nils-herrmann/clean_scirepeval huggingface dataset:
Acquiring linear subspaces for face recognition under variable lighting

Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: a large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.
Comparison of HEVC coding schemes for tile-based viewport-adaptive streaming of omnidirectional video

Virtual reality applications make use of 360-degree panoramic or omnidirectional video with high resolution and high frame rate in order to create the immersive experience to the user. The user views only a portion of the captured 360-degree scene at each time instant, hence streaming the whole omnidirectional video in highest quality is not efficient. In order to alleviate the problem of bandwidth wastage, viewport-adaptive encoding and streaming schemes have been proposed. In these schemes, part of the captured scene that is within the viewer's field of view is delivered at highest quality while the rest of the scene in a lower quality. In this work, three tile-based viewport-adaptive methods using motion-constrained tile sets (MCTS), region-of-interest scalability and simulcast approach have been studied for streaming omnidirectional content. In the performed experiments with various tiling arrangements, MCTS-based scheme required highest bitrate compared to other methods. The scalable coding scheme provided the highest performance in terms of streaming bitrate saving on average up to 53% and 35% compared to streaming the whole omnidirectional video and MCTS-based method, respectively.
The other side of the engram: experience-driven changes in neuronal intrinsic excitability

Modern theories of memory storage have largely focused on persistent, experience-dependent changes in synaptic function such as long-term potentiation and depression. But in addition to these synaptic changes, certain learning tasks produce enduring changes in the intrinsic excitability of neurons by changing the function of voltage-gated ion channels, a change that can produce broader, even neuron-wide changes in synaptic throughput. We will consider the evidence for persistent changes in intrinsic neuronal excitability — what we will call intrinsic plasticity — that is produced by training in behaving animals and by artificial patterns of activation in brain slices and neuronal cultures. These intrinsic changes might function as part of the engram itself, or as a related phenomenon such as a trigger for the consolidation or adaptive generalization of memories.
Neural Aggregation Network for Video Face Recognition

This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy.
Metrics for human driving of multiple robots

A goal of human-robot interaction is to expand the capability of human beings so that they can control multiple robots simultaneously. This provides leverage for human attention beyond single robot interaction. The number of such robots that can be operated is called the fan-out of a human-robot team. We define the concepts of neglect-tolerance as a measure of autonomy and interaction-effort as a measure of required human attention. A model for fan-out is presented that predicts a human's ability to control multiple robots. We validate this model using robot simulations and user studies. Using the validated model we demonstrate enhanced task performance on physical robots as predicted by the model.
Hop-by-Hop Message Authenticationand Source Privacy in WirelessSensor Networks

Message authentication is one of the most effective ways to thwart unauthorized and corrupted messages from being forwarded in wireless sensor networks (WSNs). For this reason, many message authentication schemes have been developed, based on either symmetric-key cryptosystems or public-key cryptosystems. Most of them, however, have the limitations of high computational and communication overhead in addition to lack of scalability and resilience to node compromise attacks. To address these issues, a polynomial-based scheme was recently introduced. However, this scheme and its extensions all have the weakness of a built-in threshold determined by the degree of the polynomial: when the number of messages transmitted is larger than this threshold, the adversary can fully recover the polynomial. In this paper, we propose a scalable authentication scheme based on elliptic curve cryptography (ECC). While enabling intermediate nodes authentication, our proposed scheme allows any node to transmit an unlimited number of messages without suffering the threshold problem. In addition, our scheme can also provide message source privacy. Both theoretical analysis and simulation results demonstrate that our proposed scheme is more efficient than the polynomial-based approach in terms of computational and communication overhead under comparable security levels while providing message source privacy.
Exploring venue popularity in foursquare

In this paper, we provide a detailed analysis on the venue popularity in Foursquare, a leading location-based social network. By collecting 2.4 million venues from 14 geographic regions all over the world, we study the common characteristics of popular venues, and make the following observations. First, venues with more complete profile information are more likely to be popular. Second, venues in the Food category attract the most (43%) public tips (comments) by users, and the Travel & Transport category is the most popular category with the highest per venue check-ins, i.e., each venue in this category attracts on average 376 check-ins. Moreover, the stickiness of users checking in venues in the residence, office, and school categories is higher than in other categories. Last but not least, in general, old venues created at the early stage of Foursquare are more popular than new venues. Our results help to understand the factors that cause venues to become popular, and have applications in venue recommendations and advertisement in location based social networks.
Image Representation Using 2D Gabor Wavelets

This paper extends to two dimensions the frame criterion developed by Daubechies for one-dimensional wavelets, and it computes the frame bounds for the particular case of 2D Gabor wavelets. Completeness criteria for 2D Gabor image representations are important because of their increasing role in many computer vision applications and also in modeling biological vision, since recent neurophysiological evidence from the visual cortex of mammalian brains suggests that the filter response profiles of the main class of linearly-responding cortical neurons (called simple cells) are best modeled as a family of self-similar 2D Gabor wavelets. We therefore derive the conditions under which a set of continuous 2D Gabor wavelets will provide a complete representation of any image, and we also find self-similar wavelet parameterizations which allow stable reconstruction by summation as though the wavelets formed an orthonormal basis. Approximating a “tight frame” generates redundancy which allows low-resolution neural responses to represent high-resolution images, as we illustrate by image reconstructions with severely quantized 2D Gabor coefficients.
Optimal transition maneuvers for a class of V/STOL aircraft

This paper focuses on the problem of computing optimal transition maneuvers for a particular class of tail-sitter aircraft able to switch their flight configuration from hover to level flight and vice versa. Both minimum-time and minimum-energy optimal transition problems are formulated and solved numerically in order to compute reference maneuvers to be employed by the onboard flight control system to change the current flight condition. In order to guide the numerical computation and to validate its results, in a first stage approximated solutions are obtained as a combination of a finite number of motion primitives corresponding to analytical trajectories of approximated dynamic models. The approximated solution is then employed to generate an initial guess for the numerical computation applied to a more accurate dynamic model. Numerical trajectories computed for a small scale prototype of tail-sitter aircraft are finally presented, showing the effectiveness of the proposedmethodology to deal with the complex dynamics governing this kind of systems.
The NTNU-YZU System in the AESW Shared Task: Automated Evaluation of Scientific Writing Using a Convolutional Neural Network

This study describes the design of the NTNU-YZU system for the automated evaluation of scientific writing shared task. We employ a convolutional neural network with the Word2Vec/GloVe embedding representation to predict whether a sentence needs language editing. For the Boolean prediction track, our best F-score of 0.6108 ranked second among the ten submissions. Our system also achieved an F-score of 0.7419 for the probabilistic estimation track, ranking fourth among the nine submissions.
Consumption symbols as carriers of culture: a study of Japanese and Spanish brand personality constructs.

This research argues that the meaning embedded in consumption symbols, such as commercial brands, can serve to represent and institutionalize the values and beliefs of a culture. Relying on a combined emic-etic approach, the authors conducted 4 studies to examine how symbolic and expressive attributes associated with commercial brands are structured and how this structure varies across 3 cultures. Studies 1 and 2 revealed a set of "brand personality" dimensions common to both Japan and the United States (Sincerity, Excitement, Competence, and Sophistication), as well as culture-specific Japanese (Peacefulness) and American (Ruggedness) dimensions. Studied 3 and 4, which extended this set of findings to Spain, yielded brand personality dimensions common to both Spain and the United States (Sincerity, Excitement, and Sophistication), plus nonshared Spanish (Passion) and American (Competence and Ruggedness) dimensions. The meaning of these brand personality dimensions is discussed in the context of cross-cultural research on values and affect, globalization issues, and cultural frame shifting.
A meta-analytic review of gender differences in perceptions of sexual harassment.

Research on gender differences in perceptions of sexual harassment informs an ongoing legal debate regarding the use of a reasonable person standard instead of a reasonable woman standard to evaluate sexual harassment claims. The authors report a meta-analysis of 62 studies of gender differences in harassment perceptions. An earlier quantitative review combined all types of social-sexual behaviors for a single meta-analysis; the purpose of this study was to investigate whether the magnitude of the female-male difference varies by type of behavior. An overall standardized mean difference of 0.30 was found, suggesting that women perceive a broader range of social-sexual behaviors as harassing. However, the meta-analysis also found that the female-male difference was larger for behaviors that involve hostile work environment harassment, derogatory attitudes toward women, dating pressure, or physical sexual contact than sexual propositions or sexual coercion.
ProteusTM: Abstraction Meets Performance in Transactional Memory

The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no universal implementation fits each and every workload. In fact, the best TM in a given workload can reveal to be disastrous for another one. This forces developers to face the complex task of tuning TM implementations, which significantly hampers their wide adoption. In this paper, we address the challenge of automatically identifying the best TM implementation for a given workload. Our proposed system, ProteusTM, hides behind the TM interface a large library of implementations. Underneath, it leverages a novel multi-dimensional online optimization scheme, combining two popular learning techniques: Collaborative Filtering and Bayesian Optimization.
 We integrated ProteusTM in GCC and demonstrate its ability to switch between TMs and adapt several configuration parameters (e.g., number of threads). We extensively evaluated ProteusTM, obtaining average performance <3% from optimal, and gains up to 100x over static alternatives.
X-Stream: edge-centric graph processing using streaming partitions

X-Stream is a system for processing both in-memory and out-of-core graphs on a single shared-memory machine. While retaining the scatter-gather programming model with state stored in the vertices, X-Stream is novel in (i) using an edge-centric rather than a vertex-centric implementation of this model, and (ii) streaming completely unordered edge lists rather than performing random access. This design is motivated by the fact that sequential bandwidth for all storage media (main memory, SSD, and magnetic disk) is substantially larger than random access bandwidth.
 We demonstrate that a large number of graph algorithms can be expressed using the edge-centric scatter-gather model. The resulting implementations scale well in terms of number of cores, in terms of number of I/O devices, and across different storage media. X-Stream competes favorably with existing systems for graph processing. Besides sequential access, we identify as one of the main contributors to better performance the fact that X-Stream does not need to sort edge lists during preprocessing.
An Analysis of Statistical and Syntactic Phrases

As the amount of textual information available through the World Wide Web grows, there is a growing need for high-precision IR systems that enable a user to nd useful information from the masses of available textual data. Phrases have traditionally been regarded as precision-enhancing devices and have proved useful as content-identiiers in representing documents. In this study, we compare the usefulness of phrases recognized using linguistic methods and those recognized by statistical techniques. We focus in particular on high-precision retrieval. We discover that once a good basic ranking scheme is being used, the use of phrases does not have a major eeect on precision at high ranks. Phrases are more useful at lower ranks where the connection between documents and relevance is more tenuous. Also, we nd that the syntactic and statistical methods for recognizing phrases yield comparable performance.
Query by Committee

We propose an algorithm called query by commitee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms.
WordNet: : Similarity - Measuring the Relatedness of Concepts

WordNet::Similarity is a freely available software package that makes it possible to measure the semantic similarity or relatedness between a pair of concepts (or word senses). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return a numeric value that represents the degree to which they are similar or related.
A high performance 76.5 GHz FMCW RADAR for advanced driving assistance system

Frequency modulated Continuous Wave (FMCW) is continuous wave RADAR widely used for Intelligent Adaptive Cruise Control (ACC) and Collision Warning System (CWS). The paper presents a design and simulation of millimeter wave FMCW RADAR. Paper introduces a mathematical modeling of a FMCW RADAR including its target model. Design of a 76.5 GHz FMCW RADAR & its target model by using Advanced Design System (ADS) software and target range & velocity at various conditions are employed in the simulated FMCW RADAR system to verify the operation of the system.
Approximate graph mining with label costs

Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.
Combining heterogeneous service technologies for building an Internet of Things middleware

0140-3664/$ see front matter 2011  A doi:10.1016/j.comcom.2011.11.003 ⇑ Corresponding author. E-mail addresses: kiev.gama@imag.fr (K. Ga (L. Touseau), didier.donsez@imag.fr (D. Donsez). Radio-frequency identification (RFID) is a technology that allows ordinary objects to be uniquely identified by ‘‘smart tags’’ which are also capable of storing small quantities of data. The term Internet of Things was originated from a vision strongly coupled with supply-chain concerns and RFID tagged objects. However the idea of such Internet of Things has evolved in a wider sense, referring now to a ubiquitous object society combining RFID, sensor networks and pervasive computing technologies. This scenario involves different requirements such as heterogeneity and dynamicity of objects, sensors, applications and protocols as well as the need for allowing the dynamic evolution of such applications. These issues seemed to be easily addressed if the principles of service-oriented computing (SOC), like loose coupling and heterogeneity, are used for constructing such architectures and applications. In this paper we underline what benefits SOC can offer to constructing a middleware for the Internet of Things. These concepts have been applied in a service-oriented middleware that tries to leverage the existing Internet of Things architectural concepts by using SOC principles in order to bring more flexibility and dynamicity. We describe the approaches used in that middleware and the lessons learned from that experience. This middleware was initially tested on an application for tracking and monitoring supply-chain objects, and later extended to target wider application domains that are also described in this paper. The project described here has become part of the OW2 AspireRFID open-source project.
Middleware for Internet of Things: A Study

The significant progress in smart devices has lead to the shifting of actual Internet to the Internet of Things (IoT). Internet of Things is invading our real lives. In this new Internet vision, trillions of smart dynamic objects and areas will be connected to the Internet and interact between each others to achieve common applications. This new Internet vision requires suitable IoT infrastructure that offers the main Internet of Things functionalities to facilitate the development of IoT applications. Middleware is the software layer between technological and application layers. It is mainly used to afford common services and functions to applications and to abstract implementation details to ease more complex application development. In this paper, we review the main challenges facing Internet of Things middleware and survey the most well known middleware that have been proposed in this field.