Trust Networks: Interpersonal, Social, And Sensor,
2011
Wright State University - Main Campus
Trust Networks: Interpersonal, Social, And Sensor, Krishnaprasad Thirunarayan, Pramod Anantharam
Kno.e.sis Publications
Trust relationships occur naturally in many diverse contexts such as ecommerce, interpersonal interactions, social networks, sensor web, etc. As agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update become critical. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics or computation in many situations. In this beginner's level tutorial, we motivate the trust problem, explain the relevant concepts, summarize research in modeling trust and gleaning trustworthiness, and discuss challenges confronting us in this process.
Double Updating Online Learning,
2011
Nanyang Technological University
Double Updating Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Research Collection School Of Computing and Information Systems
In most kernel based online learning algorithms, when an incoming instance is misclassified, it will be added into the pool of support vectors and assigned with a weight, which often remains unchanged during the rest of the learning process. This is clearly insufficient since when a new support vector is added, we generally expect the weights of the other existing support vectors to be updated in order to reflect the influence of the added support vector. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short, that explicitly addresses this problem. …
Rethinking Our Mobility: Supporting Our Patrons Where They Live,
2011
George Washington University
Rethinking Our Mobility: Supporting Our Patrons Where They Live, Alexandra Gomes, Elizabeth Palena Hall, Laura E. Abate
Himmelfarb Library Faculty Posters and Presentations
In anticipation of the release of a mobile VPN to access the George Washington University wireless network, the Himmelfarb Health Sciences Library began developing materials and services to provide support to patrons. This poster is an outline of the mobile services that were implemented to reach patrons where they live.
Adoption Of Cloud Computing Services In Malaysia : A Quantitative Study Using Technology Acceptance Model And Innovation Diffusion Theory.,
2011
Universiti Malaya
Adoption Of Cloud Computing Services In Malaysia : A Quantitative Study Using Technology Acceptance Model And Innovation Diffusion Theory., Izleen Rahayu Ab Rahman
Student Works (2010-2019)
In today’s IT world, organizations as well as individuals have choices in adopting IT in their day-to-day individual as well as business requirements. Cloud Computing, is one of the latest phenomena that arises from sharing of resources and consolidation and standardization of resources in a centralized facilities. The sharing of the resources gives various advantages to the users to start its usage as convenient as no start-up CAPEX investment. This proposed research will contribute to a new knowledge, especially in identifying the relationship between factors that determine ones decision in adopting cloud computing among Malaysian internet users. This underlying research …
Continuous Nearest Neighbor Search In The Presence Of Obstacles,
2011
Singapore Management University
Continuous Nearest Neighbor Search In The Presence Of Obstacles, Yunjun Gao, Baihua Zheng, Gang Chen, Chun Chen, Qing Li
Research Collection School Of Computing and Information Systems
Despite the ubiquity of physical obstacles (e.g., buildings, hills, and blindages, etc.) in the real world, most of spatial queries ignore the obstacles. In this article, we study a novel form of continuous nearest-neighbor queries in the presence of obstacles, namely continuous obstructed nearest-neighbor (CONN) search, which considers the impact of obstacles on the distance between objects. Given a data setP, an obstacle set O, and a query line segment q, in a two-dimensional space, a CONN query retrieves the nearest neighbor p ∈ P of each point p′ on q according to the obstructed distance, the shortest path between …
Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 4: Predictive Analysis - Regression Modeling,
2011
Cleveland State University
Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 4: Predictive Analysis - Regression Modeling, Mark Salling, Gregory Soltis, Charles Post, Sharon Bliss, Ellen Cyran
All Maxine Goodman Levin School of Urban Affairs Publications
A series of reports detailing the residential and work space location preferences of Cuyahoga county's artists.
Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 3: Attitudinal Analysis - Artist Housing And Space Survey,
2011
Cleveland State University
Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 3: Attitudinal Analysis - Artist Housing And Space Survey, Mark Salling, Gregory Soltis, Charles Post, Sharon Bliss, Ellen Cyran
All Maxine Goodman Levin School of Urban Affairs Publications
A series of reports detailing the residential and work space location preferences of Cuyahoga county's artists.
Adaptation Of The Nevada Climate Change Data Portal Web Interface To Small-Screen Mobile Devices,
2011
University of Nevada, Reno
Adaptation Of The Nevada Climate Change Data Portal Web Interface To Small-Screen Mobile Devices, Tsvetan Komarov
Festival of Communities: UG Symposium (Posters)
Robust and convenient access to the Nevada Climate Change Data Portal is vital for the project’s success, because of the researchers’ need to gather and analyze large volumes of data with minimal effort. However, the current version of the data portal web interface is not optimized for small-screen mobile devices such as mobile phones, PDAs, iPads, NetBooks, and others. The proposed research will address this issue by exploring the current methods for creating a client-aware web interface adaptable to the variety of small-screen devices, designing and implementing the most appropriate solution, and finally, user testing of the implemented solution.
Assessing Differences Between Physician's Realized And Anticipated Gains From Electronic Health Record Adoption,
2011
Cleveland State University
Assessing Differences Between Physician's Realized And Anticipated Gains From Electronic Health Record Adoption, Lori T. Peterson, Eric W. Ford, John Eberhardt, T. R. Huerta
Business Faculty Publications
Return on investment (ROI) concerns related to Electronic Health Records (EHRs) are a major barrier to the technology’s adoption. Physicians generally rely upon early adopters to vet new technologies prior to putting them into widespread use. Therefore, early adopters’ experiences with EHRs play a major role in determining future adoption patterns. The paper’s purposes are: (1) to map the EHR value streams that define the ROI calculation; and (2) to compare Current Users’ and Intended Adopters’ perceived value streams to identify similarities, differences and governing constructs. Primary data was collected by the Texas Medical Association, which surveyed 1,772 physicians on …
Users Positions In Social Networks,
2011
San Jose State University
Users Positions In Social Networks, Jasim Qazi
Master's Projects
Social networks are a new phase in human interaction: using
technology to connect people online, the social nehvorks of today have
become a central part of the lives of millions of people. People use social
networks for sharing various infonrmation with their friends and family. This
information can take the forn of text, video, images, sound etc. and it is what
forms the collection of dats in social networks.
As social networks gain popularity and as more and more people start
using social networks, it has become more important now to understand the
inner structures of social networks and understand …
Recipe Suggestion Tool,
2011
San Jose State University
Recipe Suggestion Tool, Sakuntala Padmapriya Gangaraju
Master's Projects
ABSTRACT
There is currently a great need for a tool to search cooking recipes based on ingredients. Current search engines do not provide this feature. Most of the recipe search results in current websites are not efficiently clustered based on relevance or categories resulting in a user getting lost in the huge search results presented.
Clustering in information retrieval is used for higher efficiency and better presentation of information to the user. Clustering puts similar documents in the same cluster. If a document is relevant to a query, then the documents in the same cluster are also relevant.
The goal …
Smart Search: A Firefox Add-On To Compute A Web Traffic Ranking,
2011
San Jose State University
Smart Search: A Firefox Add-On To Compute A Web Traffic Ranking, Vijaya Pamidi
Master's Projects
Search engines results are typically ordered according to some notion of importance of a web page as well as relevance of the content of a web page to a query. Web page importance is usually calculated based on some graph theoretic properties of the web. Another common technique to measure page importance is to make use of the traffic that goes to a particular web page as measured by a browser toolbar. Currently, there are some traffic ranking tools available like www.alexa.com, www.ranking.com, www.compete.com that give such analytic as to the number of users who visit a web site. Alexa …
Learning Feature Dependencies For Noise Correction In Biomedical Prediction,
2011
Institute for Infocomm Research, Singapore
Learning Feature Dependencies For Noise Correction In Biomedical Prediction, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
The presence of noise or errors in the stated feature values of biomedical data can lead to incorrect prediction. We introduce a Bayesian Network-based Noise Correction framework named BN-NC. After data preprocessing, a Bayesian Network (BN) is learned to capture the feature dependencies. Using the BN to predict each feature in turn, BN-NC estimates a feature's error rate as the deviation between its predicted and stated values in the training data, and allocates the appropriate uncertainty to its subsequent findings during prediction. BN-NC automatically generates a probabilistic rule to explain BN prediction on the class variable using the feature values …
Mkboost: A Framework Of Multiple Kernel Boosting,
2011
Singapore Management University
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based …
A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints,
2011
Nanyang Technological University
A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Previous studies of Non-Parametric Kernel Learning (NPKL) usually formulate the learning task as a Semi-Definite Programming (SDP) problem that is often solved by some general purpose SDP solvers. However, for N data examples, the time complexity of NPKL using a standard interior-point SDP solver could be as high as O(N6.5), which prohibits NPKL methods applicable to real applications, even for data sets of moderate size. In this paper, we present a family of efficient NPKL algorithms, termed "SimpleNPKL", which can learn non-parametric kernels from a large set of pairwise constraints efficiently. In particular, we propose two efficient SimpleNPKL algorithms. One …
Predicting Item Adoption Using Social Correlation,
2011
Singapore Management University
Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to …
Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation,
2011
Singapore Management University
Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou
Research Collection School Of Computing and Information Systems
Adaptation techniques based on importance weighting were shown effective for RankSVM and RankNet, viz., each training instance is assigned a target weight denoting its importance to the target domain and incorporated into loss functions. In this work, we extend RankBoost using importance weighting framework for ranking adaptation. We find it non-trivial to incorporate the target weight into the boosting-based ranking algorithms because it plays a contradictory role against the innate weight of boosting, namely source weight that focuses on adjusting source-domain ranking accuracy. Our experiments show that among three variants, the additive weight-based RankBoost, which dynamically balances the two types …
Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection,
2011
Nanyang Technological University
Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection, Bin Li, Steven C. H. Hoi, Vivekanand Gopalkrishnan
Research Collection School Of Computing and Information Systems
Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this article, we propose a novel learning-to-trade algorithm termed CO Relation-driven Nonparametric learning strategy (CORN) for actively trading stocks. CORN effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, and show that it can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpass a variety …
Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection,
2011
Nanyang Technological University
Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivek Gopalkrishnan
Research Collection School Of Computing and Information Systems
On-line portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing on-line portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidences show that the stock price relatives may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel on-line portfolio selection strategy named ``Confidence Weighted Mean Reversion'' (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, …
Two-Layer Multiple Kernel Learning,
2011
Nanyang Technological University
Two-Layer Multiple Kernel Learning, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple Kernel Learning (MKL) aims to learn kernel machines for solving a real machine learning problem (e.g. classification) by exploring the combinations of multiple kernels. The traditional MKL approach is in general “shallow” in the sense that the target kernel is simply a linear (or convex) combination of some base kernels. In this paper, we investigate a framework of Multi-Layer Multiple Kernel Learning (MLMKL) that aims to learn “deep” kernel machines by exploring the combinations of multiple kernels in a multi-layer structure, which goes beyond the conventional MKL approach. Through a multiple layer mapping, the proposed MLMKL framework offers higher …
