189,495 research outputs found
Conflict Detection for Model Versioning Based on Graph Modifications: Long Version
In model-driven engineering, models are primary artifacts and can evolve heavily during their life cycle. Therefore, versioning of models is a key technique which has to be offered by an integrated development environment for model-driven engineering. In contrast to text-based versioning systems we present an approach which takes abstract syntax structures in model states and operational features into account. Considering the abstract syntax of models as graphs, we define model revisions as graph modifications which are not necessarily rule-based. Building up on the DPO approach to graph transformations, we define two different kinds of conflict detection: (1) the check for operation-based conflicts, and (2) the check for state-based conflicts on results of merged graph modifications
A manifold learning approach to target detection in high-resolution hyperspectral imagery
Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying targets such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m \u3c\u3c d. In the remote sensing community, this has led to a recent increase in the use of manifold learning, which aims to characterize the embedded lower-dimensional, non-linear manifold upon which the hyperspectral data inherently lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation; the artificial target manifold helps to guide the separation of the target data from the background data in the new, lower-dimensional manifold coordinates. Then, target detection is performed in the manifold space
Set2Box: Similarity Preserving Representation Learning of Sets
Sets have been used for modeling various types of objects (e.g., a document
as the set of keywords in it and a customer as the set of the items that she
has purchased). Measuring similarity (e.g., Jaccard Index) between sets has
been a key building block of a wide range of applications, including,
plagiarism detection, recommendation, and graph compression. However, as sets
have grown in numbers and sizes, the computational cost and storage required
for set similarity computation have become substantial, and this has led to the
development of hashing and sketching based solutions. In this work, we propose
Set2Box, a learning-based approach for compressed representations of sets from
which various similarity measures can be estimated accurately in constant time.
The key idea is to represent sets as boxes to precisely capture overlaps of
sets. Additionally, based on the proposed box quantization scheme, we design
Set2Box+, which yields more concise but more accurate box representations of
sets. Through extensive experiments on 8 real-world datasets, we show that,
compared to baseline approaches, Set2Box+ is (a) Accurate: achieving up to
40.8X smaller estimation error while requiring 60% fewer bits to encode sets,
(b) Concise: yielding up to 96.8X more concise representations with similar
estimation error, and (c) Versatile: enabling the estimation of four
set-similarity measures from a single representation of each set.Comment: Accepted by ICDM 202
3D point cloud video segmentation oriented to the analysis of interactions
Given the widespread availability of point cloud data from consumer depth sensors, 3D point cloud segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in real world 3D data compared to 2D images. This also implies that the classical color segmentation challenges have shifted to RGBD data, and new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. Meanwhile, the lack of 3D point cloud ground truth labeling also limits the development and comparison among methods in 3D point cloud segmentation. In this paper, we present two contributions: a novel graph based point cloud segmentation method for RGBD stream data with interacting objects and a new ground truth labeling for a previously published data set. This data set focuses on interaction (merge and split between ’object’ point clouds), which differentiates itself from the few existing labeled RGBD data sets which are more oriented to Simultaneous Localization And Mapping (SLAM) tasks. The proposed point cloud segmentation method is evaluated with the 3D point cloud ground truth labeling. Experiments show the promising result of our approach.Postprint (published version
Supporting social innovation through visualisations of community interactions
Online communities that form through the introduction of sociotechnical platforms require significant effort to cultivate and sustain. Providing open, transparent information on community behaviour can motivate participation from community members themselves, while also providing platform administrators with detailed interaction dynamics. However, challenges arise in both understanding what information is conducive to engagement and sustainability, and then how best to represent this information to platform stakeholders. Towards a better understanding of these challenges, we present the design, implementation, and evaluation of a set of simple visualisations integrated into a Collective Awareness Platform for Social Innovation platform titled commonfare.net. We discuss the promise and challenge of bringing social innovation into the digital age, in terms of supporting sustained platform use and collective action, and how the introduction of community visualisations has been directed towards achieving this goal
FraudDroid: Automated Ad Fraud Detection for Android Apps
Although mobile ad frauds have been widespread, state-of-the-art approaches
in the literature have mainly focused on detecting the so-called static
placement frauds, where only a single UI state is involved and can be
identified based on static information such as the size or location of ad
views. Other types of fraud exist that involve multiple UI states and are
performed dynamically while users interact with the app. Such dynamic
interaction frauds, although now widely spread in apps, have not yet been
explored nor addressed in the literature. In this work, we investigate a wide
range of mobile ad frauds to provide a comprehensive taxonomy to the research
community. We then propose, FraudDroid, a novel hybrid approach to detect ad
frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI
state transition graphs and collects their associated runtime network traffics,
which are then leveraged to check against a set of heuristic-based rules for
identifying ad fraudulent behaviours. We show empirically that FraudDroid
detects ad frauds with a high precision (93%) and recall (92%). Experimental
results further show that FraudDroid is capable of detecting ad frauds across
the spectrum of fraud types. By analysing 12,000 ad-supported Android apps,
FraudDroid identified 335 cases of fraud associated with 20 ad networks that
are further confirmed to be true positive results and are shared with our
fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure
A knowledge model for the development of a framework for hypnogram construction
The final publication is available via http://dx.doi.org/10.1016/j.knosys.2016.11.016[Abstract] We describe a proposal of a knowledge model for the development of a framework for hypnogram construction from intelligent analysis of pulmonology and electrophysiological signals. Throughout the twentieth century, after the development of electroencephalography (EEG) by Hans Berger, there have been multiple studies on human sleep and its structure. Polysomnography (PSG), a sleep study from several biophysiological variables, gives us the hypnogram, a graphic representation of the stages of sleep as a function of time. This graph, when analyzed in conjunction with other physiological parameters, such as the heart rate or the amount of oxygen in arterial blood, has become a valuable diagnostic tool for different clinical problems that can occur during sleep and that often cause poor quality sleep. Currently, the gold standard for the detection of sleep events and for the correct classification of sleep stages are the rules published by the American Academy of Sleep Medicine (AASM), version 2.2. Based on the standards available to date, different studies on methods of automatic analysis of sleep and its stages have been developed but because of the different development and validation procedures used in existing methods, a rigorous and useful comparative analysis of results and their ability to correctly classify sleep stages is not possible. In this sense, we propose an approach that ensures that sleep stage classification task is not affected by the method for extracting PSG features and events. This approach is based on the development of a knowledge-intensive base system (KBS) for classifying sleep stages and building the corresponding hypnogram. For this development we used the CommonKADS methodology, that has become a de facto standard for the development of KBSs. As a result, we present a new knowledge model that can be used for the subsequent development of an intelligent system for hypnogram construction that allows us to isolate the process of signal processing to identify sleep stages so that the hypnograms obtained become comparable, independently of the signal analysis techniques.Xunta de Galicia; GRC2014/035Ministerio de Economía y Competitividad; TIN2013-40686-
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