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- Twitter (4)
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- Semantic Web (2)
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- Computing (1)
- Contrast Subspace (1)
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- Crisis (1)
- Crisis Coordination (1)
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- Dirichlet Distribution (1)
- Disaster Response (1)
- Document Retrieval (1)
- Documentation–assessment–monitoring–action (1)
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Articles 1 - 25 of 25
Full-Text Articles in Science and Technology Studies
Anonymized Video Analysis Methods And Systems, Marjorie Skubic, James M. Keller, Fang Wang, Derek T. Anderson, Erik Stone, Robert H. Luke Iii, Tanvi Banerjee, Marilyn J. Rantz
Anonymized Video Analysis Methods And Systems, Marjorie Skubic, James M. Keller, Fang Wang, Derek T. Anderson, Erik Stone, Robert H. Luke Iii, Tanvi Banerjee, Marilyn J. Rantz
Kno.e.sis Publications
Methods and systems for anonymized video analysis are described. In one embodiment, a first silhouette image of a person in a living unit may be accessed. The first silhouette image may be based on a first video signal recorded by a first video camera. A second silhouette image of the person in the living unit may be accessed. The second silhouette image may be of a different view of the person than the first silhouette image. The second silhouette image may be based on a second video signal recorded by a second video camera. A three-dimensional model of the person …
Protecting Web Servers From Web Robot Traffic, Derek Doran
Protecting Web Servers From Web Robot Traffic, Derek Doran
Kno.e.sis Publications
No abstract provided.
Triad-Based Role Discovery For Large Social Systems, Derek Doran
Triad-Based Role Discovery For Large Social Systems, Derek Doran
Kno.e.sis Publications
The social role of a participant in a social system conceptualizes the circumstances under which she chooses to interact with others, making their discovery and analysis important for theoretical and practical purposes. In this paper, we propose a methodology to detect such roles by utilizing the conditional triad censuses of ego-networks. These censuses are a promising tool for social role extraction because they capture the degree to which basic social forces push upon a user to interact with others in a system. Clusters of triad censuses, inferred from network samples that preserve local structural properties, define the social roles. The …
An Analysis Of Mayo Clinic Search Query Logs For Cardiovascular Diseases, Ashutosh Sopan Jadhav, Amit P. Sheth, Jyotishman Pathak
An Analysis Of Mayo Clinic Search Query Logs For Cardiovascular Diseases, Ashutosh Sopan Jadhav, Amit P. Sheth, Jyotishman Pathak
Kno.e.sis Publications
Increasingly, individuals are taking active participation in learning and managing their health by leveraging online resources. Understanding online health information searching behavior can help us to study what health topics users search for and how search queries are formulated. In this work, we analyzed 10 million cardiovascular diseases (CVD) related search queries from MayoClinic.com. We performed semantic analysis on the queries using UMLS MetaMap and analyzed structural and textual properties as well as linguistic characteristics of the queries.
Discovering Perceptions In Online Social Media: A Probabilistic Approach, Derek Doran, Swapna S. Gokhale, Aldo Dagnino
Discovering Perceptions In Online Social Media: A Probabilistic Approach, Derek Doran, Swapna S. Gokhale, Aldo Dagnino
Kno.e.sis Publications
People across the world habitually turn to online social media to share their experiences, thoughts, ideas, and opinions as they go about their daily lives. These posts collectively contain a wealth of insights into how masses perceive their surroundings. Therefore, extracting people’s perceptions from social media posts can provide valuable information about pertinent issues such as public transportation, emergency conditions, and even reactions to political actions or other activities. This paper proposes a novel approach to extract such perceptions from a corpus of social media posts originating from a given broad geographical region. The approach divides the broad region into …
Online Information Searching For Cardiovascular Diseases: An Analysis Of Mayo Clinic Search Query Logs, Ashutosh Sopan Jadhav, Amit P. Sheth, Jyotishman Pathak
Online Information Searching For Cardiovascular Diseases: An Analysis Of Mayo Clinic Search Query Logs, Ashutosh Sopan Jadhav, Amit P. Sheth, Jyotishman Pathak
Kno.e.sis Publications
Since the early 2000’s, Internet usage for health information searching has increased significantly. Studying search queries can help us to understand users “information need” and how do they formulate search queries (“expression of information need”). Although cardiovascular diseases (CVD) affect a large percentage of the population, few studies have investigated how and what users search for CVD. We address this knowledge gap in the community by analyzing a large corpus of 10 million CVD related search queries from MayoClinic.com. Using UMLS MetaMap and UMLS semantic types/concepts, we developed a rule-based approach to categorize the queries into 14 health categories. We …
Data Analytics For Power Utility Storm Planning, Lan Lin, Aldo Dagnino, Derek Doran, Swapna S. Gokhale
Data Analytics For Power Utility Storm Planning, Lan Lin, Aldo Dagnino, Derek Doran, Swapna S. Gokhale
Kno.e.sis Publications
As the world population grows, recent climatic changes seem to bring powerful storms to populated areas. The impact of these storms on utility services is devastating. Hurricane Sandy is a recent example of the enormous damages that storms can inflict on infrastructure, society, and the economy. Quick response to these emergencies represents a big challenge to electric power utilities. Traditionally utilities develop preparedness plans for storm emergency situations based on the experience of utility experts and with limited use of historical data. With the advent of the Smart Grid, utilities are incorporating automation and sensing technologies in their grids and …
A Keyword Sense Disambiguation Based Approach For Noise Filtering In Twitter, Sanjaya Wijeratne, Bahareh R. Heravi
A Keyword Sense Disambiguation Based Approach For Noise Filtering In Twitter, Sanjaya Wijeratne, Bahareh R. Heravi
Kno.e.sis Publications
In this paper, we describe an approach to filter out noisy data generated by keywords-based tweet filtering methods by performing Word Sense Disambiguation on those keywords used to collect tweets. We present the noise filtering problem as a binary classification problem and discuss our evaluation strategy which is to be carried out in future.
Rasp-Qs: Efficient And Confidential Query Services In The Cloud, Zohreh S. Alavi, Lu Zhou, James L. Powers, Keke Chen
Rasp-Qs: Efficient And Confidential Query Services In The Cloud, Zohreh S. Alavi, Lu Zhou, James L. Powers, Keke Chen
Kno.e.sis Publications
Hosting data query services in public clouds is an attractive solution for its great scalability and significant cost savings. However, data owners also have concerns on data privacy due to the lost control of the infrastructure. This demonstration shows a prototype for efficient and confidential range/kNN query services built on top of the random space perturbation (RASP) method. The RASP approach provides a privacy guarantee practical to the setting of cloudbased computing, while enabling much faster query processing compared to the encryption-based approach. This demonstration will allow users to more intuitively understand the technical merits of the RASP approach via …
Document Retrieval Using Predication Similarity, Kalpa Gunaratna
Document Retrieval Using Predication Similarity, Kalpa Gunaratna
Kno.e.sis Publications
Document retrieval has been an important research problem over many years in the information retrieval community. State-of-the-art techniques utilize various methods in matching documents to a given document including keywords, phrases, and annotations. In this paper, we propose a new approach for document retrieval that utilizes predications (subject-predicate-object triples) extracted from the documents. We represent documents as sets of predications. We measure the similarity between predications to compute the similarity between documents. Our approach utilizes the hierarchical information available in ontologies in computing concept-concept similarity, making the approach flexible. Predication-based document similarity is more precise and forms the basis for …
A Novel Web-Based Depth Video Rewind Approach Toward Fall Preventive Interventions In Hospitals, Moein Enayati, Tanvi Banerjee, Mihail Popescu, Marjorie Skubic, Marilyn J. Rantz
A Novel Web-Based Depth Video Rewind Approach Toward Fall Preventive Interventions In Hospitals, Moein Enayati, Tanvi Banerjee, Mihail Popescu, Marjorie Skubic, Marilyn J. Rantz
Kno.e.sis Publications
Falls in the hospital rooms are considered a huge burden on healthcare costs. They can lead to injuries, extended length of stay, and increase in cost for both the patients and the hospital. It can also lead to emotional trauma for the patients and their families [1]. Having Microsoft Kinects installed in the hospital rooms to capture and process every movement in the room, we deployed our previously developed fall-detection system to detect naturally occurring falls, generate a real-time fall alarm and broadcast it to hospital nurses for immediate intervention. These systems also store a processed and reduced version …
Assisting Coordination During Crisis: A Domain Ontology Based Approach To Infer Resource Needs From Tweets, Shreyansh Bhatt, Hemant Purohit, Andrew J. Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach
Assisting Coordination During Crisis: A Domain Ontology Based Approach To Infer Resource Needs From Tweets, Shreyansh Bhatt, Hemant Purohit, Andrew J. Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach
Kno.e.sis Publications
Ubiquitous social media during crises provides citizen reports on the situation, needs and supplies. Previous research extracts resource needs directly from the text (e.g. "Power cut to Coney Island and Brighton beach" indicates a power need). This approach assumes that citizens derive and write about specific needs from their observations, properly specified for the emergency response system, an assumption that is not consistent with general conversational behavior. In our study, Twitter messages (tweets) from Hurricane Sandy in 2012 clearly indicate power blackouts, but not their probable implications (e.g. loss of power to hospital life support systems). We use a domain …
Semantics-Enhanced Geoscience Interoperability, Analytics, And Applications, Krishnaprasad Thirunarayan, Amit P. Sheth
Semantics-Enhanced Geoscience Interoperability, Analytics, And Applications, Krishnaprasad Thirunarayan, Amit P. Sheth
Kno.e.sis Publications
We present our research ideas for developing cyberinfrastructure for Geoscience applications developed in the context of the EarthCube initiative, and our NSF-sponsored work on incorporating spatial-temporal-thematic semantics for enhanced querying and feature extraction from sensor data streams.
Semantic Modelling Of Smart City Data, Stefan Bischof, Athanasios Karapantelakis, Cosmin-Septimiu Nechifor, Amit P. Sheth, Alessandra Mileo, Payam Barnaghi
Semantic Modelling Of Smart City Data, Stefan Bischof, Athanasios Karapantelakis, Cosmin-Septimiu Nechifor, Amit P. Sheth, Alessandra Mileo, Payam Barnaghi
Kno.e.sis Publications
Cities present an opportunity for rendering Web of Things-enabled services. According to the World Health Organization, population in cities will double by the middle of this century, while cities deal with increasingly pressing issues such as environmental sustainability, economic growth and citizen mobility. In this paper, we propose a discussion around the need for common semantic descriptions for smart city data to facilitate future services in "smart cities". We present examples of data that can be collected from cities, discuss issues around this data and put forward some preliminary thoughts for creating a semantic description model to describe and help …
Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth
Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth
Kno.e.sis Publications
Many machine learning datasets are noisy with a substantial number of mislabeled instances. This noise yields sub-optimal classification performance. In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowdsource annotations. We describe computationally cheap feature weighting techniques and a novel non-linear distribution spreading algorithm that can be used to iteratively and interactively correcting mislabeled instances to significantly improve annotation quality at low cost. Eight different emotion extraction experiments on Twitter data demonstrate that our approach is just as effective as more computationally expensive techniques. Our techniques save a considerable …
Mining Contrast Subspaces, Lei Duan, Guanting Tang, Jian Pei, James Bailey, Guozhu Dong, Akiko Campbell, Changjie Tang
Mining Contrast Subspaces, Lei Duan, Guanting Tang, Jian Pei, James Bailey, Guozhu Dong, Akiko Campbell, Changjie Tang
Kno.e.sis Publications
In this paper, we tackle a novel problem of mining contrast subspaces. Given a set of multidimensional objects in two classes C+ and C− and a query object o, we want to find top-k subspaces S that maximize the ratio of likelihood of o in C+ against that in C−. We demonstrate that this problem has important applications, and at the same time, is very challenging. It even does not allow polynomial time approximation. We present CSMiner, a mining method with various pruning techniques. CSMiner is substantially faster than the baseline method. Our …
With Whom To Coordinate, Why And How In Ad-Hoc Social Media Communications During Crisis Response, Hemant Purohit, Shreyansh Bhatt, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John M. Flach
With Whom To Coordinate, Why And How In Ad-Hoc Social Media Communications During Crisis Response, Hemant Purohit, Shreyansh Bhatt, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John M. Flach
Kno.e.sis Publications
During crises affected people, well-wishers, and observers join social media communities to discuss the event. They often share useful information relevant to response coordination, for example, specific resource needs. However, responders face the challenge of massive data overload and lack the time to monitor social media traffic for important information. Analysis shows that only a small number of event related conversations are actionable. Moreover, responders do not know which sources are trustworthy. To address these challenges, response teams may apply manual filtering methods, resulting in limited coverage and quality. We propose a framework and interface for extracting specific resource-related information …
Hierarchical Interest Graph From Tweets, Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit P. Sheth
Hierarchical Interest Graph From Tweets, Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit P. Sheth
Kno.e.sis Publications
Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is the identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as a Hierarchical Interest Graph. To create such graphs, we utilize users' tweets to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt …
Leveraging Social Media And Web Of Data For Crisis Response Coordination, Carlos Castillo, Fernando Diaz, Hemant Purohit
Leveraging Social Media And Web Of Data For Crisis Response Coordination, Carlos Castillo, Fernando Diaz, Hemant Purohit
Kno.e.sis Publications
There is an ever increasing number of users in social media (1B+ Facebook users, 500M+ Twitter users) and ubiquitous mobile access (6B+ mobile phone subscribers) who share their observations and opinions. In addition, the Web of Data and existing knowledge bases keep on growing at a rapid pace. In this scenario, we have unprecedented opportunities to improve crisis response by extracting social signals, creating spatio-temporal mappings, performing analytics on social and Web of Data, and supporting a variety of applications. Such applications can help provide situational awareness during an emergency, improve preparedness, and assist during the rebuilding/recovery phase of a …
Comparative Trust Management With Applications: Bayesian Approaches Emphasis, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth
Comparative Trust Management With Applications: Bayesian Approaches Emphasis, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth
Kno.e.sis Publications
Trust relationships occur naturally in many diverse contexts such as collaborative systems, e-commerce, interpersonal interactions, social networks, and semantic sensor web. As agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update becomes critical. There is a need to find online substitutes for traditional (direct or face-to-face) cues to derive measures of trust, and create efficient and robust systems for managing trust in order to support decision-making. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its …
Cursing In English On Twitter, Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, Amit P. Sheth
Cursing In English On Twitter, Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, Amit P. Sheth
Kno.e.sis Publications
Cursing is not uncommon during conversations in the physical world: 0.5% to 0.7% of all the words we speak are curse words, given that 1% of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in people's cursing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform - Twitter, involving the analysis of …
Location Prediction Of Twitter Users Using Wikipedia, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
Location Prediction Of Twitter Users Using Wikipedia, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
Kno.e.sis Publications
The mining of user generated content in social media has proven very effective in domains ranging from personalization and recommendation systems to crisis management. The knowledge of online users locations makes their tweets more informative and adds another dimension to their analysis. Existing approaches to predict the location of Twitter users are purely data-driven and require large training data sets of geo-tagged tweets. The collection and modelling process of tweets can be time intensive. To overcome this drawback, we propose a novel knowledge based approach that does not require any training data. Our approach uses information in Wikipedia, about cities …
Finding Them Before They Find Us: Informatics, Parasites, And Environments In Accelerating Climate Change, Daniel R. Brooks, Eric P. Hoberg, Walter A. Boeger, Scott Lyell Gardner, Kurt E. Galbreath, David Herczeg, Hugo H. Mejía-Madrid, S. Elizabeth Rácz, Altangerel Tsogtsaikhan Dursahinhan
Finding Them Before They Find Us: Informatics, Parasites, And Environments In Accelerating Climate Change, Daniel R. Brooks, Eric P. Hoberg, Walter A. Boeger, Scott Lyell Gardner, Kurt E. Galbreath, David Herczeg, Hugo H. Mejía-Madrid, S. Elizabeth Rácz, Altangerel Tsogtsaikhan Dursahinhan
Harold W. Manter Laboratory of Parasitology: Faculty and Staff Publications
Parasites are agents of disease in humans, livestock, crops, and wildlife and are powerful representations of the ecological and historical context of the diseases they cause. Recognizing a nexus of professional opportunities and global public need, we gathered at the Cedar Point Biological Station of the University of Nebraska in September 2012 to formulate a cooperative and broad platform for providing essential information about the evolution, ecology, and epidemiology of parasites across host groups, parasite groups, geographical regions, and ecosystem types. A general protocol, documentation–assessment–monitoring–action (DAMA), suggests an integrated proposal to build a proactive capacity to understand, anticipate, and respond …
What Information About Cardiovascular Diseases Do People Search Online?, Ashutosh Sopan Jadhav, Stephen Wu, Amit P. Sheth, Jyotishman Pathak
What Information About Cardiovascular Diseases Do People Search Online?, Ashutosh Sopan Jadhav, Stephen Wu, Amit P. Sheth, Jyotishman Pathak
Kno.e.sis Publications
The objective of this study is to understand the types of health information (health topics) that users search online for Cardiovascular Diseases, by performing categorization of health search queries (from Mayoclinic.com) using UMLS MetaMap based on UMLS concepts and semantic types.
Alignment And Dataset Identification Of Linked Data In Semantic Web, Kalpa Gunaratna, Sarasi Lalithsena, Amit P. Sheth
Alignment And Dataset Identification Of Linked Data In Semantic Web, Kalpa Gunaratna, Sarasi Lalithsena, Amit P. Sheth
Kno.e.sis Publications
The Linked Open Data (LOD) cloud has gained significant attention in the Semantic Web community over the past few years. With rapid expansion in size and diversity, it consists of over 800 interlinked datasets with over 60 billion triples. These datasets encapsulate structured data and knowledge spanning over varied domains such as entertainment, life sciences, publications, geography, and government. Applications can take advantage of this by using the knowledge distributed over the interconnected datasets, which is not realistic to find in a single place elsewhere. However, two of the key obstacles in using the LOD cloud are the limited support …