A system and a method is proposed for collecting and aggregating crowd-sourced data from data files based on parameters and measures of relevance of underlying content provided by the intelligent crowd. A user's d...
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ISBN:
(纸本)9783319089799;9783319089782
A system and a method is proposed for collecting and aggregating crowd-sourced data from data files based on parameters and measures of relevance of underlying content provided by the intelligent crowd. A user's data may be combined with already existing collective data to generate relevant mark-ups for a document or other consumable data file, such as audio or video. The marked-up version of the document or data fie is then displayed to other users to, inter alia, enhance efficiency and comprehension for reading, listening or viewing.
The proceedings contain 88 papers. Invited papers. The special focus in this conference is on Human-computer interactions, Robot control, embedded and navigation systems, Bio-data analysis and mining, Biomedical signa...
ISBN:
(纸本)9783319023083
The proceedings contain 88 papers. Invited papers. The special focus in this conference is on Human-computer interactions, Robot control, embedded and navigation systems, Bio-data analysis and mining, Biomedical signal processing, Image and sound processing, Rough and fuzzy systems, patternrecognition, algorithms and optimization, Computer networks and mobile technologies and data management systems. The topics include: patternrecognition with non-euclidean similarities;Case-based reasoning and the statistical challenges II;Robust adaptive predictivemodeling and data deluge;A visual excursion into parallel coordinates (extended abstract);SOM based segmentation of visual stimuli in diagnosis and therapy of neuropsychological disorders;Independent interactive testing of interactive relational systems;Hypothesis-driven interactive classification based on AVO;Wrist localization in color images for hand gesture recognition;Bimodal speech recognition for robot applications;Developing and implementation of thewalking robot control system;Programming of industrial object simulators in proficy HMI/SCADA iFIX system;KUKA robotmotion planning using the 1742 NI smart camera;Visual simultaneous localization and mapping with direct orientation change measurements;Managing system architecture for multi-rotor autonomous flying platform-practical aspects;Calculation of the location coordinates of an object observed by a camera;SMAC-GPS and radar data integration to set the status of the objects in secure areas;Comparison of connectionist and rough set based knowledge discovery methods in search for selection in genes implicated in human familial cancer;Bit-parallel algorithm for the block variant of the merged longest common subsequence problem and Improvement of FP-growth algorithm formining description-oriented rules.
Head pose estimation is important in human-machine interfaces. However, illumination variation, occlusion and low image resolution make the estimation task difficult. Hence, a Dirichlet-tree distribution enhanced Rand...
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Zero-one data is frequently encountered in the field of datamining. A banded pattern in zero-one data is one where the attributes (columns) and records (rows) are organized in such a way that the "ones" are...
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ISBN:
(纸本)9783319089799;9783319089782
Zero-one data is frequently encountered in the field of datamining. A banded pattern in zero-one data is one where the attributes (columns) and records (rows) are organized in such a way that the "ones" are arranged along the leading diagonal. The significance is that rearranging zero-one data so as to feature bandedness enhances the operation of some datamining algorithms that work with zero-one data. The fact that a dataset features banding may also be of interest in its own right with respect to various application domains. In this paper an effective banding algorithm is presented designed to reveal banding in 2D data by rearranging the ordering of columns and rows. The challenge is the large number of potential row and column permutations. To address this issue a column and row scoring mechanism is proposed that allows columns and rows to be ordered so as to reveal bandedness without the need to consider large numbers of permutations. This mechanism has been incorporated into the Banded patternmining (BPM) algorithm proposed in this paper. The operation of BPM is fully discussed. A Complete evaluation of the BPM algorithm is also presented clearly indicating the advantages offered by BPM with respect to a number of competitor algorithms in the context of a collection of UCI datasets.
This work addresses the problem of the recognition of human activities in Ambient Assisted Living (AAL) scenarios. The ultimate goal of a good AAL system is to learn and recognise behaviours or routines of the person ...
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This paper constructs a feature vector representing intraday USD/COP transaction prices and order book dynamics using zig-zag patterns. A Hierarchical Hidden Markov Model and its representation as Dynamic Bayesian Net...
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ISBN:
(纸本)9783319089799;9783319089782
This paper constructs a feature vector representing intraday USD/COP transaction prices and order book dynamics using zig-zag patterns. A Hierarchical Hidden Markov Model and its representation as Dynamic Bayesian Network are used to model the market sentiment dynamics choosing from uptrend or downtrend latent regimes based on observed feature vector realizations. The HHMM learned a natural switching buy/uptrend sell/downtrend trading strategy using a Training-Validation framework over one month of market data. The model was tested on the following two months showing promising performance results.
This paper provides a method for automated record linkage in the historical domain based on collective entity resolution. Multiple records are considered for linkage simultaneously, using plausible record sequences as...
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ISBN:
(纸本)9783319089799;9783319089782
This paper provides a method for automated record linkage in the historical domain based on collective entity resolution. Multiple records are considered for linkage simultaneously, using plausible record sequences as a substitute for pair-wise record similarity measures such as string edit distance. The method is applied to the problem of family reconstruction from historical archives. A benchmark evaluation shows that the approach provides a computationally efficient way to produce family reconstructions which are useful in practise. Further improvements in linkage accuracy are expected by addressing data issues and linkage assumption violations.
Massive data sources resulting from human interactions with the Internet may offer a new perspective on the behavior of market. By analyzing Google query database for search terms related to movies, we present a metho...
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Many machinelearning methods have been applied on Named Entity recognition (NER). Such methods generally build on a large manually-annotated training set. However, the training set is usually limited as human labelin...
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ISBN:
(纸本)9781479947195
Many machinelearning methods have been applied on Named Entity recognition (NER). Such methods generally build on a large manually-annotated training set. However, the training set is usually limited as human labeling is costly and time consuming. Compare to the training set, the unlabeled corpus is usually much bigger and contains rich information about language. In this paper, a hybrid Deep Neural Network (DNN) is proposed to take advantage of the implicit information embedded in the un-labeled corpus. The experiments show that F1-score is improved from 85% to 90% (person name), from 75% to 81% (location name), and from 74% to 78% (organization name), compared with Conditional Random Fields (CRFs).
Generally, in machinelearning applications, the problem of missing data has significant effect on the prediction performance. For a given missing data problem, it is not straightforward to select a treatment approach...
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ISBN:
(纸本)9783319023090
Generally, in machinelearning applications, the problem of missing data has significant effect on the prediction performance. For a given missing data problem, it is not straightforward to select a treatment approach in combination with a classification model due to several factors such as the pattern of data and nature of missing data. The selection becomes more difficult for applications such as intelligent lighting, where there is high degree of randomness in the pattern of data. In this paper, we study pairs of probabilistic missing data treatment methods and classification models to identify the best pair for a dataset gathered from an office environment for intelligent lighting. We evaluate the performance in simulations using a new metric called Relevance Score. Experimental results show that the CPOF (Conditional Probability based only on the Outcome and other Features) method in combination with the DecisionTable (DT) classifier is the most suitable pair for implementation.
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