scholarly journals Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges

2021 ◽  
Author(s):  
Michael Sioutis ◽  
Diedrich Wolter
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Real World ◽  
Temporal Reasoning ◽  
Current Status ◽  
Highly Active ◽  
Future Challenges ◽  
Symbolic Ai ◽  
Spatio Temporal ◽  
Time Critical

Qualitative Spatial & Temporal Reasoning (QSTR) is a major field of study in Symbolic AI that deals with the representation and reasoning of spatio- temporal information in an abstract, human-like manner. We survey the current status of QSTR from a viewpoint of reasoning approaches, and identify certain future challenges that we think that, once overcome, will allow the field to meet the demands of and adapt to real-world, dynamic, and time-critical applications of highly active areas such as machine learning and data mining.

scholarly journals Dimension Reduction for Objects Composed of Vector Sets

2017 ◽  
Vol 27 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Marton Szemenyei ◽  
Ferenc Vajda
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Probability Distribution ◽  
Dimension Reduction ◽  
Pose Estimation ◽  
Real World ◽  
Single Object ◽  
Real World Datasets

Abstract Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution. Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data

Cyber Crime ◽  
2013 ◽  
pp. 395-415 ◽  
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

scholarly journals An application of machine learning based on real-world data: Mining features of fibrinogen in clinical stages of lung cancer between sexes

2021 ◽  
Vol 9 (8) ◽  
pp. 623-623
Author(s):  
Fangtao Yin ◽  
Hongyu Zhu ◽  
Songlin Hong ◽  
Chen Sun ◽  
Jie Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Clinical Stages

Characterizing glacial processes applying classical beamforming and machine learning

2020 ◽  
Author(s):  
Josefine Umlauft ◽  
Philippe Roux ◽  
Florent Gimbert ◽  
Albanne Lecointre ◽  
Bertrand Rouet-LeDuc ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dynamic Environment ◽  
Structural Features ◽  
Climatic Conditions ◽  
Data Set ◽  
Matched Field Processing ◽  
Highly Active ◽  
Glacial Dynamics ◽  
Spatio Temporal ◽  
Statistical Consistency

<p>The cryosphere is a highly active and dynamic environment that rapidly responds to changing climatic conditions. processes behind are poorly understood they remain challenging to observe. <span>Glacial dynamics are</span> strongly intermittent in time and heterogeneous in space. Thus, monitoring with high spatio-temporal resolution is essential. In course of the RESOLVE project, continuous seismic observations were obtained using a dense seismic network (100 nodes, Ø 700 m) installed on the Argentière Glacier (French Alpes) during May in 2018. This unique data set offers the chance to study targeted processes and dynamics within the cryosphere on a local scale in detail.</p><p align="justify">We classical beamforming within the of the array (matched field processing) and unsupervised machine learning<span> techniques</span> to identify, cluster and locate seismic sources in 5D (x, y, z, velocity, time). Sources located with high resolution and accuracy related to processes and activity within the ice body, e.g. the geometry and dynamics of crevasses or the interaction at the glacier/bedrock interface, depending on the meteorological conditions such as daily temperature fluctuations or snow fall. <span>Our preliminary</span> results indicate strong potential in poorly resolved sources, which can be observed with statistical consistency reveal new insights into structural features/ physical properties of the glacier (e.g. analysis of scatterers).</p>

scholarly journals Virtual to Real-World Transfer Learning: A Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1491
Author(s):  
Mahesh Ranaweera ◽  
Qusay H. Mahmoud
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Transfer Learning ◽  
Real World ◽  
High Performance ◽  
Research Area ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Current Status ◽  
The Real

Machine learning has become an important research area in many domains and real-world applications. The prevailing assumption in traditional machine learning techniques, that training and testing data should be of the same domain, is a challenge. In the real world, gathering enough training data to create high-performance learning models is not easy. Sometimes data are not available, very expensive, or dangerous to collect. In this scenario, the concept of machine learning does not hold up to its potential. Transfer learning has recently gained much acclaim in the field of research as it has the capability to create high performance learners through virtual environments or by using data gathered from other domains. This systematic review defines (a) transfer learning; (b) discusses the recent research conducted; (c) the current status of transfer learning and finally, (d) discusses how transfer learning can bridge the gap between the virtual and the real.

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and its Application to (Vertically) Partitioned Spatio-Temporal Data

2013 ◽  
pp. 45-65
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

Data Mining and the KDD Process

2018 ◽  
pp. 1919-1933
Author(s):  
Ana Funes ◽  
Aristides Dasso
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Research And Development ◽  
Knowledge Discovery ◽  
Knowledge Extraction ◽  
Knowledge Discovery In Databases ◽  
Special Focus ◽  
New Methods ◽  
Future Challenges

Nowadays, there exists an increasing number of applications where analysis and discovery of new patterns have fueled the research and development of new methods, all related to Machine Learning, Knowledge Extraction, Knowledge Discovery in Databases or KDD, and Data Mining. The development of Data Mining and other related disciplines has benefited from the existence of large volumes of data proceeding from the most diverse sources and domains. KDD process and methods of Data Mining allows for the discovery of knowledge in data that is hidden to humans, presenting this knowledge under different ways. In this chapter, an overview of the KDD process with special focus in the phase of Data Mining is given. A discussion on Data Mining tasks and methods, a possible classification of them, the relation of Data Mining to other disciplines, and an overview of future challenges in the field are also given.

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