4,409 research outputs found
Forecasting Player Behavioral Data and Simulating in-Game Events
Understanding player behavior is fundamental in game data science. Video
games evolve as players interact with the game, so being able to foresee player
experience would help to ensure a successful game development. In particular,
game developers need to evaluate beforehand the impact of in-game events.
Simulation optimization of these events is crucial to increase player
engagement and maximize monetization. We present an experimental analysis of
several methods to forecast game-related variables, with two main aims: to
obtain accurate predictions of in-app purchases and playtime in an operational
production environment, and to perform simulations of in-game events in order
to maximize sales and playtime. Our ultimate purpose is to take a step towards
the data-driven development of games. The results suggest that, even though the
performance of traditional approaches such as ARIMA is still better, the
outcomes of state-of-the-art techniques like deep learning are promising. Deep
learning comes up as a well-suited general model that could be used to forecast
a variety of time series with different dynamic behaviors
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Functional connectivity inference from time-series of events and application to computer networks
Today’s commercial and Internet Service Provider’s networks are large, heterogeneous, fast-evolving and commonly emit millions of events per second. Monitoring, responding to, and predicting incidents is a key challenge for network management operators. This thesis argues that the concept of functional connectivity, widely used in neuroscience and defined in terms of the presence of statistical dependencies between nodes, can unlock informational value about event logs and assist operators in identifying (and even predicting) service outages. However, existing functional connectivity inference methods are not adapted to event data from computer networks. These methods may either require unavailable models of event propagation, be computationally too costly for large networks and/or long recordings, be not adapted to sparse and discrete activity, and/or assume a static network topology. We thus first describe in depth a major commercial network to highlight the challenges faced by network operators and the opportunities offered by thinking in terms of functional connectivities. Next, using a pair of independent Bernoulli processes as reference, we develop a new statistic aimed at measuring coupling strength by quantifying deviation from independence. However, because many statistics will be large by chance, identifying functional edges from the distribution of statistics over every pair of nodes is challenging. Hence, we then develop a method that infers, in specific contexts, the function that associates each statistic to the probability it accounts for the presence of a functional edge. Next, using the previously described statistic, we propose a novel framework to infer a time-varying functional topology from the time-series of emitted events. Applying this paradigm to two major commercial networks, we show that it can reveal a priori unknown groups of devices providing particular services. Finally, we argue that this thesis has implications beyond assisting network monitoring, such as enabling more robust inference of functional connectivity in neuroscience
Computational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives.
In the past few years, improvements in the data acquisition technology have decreased the time interval of data gathering. Consequently, institutions have stored huge amounts of data such as climate time series and remote sensing images. Computational models to filter, transform, merge and analyze data from many different areas are complex and challenging. The complexity increases even more when combining several knowledge domains. Examples are research in climatic changes, biofuel production and environmental problems. A possible solution to the problem is the association of several computational techniques. Accordingly, this paper presents a framework to analyze, monitor and visualize climate and remote sensing data by employing methods based on fractal theory, data mining and visualization techniques. Initial experiments showed that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive. Sugar cane is the main source to ethanol production in Brazil, and has a strategic importance for the country economy and to guarantee the Brazilian self-sufficiency in this important, renewable source of energy.CSBC 2009
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
The increasing use of electronic forms of communication presents new
opportunities in the study of mental health, including the ability to
investigate the manifestations of psychiatric diseases unobtrusively and in the
setting of patients' daily lives. A pilot study to explore the possible
connections between bipolar affective disorder and mobile phone usage was
conducted. In this study, participants were provided a mobile phone to use as
their primary phone. This phone was loaded with a custom keyboard that
collected metadata consisting of keypress entry time and accelerometer
movement. Individual character data with the exceptions of the backspace key
and space bar were not collected due to privacy concerns. We propose an
end-to-end deep architecture based on late fusion, named DeepMood, to model the
multi-view metadata for the prediction of mood scores. Experimental results
show that 90.31% prediction accuracy on the depression score can be achieved
based on session-level mobile phone typing dynamics which is typically less
than one minute. It demonstrates the feasibility of using mobile phone metadata
to infer mood disturbance and severity.Comment: KDD 201
R-CAD: Rare Cyber Alert Signature Relationship Extraction Through Temporal Based Learning
The large number of streaming intrusion alerts make it challenging for security analysts to quickly identify attack patterns. This is especially difficult since critical alerts often occur too rarely for traditional pattern mining algorithms to be effective. Recognizing the attack speed as an inherent indicator of differing cyber attacks, this work aggregates alerts into attack episodes that have distinct attack speeds, and finds attack actions regularly co-occurring within the same episode. This enables a novel use of the constrained SPADE temporal pattern mining algorithm to extract consistent co-occurrences of alert signatures that are indicative of attack actions that follow each other. The proposed Rare yet Co-occurring Attack action Discovery (R-CAD) system extracts not only the co-occurring patterns but also the temporal characteristics of the co-occurrences, giving the `strong rules\u27 indicative of critical and repeated attack behaviors. Through the use of a real-world dataset, we demonstrate that R-CAD helps reduce the overwhelming volume and variety of intrusion alerts to a manageable set of co-occurring strong rules. We show specific rules that reveal how critical attack actions follow one another and in what attack speed
Time Series Mining: Shapelet Discovery, Ensembling, and Applications
Time series is a prominent class of temporal data sequences that has the properties of being equally spaced in time, chronologically ordered, and highly dimensional. Time series classification is an important branch of time series mining. Existing time series classifiers operate either on row data in the time domain or into an alternate data space in the shapelets or frequency domains. Combining time series classifiers, is another powerful technique used to improve the classification accuracy. It was demonstrated that different classifiers can be expert in predicting different subset of classes over others. The challenge lies in learning the expertise of different base learners. In addition, the high dimensionality characteristic of time series data makes it difficult to visualize their distribution. In this thesis we developed a new time series ensembling methods in order to improve the predictive performance, investigated the interpretability of classifiers by leveraging the power of deep learning models and adjusting them to provide visual shapelets as a by-product of the classification task. Finally, we show application through problems of solar energetic particle events prediction
Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.
Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox-called seqNMF-with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs
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