8563061
Concurrent Automata and Parallel Architecture
26942198
An Efficient Algorithm for Computing Core Attributes in Database Systems
A new approach to computing core attributes in rough set theory is proposed based on relational database systems. Especially, the fundamental operators of relational algebra are used to define the necessary and sufficient conditions that an attribute is a core attribute under the condition of the data table being consistent. Furthermore, inconsistent data table can be easily checked out and noise data can be efficiently eliminated. With this approach, we present an efficient and scalable algorithm for computing core attributes.
7528349
Semantic based clustering of Web documents
A new methodology that structures the semantics of a collection of documents into the geometry of a simplicial complex is developed: a primitive concept is represented by a top dimension simplex, and a connected component represents a concept. Based on these structures, documents can be clustered into some meaningful classes. Experiments with three different data sets from web pages and medical literature have shown that the proposed unsupervised clustering approach performs significantly better than traditional clustering algorithms, such as k-means, AutoClass and hierarchical clustering (HAC). This abstract geometric model seems have captured the intrinsic semantics of the documents.
19867080
Context-Free Fuzzy Sets in Data Mining Context
Conventional studies on rule discovery and rough set methods mainly focus on acquisition of rules, the targets of which have mutually exclusive supporting sets. However, mutual exclusiveness does not always hold in real-world databases, where conventional probabilstic approaches cannot be applied. In this paper, first, we show that these phenomena are easily found in data mining contexts: when we apply attribute-oriented generalization to attributes in databases, generalized attributes will have fuzziness for classification. Secondly, we show that real-world databases may have fuzzy contexts. Then, finally, these contexts should be analyzed by using fuzzy techniques, where context-free fuzzy sets will be a key idea.
9338940
Information flows in e-government
Classical MAC and DAC are reexamined in the light of e-government. In this paper, we focus on the information flows, which have been addressed by classical MAC, in my view, somewhat inadequately, and never been addressed by classical DAC.
34363254
Feature Subset Selection Based on Relative Dependency between Attributes
Feature subset selection is an importent component of knowledge discovery and data mining systems to help reduce the data dimensionality. Rough sets theory provides a mechanism of selecting feature subsets. In the rough set community, most feature subset selection algorithms are attributes reduct-oriented; that is, finding minimum reducts of the conditional attributes of a decision table. Two main approaches to finding attribute reducts are categorized as discernibility functions-based and attribute dependency-based. These algorithms, however, suffer from intensive computations of either discernibility functions for the former or positive regions for the latter. In this paper, we propose a new concept, called relative attribute dependency, and present a sufficient and necessary condition of the minimum conditional attributes reduct of a decision table represented with the relative attribute dependency. The relative attribute dependency can be calculated by counting the distinct rows of the sub-decision table, instead of generating discernibility functions or positive regions. Thus the computation efficiency of minimum reducts are highly improved. We develop two algorithms for finding minimum reducts of the conditional attributes, one brute-force algorithm and the other heuristic algorithm using attribute entropy as the heuristic function. We also show the results of these algorithms by an illustrative example.
14241577
How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NARX networks perform much better than conventional recurrent neural networks for learning certain simple long-term dependency problems. The intuitive explanation for this behavior is that the output memories of a NARX network can be manifested as jump-ahead connections in the time-unfolded network. These jump-ahead connections can propagate gradient information more efficiently, thus reducing the sensitivity of the network to long-term dependencies. This work gives empirical justification to our hypothesis that similar improvements in learning long-term dependencies can be achieved with other classes of recurrent neural network axchitectures simply by increasing the order of the embedded memory. In particular we explore the impact of learning simple long-term dependency problems on three classes of recurrent neural network architectures: globally recurrent networks, locally recurrent networks, and NARX (output feedback) networks.Comparing the performance of these architectures with different orders of embedded memory on two simple long-term dependencies problems shows that all of these classes of network architectures demonstrate significant improvement on learning long-term dependencies when the orders of embedded memory are increased. These results can be important to a user comfortable with a specific recurrent neural network architecture because simply increasing the embedding memory order of that architecture will make it more robust to the problem of long-term dependency learning.
12985850
Learning a class of large finite state machines with a recurrent neural network
Abstract One of the issues in any learning model is how it scales with problem size. The problem of learning finite state machine (FSMs) from examples with recurrent neural networks has been extensively explored. However, these results are somewhat disappointing in the sense that the machines that can be learned are too small to be competitive with existing grammatical inference algorithms. We show that a type of recurrent neural network (Narendra & Parthasarathy, 1990, IEEE Trans. Neural Networks, 1, 4–27) which has feedback but no hidden state neurons can learn a special type of FSM called a finite memory machine (FMM) under certain constraints. These machines have a large number of states (simulations are for 256 and 512 state FMMs) but have minimal order, relatively small depth and little logic when the FMM is implemented as a sequential machine.
31273280
Adaptive-Hierarchical-Filtering Technique for Image Enhancement
This paper describes a novel adaptive-hierarchical-filtering technique to achieve high-quality image enhancement when the image possesses the artifact of moire pattern during the reproduction of the image by different computer peripherals such as color copier, or scanners plus printers.Commercial magazine images are halftoned images. Unacceptable noises and moire distortion may result when halftone images are copied (i.e., scanned and printed). In this paper, we analyze the formation of moire patterns in the frequency and spatial domain. Based on the analysis, a set of hierarchical filter is developed to suppress the moire artifacts and enhance the image adaptively. The hierarchical filter consists of a set of variable-length low-pass filters and high-pass filters. The low-pass filters have a nice inheritance of canceling aliased low frequency components (moire distortion). High-pass filtering is also applied to sharpen image edges. An image classifier is developed to determine that an edge is either a global true edge (for sharpening enhancement) or a local halftone's micro-structural edge (for LPF for moire reduction), so that adaptive filter technique can be applied to achieve the smooth transition between sharp edges and smooth halftone regions. Thus, we achieves overall high-quality output images.Experimental results have been shown the effectiveness of the presented technique that works well on wide combinations of above-mentioned 6 factors, which will be explained during the following sections, for high-quality magazine image reproduction.
29878206
An architecture for interoperable program understanding tools
Program understanding tools are currently not interoperable, leading researchers to waste significant resources reinventing already existing tools. Even commercial environments that have been designed to support the construction of program understanding tools have serious flaws in this regard. This paper discusses CORUM (Common Object-based Re-engineering Unified Model), an architecture to support interoperability between program understanding tools, and it provides several examples of CORUM's use in the construction of new tools for concept recognition and program visualization.
23641664
Mediating Organizational Innovation: A Study of Implementation of Digital Technologies in TV News
Successful implementation of IT innovations requires mutual adjustment of technical features and social practices and structures of an organization. In this paper, we develop a synthesized model for mediating organizational innovation, drawn from Roger's model for organizational diffusion of innovation and Orlikowski et al.'s (2004) model for technology use mediation, to investigate the implementation of digital technologies in TV news. Our analysis of two case studies of TV news stations highlights the phases of mediation that occurred in these organizations and points to differences in mediation styles from previous studies. We suggest that scope of the implementation and temporal pressure in the work context were important dimensions in the mediation process