nils-herrmann/clean_scirepeval huggingface dataset:
Extensible Authentication Protocol (EAP) Key Management Framework

Status of This Memo This document specifies an Internet standards track protocol for the Internet community, and requests discussion and suggestions for improvements. Please refer to the current edition of the "Internet Official Protocol Standards" (STD 1) for the standardization state and status of this protocol. Distribution of this memo is unlimited.  The Extensible Authentication Protocol (EAP), defined in RFC 3748, enables extensible network access authentication. This document specifies the EAP key hierarchy and provides a framework for the transport and usage of keying material and parameters generated by EAP authentication algorithms, known as "methods". It also provides a detailed system-level security analysis, describing the conditions under which the key management guidelines described in RFC 4962 can be satisfied.
Modeling Dynamic Hair as a Continuum

In this paper we address the difficult problem of hair dynamics, particularly hair-hair and hair-air interactions. To model these interactions, we propose to consider hair volume as a continuum. Subsequently, we treat the interaction dynamics to be fluid dynamics. This proves to be a strong as well as viable approach for an otherwise very complex phenomenon. However, we retain the individual character of hair, which is vital to visually realistic rendering of hair animation. For that, we develop an elaborate model for stiffness and inertial dynamics of individual hair strand. Being a reduced coordinate formulation, the stiffness dynamics is numerically stable and fast. We then unify the continuum interaction dynamics and the individual hair’s stiffness dynamics.
Event perception: a mind-brain perspective.

People perceive and conceive of activity in terms of discrete events. Here the authors propose a theory according to which the perception of boundaries between events arises from ongoing perceptual processing and regulates attention and memory. Perceptual systems continuously make predictions about what will happen next. When transient errors in predictions arise, an event boundary is perceived. According to the theory, the perception of events depends on both sensory cues and knowledge structures that represent previously learned information about event parts and inferences about actors' goals and plans. Neurological and neurophysiological data suggest that representations of events may be implemented by structures in the lateral prefrontal cortex and that perceptual prediction error is calculated and evaluated by a processing pathway, including the anterior cingulate cortex and subcortical neuromodulatory systems.
Combining and measuring the benefits of bimanual pen and direct-touch interaction on horizontal interfaces

Many research projects have demonstrated the benefits of bimanual interaction for a variety of tasks. When choosing bimanual input, system designers must select the input device that each hand will control. In this paper, we argue for the use of pen and touch two-handed input, and describe an experiment in which users were faster and committed fewer errors using pen and touch input in comparison to using either touch and touch or pen and pen input while performing a representative bimanual task. We present design principles and an application in which we applied our design rationale toward the creation of a learnable set of bimanual, pen and touch input commands.
Fast Implementation of General Matrix-Vector Multiplication (GEMV) on Kepler GPUs

This paper proposes a fast implementation method for the general matrix-vector multiplication (GEMV) routine, which is one of the level-2 Basic Linear Algebra Subprograms (BLAS) subroutines, for a column-major and non-transposed matrix on NVIDIA Kepler architecture graphics processing units (GPUs). We began by implementing the GEMV kernel using typical blocking techniques for shared-memory and register along with 128-bit vector load/store instructions. In our initial investigation, we found that even though the kernel could approach actual peak GPU throughput at some matrix sizes, performance fluctuates periodically depending on the problem size. In our next step, we investigated the reason for the fluctuations using a performance model based on a thread-block scheduling mechanism, and then created a method of determining optimal thread-block sizes that avoids those fluctuations. As the results show, when run on two Kepler architecture GPUs, our single-precision GEMV (SGEMV) routine achieved better performance in terms of both throughput and performance stability (with respect to the problem size) when compared to existing implementations: CUBLAS 6.5, MAGMA 1.4.1 and KBLAS 1.0. Our implementation techniques can be used not only for SGEMV but also double-precision (DGEMV), single-complex (CGEMV), and double-complex (ZGEMV). While this paper discusses primarily Kepler architecture, we also explore the performance of proposal implementation on Maxwell architecture, which is the next generation of Kepler architecture.
Beyond diathesis stress: differential susceptibility to environmental influences.

Evolutionary-biological reasoning suggests that individuals should be differentially susceptible to environmental influences, with some people being not just more vulnerable than others to the negative effects of adversity, as the prevailing diathesis-stress view of psychopathology (and of many environmental influences) maintains, but also disproportionately susceptible to the beneficial effects of supportive and enriching experiences (or just the absence of adversity). Evidence consistent with the proposition that individuals differ in plasticity is reviewed. The authors document multiple instances in which (a) phenotypic temperamental characteristics, (b) endophenotypic attributes, and (c) specific genes function less like "vulnerability factors" and more like "plasticity factors," thereby rendering some individuals more malleable or susceptible than others to both negative and positive environmental influences. Discussion focuses upon limits of the evidence, statistical criteria for distinguishing differential susceptibility from diathesis stress, potential mechanisms of influence, and unknowns in the differential-susceptibility equation.
3D Model Retrieval with Spherical Harmonics and Moments

We consider 3D object retrieval in which a polygonal mesh serves as a query and similar objects are retrieved from a collection of 3D objects. Algorithms proceed first by a normalization step in which models are transformed into canonical coordinates. Second, feature vectors are extracted and compared with those derived from normalized models in the search space. In the feature vector space nearest neighbors are computed and ranked. Retrieved objects are displayed for inspection, selection, and processing. Our feature vectors are based on rays cast from the center of mass of the object. For each ray the object extent in the ray direction yields a sample of a function on the sphere. We compared two kinds of representations of this function, namely spherical harmonics and moments. Our empirical comparison using precision-recall diagrams for retrieval results in a data base of 3D models showed that the method using spherical harmonics performed better.
A review of image warping methods

Image warping is a transformation which maps all positions in one image plane to positions in a second plane. It arises in many image analysis problems, whether in order to remove optical distortions introduced by a camera or a particular viewing perspective, to register an image with a map or template, or to align two or more images. The choice of warp is a compromise between a smooth distortion and one which achieves a good match. Smoothness can be ensured by assuming a parametric form for the warp or by constraining it using di erential equations. Matching can be speci ed by points to be brought into alignment, by local measures of correlation between images, or by the coincidence of edges. Parametric and nonparametric approaches to warping, and matching criteria, are reviewed.
Monopolar Patch Antenna With Dual-Band and Wideband Operations

The designs of dual-band and wide-band microstrip patch antennas with conical radiation patterns are presented in this paper. The antenna is composed of a square-ring patch that is shorted to the ground plane through four shorting walls. Three resonant modes with conical radiation patterns can be simultaneously excited in the antenna structure by a patch-loaded coaxial probe inside the square-ring patch, and they can be designed as a dual-band operation. Moreover, by adjusting the width of the shorting walls, the three modes can be coupled together to realize a wide-band operation. From the obtained results, the 10 dB impedance bandwidths at lower and higher operating frequencies are respectively 42 and 8% for the dual-band antenna design, and the wide-band design exhibits an impedance bandwidth of about 70%.
Perceived learning environment and students ’ emotional experiences : A multilevel analysis of mathematics classrooms *

A multilevel approach was used to analyse relationships between perceived classroom environments and emotions in mathematics. Based on Pekrun’s (2000) [A social-cognitive, control-value theory of achievement emotions. In J. Heckhausen (Ed.), Motivational psychology of human development (pp. 143e163)] social-cognitive, control-value theory of achievement emotions, we hypothesized that environmental characteristics conveying control and value to the students would be related to their experience of enjoyment, anxiety, anger, and boredom in mathematics. Multilevel modelling of data from 1623 students from 69 classes (grades 5e10) confirmed close relationships between environmental variables and emotional experiences that functioned predominantly at the individual level. Compositional effects further revealed that classes’ aggregate environment perceptions as well as their compositions in terms of aggregate achievement and gender ratio were additionally linked to students’ emotions in mathematics. Methodological and practical implications of the findings are discussed.
ACQUINE: aesthetic quality inference engine - real-time automatic rating of photo aesthetics

We present ACQUINE - Aesthetic Quality Inference Engine, a publicly accessible system which allows users to upload their photographs and have them rated automatically for aesthetic quality. The system integrates a support vector machine based classifier which extracts visual features on the fly and performs real-time classification and prediction. As the first publicly available tool for automatically determining the aesthetic value of an image, this work is a significant first step in recognizing human emotional reaction to visual stimulus. In this paper, we discuss fundamentals behind this system, and some of the challenges faced while creating it. We report statistics generated from over 140,000 images uploaded by Web users. The system is demonstrated at http://acquine.alipr.com.
DeepMob: Learning Deep Knowledge of Human Emergency Behavior and Mobility from Big and Heterogeneous Data

The frequency and intensity of natural disasters has increased significantly in recent decades, and this trend is expected to continue. Hence, understanding and predicting human evacuation behavior and mobility will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, existing models are shallow models, and it is difficult to apply them for understanding the “deep knowledge” of human mobility. Therefore, in this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data), and we build an intelligent system, namely, DeepMob, for understanding and predicting human evacuation behavior and mobility following different types of natural disasters. The key component of DeepMob is based on a deep learning architecture that aims to understand the basic laws that govern human behavior and mobility following natural disasters, from big and heterogeneous data. Furthermore, based on the deep learning model, DeepMob can accurately predict or simulate a person’s future evacuation behaviors or evacuation routes under different disaster conditions. Experimental results and validations demonstrate the efficiency and superior performance of our system, and suggest that human mobility following disasters may be predicted and simulated more easily than previously thought.
Learning Everything about Anything: Webly-Supervised Visual Concept Learning

Recognition is graduating from labs to real-world applications. While it is encouraging to see its potential being tapped, it brings forth a fundamental challenge to the vision researcher: scalability. How can we learn a model for any concept that exhaustively covers all its appearance variations, while requiring minimal or no human supervision for compiling the vocabulary of visual variance, gathering the training images and annotations, and learning the models? In this paper, we introduce a fully-automated approach for learning extensive models for a wide range of variations (e.g. actions, interactions, attributes and beyond) within any concept. Our approach leverages vast resources of online books to discover the vocabulary of variance, and intertwines the data collection and modeling steps to alleviate the need for explicit human supervision in training the models. Our approach organizes the visual knowledge about a concept in a convenient and useful way, enabling a variety of applications across vision and NLP. Our online system has been queried by users to learn models for several interesting concepts including breakfast, Gandhi, beautiful, etc. To date, our system has models available for over 50, 000 variations within 150 concepts, and has annotated more than 10 million images with bounding boxes.
Multiview Supervised Dictionary Learning in Speech Emotion Recognition

Recently, a supervised dictionary learning (SDL) approach based on the Hilbert-Schmidt independence criterion (HSIC) has been proposed that learns the dictionary and the corresponding sparse coefficients in a space where the dependency between the data and the corresponding labels is maximized. In this paper, two multiview dictionary learning techniques are proposed based on this HSIC-based SDL. While one of these two techniques learns one dictionary and the corresponding coefficients in the space of fused features in all views, the other learns one dictionary in each view and subsequently fuses the sparse coefficients in the spaces of learned dictionaries. The effectiveness of the proposed multiview learning techniques in using the complementary information of single views is demonstrated in the application of speech emotion recognition (SER). The fully-continuous sub-challenge (FCSC) of the AVEC 2012 dataset is used in two different views: baseline and spectral energy distribution (SED) feature sets. Four dimensional affects, i.e., arousal, expectation, power, and valence are predicted using the proposed multiview methods as the continuous response variables. The results are compared with the single views, AVEC 2012 baseline system, and also other supervised and unsupervised multiview learning approaches in the literature. Using correlation coefficient as the performance measure in predicting the continuous dimensional affects, it is shown that the proposed approach achieves the highest performance among the rivals. The relative performance of the two proposed multiview techniques and their relationship are also discussed. Particularly, it is shown that by providing an additional constraint on the dictionary of one of these approaches, it becomes the same as the other.
A contour processing method for fast binary neighbourhood operations

In this paper new fast algorithms for erosion, dilation, propagation and skeletonization are presented. The key principle of the algorithms is to process object contours. A queue is implemented to store the contours in each iteration for the next iteration. The contours can be passed from one operation to another as well. Contour filling and object labelling become available by minor modifications of the basic operations. The time complexity of the algorithms is linear with the number of contour elements to be processed. The algorithms prove to be faster than any other known algorithms..
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry

Current approaches for visual--inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time; this problem is further emphasized by the fact that inertial measurements come at high rate, hence, leading to the fast growth of the number of variables in the optimization. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes into single relative motion constraints. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group. We formally discuss the generative measurement model as well as the nature of the rotation noise and derive the expression for the maximum a posteriori state estimator. Our theoretical development enables the computation of all necessary Jacobians for the optimization and a posteriori bias correction in analytic form. The second contribution is to show that the preintegrated inertial measurement unit model can be seamlessly integrated into a visual--inertial pipeline under the unifying framework of factor graphs. This enables the application of incremental-smoothing algorithms and the use of a structureless model for visual measurements, which avoids optimizing over the 3-D points, further accelerating the computation. We perform an extensive evaluation of our monocular VIO pipeline on real and simulated datasets. The results confirm that our modeling effort leads to an accurate state estimation in real time, outperforming state-of-the-art approaches.
Fusion of color and infrared video for moving human detection

We approach the task of human silhouette extraction from color and thermal image sequences using automatic image registration. Image registration between color and thermal images is a challenging problem due to the difficulties associated with finding correspondence. However, the moving people in a static scene provide cues to address this problem. In this paper, we propose a hierarchical scheme to automatically find the correspondence between the preliminary human silhouettes extracted from synchronous color and thermal image sequences for image registration. Next, we discuss strategies for probabilistically combining cues from registered color and thermal images for improved human silhouette detection. It is shown that the proposed approach achieves good results for image registration and human silhouette extraction. Experimental results also show a comparison of various sensor fusion strategies and demonstrate the improvement in performance over nonfused cases for human silhouette extraction. 2006  on behalf of Pattern Recognition Society.
Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling

Much natural data is hierarchical in nature. Moreover, this hierarchy is often shared between different instances. We introduce the nested Chinese Restaurant Franchise Process to obtain both hierarchical tree-structured representations for objects, akin to (but more general than) the nested Chinese Restaurant Process while sharing their structure akin to the Hierarchical Dirichlet Process. Moreover, by decoupling the structure generating part of the process from the components responsible for the observations, we are able to apply the same statistical approach to a variety of user generated data. In particular, we model the joint distribution of microblogs and locations for Twitter for users. This leads to a 40% reduction in location uncertainty relative to the best previously published results. Moreover, we model documents from the NIPS papers dataset, obtaining excellent perplexity relative to (hierarchical) Pachinko allocation and LDA.
End-to-End Deep Learning for Person Search

Existing person re-identification (re-id) benchmarks and algorithms mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be found from whole images. To close the gap, we investigate how to localize and match query persons from the scene images without relying on the annotations of candidate boxes. Instead of breaking it down into two separate tasks—pedestrian detection and person re-id, we propose an end-to-end deep learning framework to jointly handle both tasks. A random sampling softmax loss is proposed to effectively train the model under the supervision of sparse and unbalanced labels. On the other hand, existing benchmarks are small in scale and the samples are collected from a few fixed camera views with low scene diversities. To address this issue, we collect a largescale and scene-diversified person search dataset, which contains 18,184 images, 8,432 persons, and 99,809 annotated bounding boxes1. We evaluate our approach and other baselines on the proposed dataset, and study the influence of various factors. Experiments show that our method achieves the best result.
Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills?

This article presents a longitudinal study with four children with autism, who were exposed to a humanoid robot over a period of several months. The longitudinal approach allowed the children time to explore the space of robot–human, as well as human–human interaction. Based on the video material documenting the interactions, a quantitative and qualitative analysis was conducted. The quantitative analysis showed an increase in duration of pre-defined behaviours towards the later trials. A qualitative analysis of the video data, observing the children’s activities in their interactional context, revealed further aspects of social interaction skills (imitation, turn-taking and role-switch) and communicative competence that the children showed. The results clearly demonstrate the need for, and benefits of, long-term studies in order to reveal the full potential of robots in the therapy and education of children with autism.
Efficient temporal mining of micro-blog texts and its application to event discovery

In this paper we present a novel method for clustering words in micro-blogs, based on the similarity of the related temporal series. Our technique, named SAX*, uses the Symbolic Aggregate ApproXimation algorithm to discretize the temporal series of terms into a small set of levels, leading to a string for each. We then define a subset of “interesting” strings, i.e. those representing patterns of collective attention. Sliding temporal windows are used to detect co-occurring clusters of tokens with the same or similar string. To assess the performance of the method we first tune the model parameters on a 2-month 1 % Twitter stream, during which a number of world-wide events of differing type and duration (sports, politics, disasters, health, and celebrities) occurred. Then, we evaluate the quality of all discovered events in a 1-year stream, “googling” with the most frequent cluster n-grams and manually assessing how many clusters correspond to published news in the same temporal slot. Finally, we perform a complexity evaluation and we compare SAX* with three alternative methods for event discovery. Our evaluation shows that SAX* is at least one order of magnitude less complex than other temporal and non-temporal approaches to micro-blog clustering.