nils-herrmann/clean_scirepeval huggingface dataset:
Grouping and Aggregate queries Over Semantic Web Databases

As a growing number of applications represent data as semantic graphs like RDF (Resource Description Format) and the many entity-attribute-value formats, query languages for such data are being required to support operations beyond graph pattern matching and inference queries. Specifically the ability to express aggregate queries is an important feature which is either lacking or is implemented with little attention to the peculiarities of the data model. In this paper, we study the meaning and implementation of grouping and aggregate queries over RDF graphs. We first define grouping and aggregate operators algebraically and then show how the SPARQL query language can be extended to express grouping and aggregate queries.
An integrated system for cooperative man-machine interaction

To establish robotic application in human environments as, e.g. offices or private homes the robotic systems must be instructable by ordinary users in a natural way. In interpersonal communication humans usually apply different sensory information and are capable of integrating all perceptual cues fast and consistently. Additionally, knowledge acquired during the communication process is directly used to resolve ambiguities. As a step towards realizing similar capabilities in automatic devices this paper presents an integrated system combining automatic speech processing and image understanding. The system is intended to be an intelligent interface of a robot which manipulates objects in its surroundings according to the instructions of a human. The enhanced capabilities necessary for carrying out a multimodal man-machine dialog are realized by combining statistical and declarative methods for inference and knowledge representation. The effectiveness of this approach is demonstrated using an exemplary dialog from our construction task domain.
Reverse Indexing for Reading Graffiti Tags

In this paper, we consider the problem of automatically reading graffiti tags. As a preparatory step, we create a large set of synthetic graffiti-like characters, generated from publicly available true type fonts. For each character in the database, we extract a number of scale independent local binary descriptors. Then, using binary non negative matrix factorization, a sufficient number of basis functions are learned. Basis function coefficients of novel images can then be directly used for hashing characters from the database of prototypes. Finally, graffiti tags are recognized by means of a localized, spatial voting scheme.
Archetypical motion: Supervised game behavior learning with Archetypal Analysis

The problem of creating believable game AI poses numerous challenges for computational intelligence research. A particular challenge consists in creating human-like behaving game bots by means of applying machine learning to game-play data recorded by human players. In this paper, we propose a novel, biologically inspired approach to behavior learning for video games. Our model is based on the idea of movement primitives and we use Archetypal Analysis to determine elementary movements from data in order to represent any player action in terms of convex combinations of archetypal motions. Given these representations, we use supervised learning in order to create a system that is able to synthesize appropriate motion behavior during a game. We apply our model to teach a first person shooter game bot how to navigate in a game environment. Our results indicate that the model is able to simulate human-like behavior at lower computational costs than previous approaches.
Convex Non-negative Matrix Factorization in the Wild

Non-negative matrix factorization (NMF) has recently received a lot of attention in data mining, information retrieval, and computer vision. It factorizes a non-negative input matrix V into two non-negative matrix factors V = WH such that W describes "clusters" of the datasets. Analyzing genotypes, social networks, or images, it can be beneficial to ensure V to contain meaningful ``cluster centroids'', i.e., to restrict W to be convex combinations of data points. But how can we run this convex NMF in the wild, i.e., given millions of data points? Triggered by the simple observation that each data point is a convex combination of vertices of the data convex hull, we propose to restrict W further to be vertices of the convex hull. The benefits of this convex-hull NMF approach are twofold. First, the expected size of the convex hull of the candidate set typically grows much slower than the data set. Second, distance preserving low-dimensional embeddings allow one to compute candidate vertices efficiently. Our extensive experimental evaluation shows that convex-hull NMF compares favorably to convex NMF for large data sets both in terms of speed and reconstruction quality. Moreover, we show that our method can easily be applied to large-scale, real-world data sets, in our case consisting of 1.6 million images respectively 160 million votes on World of Warcraft guilds.
Alternative similarity functions for graph kernels

Given a bipartite graph of collaborative ratings, the task of recommendation and rating prediction can be modeled with graph kernels. We interpret these graph kernels as the inverted squared Euclidean distance in a space defined by the underlying graph and show that this inverted squared Euclidean similarity function can be replaced by other similarity functions. We evaluate several such similarity functions in the context of collaborative item recommendation and rating prediction, using the exponential diffusion kernel, the von Neumann kernel, and the random forest kernel as a basis. We find that the performance of graph kernels for these tasks can be increased by using these alternative similarity functions.
Analyzing pedestrian behavior in crowds for automatic detection of congestions

Congestions in pedestrian traffic typically occur when the number of pedestrians exceeds the capacity of pedestrian facilities. In some cases, the pedestrian density reaches a critical level which may lead to a crowd stampede as happens rather frequently at mass gatherings, in stadiums or at train stations. In the past, research has focused on improving simulations of crowd motion in order to identify potentially dangerous locations and to direct pedestrian streams. Recently, works towards the automatic real-time detection of critical mass behavior based on optical flow computations have been proposed. In this paper, we verify these approaches by analyzing mircoscopic pedestrian behavior in congestions and conducting experiments on synthetic as well as on real datasets.
Learning assembly sequence plans using functional models

The representation of knowledge about assembled objects is a fundamental requirement, if computer systems are used to supervise construction tasks. Depending on the complexity of the task and the characteristics of the objects forming its basis the process of modeling may be burdensome. We introduce a method to model classes of assemblies instead of individual assembled objects. Based on functional properties of the involved objects this general model is used to derive hierarchical descriptions of individual assemblies automatically. Furthermore, the model allows the generation of high level assembly sequence plans.
Informed Haar-Like Features Improve Pedestrian Detection

We propose a simple yet effective detector for pedestrian detection. The basic idea is to incorporate common sense and everyday knowledge into the design of simple and computationally efficient features. As pedestrians usually appear up-right in image or video data, the problem of pedestrian detection is considerably simpler than general purpose people detection. We therefore employ a statistical model of the up-right human body where the head, the upper body, and the lower body are treated as three distinct components. Our main contribution is to systematically design a pool of rectangular templates that are tailored to this shape model. As we incorporate different kinds of low-level measurements, the resulting multi-modal & multi-channel Haar-like features represent characteristic differences between parts of the human body yet are robust against variations in clothing or environmental settings. Our approach avoids exhaustive searches over all possible configurations of rectangle features and neither relies on random sampling. It thus marks a middle ground among recently published techniques and yields efficient low-dimensional yet highly discriminative features. Experimental results on the INRIA and Caltech pedestrian datasets show that our detector reaches state-of-the-art performance at low computational costs and that our features are robust against occlusions.
Content-based image retrieval by multimodal interaction

Due to the size of today's professional image databases, the standard approach to content-based image retrieval is to interactively navigate through the content. However, most people whose job necessitates working with such databases do not have a technical background. Commercial practice thus requires efficient retrieval techniques as well as navigation interfaces that are intuitive to use and easy to learn. In this paper we introduce a system for interactive image retrieval that combines different approaches to feature based queries. Furthermore, it allows multimodal interaction because apart from conventional input devices like mouse and keyboard, it is possible to operate the system using a touch screen or even natural language. Besides technical details and results on retrieval accuracy, we also present results of usability experiments which underline that users well appreciate multimodal interfaces for image retrieval.
The Good, the Bad, and the Ugly: Predicting Aesthetic Image Labels

Automatic classification of the aesthetic content of a picture is one of the challenges in the emerging discipline of computational aesthetics. Any suitable solution must cope with the facts that aesthetic experiences are highly subjective and that a commonly agreed upon theory of their psychological constituents is still missing. In this paper, we present results obtained from an empirical basis of several thousand images. We train SVM based classifiers to predict aesthetic adjectives rather than aesthetic scores and we introduce a probabilistic post processing step that alleviates effects due to misleadingly labeled training data. Extensive experimentation indicates that aesthetics classification is possible to a large extent. In particular, we find that previously established low-level features are well suited to recognize beauty. Robust recognition of unseemliness, on the other hand, appears to require more high-level analysis.
Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning

Major commercial (AAA) games increasingly transit to a semi-persistent or persistent format in order to extend the value of the game to the player, and to add new sources of revenue beyond basic retail sales. Given this shift in the design of AAA titles, game analytics needs to address new types of problems, notably the problem of forecasting future player behavior. This is because player retention is a key factor in driving revenue in semi-persistent titles, for example via downloadable content. This paper introduces a model for predicting retention of players in AAA games and provides a tensor-based spatio-temporal model for analyzing player trajectories in 3D games. We show how knowledge as to trajectories can help with predicting player retention. Furthermore, we describe two new algorithms for three way DEDICOM including a fast gradient method and a seminonnegative constrained method. These approaches are validated against a detailed behavioral data set from the AAA open-world game Just Cause 2.
Malware Detection on Mobile Devices Using Distributed Machine Learning

This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system monitors mobile user activity in a distributed and privacy-preserving way using a statistical classification model which is evolved by training with examples of both normal usage patterns and unusual behavior. The system is evaluated using the MIT reality mining data set. The results indicate that the distributed learning system trains quickly and performs reliably. Moreover, it is robust against failures of individual components.
Automatic detection of dangerous motion behavior in human crowds

Tragically, mass gatherings such as music festivals, sports events or pilgrimage quite often end in terrible crowd disasters with many victims. In the past, research focused on developing physical models that model human behavior in order to simulate pedestrian flows and to identify potentially hazardous locations. However, no automatic systems for detection of dangerous motion behavior in crowds exist. In this paper, we present an automatic system for the detection and early warning of dangerous situations during mass events. It is based on optical flow computations and detects patterns of crowd motion that are characteristic for hazardous congestions. By applying an online change-point detection algorithm, the system is capable of identifying changes in pedestrian flow and thus alarms security personnel to take necessary actions.
Applying Ensembles of Multilinear Classifiers in the Frequency Domain

Ensemble methods such as bootstrap, bagging or boosting have had a considerable impact on recent developments in machine learning, pattern recognition and computer vision. Theoretical and practical results alike have established that, in terms of accuracy, ensembles of weak classifiers generally outperform monolithic solutions. However, this comes at the cost of an extensive training process. The work presented in this paper results from projects on advanced human machine interaction. In scenarios like ours, online learning is a major requirement, and lengthy training is prohibitive. We therefore propose a different approach to ensemble learning. Instead of a set of weak classifiers, we combine strong, separable, multilinear discriminant functions. These are especially suited for computer vision: they train very quickly and allow for rapid classification of image content. Training different classifiers for different contexts or on semantically organized data provides ensembles of experts. We collapse a set of experts into a single multilinear function and thus achieve the same runtime for arbitrarily many classifiers as for a single one. Moreover, carrying out the classification in the frequency domain results in faster framerates. Experiments with image sequences recorded in typical home environments show that our ensemble training schemes yield high accuracy on unconstrained and cluttered data.
Classification of Team Behaviors in Sports Video Games

This paper considers the application of pattern recognition techniques in modern computer games. Towards the problem of realizing more life-like behavior for artificial game characters, we record the network traffic of online multiplayer games. Dealing with a soccer game, we cluster these data and train HMMs in order to achieve fast and robust recognition of behaviors and actions in the virtual game world. Experimental results indicate that pattern recognition and machine learning provide an auspicious avenue towards more convincing artificial characters
Behavior evolution in Tomb Raider Underworld

Behavioral datasets from major commercial game titles of the “AAA” grade generally feature high dimensionality and large sample sizes, from tens of thousands to millions, covering time scales stretching into several years of real-time, and evolving user populations. This makes dimensionality-reduction methods such as clustering and classification useful for discovering and defining patterns in player behavior. The goal from the perspective of game development is the formation of behavioral profiles that provide actionable insights into how a game is being played, and enables the detection of e.g. problems hindering player progression. Due to its unsupervised nature, clustering is notably useful in cases where no prior-defined classes exist. Previous research in this area has successfully applied clustering algorithms to behavioral datasets from different games. In this paper, the focus is on examining the behavior of 62,000 players from the major commercial game Tomb Raider: Underworld, as it unfolds from the beginning of the game and throughout the seven main levels of the game. Where previous research has focused on aggregated behavioral datasets spanning an entire game, or conversely a limited slice or snapshot viewed in isolation, this is to the best knowledge of the authors the first study to examine the application of clustering methods to player behavior as it evolves throughout an entire game.
Efficient Mesoscopic Simulations for Persistent Agents in 3D-Applications and Games

To save computational resources, traffic simulations in virtual environments and digital games often remove entities from the simulation once they leave the user's visual field of view. This can lead to inconsistencies within the simulated world and break the immersive effect. To counter this effect, we propose a system consisting of a regular microscopic simulation around the user and an additional less detailed simulation layer beyond the user's immediate surroundings. The new layer performs a mesoscopic simulation based on the FastLane model, utilizing elements from queuing theory. With this hybrid approach it is possible to simulate a reasonable amount of detailed traffic participants as well as a much larger number of less detailed, but persistent traffic participants. As a result it is possible to simulate entities with complex behavior close to the user while maintaining reasonable traffic densities throughout the entire system.
Exploring Human Vision Driven Features for Pedestrian Detection

Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture. Our main contributions are the first to design a local statistical multichannel descriptor to incorporate both color and gradient information. Second, we introduce a multidirection and multiscale contrast scheme based on grid cells to integrate expressive local variations. Contributing to the issue of selecting most discriminative features for assessing and classification, we perform extensive comparisons with respect to statistical descriptors, contrast measurements, and scale structures. By this way, we obtain reasonable results under various configurations. Empirical findings from applying our optimized detector on the INRIA and Caltech pedestrian datasets show that our features yield state-of-the-art performance in pedestrian detection.
Benefits of Separable, Multilinear Discriminant Classification

This paper presents an empirical investigation of the merits of tensor-based discriminant classification for visual object detection. First, we briefly discuss 2D separable discriminant analysis for grey value image analysis. Then, we contrast this tensorial approach with classical linear discriminant analysis. Our findings on a standard data set for object detection in natural environments show that, for the task of image analysis, tensor-based discriminant classifiers perform very robust. They learn and run faster and also generalize better than conventional techniques based on vectorial representations of the data
Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series

We analyze time series which indicate how collective attention to social media services or Web-based businesses evolves over time. Data was gathered from Goolge Trends and consists of discrete time series of varying duration. Following the related literature, we fit Weibull distributions to the data. Given the two parameters of its fitted model, we embed each time series in a low-dimensional space and apply kernel archetypal analysis based on the Kullback-Leibler divergence for clustering. Our results reveal strong regularities in the dynamics of collective attention to social media and thus illustrate the potential of advanced pattern recognition techniques in the emerging area of Web science.
Age Recognition in the Wild

In this paper, we present a novel approach to age recognition from facial images. The method we propose, combines several established features in order to characterize facial characteristics and aging patterns. Since we explicitly consider age recognition in the wild, i.e. vast amounts of unconstrained Internet images, the methods we employ are tailored towards speed and efficiency. For evaluation, we test different classifiers on common benchmark data and a new data set of unconstrained images harvested from the Internet. Extensive experimental evaluation shows state of the art performance on the benchmarks, very high accuracy for the novel data set, and superior runtime performance; to our knowledge, this is the first time that automatic age recognition is carried out on a large Internet data set.
Tensor-Based Filter Design using Kernel Ridge Regression

Tensor-based approaches to visual object detection can drastically reduce the number of parameters in the training process. Compared to their vector-based counterparts, tensor methods therefore train faster, better manage noisy or corrupted training samples, and are less prone to over-fitting. In this paper, we show how to incorporate the kernel trick into tensor-based filter design. Dealing with object detection in cluttered natural environments, the method is shown to cope with substantially varying training data and a cascade of only two kernel tensor-filters is demonstrated to provide very reliable results.
Detecting abnormal gait

Analyzing human gait has become popular in computer vision. So far, however, contributions to this topic almost exclusively considered the problem of person identification. In this paper, we view gait analysis from a different angle and shall examine its use as a means to deduce the physical condition of people. Understanding the detection of unusual movement patterns as a two class problem leads to the idea of using support vector machines for classification. We thus present a homeomorphisms between 2D lattices and binary shapes that provides a robust vector space embedding of body silhouettes. Experimental results underline that feature vectors obtained from this scheme are well suited to detect abnormal gait wavering, faltering, and falling can be detected reliably across individuals without tracking or recognizing limbs or body parts.
Usability Evaluation for Image Retrieval Beyond Desktop Applications

Interactivity is a key concept in modern content-based retrieval. Therefore, in addition to the ability to learn from user generated data, easy and intuitive to use interfaces are an important area of research in (multi)media retrieval. In this contribution, we focus on the latter aspect and present how different modalities like speech and gestures on super sized touch screen facilities may be integrated to accomplish the goal of intuitive interaction. In order to evaluate our approach, we conducted a series of usability experiments. Their results demonstrate that our multimodal user interface allows for both, comfortable and successful interactive image retrieval