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.
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.
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.
13866418
The Lutonium: a sub-nanojoule asynchronous 8051 microcontroller
We describe the Lutonium, an asynchronous 8051 microcontroller designed for low Et/sup 2/. In 0.18 /spl mu/m CMOS, at nominal 1.8 V, we expect a performance of 0.5 nJ per instruction at 200 MIPS. At 0.5 V, we expect 4 MIPS and 40 pJ/instruction, corresponding to 25,000 MIPS/Watt. We describe the structure of a fine-grain pipeline optimized for Et/sup 2/ efficiency, some of the peripherals implementation, and the advantages of an asynchronous implementation of a deep-sleep mechanism.
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.
1642899
Isomorphic Strategy for Processor Allocation in k-Ary n-Cube Systems
Due to its topological generality and flexibility, the k-ary n-cube architecture has been actively researched for various applications. However, the processor allocation problem has not been adequately addressed for the k-ary n-cube architecture, even though it has been studied extensively for hypercubes and meshes. The earlier k-ary n-cube allocation schemes based on conventional slice partitioning suffer from internal fragmentation of processors. In contrast, algorithms based on job-based partitioning alleviate the fragmentation problem but require higher time complexity. This paper proposes a new allocation scheme based on isomorphic partitioning, where the processor space is partitioned into higher dimensional isomorphic subcubes. The proposed scheme minimizes the fragmentation problem and is general in the sense that any size request can be supported and the host architecture need not be isomorphic. Extensive simulation study reveals that the proposed scheme significantly outperforms earlier schemes in terms of mean response time for practical size k-ary and n-cube architectures. The simulation results also show that reduction of external fragmentation is more substantial than internal fragmentation with the proposed scheme.
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.
2712263
How users interact with biodiversity information using TaxonTree
Biodiversity databases have recently become widely available to the public and to other researchers. To retrieve information from these resources, users must understand the underlying data schemas even though they often are not content experts. Many other domains share this problem.We developed an interface, TaxonTree, to visualize the taxonomic hierarchy of animal names. We applied integrated searching and browsing so that users need not have complete knowledge either of appropriate keywords or the organization of the data.Our qualitative user study of TaxonTree in an undergraduate course is the first to describe usage patterns in the biodiversity domain. We found that tree-based interaction and visualization aided users' understanding of the data. Most users approached biodiversity data by browsing, using common, general knowledge rather than the scientific keyword expertise necessary to search using traditional interfaces. Users with different levels of interest in the domain had different interaction preferences.