nils-herrmann/clean_scirepeval huggingface dataset:
Markov constraints: steerable generation of Markov sequences

Markov chains are a well known tool to model temporal properties of many phenomena, from text structure to fluctuations in economics. Because they are easy to generate, Markovian sequences, i.e. temporal sequences having the Markov property, are also used for content generation applications such as text or music generation that imitate a given style. However, Markov sequences are traditionally generated using greedy, left-to-right algorithms. While this approach is computationally cheap, it is fundamentally unsuited for interactive control. This paper addresses the issue of generating steerable Markovian sequences. We target interactive applications such as games, in which users want to control, through simple input devices, the way the system generates a Markovian sequence, such as a text, a musical sequence or a drawing. To this aim, we propose to revisit Markov sequence generation as a branch and bound constraint satisfaction problem (CSP). We propose a CSP formulation of the basic Markovian hypothesis as elementary Markov Constraints (EMC). We propose algorithms that achieve domain-consistency for the propagators of EMCs, in an event-based implementation of CSP. We show how EMCs can be combined to estimate the global Markovian probability of a whole sequence, and accommodate for different species of Markov generation such as fixed order, variable-order, or smoothing. Such a formulation, although more costly than traditional greedy generation algorithms, yields the immense advantage of being naturally steerable, since control specifications can be represented by arbitrary additional constraints, without any modification of the generation algorithm. We illustrate our approach on simple yet combinatorial chord sequence and melody generation problems and give some performance results.
Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives

After 40 years of investigation, steady-state visually evoked potentials (SSVEPs) have been shown to be useful for many paradigms in cognitive (visual attention, binocular rivalry, working memory, and brain rhythms) and clinical neuroscience (aging, neurodegenerative disorders, schizophrenia, ophthalmic pathologies, migraine, autism, depression, anxiety, stress, and epilepsy). Recently, in engineering, SSVEPs found a novel application for SSVEP-driven brain-computer interface (BCI) systems. Although some SSVEP properties are well documented, many questions are still hotly debated. We provide an overview of recent SSVEP studies in neuroscience (using implanted and scalp EEG, fMRI, or PET), with the perspective of modern theories about the visual pathway. We investigate the steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation. Next, we describe the SSVEP-BCI paradigm and review recently developed SSVEP-based BCI systems. Lastly, we outline future research directions related to basic and applied aspects of SSVEPs.
CarSafe: a driver safety app that detects dangerous driving behavior using dual-cameras on smartphones

Driving while being tired or distracted is dangerous. We are developing the CafeSafe app for Android phones, which fuses information from both front and back cameras and others embedded sensors on the phone to detect and alert drivers to dangerous driving conditions in and outside of the car. CarSafe uses computer vision and machine learning algorithms on the phone to monitor and detect whether the driver is tired or distracted using the front camera while at the same time tracking road conditions using the back camera. CarSafe is the first dual-camera application for smart-phones.
Vision-Based Unmanned Aerial Vehicle Navigation Using Geo-Referenced Information

This paper investigates the possibility of augmenting an Unmanned Aerial Vehicle (UAV) navigation system with a passive video camera in order to cope with long-term GPS outages. The paper proposes a vision-based navigation architecture which combines inertial sensors, visual odometry, and registration of the on-board video to a geo-referenced aerial image. The vision-aided navigation system developed is capable of providing high-rate and drift-free state estimation for UAV autonomous navigation without the GPS system. Due to the use of image-to-map registration for absolute position calculation, drift-free position performance depends on the structural characteristics of the terrain. Experimental evaluation of the approach based on offline flight data is provided. In addition the architecture proposed has been implemented on-board an experimental UAV helicopter platform and tested during vision-based autonomous flights.
Distributed MAP Inference for Undirected Graphical Models

Graphical models have widespread uses in information extraction and natural language processing. Recent improvements in approximate inference techniques [1, 2, 3, 4] have allowed exploration of dense models over a large number of variables. These applications include coreference resolution [5, 6], relation extraction [7], and joint inference [8, 9, 10]. But as the graphs grow to web scale, even these inference techniques become infeasible, motivating the need for scalable, parallelizable solutions.
Variational Shape from Light Field

In this paper we propose an efficient method to calculate a highquality depth map from a single raw image captured by a light field or plenoptic camera. The proposed model combines the main idea of Active Wavefront Sampling (AWS) with the light field technique, i.e. we extract so-called sub-aperture images out of the raw image of a plenoptic camera, in such a way that the virtual view points are arranged on circles around a fixed center view. By tracking an imaged scene point over a sequence of sub-aperture images corresponding to a common circle, one can observe a virtual rotation of the scene point on the image plane. Our model is able to measure a dense field of these rotations, which are inversely related to the scene depth.
Febrl -: an open source data cleaning, deduplication and record linkage system with a graphical user interface

Matching records that refer to the same entity across data-bases is becoming an increasingly important part of many data mining projects, as often data from multiple sources needs to be matched in order to enrich data or improve its quality. Significant advances in record linkage techniques have been made in recent years. However, many new techniques are either implemented in research proof-of-concept systems only, or they are hidden within expensive 'black box' commercial software. This makes it difficult for both researchers and practitioners to experiment with new record linkage techniques, and to compare existing techniques with new ones. The Febrl ( system aims to fill this gap. It contains many recently developed techniques for data cleaning, deduplication and record linkage, and encapsulates them into a graphical user interface (GUI). Febrl thus allows even inexperienced users to learn and experiment with both traditional and new record linkage techniques. Because Febrl is written in Python and its source code is available, it is fairly easy to integrate new record linkage techniques into it. Therefore, Febrl can be seen as a tool that allows researchers to compare various existing record linkage techniques with their own ones, enabling the record linkage research community to conduct their work more efficiently. Additionally, Febrl is suitable as a training tool for new record linkage users, and it can also be used for practical linkage projects with data sets that contain up to several hundred thousand records.
B4. Analysis and design of single section and three-section ultra-wideband quadrature hybrid couplers

This paper presents analysis and design of two different slot coupled ultra-wideband quadrature couplers, employing a single section and a three-section configuration. A design procedure based on analysis of different design parameters of both circuit configurations is proposed. The simulation results of the single section coupler shows an amplitude imbalance ± 1dB form 4.8-10.6-GHz. The return loss and the isolation are better than 19dB and 25dB, respectively. The manufactured single section can exhibit 6° phase imbalance from 3-8 GHz. An improved three-section slot coupled hybrid coupler is designed by adding strips and slots of elliptical shape on the both sides of the tapered hexagon. The final circuit of three section coupler can exhibit a coupling of 3dB ±0.75 dB from 3.1-10.6-GHz. The corresponding return loss and isolation is the order of 22 dB and 26 dB, respectively. A phase deviation of ±1.8° between the ports is confirmed.
Real-time 3D head pose and facial landmark estimation from depth images using triangular surface patch features

We present a real-time system for 3D head pose estimation and facial landmark localization using a commodity depth sensor. We introduce a novel triangular surface patch (TSP) descriptor, which encodes the shape of the 3D surface of the face within a triangular area. The proposed descriptor is viewpoint invariant, and it is robust to noise and to variations in the data resolution. Using a fast nearest neighbor lookup, TSP descriptors from an input depth map are matched to the most similar ones that were computed from synthetic head models in a training phase. The matched triangular surface patches in the training set are used to compute estimates of the 3D head pose and facial landmark positions in the input depth map. By sampling many TSP descriptors, many votes for pose and landmark positions are generated which together yield robust final estimates. We evaluate our approach on the publicly available Biwi Kinect Head Pose Database to compare it against state-of-the-art methods. Our results show a significant improvement in the accuracy of both pose and landmark location estimates while maintaining real-time speed.
Large scale multi-label classification via metalabeler

The explosion of online content has made the management of such content non-trivial. Web-related tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multi-label categorization systems on a large scale. Existing multi-label classification methods either do not scale or have unsatisfactory performance. In this work, we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive cross-validation. Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods. Moreover, MetaLabeler scales to millions of multi-labeled instances and can be deployed easily. This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo!, yielding a significant improvement in performance.
LASyM: A Learning Analytics System for MOOCs

Nowadays, the Web has revolutionized our vision as to how deliver courses in a radically transformed and enhanced way. Boosted by Cloud computing, the use of the Web in education has revealed new challenges and looks forward to new aspirations such as MOOCs (Massive Open Online Courses) as a technology-led revolution ushering in a new generation of learning environments. Expected to deliver effective education strategies, pedagogies and practices, which lead to student success, the massive open online courses, considered as the “linux of education”, are increasingly developed by elite US institutions such MIT, Harvard and Stanford by supplying open/distance learning for large online community without paying any fees, MOOCs have the potential to enable free university-level education on an enormous scale. Nevertheless, a concern often is raised about MOOCs is that a very small proportion of learners complete the course while thousands enrol for courses. In this paper, we present LASyM, a learning analytics system for massive open online courses. The system is a Hadoop based one whose main objective is to assure Learning Analytics for MOOCs’ communities as a mean to help them investigate massive raw data, generated by MOOC platforms around learning outcomes and assessments, and reveal any useful information to be used in designing learning-optimized MOOCs. To evaluate the effectiveness of the proposed system we developed a method to identify, with low latency, online learners more likely to drop out. Cloud Computing; MOOCs; Hadoop; Learning
Plenoptic depth estimation from multiple aliased views

With a sampled light field acquired from a plenoptic camera, several low-resolution views of the scene are available from which to infer depth. Unlike traditional multiview stereo, these views may be highly aliased due to the sparse sampling lattice in space, which can lead to reconstruction errors. We first analyse the conditions under which aliasing is a problem, and discuss the trade-offs for different parameter choices in plenoptic cameras. We then propose a method to compensate for the aliasing, whilst fusing the information from the multiple views to correctly recover depth maps. We show results on synthetic and real data, demonstrating the effectiveness of our method.
Patterns of Emergent Leadership in Virtual Teams

This study observed naturally occurring emergent leadership behavior in distributed virtual teams. The goal of the study was to understand how leadership behaviors emerge and are distributed in these kinds of teams. Archived team interaction captured during the course of a virtual collaboration exercise was analyzed using an a priori content analytic scheme derived from behaviorally-based leadership theory to capture behavior associated with leadership in virtual environments. The findings lend support to the notion that behaviorally-based leadership theory can provide insights into emergent leadership in virtual environments. This study also provides additional insights into the patterns of leadership that emerge in virtual environments and relationship to leadership behaviors.
Cognitive Adaptations for Social Exchange

Is it not reasonable to anticipate that our understanding of the human mind would be aided greatly by knowing the purpose for which it was designed?  The human mind is the most complex natural phenomenon humans have yet encountered , and Darwin's gift to those who wish to understand it is a knowledge of the process that created it and gave it its distinctive organization: evolution. Because we know that the human mind is the product of the evolutionary process, we know something vitally illuminating: that, aside from those properties acquired by chance, the mind consists of a set of adaptations, designed to solve the long-standing adaptive problems humans encountered as hunter-gatherers. Such a view is uncontroversial to most behavioral scientists when applied to topics such as vision or balance. Yet adaptation-ist approaches to human psychology are considered radical-or even transparently false-when applied to most other areas of human thought and action, especially social behavior. Nevertheless, the logic of the adaptationist postion is completely general , and a dispassionate evaluation of its implications leads to the expectation that humans should have evolved a constellation of cognitive adaptations to social life. Our ancestors have been members of social groups and engaging in social interactions for millions and probably tens of millions of years. To behave adaptively, they not only needed to construct a spatial map of the objects disclosed to them by their retinas, but a social map of the persons, relationships, motives, interactions, emotions, and intentions that made up their social world. Our view, then, is that humans have a faculty of social cognition, consisting of a rich collection of dedicated, functionally specialized, interrelated modules (i.e., functionally isolable subunits, mechanisms, mental organs, etc.), organized to collectively guide thought and behavior with respect to the evolutionarily recurrent adaptive problems posed by the social world. Nonetheless, if such a view has merit, it not only must be argued for on theoretical grounds-however compelling-but also must be substantiated by experimental evidence, as well as by converging lines of empirical s u p Port drawn from related fieldssuch as neuroscience, linguistics, and anthropology. The 164 COOPERATION eventual goal is to recover out of carefully designed experimental studies "high-reso-lution" maps of the intricate mechanisms involved. Such an approach is intended to exploit the signal virtue of cognitive psychology: With its emphasis on mechanisms, cognitive approaches allow causal pathways to be precisely specified …
A Low-Cost Wideband Circularly Polarized Slot Array With Integrated Feeding Network and Reduced Height

This letter proposes a wideband unidirectional circularly polarized (CP) slot array with a truly planar structure, which integrates the feeding network on a single substrate. The height of the array is reduced by half compared to traditional arrays with a reflector. Four parasitic slots are etched at the edges of the ground plane to improve the gain performance when the height is reduced. The measured results show that the bandwidths (BWs) for impedance, axial ratio (AR), and gain are all more than 23%, and these three bandwidths almost cover the same frequency band. The proposed array has a good unidirectional pattern and can be used in base-station applications.
A survey of data provenance in e-science

Data management is growing in complexity as large-scale applications take advantage of the loosely coupled resources brought together by grid middleware and by abundant storage capacity. Metadata describing the data products used in and generated by these applications is essential to disambiguate the data and enable reuse. Data provenance, one kind of metadata, pertains to the derivation history of a data product starting from its original sources.In this paper we create a taxonomy of data provenance characteristics and apply it to current research efforts in e-science, focusing primarily on scientific workflow approaches. The main aspect of our taxonomy categorizes provenance systems based on why they record provenance, what they describe, how they represent and store provenance, and ways to disseminate it. The survey culminates with an identification of open research problems in the field.
CLUSTERGEN: a statistical parametric synthesizer using trajectory modeling

Unit selection synthesis has shown itself to be capable of producing high quality natural sounding synthetic speech when constructed from large databases of well-recorded, well-labeled speech. However, the cost in time and expertise of building such voices is still too expensive and specialized to be able to build individual voices for everyone. The quality in unit selection synthesis is directly related to the quality and size of the database used. As we require our speech synthesizers to have more variation, style and emotion, for unit selection synthesis, much larger databases will be required. As an alternative, more recently we have started looking for parametric models for speech synthesis, that are still trained from databases of natural speech but are more robust to errors and allow for better modeling of variation. This paper presents the CLUSTERGEN synthesizer which is implemented within the Festival/FestVox voice building environment. As well as the basic technique, three methods of modeling dynamics in the signal are presented and compared: a simple point model, a basic trajectory model and a trajectory model with overlap and add.
An augmented reality interface for visualizing and interacting with virtual content

In this paper, a novel AR interface is proposed that provides generic solutions to the tasks involved in augmenting simultaneously different types of virtual information and processing of tracking data for natural interaction. Participants within the system can experience a real-time mixture of 3D objects, static video, images, textual information and 3D sound with the real environment. The user-friendly AR interface can achieve maximum interaction using simple but effective forms of collaboration based on the combinations of human–computer interaction techniques. To prove the feasibility of the interface, the use of indoor AR techniques are employed to construct innovative applications and demonstrate examples from heritage to learning systems. Finally, an initial evaluation of the AR interface including some initial results is presented.
The Time Has Come Bayesian Methods for Data Analysis in the Organizational Sciences

The use of Bayesian methods for data analysis is creating a revolution in fields ranging from genetics to marketing. Yet, results of our literature review, including more than 10,000 articles published in 15 journals from January 2001 and December 2010, indicate that Bayesian approaches are essentially absent from the organizational sciences. Our article introduces organizational science researchers to Bayesian methods and describes why and how they should be used. We use multiple linear regression as the framework to offer a step-by-step demonstration, including the use of software, regarding how to implement Bayesian methods. We explain and illustrate how to determine the prior distribution, compute the posterior distribution, possibly accept the null value, and produce a write-up describing the entire Bayesian process, including graphs, results, and their interpretation. We also offer a summary of the advantages of using Bayesian analysis and examples of how specific published research based on frequentist analysis-based approaches failed to benefit from the advantages offered by a Bayesian approach and how using Bayesian analyses would have led to richer and, in some cases, different substantive conclusions. We hope that our article will serve as a catalyst for the adoption of Bayesian methods in organizational science research.
Deep Reinforcement Learning With Visual Attention for Vehicle Classification

Automatic vehicle classification is crucial to intelligent transportation system, especially for vehicle-tracking by police. Due to the complex lighting and image capture conditions, image-based vehicle classification in real-world environments is still a challenging task and the performance is far from being satisfactory. However, owing to the mechanism of visual attention, the human vision system shows remarkable capability compared with the computer vision system, especially in distinguishing nuances processing. Inspired by this mechanism, we propose a convolutional neural network (CNN) model of visual attention for image classification. A visual attention-based image processing module is used to highlight one part of an image and weaken the others, generating a focused image. Then the focused image is input into the CNN to be classified. According to the classification probability distribution, we compute the information entropy to guide a reinforcement learning agent to achieve a better policy for image classification to select the key parts of an image. Systematic experiments on a surveillance-nature dataset which contains images captured by surveillance cameras in the front view, demonstrate that the proposed model is more competitive than the large-scale CNN in vehicle classification tasks.