constant throughput despite target depth differences and produce better models of performance.
A hybrid data analytic approach to predict college graduation status and its determinative factors

The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred to as completion rates, directly impact university rankings and represent a measurement of institutional performance and student success. In recent years, there has been a concerted effort by federal and state governments to increase the transparency and accountability of institutions, making “graduation rates” an important and challenging university goal. In line with this, the main purpose of this paper is to propose a hybrid data analytic approach which can be flexibly implemented not only in the USA but also at various colleges across the world which would help predict the graduation status of undergraduate students due to its generic nature. It is also aimed at providing a means of determining and ranking the critical factors of graduation status.  This study focuses on developing a novel hybrid data analytic approach to predict the degree completion of undergraduate students at a four-year public university in the USA. Via the deployment of the proposed methodology, the data were analyzed using three popular data mining classifications methods (i.e. decision trees, artificial neural networks, and support vector machines) to develop predictive degree completion models. Finally, a sensitivity analysis is performed to identify the relative importance of each predictor factor driving the graduation.  The sensitivity analysis of the most critical factors in predicting graduation rates is determined to be fall-term grade-point average, housing status (on campus or commuter), and which high school the student attended. The least influential factors of graduation status are ethnicity, whether or not a student had work study, and whether or not a student applied for financial aid. All three data analytic models yielded high accuracies ranging from 71.56 to 77.61 percent, which validates the proposed model.  This study presents uniqueness in that it presents an unbiased means of determining the driving factors of college graduation status with a flexible and powerful hybrid methodology to be implemented at other similar decision-making settings.
Denoising without access to clean data using a partitioned autoencoder

Training a denoising autoencoder neural network requires access to truly clean data, a requirement which is often impractical. To remedy this, we introduce a method to train an autoencoder using only noisy data, having examples with and without the signal class of interest. The autoencoder learns a partitioned representation of signal and noise, learning to reconstruct each separately. We illustrate the method by denoising birdsong audio (available abundantly in uncontrolled noisy datasets) using a convolutional autoencoder.
Essential Layers, Artifacts, and Dependencies of Enterprise Architecture

After a period where implementation speed was more important than integration, consistency and reduction of complexity, architectural considerations have become a key issue of information management in recent years again. Enterprise architecture is widely accepted as an essential mechanism for ensuring agility and consistency, compliance and efficiency. Although standards like TOGAF and FEAF have developed, however, there is no common agreement on which architecture layers, which artifact types and which dependencies constitute the essence of enterprise architecture. This paper contributes to the identification of essential elements of enterprise architecture by (1) specifying enterprise architecture as a hierarchical, multilevel system comprising aggregation hierarchies, architecture layers and views, (2) discussing enterprise architecture frameworks with regard to essential elements, (3) proposing interfacing requirements of enterprise architecture with other architecture models and (4) matching these findings with current enterprise architecture practice in several large companies.
Algorithms for Characterizing Measured Three-Phase Unbalanced Voltage Dips

This paper addresses the problem of estimating the characteristics of three-phase unbalanced voltage dips from measured phase voltages. This is important for obtaining statistics on voltage dips and for obtaining information about the underlying event (e.g., the fault type). Two different algorithms are compared. The "six-phase algorithm" is computationally simple and easy to interpret. However, large phase-angle jumps lead to wrong estimations. The "symmetrical component algorithm" gives a correct value in almost all cases. Exceptions are events with very severe load influence on the voltages during the fault. The latter algorithm is studied in more detail through a measurement example and through a study of a range of synthetic events. The conclusion is that both algorithms have their specific application areas.
Methodologies for realizing natural-language-facilitated human-robot cooperation: A review

It is natural and efficient to use Natural Language (NL) for transferring knowledge from a human to a robot. Recently, research on using NL to support human-robot cooperation (HRC) has received increasing attention in several domains such as robotic daily assistance, robotic health caregiving, intelligent manufacturing, autonomous navigation and robot social accompany. However, a high-level review that can reveal the realization process and the latest methodologies of using NL to facilitate HRC is missing. In this review, a comprehensive summary about the methodology development of natural-language-facilitated human-robot cooperation (NLC) has been made. We first analyzed driving forces for NLC developments. Then, with a temporal realization order, we reviewed three main steps of NLC: human NL understanding, knowledge representation, and knowledge-world mapping. Last, based on our paper review and perspectives, potential research trends in NLC were discussed.
Reflection from layered surfaces due to subsurface scattering

The reflection of light from most materials consists of two major terms: the specular and the diffuse. Specular reflection may be modeled from first principles by considering a rough surface consisting of perfect reflectors, or micro-facets. Diffuse reflection is generally considered to result from multiple scattering either from a rough surface or from within a layer near the surface. Accounting for diffuse reflection by Lambert’s Cosine Law, as is universally done in computer graphics, is not a physical theory based on first principles. This paper presents a model for subsurface scattering in layered surfaces in terms of one-dimensional linear transport theory. We derive explicit formulas for backscattering and transmission that can be directly incorporated in most rendering systems, and a general Monte Carlo method that is easily added to a ray tracer. This model is particularly appropriate for common layered materials appearing in nature, such as biological tissues (e.g. skin, leaves, etc.) or inorganic materials (e.g. snow, sand, paint, varnished or dusty surfaces). As an application of the model, we simulate the appearance of a face and a cluster of leaves from experimental data describing their layer properties.
A Coaxial Line to Post-Wall Waveguide Transition for a Cost-Effective Transformer between a RF-Device and a Planar Slot-Array Antenna in 60-GHz Band

Interfaces between a coaxial structure and a post-wall waveguide are proposed as the essential components for cost-effective millimeter-wave modules. PTFE substrate is selected in terms of loss and manufacturability. The reflection and the transmission characteristics are investigated. The short-stepped and the short-taper-stepped feeding structures provide 14.7% and 13.2% bandwidths for the reflection smaller than −15 dB, respectively. The 46 × 40 mm2 size antenna fed by the shortstepped structure in PTFE substrate gives 27.3 dBi with 58.2% efficiency at 60.0 GHz. Feeding structures in PTFE substrate fulfill electrical and manufacturing demands in millimeter-wave bands. key words: home-link system, coaxial, post-wall waveguide, millimeterwave band, PTFE
Dissociation of reward anticipation and outcome with event-related fMRI.

Reward processing involves both appetitive and consummatory phases. We sought to examine whether reward anticipation vs outcomes would recruit different regions of ventral forebrain circuitry using event-related fMRI. Nine healthy volunteers participated in a monetary incentive delays task in which they either responded to a cued target for monetary reward, responded to a cued target for no reward, or did not respond to a cued target during scanning. Multiple regression analyses indicated that while anticipation of reward vs non-reward activated foci in the ventral striatum, reward vs non-reward outcomes activated foci in the ventromedial frontal cortex. These findings suggest that reward anticipation and outcomes may differentially recruit distinct regions that lie along the trajectory of ascending dopamine projections.
Integrating Out Multinomial Parameters in Latent Dirichlet Allocation and Naive Bayes for Collapsed Gibbs Sampling

This note shows how to integrate out the multinomial parameters for latent Dirichlet allocation (LDA) and naive Bayes (NB) models. This allows us to perform Gibbs sampling without taking multinomial parameter samples. Although the conjugacy of the Dirichlet priors makes sampling the multinomial parameters relatively straightforward, sampling on a topic-by-topic basis provides two advantages. First, it means that all samples are drawn from simple discrete distributions with easily calculated parameters. Second, and more importantly, collapsing supports fully stochastic Gibbs sampling where the model is updated after each word (in LDA) or document (in NB) is assigned a topic. Typically, more stochastic sampling leads to quicker convergence to the stationary state of the Markov chain made up of the Gibbs samples.
The implications of embodiment for behavior and cognition: animal and robotic case studies

In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. ‘intelligence requires a body’, the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of ‘body schema’ and ‘forward models’ as a natural extension of the embodied approach toward first representations.
A Bayesian Approach to Learning Causal Networks

Whereas acausal Bayesian networks rep­ resent probabilistic independence, causal Bayesian networks represent causal relation­ ships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal net­ works are fairly well developed. These meth­ ods often employ assumptions to facilitate the construction of priors, including the as­ sumptions of parameter independence, pa­ rameter modularity, and likelihood equiva­ lence. We show that although these assump­ tions also can be appropriate for learning causal networks, we need additional assump­ tions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component in­ dependence. We show that these new as­ sumptions, when combined with parame­ ter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.
Neurocognitive poetics: methods and models for investigating the neuronal and cognitive-affective bases of literature reception

A long tradition of research including classical rhetoric, esthetics and poetics theory, formalism and structuralism, as well as current perspectives in (neuro)cognitive poetics has investigated structural and functional aspects of literature reception. Despite a wealth of literature published in specialized journals like Poetics, however, still little is known about how the brain processes and creates literary and poetic texts. Still, such stimulus material might be suited better than other genres for demonstrating the complexities with which our brain constructs the world in and around us, because it unifies thought and language, music and imagery in a clear, manageable way, most often with play, pleasure, and emotion (Schrott and Jacobs, 2011). In this paper, I discuss methods and models for investigating the neuronal and cognitive-affective bases of literary reading together with pertinent results from studies on poetics, text processing, emotion, or neuroaesthetics, and outline current challenges and future perspectives.
N-gram IDF: A Global Term Weighting Scheme Based on Information Distance

This paper first reveals the relationship between Inverse Document Frequency (IDF), a global term weighting scheme, and information distance, a universal metric defined by Kolmogorov complexity. We concretely give a theoretical explanation that the IDF of a term is equal to the distance between the term and the empty string in the space of information distance in which the Kolmogorov complexity is approximated using Web documents and the Shannon-Fano coding. Based on our findings, we propose N-gram IDF, a theoretical extension of IDF for handling words and phrases of any length. By comparing weights among N-grams of any N, N-gram IDF enables us to determine dominant N-grams among overlapping ones and extract key terms of any length from texts without using any NLP techniques. To efficiently compute the weight for all possible N-grams, we adopt two string processing techniques, i.e., maximal substring extraction using enhanced suffix array and document listing using wavelet tree. We conducted experiments on key term extraction and Web search query segmentation, and found that N-gram IDF was competitive with state-of-the-art methods that were designed for each application using additional resources and efforts. The results exemplified the potential of N-gram IDF.
The flipped classroom: for active, effective and increased learning – especially for low achievers

Higher education has been pressured to shift towards more flexible, effective, active, and student-centered teaching strategies that mitigate the limitations of traditional transmittal models of education. Lately, the flipped classroom model has been suggested to support this transition. However, research on the use of flipped classroom in higher education is in its infancy and little is known about student’s perceptions of learning through flipped classroom. This study examined students’ perceptions of flipped classroom education in a last year university course in research methods. A questionnaire was administered measuring students’ (n = 240) perceptions of flipped classroom in general, video as a learning tool, and Moodle (Learning Management System) as a supporting tool within the frame of a flipped classroom model. The results revealed that a large majority of the students had a positive attitude towards flipped classroom, the use of video and Moodle, and that a positive attitude towards flipped classroom was strongly correlated to perceptions of increased motivation, engagement, increased learning, and effective learning. Low achievers significantly reported more positively as compared to high achievers with regards to attitudes towards the use of video as a learning tool, perceived increased learning, and perceived more effective learning.
Analyzing the Performance of Multilayer Neural Networks for Object Recognition

In the last two years, convolutional neural networks (CNNs) have achieved an impressive suite of results on standard recognition datasets and tasks. CNN-based features seem poised to quickly replace engineered representations, such as SIFT and HOG. However, compared to SIFT and HOG, we understand much less about the nature of the features learned by large CNNs. In this paper, we experimentally probe several aspects of CNN feature learning in an attempt to help practitioners gain useful, evidence-backed intuitions about how to apply CNNs to computer vision problems.
A printed inverted-F antenna for circular polarization

In this paper, a printed inverted-F antenna for circular polarization is proposed. A rectangular element is loaded at the feeding element perpendicularly. The circular polarized wave is radiated in the frequency range between the first resonant frequency and the second resonant frequency produced by the rectangular element. The relationships between the geometrical parameters of the loaded rectangular element and the antenna characteristics (return loss and axial ratio) are investigated.
Stationary Frame Induction Motor Feed Forward Current Controller With Back EMF Compensation

In this paper, a novel induction motor (IM) stator current controller is proposed based on the stationary frame feed forward controlling structure. The proposed regulator represents an improved variant of the existing stationary and rotational frame IM stator current regulators based on the back EMF compensation. The paper novelty consists of the stationary frame controlling structure based on two feed forward actions that enable asymptotic reference tracking. Namely, the novel stationary frame regulator includes a proportional integral (PI) regulation term combined with a stator resistance voltage drop and back EMF feed forward compensations, with the back EMF compensation based on the stator flux value estimated by means of a programmable low-pass filter. The feedback term is based on a PI controller, with the corresponding parameter tuning procedure outlined in the paper. The simulation runs are included in the paper in order to examine the regulator performance in relation to the factors critical to the IM stator current regulation: variable IM model parameter values, transport delay introduced in the control loop, variable stator frequency, and rotor speed values. Also, the experimental tests are presented and carried out on an experimental setup for different operating conditions typical for an IM drive.
Global change and the ecology of cities.

Urban areas are hot spots that drive environmental change at multiple scales. Material demands of production and human consumption alter land use and cover, biodiversity, and hydrosystems locally to regionally, and urban waste discharge affects local to global biogeochemical cycles and climate. For urbanites, however, global environmental changes are swamped by dramatic changes in the local environment. Urban ecology integrates natural and social sciences to study these radically altered local environments and their regional and global effects. Cities themselves present both the problems and solutions to sustainability challenges of an increasingly urbanized world.
Image Processing using IP Core Generator through FPGA

Xilinx CORE Generator System generates and delivers parameterizable cores optimized for Xilinx FPGAs. CORE Generator is mainly used to create high density, high performance designs in Xilinx FPGAs in less time. The CORE Generator is included with the ISE WebPack and ISE Foundation software and comes with an extensive library of Xilinx LogiCORE IP. These include DSP functions, memories, storage elements, math functions and a variety of basic elements. Xilinx provides a flexible Block Memory Generator core to create compact, high-performance memories running at up to 450 MHz. Block Memory Generator provides single port and dual port block memory. These memory types differ in selection of operating modes.Matlab tool is used to convert the image that is being processed to .coe file format.
Imagination-Augmented Agents for Deep Reinforcement Learning

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several baselines.