31,664 research outputs found
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
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
Behavioral constraint template-based sequence classification
In this paper we present the interesting Behavioral Constraint Miner (iBCM), a new approach towards classifying sequences. The prevalence of sequential data, i.e., a collection of ordered items such as text, website navigation patterns, traffic management, and so on, has incited a surge in research interest towards sequence classification. Existing approaches mainly focus on retrieving sequences of itemsets and checking their presence in labeled data streams to obtain a classifier. The proposed iBCM approach, rather than focusing on plain sequences, is template-based and draws its inspiration from behavioral patterns used for software verification. These patterns have a broad range of characteristics and go beyond the typical sequence mining representation, allowing for a more precise and concise way of capturing sequential information in a database. Furthermore, it is possible to also mine for negative information, i.e., sequences that do not occur. The technique is benchmarked against other state-of-the-art approaches and exhibits a strong potential towards sequence classification. Code related to this chapter is available at: http://feb.kuleuven.be/public/u0092789/status: publishe
An investigation of a deep learning based malware detection system
We investigate a Deep Learning based system for malware detection. In the
investigation, we experiment with different combination of Deep Learning
architectures including Auto-Encoders, and Deep Neural Networks with varying
layers over Malicia malware dataset on which earlier studies have obtained an
accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these
results were done using extensive man-made custom domain features and investing
corresponding feature engineering and design efforts. In our proposed approach,
besides improving the previous best results (99.21% accuracy and a False
Positive Rate of 0.19%) indicates that Deep Learning based systems could
deliver an effective defense against malware. Since it is good in automatically
extracting higher conceptual features from the data, Deep Learning based
systems could provide an effective, general and scalable mechanism for
detection of existing and unknown malware.Comment: 13 Pages, 4 figure
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