5239541
Link prediction approach to collaborative filtering
Recommender systems can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms
21266732
User Misconceptions of Information Retrieval Systems
In this paper, we report results of an investigation where thirty subjects were observedperforming subject-based search in an online catalog system. The observationshave revealed a range of misconceptions users have when performing subject-basedsearch. We have developed a taxonomy that characterizes these misconceptions andhypotheses about the causes of the misconceptions. Directions for improving searchperformance are also suggested.
19452291
Cross-Jurisdictional Activity Networks to Support Criminal Investigations
Border and transportation security is a critical part of the Department of Homeland Security's (DHS) national strategy. DHS strategy calls for the creation of "smart borders" where information from local, state, federal, and international sources can be combined to support risk-based management tools for border-management agencies. Sharing cross-jurisdictional law enforcement data is important because criminals take advantage of the fact that information sharing between law enforcement jurisdictions is very limited. Challenges to information integration in this important domain include policy and privacy concerns, data security requirements, semantic or schema-level matching of legacy system data, and identity matching. The BorderSafe project leverages the skills and experiences of a diverse group of researchers, law enforcement officials, and government agencies to address data sharing needs in the Southwest United States. Our demo presents a framework for combining law enforcement data across jurisdictions to support criminal investigation and describes how that framework was used in combining three large, overlapping datasets to produce an integrated system for visualizing criminal activity networks.
40127439
Authorship Analysis in Cybercrime Investigation
Criminals have been using the Internet to distribute a wide range of illegal materials globally in an anonymous manner, making criminal identity tracing difficult in the cybercrime investigation process. In this study we propose to adopt the authorship analysis framework to automatically trace identities of cyber criminals through messages they post on the Internet. Under this framework, three types of message features, including style markers, structural features, and content-specific features, are extracted and inductive learning algorithms are used to build feature-based models to identify authorship of illegal messages. To evaluate the effectiveness of this framework, we conducted an experimental study on data sets of English and Chinese email and online newsgroup messages. We experimented with all three types of message features and three inductive learning algorithms. The results indicate that the proposed approach can discover real identities of authors of both English and Chinese Internet messages with relatively high accuracies.
35410760
Collaborative Workflow Management for Interagency Crime Analysis
To strengthen homeland security, there is a critical need for new tools that can facilitate real time collaboration among various law enforcement agencies. Through a field study, we find that law enforcement work is knowledge intensive and involves complex collaborative processes interrelating a large number of disparate units in a loosely defined virtual organization. To support knowledge intensive collaboration, we propose a new workflow centric framework to seamlessly integrate previously separate techniques from the fields of information retrieval and workflow management. Specifically, we develop a collaborative workflow management framework for interagency crime analysis. The key contribution of our research is that by integrating various state-of-the-art techniques innovatively, the proposed system can support real time collaboration processes in a virtual organization that evolves dynamically.
38977435
Sharing and Visualizing Infectious Disease Datasets Using the WNV-BOT Portal System
Infectious disease outbreaks are critical threats to public health and national security. Information systems play a central role in developing an effective comprehensive approach to prevent, detect, respond to, and manage infectious disease outbreaks of plants, animals, and humans.
16294852
Interactive volume visualization on a heterogeneous message-passing multicomputer
This paper describes VOL2, an interactive general-purpose volume renderer based on ray casting and implemented on Pixel-Planes 5, a distributed-memory, message-passing multicomputer. VOL2 is a pipelined renderer using image-space task parallelism and object-space data partitioning. We describe the parallelization and load balancing techniques used in order to achieve interactive response and near-real-time frame rates. We also present a number of applications for our system and derive some general conclusions about operation of image-order rendering algorithms on message-passing multicomputers.
33677672
A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting
In this paper we present an application of hybrid neural network approaches and an assessment of the effects of missing data on motorway traffic flow forecasting. Two hybrid approaches are developed using a Self-Organising Map (SOM) to initially classify traffic into different states. The first hybrid approach includes four Auto-Regressive Integrated Moving Average (ARIMA) models, whilst the second uses two Multi-Layer Perception (MLP) models. It was found that the SOM/ARIMA hybrid approach out-performs all individual ARIMA models, whilst the SOM/MLP hybrid approach achieves superior forecasting performance to all models used in this study, including three naïve models. The effects of different proportions of missing data on Neural Network (NN) performance when forecasting traffic flow are assessed and several initial substitution options to replace missing data are discussed. Over-all, it is shown that ARIMA models are more sensitive to the percentage of missing data than neural networks in this context.
16884480
Overload control in QoS-aware web servers
With the explosive use of Internet, contemporary web servers are susceptible to overloads during which their services deteriorate drastically and often lead to denial of services. Overloads are of more serious concerns for QoS-aware servers. Evaluation of performance of QoS-aware servers in terms of the number of request completion is not very meaningful. A better measure would be the number of completed sessions. In this paper, we proposed two methods to prevent and control overloads in web servers by utilizing session-based relationship among HTTP requests. We first exploited the dependence among session-based requests by analyzing and predicting the reference patterns. Using the dependency relationships, we have derived traffic conformation functions that can be used for capacity planning and overload prevention in web servers. Second, we have proposed a dynamic weighted fair sharing (DWFS) scheduling algorithm to control overloads in web servers. DWFS is distinguished from other scheduling algorithms in the sense that it aims to avoid processing of requests that belong to sessions that are likely to be aborted in the near future. The experimental results demonstrate that DWFS can improve server responsiveness by as high as 50% while providing QoS support through service differentiation for a class of application environment.
6295939
Dental Biometrics: Human Identification Using Dental Radiographs
The goal of forensic dentistry is to identify people based on their dental records, mainly as radiograph images. In this paper we attempt to set forth the foundations of a biometric system for semi-automatic processing and matching of dental images, with the final goal of human identification. Given a dental record, usually as a postmortem (PM) radiograph, we need to search the database of antemortem (AM) radiographs to determine the identity of the person associated with the PM image. We use a semi-automatic method to extract shapes of the teeth from the AM and PM radiographs, and find the affine transform that best fits the shapes in the PM image to those in the AM images. A ranking of matching scores is generated based on the distance between the AM and PM tooth shapes. Initial experimental results on a small database of radiographs indicate that matching dental images based on tooth shapes and their relative positions is a feasible method for human identification.
15665012
Progressive auction based resource allocation in service-oriented architecture
The emerging paradigm of distributed Web services has promised seamless business integration across the Internet that shares not only computing power but also applications. One major challenge is resource allocation to each end user in an economically efficient and low latency manner. A common solution is proportional sharing where each user gets his resource in proportion to the predetermined weight. However, this does not allow users to differentiate the value of their services and to dynamically adjust the weights. A resource reservation mechanism may improve the information and service sharing but usually introduces a high latency. In this paper, we propose a progressive auction based market mechanism for resource allocation that allows user differentiation of the service value and ensures the resource acquisition latency to be limited only to the communication delay in service-oriented architecture (SOA) systems. We present a service sharing prototype for selecting services for further business process composition or solution creation and show experimental results on the formation of reliable user ranking in resource sharing through the progressive auction.