16267823
Variable selection and ranking for analyzing automobile traffic accident data
Variable ranking and feature selection are important concepts in data mining and machine learning. This paper introduces a new variable ranking technique named Sum Max Gain Ratio (SMGR). The new technique is evaluated within the domain of traffic accident data and against a more generalized dataset. In certain cases, SMGR is empirically shown to provide similar results to established approaches with significantly better runtime performance.
14137582
NASA's standards advisor pilot: search solutions for an intranet
With the immense progress in web technologies, lots of information can be acquired from a simple query. Unfortunately with the available tools today, the information obtained can be unfocussed and voluminous. It requires user to search through information galore before he can obtain the exact answer to the query. This arouses the need for a search technology which produces exact result in spite of simple query. This paper focuses on evaluating three search technologies - Verity Ultraseek, Copernic Enterprise search and ISYS:web for the NASA Standards Advisor Project. From the results obtained by using sample documents and sample queries it is shown that Verity Ultraseek outperformed Copernic and ISYS: web in both search capabilities and retrieval performance.
17812918
Acquiring and delivering lessons learned for NASA scientists and engineers: a dynamic approach
Web forms are a common mechanism for collecting information online. They pose some limitations which negatively affect ease and flexibility of user interaction. These limitations are more pronounced when collecting information that can not be broken down into well defined and agreed upon structures. Lessons Learned or any kind of 'knowledge' is an example of such information. In this paper we introduce a concept of 'Dynamic Forms' which is aimed at enhancing user interaction while collecting unstructured information, 'Lessons Learned' in particular.
2351489
An Empirical Study Using Task Assignment Patterns to Improve the Accuracy of Software Effort Estimation
In most software development organizations, there is seldom a one-to-one mapping between software developers and development tasks. It is frequently necessary to concurrently assign individuals to multiple tasks and to assign more than one individual to work cooperatively on a single task. A principal goal in making such assignments should be to minimize the effort required to complete each task. But what impact does the manner in which developers are assigned to tasks have on the effort requirements? This paper identifies four task assignment factors: team size, concurrency, intensity, and fragmentation. These four factors are shown to improve the predictive ability of the well-known intermediate COCOMO cost estimation model. A parsimonious effort estimation model is also derived that utilizes a subset of the task assignment factors and unadjusted function points. For the data examined, this parsimonious model is shown to have goodness of fit and quality of estimation superior to that of the COCOMO model, while utilizing fewer cost factors.
6086610
Security for fixed sensor networks
Sensor networks consist of resource-constrained sensors operating in a variety of environments. Given the severe constraints on these sensors, it is a particularly challenging problem to choose and design valid security protocols for such networks. This problem has recently given rise to new research addressing the security issues. This paper presents an overview of the important works, specifically the new mechanisms and protocols, which have been introduced or are still under development in this area.
206448594
Enhancing the Cocomo Estimation Models
In software engineering, team task assignments appear to have a significant potential impact on a project's overall success. The authors propose task assignment effort adjustment factors that can help tune existing estimation models. They show significant improvements in the predictive abilities of both Cocomo I and II by enhancing them with these factors.
18629340
Report on the 1984 Distributed Artificial Intelligence Workshop
The fifth Distributed Artificial Intclligencc Workshop tias held at the Schlumberger-Doll Research Laboratory from October 14 to 17, 1984 It was attended by 20 participants from academic and industrial institutions. As in the past,’ this workshop was designed as an informal meeting It included brief research rcport,s from individual groups along with genera1 discussion of questions of common interest. This report summarizes the general discussion and contains summaries of group presentations that have been contributed by individual speakers to scale the resources applied to a problem). Others arc motivated by problem characteristics, such as natural spatial or functional distribution. Finally, some researchers are motivated by the observation that some issues in problcm solving and reasoning (e.g., communication and coherent cooperation) are brought into sharp focus in the distributed setting. The main body of this report contains summaries written by individual attcndces of the workshop. This is, in turn, followed by a brief smnmary of the gcncral discus
6433216
XCS Applied To Mapping FPGA Architectures
This paper considers the application of XCS to the complex, real-world problem of mapping Boolean networks to technology-specific layout of field programmable gate arrays (FPGAs). The mapping is formulated as a temporal task, where the XCS's actions are to create blocks (based on an abstract Boolean network) that can be placed in the FPGA, one-at-a-time. Despite the complexity of this task, we demonstrate that the system is effective. We demonstrate the transfer of knowledge stored in an XCS rule set from a small FPGA mapping problem, to a larger problem. We present a novel technique that utilizes a problem-specific finite state machine and two populations of classifiers to treat the problem hierarchically. Final sections of the paper discuss implications and future directions for this work.
3217704
Conceptual-Model-Based Data Extraction from Multiple-Record Web Pages
Abstract Electronically available data on the Web is exploding at an ever increasing pace. Much of this data is unstructured, which makes searching hard and traditional database querying impossible. Many Web documents, however, contain an abundance of recognizable constants that together describe the essence of a document's content. For these kinds of data-rich, multiple-record documents (e.g., advertisements, movie reviews, weather reports, travel information, sports summaries, financial statements, obituaries, and many others) we can apply a conceptual-modeling approach to extract and structure data automatically. The approach is based on an ontology – a conceptual model instance – that describes the data of interest, including relationships, lexical appearance, and context keywords. By parsing the ontology, we can automatically produce a database scheme and recognizers for constants and keywords, and then invoke routines to recognize and extract data from unstructured documents and structure it according to the generated database scheme. Experiments show that it is possible to achieve good recall and precision ratios for documents that are rich in recognizable constants and narrow in ontological breadth. Our approach is less labor-intensive than other approaches that manually or semiautomatically generate wrappers, and it is generally insensitive to changes in Web-page format.
12720907
XCS for robust automatic target recognition
A primary strength of the XCS approach is its ability to create maximally accurate general rules. In automatic target recognition (ATR) there is a need for robust performance beyond so-called standard operating conditions (SOCs, those conditions for which training data is available) to extended operating conditions (EOCs, conditions of known targets that cannot be foreseen and trained for). EOCs include things like vehicle-specific variations, environmental effects (mud, etc.), unanticipated viewing angles, and articulation of components of the target (hatches, turrets, etc.). This paper presents experiments where XCS addresses structural generalization over global and local features normally used in ATR classification. In many SOCs, these features are adequate for target recognition. Our goal with XCS is to form generalized rules that utilize these features for effective ATR in EOCs. Results show that XCS is effective in this generalization task. Conclusions and future directions for research are discussed.
2984394
Augmented Reality Chinese Checkers
This paper presents an application, Augmented Reality Chinese Checkers that we created to investigate user interface issues for table top projected augmented reality entertainment applications. A new tangible interaction device, the wireless button enhanced fiducial, is introduced to support selection tasks in mixed reality environments. The Augmented Reality Chinese Checkers game is built on a framework which can be used to create other computer supported collaborative games. The system is built using the Passive Detection Framework to track the 6 degrees of freedom position in real time of marked objects in the environment. The game supports up to six players at a time.