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.
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.
18416128
Dynamic Simultaneous Multithreaded Architecture
This paper presents the Dynamic Simultaneous Multithreaded Architecture (DSMT). DSMT efficiently executes multiple threads from a single program on a SMT processor core. To accomplish this, threads are generated dynamically from a predictable flow of control and then executed speculatively. Data obtained during the single context nonspeculative execution phase of DSMT is used as a hint to speculate the posterior behavior of multiple threads. DSMT employs simple mechanisms based on state bits that keep track of inter-thread dependencies in registers and memory, synchronize thread execution, and control recovery from misspeculation. Moreover, DSMT utilizes a novel greedy policy for choosing those sections of code which provide the highest performance based on their past execution history. The DSMT architecture was simulated with a new cycle-accurate, execution-driven simulator. Our simulation results show that DSMT has very good potential to improve SMT performance, even when only a single program is available. However, we found that dynamic thread behavior together with frequent misspeculation may also produce diminishing returns in performance. Therefore, the challenge is to maximize the amount of thread-level parallelism that DSMT is capable of exploiting and at the same time reduce the frequency of misspeculations.
1452553
Understanding research trends in conferences using paperLens
PaperLens is a novel visualization that reveals trends, connections, and activity throughout a conference community. It tightly couples views across papers, authors, and references. PaperLens was developed to visualize 8 years (1995-2002) of InfoVis conference proceedings and was then extended to visualize 23 years (1982-2004) of the CHI conference proceedings. This paper describes how we analyzed the data and designed PaperLens. We also describe a user study to focus our redesign efforts along with the design changes we made to address usability issues. We summarize lessons learned in the process of design and scaling up to the larger set of CHI conference papers.
14687618
Using a Hybrid Genetic Algorithm and Fuzzy Logic for Metabolic Modeling
The identification of metabolic systems is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE's have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model. The hybrid genetic algorithm (GA) integrates a GA with the simplex method in functional optimization to improve the GA's convergence rate. We have applied this approach to modeling the rate of three enzyme reactions in E. coli central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit system behaviors observed in biochemical experiments.
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.
17892684
Refined statistical static timing analysis through
Statistical static timing analysis (SSTA) has been a popular research topic in recent years. A fundamental issue with applying SSTA in practice today is the lack of reliable and efficient statistical timing models (STM). Among many types of parameters required to be carefully modeled in an STM, spatial delay correlations are recognized as having significant impact on SSTA results. In this work, we assume that exact modeling of spatial delay correlations is quite difficult, and propose an experimental methodology to resolve this issue. The modeling accuracy requirement is relaxed by allowing SSTA to impose upper bounds and lower bounds on the delay correlations. These bounds can then be refined through learning the actual delay correlations from path delay testing on silicon. We utilize SSTA as the platform for learning and propose a Bayesian approach for learning spatial delay correlations. The effectiveness of the proposed methodology is illustrated through experiments on benchmark circuits.