327923
Implicit interest indicators
Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can ``intelligently'' determine the interest of a user and use this information to make suggestions. The common solution, ``explicit ratings'', where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A more ``intelligent'' method is to useimplicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. These implicit interest indicators have obvious advantages, including removing the cost of the user rating, and that every user interaction with the system can contribute to an implicit rating.
Current recommender systems mostly do not use implicit ratings, nor is the ability of implicit ratings to predict actual user interest well-understood. This research studies the correlation between various implicit ratings and the explicit rating for a single Web page. A Web browser was developed to record the user's actions (implicit ratings) and the explicit rating of a page. Actions included mouse clicks, mouse movement, scrolling and elapsed time. This browser was used by over 80 people that browsed more than 2500 Web pages.
Using the data collected by the browser, the individual implicit ratings and some combinations of implicit ratings were analyzed and compared with the explicit rating. We found that the time spent on a page, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest.
1099339
Cognitive Scales and Mental Models for Inclusive Design
In keeping with a user capability and product demand approach to product assessment, this paper examines the cognitive demands placed on users when interacting with consumer products. The eventual aim is to develop a set of cognitive capability scales that could be used in the analytical evaluation of product interfaces. We explore the dimensions of cognitive capability relevant to product interaction and describe how these may be used to evaluate a given design. Planned work addresses quantitative measurement of cognitive capabilities and predictive validation of capability scales.
19037738
The Virtualization Reality
A number of important challenges are associated with the deployment and configuration of contemporary computing infrastructure. Given the variety of operating systems and their many versions—including the often-specific configurations required to accommodate the wide range of popular applications—it has become quite a conundrum to establish and manage such systems.
19011421
A Host Architecture for Automobile License Plate Recognition
Automobile license plate recognition (ALPR) is becoming increasingly important to homeland security and law enforcement. There are a number of commercial vendors with tag recognition hardware and software in the ALPR market. Many of these vendors have developed open tag recognition solutions that run on laptop PCs with commodity video-capture devices as peripherals. Such PC-based solutions are quite conducive to an open architecture for data transmission and sharing between applications. In this paper, we present an architecture that supports such a commodity tag recognition solution as its centerpiece. We then discuss a recent pilot project where this architecture was implemented and tested.
10058829
A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge
Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word sense disambiguation has been extensively studied in Computational Linguistics. However, existing methods either are brittle and narrowly focus on specific topics or words, or provide only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for a word sense disambiguation system. In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input. Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice. We evaluated our approach using SemEval 2007 Task 7 (Coarse-grained English All-words Task), and our system significantly outperformed the best unsupervised system participating in SemEval 2007 and achieved the performance approaching top-performing supervised systems. Although our method was only tested with coarse-grained sense disambiguation, it can be directly applied to fine-grained sense disambiguation.
2182133
Parallel imaging: system design and limitations
Parallel imaging using relatively low acceleration factors (two to three) has become commonplace in clinical MRI. Of interest now are the practical limits to parallel imaging. Factors limiting the use of high acceleration factors are the available number of independent receiver channels and the unavoidable decrease in signal-to-noise ratio with higher acceleration factors. This paper will discuss system design for parallel imaging, including prototype 64 channel MR receivers and 64 element RF coil arrays. These systems have enabled single echo acquisition (SEA) MR imaging. Parallel imaging applications during transmit have also been suggested, and new techniques for implementing independent transmit chains will also be discussed.
18886117
An Effective Human Motion Classification Approach using Knowledge Representation in Qualitative Normalised Templates
Classification of human motion in video data is essential in numerous applications. However, problems arise as the human exhibits complex and dynamic motion that is nonlinear and time varying. In this paper, we propose a knowledge-based human motion classification framework that employs fuzzy qualitative reasoning to address these problems. Our approach utilises the rich contextual information (e.g. structural and transitional characteristic of human motion) captured in video sequence to effectively study and recognise human motion. With the aid of domain knowledge, a set of fuzzy rules are defined in the knowledge base. This work is in contrast with previous attempts that depend solely on the trajectories of the body parts. Experimental results on two classes of motion (e.g. walking and running) that result in similar motions; and a comparison with the conventional method has demonstrated and validated the effectiveness of the proposed method in improving the perception of human motion.