nils-herrmann/clean_scirepeval huggingface dataset:
Face recognition with weighted locally linear embedding

We present an approach to recognizing faces with varying appearances which also considers the relative probability of occurrence for each appearance. We propose and demonstrate extending dimensionality reduction using locally linear embedding (LLE), to model the local shape of the manifold using neighboring nodes of the graph, where the probability associated with each node is also considered. The approach has been implemented in software and evaluated on the Yale database of face images (Belhumeur et al., 1997). Recognition rates are compared with non-weighted LLE and principal component analysis (PCA), and in our setting, weighted LLE achieves superior performance.
MALADY: A Machine Learning-Based Autonomous Decision-Making System for Sensor Networks

As the capabilities of sensor networks evolve, we need to address the challenges that will help in shifting the perception of sensor networks as being merely a data-gathering network to that of a network that is capable of learning and making decisions autonomously. This shift in intelligence from the edge to the nodes in the network is particularly relevant in unattended sensor deployments where there is no continuous access to a remote base station. In this paper, we propose anarchitecture, called MALADY, which uses a machine learningapproach to enable a network of embedded sensor nodes to use the data that they have gathered to learn and make decisions in real-time within the network and thereby, become autonomous. MALADY supports supervised as well as unsupervised learning algorithms. Our implementation of the algorithms introduces some practical optimizations, in order to make them viable for nodes with limited resources. Our experimental results basedon an implementation on the MicaZ mote/TinyOS platformshow that the supervised learning technique based on lineardiscriminant analysis has a higher learning complexity, but allows a sensor node to learn about the data correlations robustly and make decisions accurately, after learning from only a few samples. In comparison, the unsupervised learning technique based on clustering has a low overhead, but requires more learning samples to achieve a high detection accuracy.
Integral correlograms and probabilistic diffusion for image tagging

We present a framework intended to assist users in the task of tagging pictures with content descriptors. Histogram- or correlogram features of manually indicated regions of interest are extracted from a few training images; probabilistic diffusion over these prototypes is used to analyze further images. Since speed is pivotal in interactive applications, we apply a fast algorithm for computing local correlograms; moreover, our diffusion-based classifier trains almost instantaneously. Experiments with images downloaded from flickr.com indicate that our method achieves good results even when trained with a single image only.
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.
Gait recognition by learning distributed key poses

Gait recognition is receiving increasing attention from computer vision researchers for its applicability in areas such as visual surveillance, access control, or smart interfaces. Most existing research attempts to model individual gait patterns as sequences of temporal templates either by determining gait cycles or by aggregating spatio-temporal information into a 2D signature. This paper presents a simple yet efficient and effective approach to gait recognition based on a contour-distance feature and key pose learning. Unlike existing work, gait patterns are modelled as a non-temporal collection of key poses distributed over gait cycles. Experimental results on a large multi-view benchmark data set exhibit high recognition accuracy and robustness against changes in viewpoint. Consequently, this paper establishes that non-temporal methods can accomplish efficient and accurate gait recognition.
Analyzing the Evolution of Social Groups in World of Warcraft®

This paper investigates the evolution of social structures in the game WORLD OF WARCRAFT®. We analyze 192 million recordings of 18 million characters belonging to 1.4 million teams, spanning a period of 4 years. Using a recent matrix factorization method, we extract lower dimensional data embeddings. The embeddings provide intuitively interpretable categorizations and we find a tendency towards guilds comprised of casual gamers. To our knowledge, this is the first study considering such a vast amount of data for analyzing groups in MMORPGs.
Bayesian Imitation of Human Behavior in Interactive Computer Games

Modern interactive computer games provide the ability to objectively record complex human behavior, offering a variety of interesting challenges to the pattern recognition community. Such recordings often represent a multiplexing of long-term strategy, mid-term tactics and short-term reactions, in addition to the more low-level details of the player's movements. In this paper, we describe our work in the field of imitation learning; more specifically, we present a mature, Bayesian-based approach to the extraction of both the strategic behavior and movement patterns of a human player, and their use in realizing a cloned artificial agent. We then describe a set of experiments demonstrating the effectiveness of our model
The Playtime Principle: Large-scale cross-games interest modeling

The collection and analysis of behavioral telemetry in digital games has in the past five years become an integral part of game development. One of the key challenges in game analytics is the development of methods for characterizing and predicting player behavior as it evolves over time. Characterizing behavior is necessary for monitoring player populations and gradually improve game design and the playing experience. Predicting behavior is necessary to describe player engagement and prevent future player churn. In this paper, methods and theory from kernel archetype analysis and random process models are utilized to evaluate the playtime behavior, i.e. time spent playing specific games as a function of time, of over 6 million players, across more than 3000 PC and console games from the Steam platform, covering a combined playtime of more than 5 billion hours. A number of conclusions can be derived from this large-scale analysis, notably that playtime as a function of time, across the thousands of games in the dataset, and irrespective of local differences in the playtime frequency distribution, can be modeled using the same model: the Weibull distribution. This suggests that there are fundamental properties governing player engagement as it evolves over time, which we here refer to as the Playtime Principle. Additionally, the analysis shows that there are distinct clusters, or archetypes, in the playtime frequency distributions of the investigated games. These archetypal groups correspond to specific playtime distributions. Finally, the analysis reveals information about player behavior across a very large dataset, showing for example that the vast majority of games are players for less than 10 hours, and very few players spend more than 30-35 hours on any specific game.
Efficient Learning for Hashing Proportional Data

Spectral hashing (SH) seeks compact binary codes of data points so that Hamming distances between codes correlate with data similarity. Quickly learning such codes typically boils down to principle component analysis (PCA). However, this is only justified for normally distributed data. For proportional data (normalized histograms), this is not the case. Due to the sum-to-unity constraint, features that are as independent as possible will not all be uncorrelated. In this paper, we show that a linear-time transformation efficiently copes with sum-to-unity constraints: first, we select a small number K of diverse data points by maximizing the volume of the simplex spanned by these prototypes; second, we represent each data point by means of its cosine similarities to the K selected prototypes. This maximum volume hashing is sensible since each dimension in the transformed space is likely to follow a von Mises (vM) distribution, and, in very high dimensions, the vM distribution closely resembles a Gaussian distribution. This justifies to employ PCA on the transformed data. Our extensive experiments validate this: maximum volume hashing outperforms spectral hashing and other state of the art techniques.
Beyond heatmaps: Spatio-temporal clustering using behavior-based partitioning of game levels

Evaluating the spatial behavior of players allows for comparing design intent with emergent behavior. However, spatial analytics for game development is still in its infancy and current analysis mostly relies on aggregate visualizations such as heatmaps. In this paper, we propose the use of advanced spatial clustering techniques to evaluate player behavior. In particular, we consider the use of DEDICOM and DESICOM, two techniques that operate on asymmetric spatial similarity matrices and can simultaneously uncover preferred locations and likely transitions between them. Our results highlight the ability of asymmetric techniques to partition game maps into meaningful areas and to retain information about player movements between these areas.
Perseus -- A Personalized Reputation System

We propose Perseus, a personalized reputation system. In Perseus, reputations comprise of three aspects: how much I personally trust another individual, how trustworthy others think the individual is, and how much I trust the opinions of others. Perseus is adaptive in the sense that user feedback is used to modify the way the different aspects are considered. We also present simulation experiments, which indicate that Perseus is robust and able to survive under extreme conditions of misbehavior. In addition, Perseus encourages individuals to rate the other party and give fair ratings. We also compare Perseus against other well-known reputation systems.
Automatic mapping of human behavior data to personality model parameters for traffic simulations in virtual environments

We present an approach towards automatic parameter identification for personality models in traffic simulation telemetry. To this end, we compare the behavior data of human and artificial drivers in the same virtual environment. We record the driving behaviors of human subjects in a car simulator and use evolutionary strategies to infer parameters of models of artificial drivers from the recorded data. We evaluate our approach in several prototypic traffic situations in which we compare the resulting artificial agents against human drivers as well as against simple baseline implementations of artificial drivers. As a result, we show that particular ranges of parameters of a driver profile can be inferred for which the simulated driving behavior does not change. We further show that precision depends on the amount of data and the scenarios in which these were recorded. The proposed method can also be applied to compare human and artificial driving behavior.
Memory consistency validation in a cognitive vision system

Ensuring the consistency of memory content is a key feature of cognitive vision systems. This work presents an approach to deal with functional dependencies of hypotheses stored in a visual active memory. By means of Bayesian networks a probabilistic approach is used to incorporate uncertainty of observations. Furthermore, a measurement to detect inconsistencies in the memory is introduced. The benefit of this validation module as part of an integrated system is shown for the task of visual surveillance in an office scenario.
Fast learning for customizable head pose recognition in robotic wheelchair control

In the PLAYBOT project, we aim at assisting disabled children at play. To this end, we are developing a semi autonomous robotic wheelchair. It is equipped with several visual sensors and a robotic manipulator and thus conveniently enhances the innate capabilities of a disabled child. In addition to a touch screen, the child may control the wheelchair using simple head movements. As control based on head posture requires reliable face detection and head pose recognition, we are in need of a robust technique that may effortlessly be tailored to individual users. In this paper, we present a multilinear classification algorithm for fast and reliable face detection. It trains within seconds and thus can easily be customized to the home environment of a disabled child. Subsequent head pose recognition is done using support vector machines. Experimental results show that this two stage approach to head pose-based robotic wheelchair control performs fast and very robust
Temporal key poses for human action recognition

In this paper, we present a simple yet effective approach to recognizing human activities from video sequences. Our approach integrates the advantages of human action recognition in static images using action key poses and motion based approaches using the variants of Motion History Images (MHI) and Motion Energy Images(MEI). We combine both methodologies to extract a new representation of temporal key poses. In an evaluation of this on well established benchmark data we achieve high recognition rates. For the task of action recognition using the MuHAVi data set, we achieve an accuracy of 98.5% in a leave-one-out cross validation procedure. For single-view action recognition using the popular Weizmann data sets, we achieve an accuracy of 100%. In more difficult evaluation setups where the number of training samples for certain individuals or views are restricted, the proposed method exceeds recently published results of other approaches. Moreover, the introduced approach is computationally efficient, robust with respect to parameter selection, and straight forward to implement as it builds on well established and understood concepts.
Tailoring Taxonomies for Efficient Text Categorization and Expert Finding

Automatic content categorization by means of taxonomies is a powerful tool for information retrieval and search technologies as it improves the accessibility of data both for humans and machines. While research on automatic categorization has mainly focused on the problem of classifier design, hardly any effort has been spent on the optimization of the taxonomy size itself. However, taxonomy tailoring may significantly improve computational efficiency and scalability of modern retrieval systems where taxonomies often consist of tens of thousands of non-uniformly distributed categories. In this paper we demonstrate empirically that small subtrees of a taxonomy already enable reliable categorization. We compare several measures for the optimal selection of sub-taxonomies and investigate to what extent a reduction affects the classification quality. We consider applications in classical document categorization and in the upcoming area of expert finding and report corresponding results obtained from experiments with standard benchmark data.
Adaptive wireless services for augmented environments

This paper presents a system that combines mobile services, ubiquitous computing, and augmented reality concepts in order to bring the kind of information-rich environment, which is currently limited to computer screens, to the physical world. This objective is achieved by constructing an architecture based on existing wireless Web application and sensor network technologies. Users of the system as well as objects in the environment are associated with individual sensors. The system collects proximity information between objects and users, which is then used by the Web application server to provide adaptive services. The users then access these location-based services over the wireless network using a standard Web browser on their mobile device, e.g. smart phone. Unlike existing GPS-based solutions, this approach allows for a proximity resolution of approximately one meter inside buildings and rooms. Thus, the augmented objects in the surrounding environment of the users become local hubs of interactive services, creating an information-rich environment that is similar to the one on the Internet. The system presented, which can be built using already existing technologies, has widespread potential applications in, for example, elderly care, education, and commerce.
Erosion Band Features for Cell Phone Image Based Plant Disease Classification

We introduce a novel set of features for a challenging image analysis task in agriculture where cell phone camera images of beet leaves are analyzed as to the presence of plant diseases. Aiming at minimal computational costs on the cellular device and highly accurate prediction results, we present an efficient detector of potential disease regions and a robust classification method based on texture features. We evaluate several first- and second-order statistical features for classifying textures of leaf spots and we find that a combination of descriptors derived on multiple erosion bands of the RGB color channels, as well as, the local binary patterns of gradient magnitudes of the extracted regions accurately distinguish between symptoms caused by five diseases, including infections of the fungi Cercospora beticola, Ramularia beticola, Uromyces betae, and Phoma betae, and the bacterium Pseudomonas syringae pv. aptata.
Integration frameworks for large scale cognitive vision systems - an evaluative study

Owing to the ever growing complexity of present day computer vision systems, system architecture has become an emerging topic in vision research. Systems that integrate numerous modules and algorithms of different I/O and time scale behavior require sound and reliable concepts for interprocess communication. Consequently, topics and methods known from software and systems engineering are becoming increasingly important. Especially, framework technologies for system integration are required. This contribution results from a cooperation between two multinational projects on cognitive vision. It discusses functional and non-functional requirements in cognitive vision and compares and assesses existing solutions.
Online k-Maxoids Clustering

We present an online learning algorithm to extract extremal prototypes from a set of data. As an online algorithm, our method can continue to learn during the application phase of a system. However, as a greedy update procedure, it may be sensitive to outliers. We therefore consider the use of extreme value theory for self-assessment and discuss how to incorporate Weibull statistics so as to increase robustness. We evaluate our approaches on synthetic as well as real world datasets to perform profiling. Our empirical results show that incorporating self-assessment not only results in better data representations but also reveals interpretable insights about the analyzed dataset.
Efficient Pedestrian Detection via Rectangular Features Based on a Statistical Shape Model

Automatic pedestrian detection for advanced driver assistance systems (ADASs) is still a challenging task. Major reasons are dynamic and complex backgrounds in street scenes and variations in clothing or postures of pedestrians. We propose a simple yet effective detector for robust pedestrian detection. Observing that pedestrians usually appear upright in video data, we employ a statistical model of the upright human body in which the head, upper body, and lower body are treated as three distinct components. Our main contribution is to systematically design a pool of rectangular features that are tailored to this shape model. As we incorporate different kinds of low-level measurements, the resulting multimodal and multichannel Haar-like features represent characteristic differences between parts of the human body but are robust against variations in clothing or environmental settings. Our approach avoids exhaustive searches over all possible configurations of rectangular features nor does it rely 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 well-established INRIA, Caltech, and KITTI pedestrian data sets show that our detector reaches state-of-the-art performance at low computational costs and that our features are robust against occlusions.
Guns, swords and data: Clustering of player behavior in computer games in the wild

Behavioral data from computer games can be exceptionally high-dimensional, of massive scale and cover a temporal segment reaching years of real-time and a varying population of users. Clustering of user behavior provides a way to discover behavioral patterns that are actionable for game developers. Interpretability and reliability of clustering results is vital, as decisions based on them affect game design and thus ultimately revenue. Here case studies are presented focusing on clustering analysis applied to high-dimensionality player behavior telemetry, covering a combined total of 260,000 characters from two major commercial game titles: the Massively Multiplayer Online Role-Playing Game Tera and the multi-player strategy war game Battlefield 2: Bad Company 2. K-means and Simplex Volume Maximization clustering were applied to the two datasets, combined with considerations of the design of the games, resulting in actionable behavioral profiles. Depending on the algorithm different insights into the underlying behavior of the population of the two games are provided.
Efficient Subframe Video Alignment Using Short Descriptors

This paper addresses the problem of video alignment. We present efficient approaches that allow for spatiotemporal alignment of two sequences. Unlike most related works, we consider independently moving cameras that capture a 3D scene at different times. The novelty of the proposed method lies in the adaptation and extension of an efficient information retrieval framework that casts the sequences as an image database and a set of query frames, respectively. The efficient retrieval builds on the recently proposed quad descriptor. In this context, we define the 3D Vote Space (VS) by aggregating votes through a multiquerying (multiscale) scheme and we present two solutions based on VS entries; a causal solution that permits online synchronization and a global solution through multiscale dynamic programming. In addition, we extend the recently introduced ECC image-alignment algorithm to the temporal dimension that allows for spatial registration and synchronization refinement with subframe accuracy. We investigate full search and quantization methods for short descriptors and we compare the proposed schemes with the state of the art. Experiments with real videos by moving or static cameras demonstrate the efficiency of the proposed method and verify its effectiveness with respect to spatiotemporal alignment accuracy.