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We consider the problem of anomaly localization in a sensor network for multivariate time-series data by computing anomaly scores for each variable separately. To estimate the sparse Gaussian graphical models (GGMs) learned from different sliding windows of the dataset, we propose a new model wherein we constrain sparsity directly through L0 constraint and apply an additional L2 regularization in...
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...
Daily climate data observations from more than 3000 climate measurement sites in the continental U.S. were mined and analyzed to derive insights and trends from climate extreme indices. Daily climate data observations were aggregated by climate divisions and analyzed to derive a new climate extremes indices data set (Threshold Exceedence Frequency, TEF). Each climate division was statistically assessed...
With an increase in the population of older adults, the number of cases with dementia also increases. People living with dementia (PLwD) exhibit various behavioral and psychological experiences; agitation and aggression being the most common. Aggressive patients with dementia can harm themselves, other patients and the staff. In the past, researchers have used actigraphy to detect incidences of agitation...
This study presents a scalable and robust approach to spatial downscaling in the context of climate downscaling. We explore the ability of four techniques to downscale a climate variable to a given location of interest. As an example, we focus on downscaling daily mean air temperature at twelve stations located across the topographically complex province of British Columbia, Canada. The techniques...
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...
Network centrality reflects node importance in networks, which is a challenging problem in social network analysis. Based on Fuzzy Set and MYCIN theory, this paper proposes a novel node centrality measuring method and models n-monkeys dataset, where n is 20. Initially, we created monkeys relationship graph and generated relationship matrix based on the monkeys' encountering times in a specific time...
Job ad data has become an essential part of the recruiting world, helping recruiters to construct views of the labor market to determine emerging skills, closest competitors, and where to get the most value for each recruiting dollar spent. Collecting this data, however, can be problematic, as job ads are posted redundantly at numerous online locations. In this paper, we detail a domain-specific near-duplicate...
Multidimensional relationships can be represented as a multi-mode network or graph, where each vertex or node corresponds to an object, and each edge or link is attributed to one of the multiple types of relationships between a pair of objects. Web search log includes users' search behavior and can also be represented as such a multi-mode network, where each vertex corresponds to a query and each...
Analyzing job hopping behavior is important for the understanding of job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow and organization competition. Traditionally, surveys are conducted on job seekers and employers to study job behavior. While surveys are good at getting direct user...
In the recruitment domain, knowing the employer industry of jobs is important to get an insight about the demand in each industry. The existing system at CareerBuilder uses an employer name normalization system and an employer knowledge base to infer the employer industry of a job. However, errors may occur during the computation of the job employer and in the construction of the employer knowledge...
The interests of individual Internet users fall into a hierarchical structure which is useful in regards to building personalized searches and recommendations. Most studies on this subject construct the interest hierarchy of a single person from the document perspective. In this study, we constructed the user interest hierarchy via user profiles. We organized 433,397 user interests, referred to here...
Graphs or networks are a natural way to analyze inter-related set of entities. When these entities are associated with a diverse number of features, each denoting a specific perspective, then the representation can be simplified by forming a network of layers (one for each feature) or multiplexes. Vertices with high centrality values in the multiplexes represent the most influential vertices. However,...
Residential Demand Response has emerged as an instrument of the modern smart grid to alleviate supply and demand imbalances of electricity. Utilizing their flexibility of electricity demand, residential households are offered monetary incentives to temporarily reduce energy consumption during times when the grid is strained due to a supply shortage. In this paper, we estimate the magnitude of reductions...
Cyberbullying refers to the use of text, images, audio and video to harass or harm individuals or groups on a repetitive and non–stop basis in online social networks. The phenomenon has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances to mitigate the consequences. We perform a detailed analysis of a large–scale real–world...
We present the results of an experiment to assess the validity of prior polarities available in sentiment lexicons. We designed a ranking task that was elicited through pairwise comparisons and compared the results to those predicted by two popular sentiment lexicons. We find that the experiment results show a moderate level of agreement between the lexicons and human judgments.
The proliferation of Web 2.0 technologies and the increasing use of computer-mediated communication resulted in a new form of written text, termed microtext. This poses new challenges to natural language processing tools which are usually designed for well-written text. This paper proposes a phonetic-based framework for normalizing microtext to plain English and, hence, improve the classification...
We present an accelerated algorithm for hierarchical density based clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a significant qualitative improvement over the popular DBSCAN algorithm. The accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while supporting variable density clusters, and eliminating the need for the difficult to tune distance scale...
Given a beginning and ending document, automated storytelling attempts to fill in intermediary documents to form a coherent story. This is a common problem for analysts; they often have two snippets of information and want to find the other pieces that relate them. Evaluation of the quality of the created stories is difficult and has routinely involved human judgment. This work extends the state of...
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