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Deploying a large number of sensing buoys is a powerful tool for oceanographic, marine biology and climate change research. In this work we address the problem of efficient data collection by a team of Unmanned Aerial Vehicles (UAV) from low-power, small buoys with previously unknown locations in complex coastal or remote oceanic regions with aerial restrictions. To tackle this problem we propose...
Reading is one of the main paths to acquire knowledge, either done traditionally on paper media or practiced on electronic devices. Efficiency varies when different reading patterns are involved. It is the objective of this research to classify reading patterns from fixation data using machine learning techniques in an attempt to understand and evaluate the reading and learning process. In our experiment,...
With the popularity of social networks, users can communicate with each other in a more convenient way. However, the increasing amount of data poses new challenges for the analysis of the social activities of the users. In this paper, we propose to visualize the heterogeneous information of user interactions in a social network in a three-dimensional way using the concept of solar systems. The target...
Incorporating user characteristics and contextual information has shown to be essential when it comes to personalized music retrieval and recommendation. To this end, the current location of a user is often exploited. However, relying solely on GPS coordinates neglects the cultural background of users, which does not necessarily coincide with political borders. In this paper, we analyze culture-specific...
Current motion-capture technologies produce continuous streams of 3D human joint trajectories. One of the challenges is to automatically annotate such streams of complex spatio-temporal data in real time. In this paper, we propose an efficient approach to label motion stream data in real time with a limited usage of main memory. Based on a set of user-defined motion profiles, each of them specified...
Data analysis performed with the use of data mining methods is nowadays very current in the field of industry. The results of this data analysis can bring new knowledge which can be applicable to a given manufacturing process and thus contribute to streamlining process management. This paper aims at the data analysis of failures emerging in the industrial process with the use of data mining methods...
This paper addresses the problem of automated structured data records extraction from web pages. In particular, we focus on the extraction of posts from online forum sites. We show that variability in the HTML structure within user generated content in forum posts can negatively affect the extraction accuracy and propose the integration of a deep learning node classifier in the popular Mining Data...
Road pixel segmentation in airborne data is an important and challenging task. Recently, a sophisticated and robust approach based on superpixels and minimum cost paths has been published. In order to find out which of the numerous features are most essential, we propose a forward-search wrapper approach for feature selection which was tested with two different classifiers and with both generic and...
This paper presents a novel local posture orientation-context descriptor, and proposes a FDDL(Fisher discriminant dictionary learning) method based on local orientation-preserving(LOP-FDDL) for sparse coding in action recognition task. To take full use of the information about the position of the local body-part related to the center of the torso, ant the spatial-temporal shape changes of the human...
Observational data resources based on the capture of clinical data in the electronic health record (EHR) have produced significant learning opportunities in many areas of medicine. These large data resources can span multiple hospital systems and employ common semantics, ontologies, and data models. They have uncovered critical safety issues for patients, and spurred observational research and clinical...
Frequent graph mining has received a lot of attention from the research community because of the increasing availability of graph data in several domains, including bioinformatics, social networks, and cyber security. On large graphs such as protein-protein interaction and gene coexpression networks, frequent subgraph mining algorithms take hours to finish. In this paper, we propose a parallel algorithm...
Human mobility has been studied extensively in various biomedical contexts with applications in clinical rehabilitation, disease diagnosis, health risk prognosis, and general performance assessments. In this paper, we present ATOMHP (Analytical Technologies to Objectively Measure Human Performance) Kinect: a system to objectively quantify human performance using the Microsoft Kinect as a single camera...
Crime prediction plays a crucial role in addressing crime, violence, conflict and insecurity in cities to promote good governance, appropriate urban planning and management. Plenty efforts have been made on developing crime prediction models by leveraging demographic data, but they failed to capture the dynamic nature of crimes in urban. Recently, with the development of new techniques for collecting...
Predictive Complex Event Processing (CEP) constitutes the next phase of CEP evolution and provides future predictive states of the partially matched complex sequences. In this paper, we demonstrate our novel predictive CEP system and show that this problem can be solved while leveraging existing data modelling, query execution and optimisation frameworks. We model the predictive detection of events...
Feature selection is the process of selecting a subset of relevant features from the larger set of collected features. As the amount of available data grows with technology, feature selection becomes a more important part of the system-design process. In real-world applications, there are several costs associated with the collection, processing, and storage of data. Given that these costs can vary...
Feature selection, as a fundamental component of building robust models, plays an important role in many machine learning and data mining tasks. Since acquiring labeled data is particularly expensive in both time and effort, unsupervised feature selection on unlabeled data has recently gained considerable attention. Without label information, unsupervised feature selection needs alternative criteria...
The primary failure mechanism in brittle materials such as ceramics, granite and some metal alloys is through the presence of defects which result in crack formation and propagation under the application of load. We are interested in studying this process of crack propagation, interaction and coalescence, which degrades the strength of the specimen. Traditionally, engineering applications that study...
Protein-protein interaction extraction research can be widely applied to the field of life science research. However, most of the machine learning based methods focus on binary PPI relation extraction, which loses rich relationship type information that is critical to the PPIs study. The rule based open information extraction methods can extract the PPI triple (i.e. “protein1, interaction word, protein2”),...
Certain environmental processes, while influential, are inherently difficult to quantify and detect using traditional time series analyses, particularly among variables with different seasonal progressions. Disturbances that only manifest in part of a season (e.g., spring defoliation) or subtle climate shifts can pose detection challenges when they occur in the presence of other variability. Increasing...
Given a database of spatial trajectories reporting the movement of a set of objects in a time frame, the problem is to discover the groups of objects that stay in close proximity within a geographical area for a significant time. To deal with the problem, techniques for the discovery of collective patterns, e.g. the meeting pattern, have been proposed. Such techniques, however, impose stringent constraints...
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