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In this paper, we will first explain the FS (familiarity and strangeness) model as a requirement for attracting people's attention and bringing about analogical thinking. After introducing the idea of shikake (triggers for behavior change) and its requirements, we propose the inclusion of the FS model as an attribute of MoDAT in order to encourage MoDAT participants to come up with new shikake ideas.
Performing statistical inference on collections of graphs is of import to many disciplines. Graph embedding, in which the vertices of a graph are mapped to vectors in a low-dimensional Euclidean space, has gained traction as a basic tool for graph analysis. Here we describe an omnibus embedding in which multiple graphs on the same vertex set are jointly embedded into a single space with a distinct...
Set-valued data is comprised of records that are sets of items, such as goods purchased by each individual. Methods of publishing and widely utilizing set-valued data while protecting personal information have been extensively studied in the field of privacy-preserving data publishing. Until now, basic models such as k-anonymity or km-anonymity could not cope with attribute inference by an adversary...
Stochastic principal component analysis (SPCA) has become a popular dimensionality reduction strategy for large, high-dimensional datasets. We derive a simplified algorithm, called Lazy SPCA, which has reduced computational complexity and is better suited for large-scale distributed computation. We prove that SPCA and Lazy SPCA find the same approximations to the principal subspace, and that the pairwise...
The Krylov subspace based information retrieval (IR) approach has been shown to provide comparable accuracy to latent semantic indexing (LSI), while providing some computational advantages. Recently, in the area of numerical linear algebra, attention has been drawn to the block Krylov subspace methods, which are shown to be more efficient than the classic Krylov subspace methods in solving linear...
Networks (i.e., graphs) appears in many high-impact applications. Often these networks are collected from different sources, at different times, at different granularities. In this talk, I will present our recent work on mining such multiple networks. First, we will present several new data models, whose key idea is to leverage networks as context to connect different data sets or different data mining...
In this paper we propose a new method to anonymize (share relevant and detailed information while not naming names) and protect data sets (minimize the utility loss) based on Factor Analysis. The method basically consists of obtaining the factors, which are uncorrelated, protecting them and undoing the transformation in order to get interpretable protected variables. We first show how to proceed when...
In this study, we have developed the video based risk recognition training tool with an eye tracking device and a motion sensor. We applied the tool on the risk recognition training in a construction company and extracted features in risk recognition of expert field overseers from their eyes and utterances during the training. As the results of the examinations, typical risk recognition processes...
Consider a problem of estimating an unknown high dimensional density whose support lies on unknown low-dimensional data manifold. This problem arises in many data mining tasks, and the paper proposes a new geometrically motivated solution for the problem in manifold learning framework, including an estimation of an unknown support of the density. Firstly, tangent bundle manifold learning problem is...
Data Jacket (DJ) is a technique for sharing information about data and for considering the potential value of datasets, with the data itself hidden, by describing the summary of data in natural language. In DJs, variables are described by variable labels (VLs), which are the names/meanings of variables. In the previous study, the matrix-based method for inferring VLs in DJs whose VLs are unknown,...
Comparing images to recommend items from an image-inventory is a subject of continued interest. Added with the scalability of deep-learning architectures the once 'manual' job of hand-crafting features have been largely alleviated, and images can be compared according to features generated from a deep convolutional neural network. In this paper, we compare distance metrics (and divergences) to rank...
To collect and explicate meaningful knowledge of a community, we propose an Activity Model based on structured knowledge. The following issues arise related to the model development: (a) difficulties in capturing activities; (b) difficulty of acquiring knowledge; and (c) difficulty in optimizing the activities to newly adopted technologies. Therefore, we are developing technologies that use on-site...
Mid-Infrared (MIR) spectroscopy has emerged as the most economically viable technology to determine milk values as well as to identify a set of animal phenotypes related to health, feeding, well-being and environment. However, Fourier transform-MIR spectra incurs a significant amount of redundant data. This creates critical issues such as increased learning complexity while performing Fog and Cloud...
In this work, we report an ongoing study that aims to apply cluster validation measures for analyzing email communications at an organizational level of a company. This analysis can be used to evaluate the company structure and to produce further recommendations for structural improvements. Our initial evaluations, based on data in the forms of emails logs and organizational structure for a large...
In this paper, an innovative approach to keyboard user monitoring (authentication), using keyboard dynamics and founded on the concept of time series analysis, is presented. The work is motivated by the need for robust authentication mechanisms in the context of on-line assessment such as those featured in many online learning platforms. Four analysis mechanisms are considered: analysis of keystroke...
This paper proposes an approach to estimating fungibility between skills given multiple information sources of those skills. An estimate of skill adjacency or fungibility or substitutability is critical for effective capacity planning, analytics and optimization in the face of changing skill requirements of an organization. The proposed approach is based on computing a similarity measure between skills,...
We present a novel and configurable synthetic data generator for evolving region trajectories that emulates certain characteristics of a given input dataset, such as the spatial position, velocity, lifespan, and geometry shape and size. This tool aims to facilitate faster prototyping and evaluation of new spatiotemporal data mining algorithms that operate on a specific type of trajectory data, of...
Effective mining of large amount of DNA and RNA fragments obtained from next generation sequencing technologies, depends on the availability of efficient analytical tools to process them. One of the important aspects of this analysis, dealing with huge number of fragments, is partitioning them based on their level of similarities. In this paper we propose a space transformation based clustering approach...
In this paper, we propose a new discriminative dictionary learning framework, called robust Label Embedding Projective Dictionary Learning (LE-PDL), for data classification. LE-PDL can learn a discriminative dictionary and the blockdiagonal representations without using the l0-norm or l1-norm sparsity regularization, since the l0 or l1-norm constraint on the coding coefficients used in the existing...
Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant...
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