20790783
An E-mail Monitoring System for Detecting Outflow of Confidential Documents
E-mails are widely used communication tool with their convenienceand efficiency. In spite of their usefulness, they are difficult to control in thate-mails are easily used as outflow path of confidential documents in an organization.In order to detect and prevent the outflow, the e-mail monitoring iswidely used. We propose a system that detects in real time the outflow of thedocuments to be protected. It is based on the automatic text categorization andmachine learning technique. The experimental result shows the high accuracyand efficiency of the method.
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.
16215166
Visual Media Retrieval Framework Using Web Service
The need for content-based image retrieval from image databases is ever increasing rapidly in many applications, such as electronic art museums, internet shopping malls, internet search engines, and medical information systems. Many such image resources have been previously developed and widely spread over the internet. In this paper, we propose a Web Service-driven architecture, named the HERMES(tHE Retrieval framework for visual MEdia Service), to support effective retrieval on large volumes of visual media resources. We explain how semantic metadata and ontology can be utilized to realize more intelligent content-based retrieval on visual media data.
33958226
Performance evaluation of a communication optimization model in network-based parallel computing
Network based distributed computing has been gaining popularity over the past decade. Many parallel programming languages and related parallel programming modes are becoming widely accepted. However, the execution of parallel applications on distributed systems has been hampered by the high communication overhead. To reduce the communication overhead and the completion time of a parallel application, we propose a key message model for parallel computing on network of workstations (NOWs). In the key message model, all messages generated in a key message path are prioritized. A key message path in a task graph is defined as the path that is optimized by the key message algorithm. All messages generated in a key message path are prioritized. Besides, the key message algorithm automatically finds the key message paths. In this paper, we first describe the algorithm that identifies the key messages to be prioritized in a parallel application, then analyze the cost of the algorithm, and finally evaluate the performance of the algorithm in a simulation. Our preliminary analysis of the algorithm shows improvement over the system which does not use prioritization scheme.
355618
Reducing pattern delay variations for screening frequency dependent defects
The delay variations of a pattern set can come from two sources: (1) Different patterns sensitize different parts of the circuit and result in different delays. (2) The same pattern, applied on different chips, results in different delays because of process variations. For structural delay testing, these pattern variations may result in difficulty for finding an optimal test clock setting, which may significantly impact the defect screening effectiveness. This paper investigates the possibility of applying statistical timing analysis techniques to reduce pattern variations for structural delay testing. We develop an efficient statistical pattern-based timing simulator and devise pattern selection algorithms for reducing such variations. By constructing pattern sets with smaller variations, we show that higher screening effectiveness can be achieved. We present experimental results to demonstrate the advantages of our techniques based on benchmark circuits.
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.
7825415
Understanding Eight Years of InfoVis Conferences Using PaperLens
We present PaperLens, a visualization that reveals connections, trends, and activity throughout the InfoVis conference community for the last 8 years. It tightly couples views across papers, authors, and references. This paper describes how we analyzed the data, the strengths and weaknesses of PaperLens, and interesting patterns and relationships we have discovered using PaperLens.
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