nils-herrmann/clean_scirepeval huggingface dataset:
The State of the Art in Flow Visualization: Dense and Texture-Based Techniques

Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture-based techniques. In this paper, dense, texture-based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio-temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods.
Animating Corrosion and Erosion

In this paper, we present a simple method for animating natural phenomena such as erosion, sedimentation, and acidic corrosion. We discretize the appropriate physical or chemical equations using finite differences, and we use the results to modify the shape of a solid body. We remove mass from an object by treating its surface as a level set and advecting it inward, and we deposit the chemical and physical byproducts into simulated fluid. Similarly, our technique deposits sediment onto a surface by advecting the level set outward. Our idea can be used for off-line high quality animations as well as interactive applications such as games, and we demonstrate both in this paper.
A compact LTCC branch-line coupler UsingModified-T equivalent-circuit modelfor transmission line

In this letter, a compact branch-line coupler is proposed by making good use of the three-dimensional layout capability of the low-temperature co-fired ceramic (LTCC) substrate. This branch-line coupler is accomplished by using lumped-inductors and lumped-capacitors to realize the modified-T equivalent-circuit model for the transmission line so that the circuit size may drastically be reduced. Specifically, a very compact LTCC branch-line coupler with a size of 0.079/spl lambda//spl times/0.0717/spl lambda/ is implemented and carefully examined, where /spl lambda/ is the wavelength of the multilayer structure at the operating frequency f/sub 0/.
Analysis of sports data by using bivariate Poisson models

Models based on the bivariate Poisson distribution are used for modelling sports data. Independent Poisson distributions are usually adopted to model the number of goals of two competing teams. We replace the independence assumption by considering a bivariate Poisson model and its extensions. The models proposed allow for correlation between the two scores, which is a plausible assumption in sports with two opposing teams competing against each other. The effect of introducing even slight correlation is discussed. Using just a bivariate Poisson distribution can improve model fit and prediction of the number of draws in football games.The model is extended by considering an inflation factor for diagonal terms in the bivariate joint distribution.This inflation improves in precision the estimation of draws and, at the same time, allows for overdispersed, relative to the simple Poisson distribution, marginal distributions. The properties of the models proposed as well as interpretation and estimation procedures are provided. An illustration of the models is presented by using data sets from football and water-polo.
Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting

We propose a novel algorithm to detect visual saliency from video signals by combining both spatial and temporal information and statistical uncertainty measures. The main novelty of the proposed method is twofold. First, separate spatial and temporal saliency maps are generated, where the computation of temporal saliency incorporates a recent psychological study of human visual speed perception. Second, the spatial and temporal saliency maps are merged into one using a spatiotemporally adaptive entropy-based uncertainty weighting approach. The spatial uncertainty weighing incorporates the characteristics of proximity and continuity of spatial saliency, while the temporal uncertainty weighting takes into account the variations of background motion and local contrast. Experimental results show that the proposed spatiotemporal uncertainty weighting algorithm significantly outperforms state-of-the-art video saliency detection models.
Big Data Analysis: Recommendation System with Hadoop Framework

Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. In this paper, we have proposed a recommendation system for the large amount of data available on the web in the form of ratings, reviews, opinions, complaints, remarks, feedback, and comments about any item (product, event, individual and services) using Hadoop Framework. We have implemented Mahout Interfaces for analyzing the data provided by review and rating site for movies.
Leader-Based Consensus

It is now well recognized that consensus is a fundamental problem one has to solve to implement reliable applications on top of unreliable asynchronous distributed systems prone to failures. It has been shown that this problem cannot be solved if the underlying asynchronous system does not satisfy additional assumptions. This paper presents a new consensus protocol based on a leader oracle (denoted O in the Iitterature). Although this protocol uses asynchronous rounds, it is not based on the rotating coordinator paradigm. As a consequence, it does not suffer from drawbacks inherent to 05-based consensus protocols that explicitly use this paradigm. As ft and OS are equivalent, the proposed protocol does not require assumptions stronger or weaker than the ones abstracted in OS. Hence, it also requires / < n/2 (where n is the number of processes and / an upper bound on the number of processes that may crash). From a design point of view, the proposed protocol is surprisingly simple. From an efficiency point of view, it allows the processes to agree in a single round when the oracle provides the processes with the same leader (a common case in practice). It is also shown that the time and message costs of the protocol can be reduced when / < ra/3. Moreover, when, in addition to the leader oracle, the system is equipped with a random oracle, the proposed protocol can be extended to provide a hybrid consensus protocol at no additional message cost.
Mining social networks using heat diffusion processes for marketing candidates selection

Social Network Marketing techniques employ pre-existing social networks to increase brands or products awareness through word-of-mouth promotion. Full understanding of social network marketing and the potential candidates that can thus be marketed to certainly offer lucrative opportunities for prospective sellers. Due to the complexity of social networks, few models exist to interpret social network marketing realistically. We propose to model social network marketing using Heat Diffusion Processes. This paper presents three diffusion models, along with three algorithms for selecting the best individuals to receive marketing samples. These approaches have the following advantages to best illustrate the properties of real-world social networks: (1) We can plan a marketing strategy sequentially in time since we include a time factor in the simulation of product adoptions; (2) The algorithm of selecting marketing candidates best represents and utilizes the clustering property of real-world social networks; and (3) The model we construct can diffuse both positive and negative comments on products or brands in order to simulate the complicated communications within social networks. Our work represents a novel approach to the analysis of social network marketing, and is the first work to propose how to defend against negative comments within social networks. Complexity analysis shows our model is also scalable to very large social networks.
Ad Hoc Autonomous Agent Teams: Collaboration without Pre-Coordination

As autonomous agents proliferate in the real world, both in software and robotic settings, they will increasingly need to band together for cooperative activities with previously unfamiliar teammates. In such ad hoc team settings, team strategies cannot be developed a priori. Rather, an agent must be prepared to cooperate with many types of teammates: it must collaborate without pre-coordination. This paper challenges the AI community to develop theory and to implement prototypes of ad hoc team agents. It defines the concept of ad hoc team agents, specifies an evaluation paradigm, and provides examples of possible theoretical and empirical approaches to challenge. The goal is to encourage progress towards this ambitious, newly realistic, and increasingly important research goal.
Information dissemination in self-organizing intervehicle networks

Intervehicle communication (IVC) is an emerging topic in research and application that is getting increasing attention from all major car manufacturers. In this paper, a novel method for scalable information dissemination in highly mobile ad hoc networks is proposed: segment-oriented data abstraction and dissemination (SODAD). With SODAD, information can be distributed in an information range multiple orders of magnitude larger than the transmission range of the air interface, even if only 1%-3% of all vehicles are equipped with an IVC system, e.g., during market introduction. By restricting the method to the dissemination of map/position-based data, scalability is achieved. In the second half of this paper, an example application for the SODAD method is presented: a self-organizing traffic-information system (SOTIS). In SOTIS, a car is equipped with a satellite navigation receiver, an IVC system, and a digital map. Each individual vehicle collects traffic information for its local area. Using the digital map, the traffic information is analyzed based on road segments. By distributing the information in the ad hoc intervehicle network using the SODAD method, a decentralized traffic information system is created. The performance of the proposed methods is evaluated using network simulation with vehicular mobility models. Simulation results for typical scenarios are presented. Furthermore, a prototype implementation based on commercially available standard hardware demonstrates the feasibility of the proposed approach.
Perception Markup Language: Towards a Standardized Representation of Perceived Nonverbal Behaviors

Modern virtual agents require knowledge about their environment, the interaction itself, and their interlocutors’ behavior in order to be able to show appropriate nonverbal behavior as well as to adapt dialog policies accordingly. Recent achievements in the area of automatic behavior recognition and understanding can provide information about the interactants’ multimodal nonverbal behavior and subsequently their affective states. In this paper, we introduce a perception markup language (PML) which is a first step towards a standardized representation of perceived nonverbal behaviors. PML follows several design concepts, namely compatibility and synergy, modeling uncertainty, multiple interpretative layers, and extensibility, in order to maximize its usefulness for the research community. We show how we can successfully integrate PML in a fully automated virtual agent system for healthcare applications.
Graph Frequency Analysis of Brain Signals

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed and recognize the most contributing and important frequency signatures at different levels of task familiarity.
We are not All Equal: Personalizing Models for Facial Expression Analysis with Transductive Parameter Transfer

Previous works on facial expression analysis have shown that person specific models are advantageous with respect to generic ones for recognizing facial expressions of new users added to the gallery set. This finding is not surprising, due to the often significant inter-individual variability: different persons have different morphological aspects and express their emotions in different ways. However, acquiring person-specific labeled data for learning models is a very time consuming process. In this work we propose a new transfer learning method to compute personalized models without labeled target data Our approach is based on learning multiple person-specific classifiers for a set of source subjects and then directly transfer knowledge about the parameters of these classifiers to the target individual. The transfer process is obtained by learning a regression function which maps the data distribution associated to each source subject to the corresponding classifier's parameters. We tested our approach on two different application domains, Action Units (AUs) detection and spontaneous pain recognition, using publicly available datasets and showing its advantages with respect to the state-of-the-art both in term of accuracy and computational cost.
Fully automatic generation of anatomical face simulation models

We present a fast, fully automatic morphing algorithm for creating simulatable flesh and muscle models for human and humanoid faces. Current techniques for creating such models require a significant amount of time and effort, making them infeasible or impractical. In fact, the vast majority of research papers use only a floating mask with no inner lips, teeth, tongue, eyelids, eyes, head, ears, etc.---and even those that build the full visual model would typically still lack the cranium, jaw, muscles, and other internal anatomy. Our method requires only the target surface mesh as input and can create a variety of models in only a few hours with no user interaction. We start with a symmetric, high resolution, anatomically accurate template model that includes auxiliary information such as feature points and curves. Then given a target mesh, we automatically orient it to the template, detect feature points, and use these to bootstrap the detection of corresponding feature curves. These curve correspondences are used to deform the surface mesh of the template model to match the target mesh. Then, the calculated displacements of the template surface mesh are used to drive a three-dimensional morph of the full template model including all interior anatomy. The resulting target model can be simulated to generate a large range of expressions that are consistent across characters using the same muscle activations. Full automation of this entire process makes it readily available to a wide range of users.
Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics

Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed relationships while limiting the semantic drift. We research bootstrapping for relationship extraction using word embeddings to find similar relationships. Experimental results show that relying on word embeddings achieves a better performance on the task of extracting four types of relationships from a collection of newswire documents when compared with a baseline using TFIDF to find similar relationships.
Dual band interleaved base station phased array antenna with optimized cross-dipole and EBG/AMC structure

In this work, a new base station antenna is proposed. Two separate frequency bands with separate radiating elements are used in each band. The frequency band separation ratio is about 1.3:1. These elements are arranged with different spacing (wider spacing for the lower frequency band, and narrower spacing for the higher frequency band). Isolation between bands inherently exists in this approach. This avoids the grating lobe effect, and mitigates the beam narrowing (dispersion) seen with fixed element spacing covering the whole wide bandwidth. A new low-profile cross dipole is designed, which is integrated in the array with an EBG/AMC structure for reducing the size of low band elements and decreasing coupling at high band.
Utilizing RFID Signaling Scheme for Localization of Stationary Objects and Speed Estimation of Mobile Objects

Mapping the physical location of nodes within a wireless sensor network (WSN) is critical in many applications such as tracking and environmental sampling. Passive RFID tags pose an interesting solution to localizing nodes because an outside reader, rather than the tag, supplies the power to the tag. Thus, utilizing passive RFID technology allows a localization scheme to not be limited to objects that have wireless communication capability because the technique only requires that the object carries a RFID tag. This paper illustrates a method in which objects can be localized without the need to communicate received signal strength information between the reader and the tagged item. The method matches tag count percentage patterns under different signal attenuation levels to a database of tag count percentages, attenuations and distances from the base station reader.
Writer identification and verification using GMM supervectors

This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the CVL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors.
Stochastic properties of the random waypoint mobility model: epoch length, direction distribution, and cell change rate

The random waypoint model is a commonly used mobility model for simulations of wireless communication networks. In this paper, we present analytical derivations of some fundamental stochastic properties of this model with respect to: (a) the length and duration of a movement epoch, (b) the chosen direction angle at the beginning of a movement epoch, and (c) the cell change rate of the random waypoint mobility model when used within the context of cellular networks. Our results and methods can be used to compare the random waypoint model with other mobility models. The results on the movement epoch duration as well as on the cell change rate enable us to make a statement about the 'degree of mobility' of a certain simulation scenario. The direction distribution explains in an analytical manner the effect that nodes tend to move back to the middle of the system area.
Detecting Network Intrusion Using a Markov Modulated Nonhomogeneous Poisson Process

Network intrusion occurs when a criminal gains access to a customer's telephone, computer, bank, or other type of account. Detecting network intrusion is an important problem that has received little attention in the statistics literature. This article proposes a Markov modulated nonhomogeneous Poisson process (MMNHPP) to monitor transactions on a customer's account for deviations from the customer's established behavior patterns. An important beneet of the MMNHPP is its ability to model the posterior probability of a criminal presence as a function of time. The MMNHPP combines aspects of the Markov modulated Poisson process and the nonhomogeneous Poisson process to model point processes exhibiting both regular patterns and irregular bursts of activity. The need to accommodate both types of behavior is demonstrated using data from two telephone accounts. MMNHPP parameters are sampled from their posterior distribution given a set of observed event times using an MCMC algorithm. The algorithm switches between drawing missing descriptions of criminal activity given model parameters and sampling model parameters given complete data. An augmented variables scheme is used to render otherwise strongly related elements of the missing data independent in their posterior distribution. Stochastic forward backward recursions for nonstationary hidden Markov models allow the augmenting variables, and thus the entire missing data vector, to be sampled by a single Gibbs step.
Sockpuppet gang detection on social media sites

Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or misleading remarks comments that praise or defame the work of others. The detection of multiple user accounts that are controlled by an individual or organization is important. Herein, we define the problem as sockpuppet gang (SPG) detection. First, we analyze user sentiment orientation to topics based on emotional phrases extracted from their posted comments. Then we evaluate the similarity between sentiment orientations of user account pairs, and build a similar-orientation network (SON) where each vertex represents a user account on a social media site. In an SON, an edge exists only if the two user accounts have similar sentiment orientations to most topics. The boundary between detected SPGs may be indistinct, thus by analyzing account posting behavior features we propose a multiple random walk method to iteratively remeasure the weight of each edge. Finally, we adopt multiple community detection algorithms to detect SPGs in the network. User accounts in the same SPG are considered to be controlled by the same individual or organization. In our experiments on real world datasets, our method shows better performance than other contemporary methods.
Development of the NAIST-Hand with Vision-based Tactile Fingertip Sensor

This paper introduces a multifingered robotic hand eNAIST-Handf and a grip force control by slip margin feedback. The developed prototype finger of the NAIST-hand has a new mechanism by which all 3 motors can be placed inside the palm without using wire-driven mechanisms. A method of grip force control is proposed using an incipient slip estimation. A new tactile sensor is designed to active the proposed control method by the NAIST-Hand. This sensor consists of a transparent semispherical gel, an embedded small camera, and a force sensor in order to implement the direct slip margin estimation. The structure and the principle of sensing are described.