863031
Component Selection to Optimize Distance Function Learning in Complex Scientific Data Sets
Analyzing complex scientific data, e.g., graphs and images, often requires comparison of features: regions on graphs, visual aspects of images and related metadata, some features being relatively more important. The notion of similarity for comparison is typically distance between data objects which could be expressed as distance between features. We refer to distance based on each feature as a component. Weights of components representing relative importance of features could be learned using distance function learning algorithms. However, it is seldom known which components optimize learning, given criteria such as accuracy, efficiency and simplicity. This is the problem we address. We propose and theoretically compare four component selection approaches: Maximal Path Traversal, Minimal Path Traversal, Maximal Path Traversal with Pruning and Minimal Path Traversal with Pruning. Experimental evaluation is conducted using real data from Materials Science, Nanotechnology and Bioinformatics. A trademarked software tool is developed as a highlight of this work.
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.
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.
2346851
CARE: An Automobile Crash Data Analysis Tool
The Critical Analysis Reporting Environment provides an efficient tool for transportation safer engineers and policymakers to use in analyzing the categorical crash data typically obtained from police reports. CAPE has proven successful in the traffic safety community for two reasons: its simplicity and its efficiency. It is currently being used in several states.
42606216
Wiring the academy (panel session): what's working, what's not, what's next, and why
The network era of computing is and will profoundly change the conduct of management education. It offers opportunities for building new types of learning relationships among faculty, departments, colleges, students, and the corporations who employ business graduates. The challenges of funding and leading organizational change at the academy to seize these opportunities, however, are quite daunting. While successes with particular courses, student groups, or executive education programs abound, few business schools have succeeded with large scale IT implementation and the cultural changes necessary to thrive and lead in the network era of computing. This set of changes is referred to as wiring the academy.
13913953
Random pulse input versus continuous current plus white noise: Are they equivalent?
Abstract We compared the behavior of the Hodgkin–Huxley (HH) model subjected to two input regimes: firstly, discrete pulses from a Poisson process, and then to a diffusion approximation, constructed to share statistical properties with the discrete pulse input. The results show that under a wide range of physiologically plausible input conditions the diffusion process is equivalent to the pulse process, in terms of output interspike interval distribution. In this respect, the HH model differs from the integrate and fire model, which has been widely used to address a variety of questions in computational neuroscience.
16581818
A Case Study of Software Process Improvement During Development
We present a case study of the use of a software process improvement method which is based on the analysis of defect data. The first step of the method is the classification of software defects using attributes which relate defects to specific process activities. Such classification captures the semantics of the defects in a fashion which is useful for process correction. The second step utilizes a machine-assisted approach to data exploration which allows a project team to discover such knowledge from defect data as is useful for process correction. We show that such analysis of defect data can readily lead a project team to improve their process during development. >
9261429
Understanding user behaviour and its metrics
We are pleased to bring together such a great group of people to speak about various aspects of user behaviour and trends, an important topic for us all. We are sometimes guilty of assuming we know what users want. Those assumptions have been challenged in recent years by sophisticated evidence-based studies, the impact of which has been further heightened by digital systems that are responding to the new and changing needs of researchers operating on the front lines of science. To start the programme, two such scientists will provide accounts of how they respond to the information challenges they face in their respective fields. During the day we will learn about the background motivations for, and situations in which, researchers increasingly operate. The challenge is to interpret what this means in terms of our own organisations. This is a timely issue for the British Library as it revisits its science, technology and medical strategy to meet its own changing needs as well as the needs of new generations of researchers. This will require adaptations to our information and knowledge services and policies, something I suspect applies to all ICSTI members here engaged in service provision. A relevant example of this is the British Library’s recent launch of the UK PubMed Central, a US–UK collaboration with the support of research funding agencies and driven by the Wellcome Trust. We are pleased that representatives of those organisations are here today. Participants from 12 countries are among us, making this a truly international gathering. I am delighted to welcome you all here today.