29035719
Extracting User Profiles from E-mails Using the Set-Oriented Classifier
More and more people rely on e-mails rather than postal letters to communicate to each other. Although e-mails are more convenient, letters still have many positive features. The ability to handle "anonymous recipient" is one of them. This paper proposes a software agent that performs the routing task as human beings for the anonymous recipient e-mails. The software agent named "TWIMC (To Whom It May Concern)" receives anonymous recipient e-mails, analyze it, and then routes the e-mail to the mostly qualified person (i.e., email account) inside the organization. The agent employs the Set-oriented Classifier System (SCS) that is a genetic algorithm classifier that uses set representation internally. The comparison of SCS with the Support Vector Machine (SVM) shows that the SCS outperforms SVM under noisy environment.
7685565
TWIMC: An Anonymous Recipient E-mail System
More and more people rely on e-mails rather than postal letters to communicate each other. Although e-mails are more convenient, letters still have many nice features. The ability to handle "anonymous recipient" is one of them. This research aims to develop a software agent that performs the routing task as human beings for the anonymous recipient e-mails. The software agent named "TWIMC (To Whom It May Concern)" receives anonymous recipient e-mails, analyze it, and then routes the e-mail to the mostly qualified person (i.e., e-mail account) inside the organization. The machine learning and automatic text categorization (ATC) techniques are applied for the task. We view each e-mail account as a category (or class) of ATC. Everyday e-mail collections for each e-mail account provide an excellent source of training data. The experiment shows the high possibility that TWIMC could be deployed in the real world.
34238463
An Efficient E-mail Monitoring System for Detecting Proprietary Information Outflow Using Broad Concept Learning
The automatic text categorization (ATC) is an emerging field that is technically based on the machine learning and information retrieval. Traditional machine learning usually builds a model that captures the behavior of the ‘concept’ from the training instances. Following this context, the ATC learns the concept from text data such as digital text documents, web pages, or e-mail texts. In this paper, we categorize the concepts that have been dealt by ATC into two groups: broad concepts and narrow concepts. Broad concepts are difficult to learn than narrow concepts. We propose a multi model approach where multiple local models are constructed to learn the broad concepts. Some experimental results showed that the multi model approach turned out to be effective in learning the confidentiality of company.
13264531
An efficient scattered data approximation using multilevel B-splines based on quasi-interpolants
In this paper, we propose an efficient approximation algorithm using multilevel B-splines based on quasi-interpolants. Multilevel technique uses a coarse to fine hierarchy to generate a sequence of bicubic B-spline functions whose sum approaches the desired interpolation function. To compute a set of control points, quasi-interpolants gives a procedure for deriving local spline approximation methods where a B-spline coefficient only depends on data points taken from the neighborhood of the support corresponding the B-spline. Experimental results show that the smooth surface reconstruction with high accuracy can be obtained from a selected set of scattered or dense irregular samples.
19160052
Architectural enhancements for network congestion control applications
Complex network protocols and various network services require significant processing capability for modern network applications. One of the important features in modern networks is differentiated service. Along with differentiated service, rapidly changing network environments result in congestion problems. In this paper, we analyze the characteristics of representative congestion control applications-scheduling and queue management algorithms, and we propose application-specific acceleration techniques that use instruction-level parallelism (ILP) and packet-level parallelism (PLP) in these applications. From the PLP perspective, we propose a hardware acceleration model based on detailed analysis of congestion control applications. In order to get large throughputs, a large number of processing elements (PEs) and a parallel comparator are designed. Such hardware accelerators provide large parallelism proportional to the number of processing elements added. A 32-PE enhancement yields 24times speedup for weighted fair queueing (WFQ) and 27times speedup for random early detection (RED). For ILP, new instruction set extensions for fast conditional operations are applied for congestion control applications. Based on our experiments, proposed architectural extensions show 10%-12% improvement in performance for instruction set enhancements. As the performance of general-purpose processors rapidly increases, defining architectural extensions (e.g., multi-media extensions (MMX) as in multimedia applications) for general-purpose processors could be an alternative solution for a wide range of network applications
22808943
Exploring content-actor paired network data using iterative query refinement with NetLens
Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper demonstrates a tool, NetLens, to explore a content-actor paired network data model. The NetLens interface was designed to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify in node-link visualizations. NetLens is general and scalable in that it applies to any dataset that can be represented with our abstract content-actor data model
283122
Electronic voting system usability issues
With recent troubles in U.S. elections, there has been a nationwide push to update voting systems. Municipalities are investing heavily in electronic voting systems, many of which use a touch screen. These systems offer the promise of faster and more accurate voting, but the current reality is that they are fraught with usability and systemic problems. This paper surveys issues relating to usability of electronic voting systems and reports on a series of studies, including one with 415 voters using new systems that the State of Maryland purchased. Our analysis shows these systems work well, but have several problems, and many voters have concerns about them.
6217758
Implementation and Performance Evaluation of M-VIA on AceNIC Gigabit Ethernet Card
This paper describes the implementation and performance of M-VIA on the AceNIC Gigabit Ethernet card. The AceNIC adapter has several notable hardware features for high-speed communication, such as jumbo frames and interrupt coalescing. The M-VIA performance charac- teristics were measured and evaluated based on these hardware features. Our results show that latency and bandwidth improvement can be ob- tained when the M-VIA data segmentation size is properly adjusted to utilize the AceNIC's jumbo frame feature. The M-VIA data segmenta- tion size of 4,096 bytes with MTU size of 4,138 bytes showed the best performance. However, larger MTU sizes did not necessarily result in better performance due to extra segmentation and DMA setup over- head. In addition, the cost of M-VIA interrupt handling can be reduced with AceNIC's hardware interrupt coalescing. When the parameters for the hardware interrupt coalescing were properly adjusted, the latency of interrupt handling was reduced by up to 170 µs.