nils-herrmann/clean_scirepeval huggingface dataset:
Predicting player churn in the wild

Free-to-Play or “freemium” games represent a fundamental shift in the business models of the game industry, facilitated by the increasing use of online distribution platforms and the introduction of increasingly powerful mobile platforms. The ability of a game development company to analyze and derive insights from behavioral telemetry is crucial to the success of these games which rely on in-game purchases and in-game advertising to generate revenue, and for the company to remain competitive in a global marketplace. The ability to model, understand and predict future player behavior has a crucial value, allowing developers to obtain data-driven insights to inform design, development and marketing strategies. One of the key challenges is modeling and predicting player churn. This paper presents the first cross-game study of churn prediction in Free-to-Play games. Churn in games is discussed and thoroughly defined as a formal problem, aligning with industry standards. Furthermore, a range of features which are generic to games are defined and evaluated for their usefulness in predicting player churn, e.g. playtime, session length and session intervals. Using these behavioral features, combined with the individual retention model for each game in the dataset used, we develop a broadly applicable churn prediction model, which does not rely on game-design specific features. The presented classifiers are applied on a dataset covering five free-to-play games resulting in high accuracy churn prediction.
A Fast, Feature-based Cluster Algorithm for Information Retrieval

The Internet is a vast resource of information. Unfortunately, finding and accessing this information is often a very cumbersome task even with existing information platforms. Searching on the WWW suffers from the fact that almost every word is ambiguous to a certain degree in the information-rich environment of the Internet. Clustering search results is a way to solve this problem. This paper introduces a novel, fast way to cluster documents based on frequent term sets.
A new evaluation measure for information retrieval systems

Some of the established approaches to evaluating text clustering algorithms for information retrieval show theoretical flaws. In this paper, we analyze these flaws and introduce a new evaluation measure to overcome them. Based on a simple yet rigorous mathematical analysis of the effect of certain parameters in cluster based retrieval, we show that certain conclusions drawn in the recent literature must be taken with a grain of salt. Our new measure, in contrast, accounts for statistical biases that have to be expected according to our analysis. A series of experiments and a comparison with results reported recently underlines that this measure is a more suitable performance indicator that allows for more meaningful interpretations.
Detecting trends in social bookmarking systems using a probabilistic generative model and smoothing

We propose a method for the detection of trends in social bookmarking systems. Compared to other work in this emerging field, our approach has a more sound statistical basis. In order to cope with the problem of vanishing probabilities due to data sparsity, we apply smoothing and show that it allows for an easy calibration of our trend detector resulting in better generalization and scalability. We test our approach on a collection of 105, 000, 000 bookmarks collected from the del.icio.us bookmarking service. To our knowledge, this is the largest corpus of a real world bookmarking service analyzed in this context. The results show that our method outperforms previously proposed methods and successfully detects trends in the data.
Tree-Based Signatures for Shape Classification

This paper reports on tree-based shape encoding and classification. We present an approach that combines characteristics from the theories of R-trees known from data base indexing and regression trees known from pattern recognition. The resulting shape representations are highly storage efficient. As they immediately transform into scale invariant signatures, we apply the Earth Mover's distance for computing shape similarities. Experimental results underline the efficacy of this approach. The required computations are simple and fast but allow for robust shape classification and clustering.
A distributed machine learning framework

A distributed online learning framework for support vector machines (SVMs) is presented and analyzed. First, the generic binary classification problem is decomposed into multiple relaxed subproblems. Then, each of them is solved iteratively through parallel update algorithms with minimal communication overhead. This computation can be performed by individual processing units, such as separate computers or processor cores, in parallel and possibly having access to only a subset of the data. Convergence properties of continuous- and discrete-time variants of the proposed parallel update schemes are studied. A sufficient condition is derived under which synchronous and asynchronous gradient algorithms converge to the approximate solution. Subsequently, a class of stochastic update algorithms, which may arise due to distortions in the information flow between units, is shown to be globally stable under similar sufficient conditions. Active set methods are utilized to decrease communication and computational overhead. A numerical example comparing centralized and distributed learning schemes indicates favorable properties of the proposed framework such as configurability and fast convergence.
Archetypal Images in Large Photo Collections

This paper presents an approach to large scale archetypal analysis. In archetypal analysis, multivariate data points are represented as sparse convex combinations of extremal elements of a data set. It therefore allows for describing data in terms of fractions of intuitively understandable elementary concepts. However, as its computation costs grow quadratically with the number of data points, the original algorithm hardly applies to practical data analysis problems. In this paper, we present a way of considerably accelerating archetypal analysis and then apply it to search for latent structures in a large collection of images.
Moving pedestrian detection based on motion segmentation

The detection of moving pedestrians is of major importance in the area of robot vision, since information about such persons and their tracks should be incorporated into reliable collision avoidance algorithms. In this paper, we propose a new approach, based on motion analysis, to detect moving pedestrians. Our main contribution is to localize moving objects by motion segmentation on an optical flow field as a preprocessing step, so as to significantly reduce the number of detection windows needed to be evaluated by a subsequent people classifier, resulting in a fast method for real-time systems. Therefore, we align detection windows with segmented motion-blobs using a height-prior rule. Finally, we apply a Histogram of Oriented Gradients (HOG) features based Support Vector Machine with Radial Basis Function kernel (RBF-SVM) to estimate a confidence for each detection window, and thereby locate potential pedestrians inside the segmented blobs. Experimental results on “Daimler mono moving pedestrian detection” benchmark show that our approach obtains a log-average miss rate of 43% in the FPPI range [10-2, 100], which is a clear improvement with respect to the naive HOG+linSVM approach and better than several other state-of-the-art detectors. Moreover, our approach also reduces runtime per frame by an order of magnitude.
A cognitive vision system for action recognition in office environments

The emerging cognitive vision paradigm is concerned with vision systems that evaluate, gather and integrate contextual knowledge for visual analysis. In reasoning about events and structures, cognitive vision systems should rely on multiple computations in order to perform robustly even in noisy domains. Action recognition in an unconstrained office environment thus provides an excellent testbed for research on cognitive computer vision. In this contribution, we present a system that consists of several computational modules for object and action recognition. It applies attention mechanisms, visual learning and contextual as well as probabilistic reasoning to fuse individual results and verify their consistency. Database technologies are used for information storage and an XML based communication framework integrates all modules into a consistent architecture.
Adapting Information Theoretic Clustering to Binary Images

We consider the problem of finding points of interest along local curves of binary images. Information theoretic vector quantization is a clustering algorithm that shifts cluster centers towards the modes of principal curves of a data set. Its runtime characteristics, however, do not allow for efficient processing of many data points. In this paper, we show how to solve this problem when dealing with data on a 2D lattice. Borrowing concepts from signal processing, we adapt information theoretic clustering to the quantization of binary images and gain significant speedup.
The Snippet Statistics of Font Recognition

This paper considers the topic of automatic font recognition. The task is to recognize a specific font from a text snippet. Unlike previous contributions, we evaluate, how the frequencies of certain letters or words influence automatic recognition systems. The evaluation provides estimates on the general feasibility of font recognition under various changing conditions. Results on a data-set containing 747 different fonts shows that precision can vary between 16% and 94%, dependent on (i) which letters are provided, (ii) how many letters are provided, and (iii) which language is used -- as these factors considerably influence the text snippet statistics. As a second contribution, we introduce a novel bag-of-features based approach to font recognition.
Evaluating integrated speech- and image understanding

The capability to coordinate and interrelate speech and vision is a virtual prerequisite for adaptive, cooperative, and flexible interaction among people. It is therefore fair to assume that human-machine interaction, too, would benefit from intelligent interfaces for integrated speech and image processing. We first sketch an interactive system that integrates automatic speech processing with image understanding. Then, we concentrate on performance assessment which we believe is an emerging key issue in multimodal interaction. We explain the benefit of time scale analysis and usability studies and evaluate our system accordingly.
Action recognition by learning discriminative key poses

This paper proposes a novel approach to pose-based human action recognition. Given a set of training images, we first extract a scale invariant contour-based pose feature from silhouettes. Then, we cluster the features in order to build a set of prototypical key poses. Based on their relative discriminative power for action recognition, we learn weights that favor distinctive key poses. Finally, classification of a novel action sequence is based on a simple and efficient weighted voting scheme that augments results with a confidence value which indicates recognition uncertainty. Our approach does not require temporal information and is applicable for action recognition from videos or still images. It is efficient and delivers real-time performance. In experimental evaluations for single-view action recognition and the multi-view MuHAVi data set, it shows high recognition accuracy.
Probabilistic Diffusion Classifiers for Object Detection

This paper presents a stochastic diffusion approach to prototype-based classification. Relations between exemplary objects and their features are modeled in a bipartite graph. A Bayesian interpretation of the model leads to a Markov chain over the set of objects. In contrast to related graph diffusion approaches, our dual treatment of objects and features easily copes with out of sample objects. Applied to problems in color object localization in unconstrained images, our method performs robust and yields promising results.
Comparing and evaluating interest points

Many computer vision tasks rely on feature extraction. Interest points are such features. This paper shows that interest points are geometrically stable under different transformations and have high information content (distinctiveness). These two properties make interest points very successful in the contest of image matching. To measure these two properties quantitatively, we introduce two evaluation criteria: repeatability rate and information content. The quality of the interest points depends on the detector used. In this paper several detectors are compared according to the criteria specified above. We determine which detector gives the best results and show that it satisfies the criteria well.
A discrete-time parallel update algorithm for distributed learning

We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithms with minimal communication overhead. Decomposing the main problem into multiple relaxed subproblems allows them to be simultaneously solved by individual computing units operating in parallel and having access to only a subset of the data. A sufficient condition is derived under which a synchronous, discrete-time gradient update algorithm converges to the approximate solution. We apply the proposed distributed learning framework in the context of automatic image tagging as a first processing layer. Initial results from corresponding experiments indicate that he proposed framework has favorable properties including efficiency, configurability, robustness, suitability for online learning, and low communication overhead.
Clustering Game Behavior Data

Recent years have seen a deluge of behavioral data from players hitting the game industry. Reasons for this data surge are many and include the introduction of new business models, technical innovations, the popularity of online games, and the increasing persistence of games. Irrespective of the causes, the proliferation of behavioral data poses the problem of how to derive insights therefrom. Behavioral data sets can be large, time-dependent and high-dimensional. Clustering offers a way to explore such data and to discover patterns that can reduce the overall complexity of the data. Clustering and other techniques for player profiling and play style analysis have, therefore, become popular in the nascent field of game analytics. However, the proper use of clustering techniques requires expertise and an understanding of games is essential to evaluate results. With this paper, we address game data scientists and present a review and tutorial focusing on the application of clustering techniques to mine behavioral game data. Several algorithms are reviewed and examples of their application shown. Key topics such as feature normalization are discussed and open problems in the context of game analytics are pointed out.
Fast, Illumination Insensitive Face Detection Based on Multilinear Techniques and Curvature Features

This paper brings together two recent developments in image analysis. We consider a new mathematical framework that provides illumination invariant descriptors for face detection. Towards fast learning and processing, we understand images and the corresponding feature maps as multilinear entities and apply higher order classifiers for image analysis and object detection. Experimental results underline that this approach indeed provides quick training, fast runtime and robust performance across a variety of illumination conditions
Center-Surround Contrast Features for Pedestrian Detection

Inspired by the human vision system, in this paper we propose a specifically organized kind of center-surround contrast features and show their suitability for pedestrian detection. These contrasts are computed from a novel combination of both local color and gradient statistics aggregated quickly for arbitrary sized square cells. We exploit our contrast features in a rich multi-scale and -direction fashion between each central cell and its neighbors and boost the significant ones for pedestrian detection. Experimental results on the INRIA and Caltech pedestrian datasets show that our method achieves state-of-the-art performance.
Image retrieval and Web 2.0 — where can we go from here?

Compared to only a few years ago, today there is an abundance of annotated image data available on the Internet. For researchers on image retrieval, this is an unforseen but welcome consequence of the rise of Web 2.0 technologies. Popular social networking and content sharing services seem to hold the key to the integration of context and semantics into retrieval. However, at least for now, it appears that this promise has to be taken with a grain of salt. In this paper, we present preliminary empirical results on the tagging behavior of power users of content sharing and social bookmarking services. Our findings suggest different promising research directions for image retrieval and we briefly discuss some of them.
An intelligent knowledge sharing system for web communities

This paper presents an expert peering system for information exchange in the knowledge society. Our system realizes an intelligent, real-time search engine for enterprise Intranets or online communities that automatically relays user queries to knowledgable specialists. According to its very nature, the system requires a sound integration of concepts drawn from various areas of Computer Science. In addition to our solutions to problems in scalable processing, data transfer, and networking, we also address issues of interface design and usability, as well as aspects of machine intelligence. Results obtained from extensive experiments demonstrate the efficiency and robustness of our system and convey its potential for next generation web services.
Action Recognition in Videos Using Nonnegative Tensor Factorization

Recognizing human actions is of vital interest in video surveillance or ambient assisted living. We consider an action as a sequence of body poses which are themselves a linear combination of body parts. In an offline procedure, nonnegative tensor factorization is used to extract basis images that represent body parts. The weighting coefficients are obtained by filtering a frame with the set of basis images. Since the basis images are obtained from nonnegative tensor factorization, they are separable and filtering can be implemented efficiently. The weighting coefficients encode dynamics and are used for action recognition. In the proposed action recognition framework, neither explicit detection and tracking of humans nor background subtraction are needed. Furthermore, for recognizing location specific actions, we implicitely take scene objects into account.
How players lose interest in playing a game: An empirical study based on distributions of total playing times

Analyzing telemetry data of player behavior in computer games is a topic of increasing interest for industry and research, alike. When applied to game telemetry data, pattern recognition and statistical analysis provide valuable business intelligence tools for game development. An important problem in this area is to characterize how player engagement in a game evolves over time. Reliable models are of pivotal interest since they allow for assessing the long-term success of game products and can provide estimates of how long players may be expected to keep actively playing a game. In this paper, we introduce methods from random process theory into game data mining in order to draw inferences about player engagement. Given large samples (over 250,000 players) of behavioral telemetry data from five different action-adventure and shooter games, we extract information as to how long individual players have played these games and apply techniques from lifetime analysis to identify common patterns. In all five cases, we find that the Weibull distribution gives a good account of the statistics of total playing times. This implies that an average player's interest in playing one of the games considered evolves according to a non-homogeneous Poisson process. Therefore, given data on the initial playtime behavior of the players of a game, it becomes possible to predict when they stop playing.
Who is doing what? Simultaneous recognition of actions and actors

Recognizing human actions in videos has become a rapidly growing area of research. Most existing research has focused only on a single aspect i.e. recognition of actions. However, humans tend to perform different actions in their own styles. In this paper, we deal with the problem of simultaneously identifying actions and the underlying styles (actors) in videos. We propose a hierarchical approach based on conventional action recognition and asymmetric bilinear modeling. Our approach is solely based on dynamics of the underlying activity. Results on the multi-actor multi-action data set IX-MAS show a high recognition rate.
Collective Attention on the Web

Understanding the dynamics of collective human attention has been called a key scientific challenge for the information age. Tackling this challenge, this monograph explores the dynamics of collective attention related to Internet phenomena such as Internet memes, viral videos, or social media platforms and Web-based businesses. To this end, we analyze time series data that directly or indirectly represent how the interest of large populations of Web users in content or services develops over time. Regardless of regional or cultural contexts, we generally observe strong regularities in time series that reflect attention dynamics and we discuss mathematical models that provide plausible explanations as to what drives the apparently dominant dynamics of rapid initial growth and prolonged decline.
A global sampling method for alpha matting

Alpha matting refers to the problem of softly extracting the foreground from an image. Given a trimap (specifying known foreground/background and unknown pixels), a straightforward way to compute the alpha value is to sample some known foreground and background colors for each unknown pixel. Existing sampling-based matting methods often collect samples near the unknown pixels only. They fail if good samples cannot be found nearby. In this paper, we propose a global sampling method that uses all samples available in the image. Our global sample set avoids missing good samples. A simple but effective cost function is defined to tackle the ambiguity in the sample selection process. To handle the computational complexity introduced by the large number of samples, we pose the sampling task as a correspondence problem. The correspondence search is efficiently achieved by generalizing a randomized algorithm previously designed for patch matching[3]. A variety of experiments show that our global sampling method produces both visually and quantitatively high-quality matting results.