• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1121-1124
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• 1987

Previous methods to map from a FORTRAN-like specification into a systolic array were difficult to find data dependencies because the specification was expressed and executed sequentially. Data-flow graph(DFG)s show data dependencies explicitly. In this paper we show a mapping tool from a DFG specification into a systolic array. We introduce the concept of a Systolic Pattern Stream(SPS) and use that concept to derive a systolic array.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.81-85
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• 2008

The coupling measurements of object oriented programs can be valuable information for various maintenance tasks and there exists a variety of metrics proposed by numerous researchers. Those metrics, however, cannot measure the strength of dependencies between classes, but only imply the existence of dependencies. Hence they are of limited value for assisting maintenance tasks such as refactoring and system decomposition, which requires information about the strength of dependency. In this paper, a coupling metric which can measure the strength of dependence as well as detect the existence of dependencies is proposed. Our coupling metric is evaluated based on the cost required for performing the maintenance tasks. We have applied the proposed coupling metric to an example of system decomposition in an effort to assess the potential benefits of our approach for maintenance tasks.

• BMB Reports
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• v.37 no.6
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• pp.648-656
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• 2004

This paper deals with the development of a predictive probabilistic model, a composite dependency-reflecting model (CDRM), which was designed to detect core promoter regions and transcription start sites (TSS) in vertebrate genomic DNA sequences, an issue of some importance for genome annotation. The model actually represents a combination of first-, second-, third- and much higher order or long-range dependencies obtained using the expanded maximal dependency decomposition (EMDD) procedure, which iteratively decomposes data sets into subsets on the basis of dependency degree and patterns inherent in the target promoter region to be modeled. In addition, decomposed subsets are modeled by using a first-order Markov model, allowing the predictive model to reflect dependency between adjacent positions explicitly. In this way, the CDRM allows for potentially complex dependencies between positions in the core promoter region. Such complex dependencies may be closely related to the biological and structural contexts since promoter elements are present in various combinations separated by various distances in the sequence. Thus, CDRM may be appropriate for recognizing core promoter regions and TSSs in vertebrate genomic contig. To demonstrate the effectiveness of our algorithm, we tested it using standardized data and real core promoters, and compared it with some current representative promoter-finding algorithms. The developed algorithm showed better accuracy in terms of specificity and sensitivity than the promoter-finding ones used in performance comparison.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1689-1706
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• 2012

The preservation of temporal dependencies among a group of processes that exchange continuous media at runtime is a key issue for emerging mobile distributed systems (MDS), such as monitoring of biosignals and interactive multiuser games. Although several works are oriented to satisfy temporal dependencies, most of them are not suitable for MDSs. In general, an MDS is characterized by the absence of global references (e.g. shared memory and wall clock), host mobility, limited processing and storage capabilities in mobile hosts, and limited bandwidth on wireless communication channels. This paper proposes an asymmetric synchronization protocol to be used at runtime in an MDS without using a common reference. One main aspect of our synchronization protocol is that it translates temporal constraints to causal dependencies of the continuous media data as seen by the mobile hosts. We simulate the protocol by considering a cellular network environment and by using MPEG-4 encoders. The simulation results show that our protocol is effective in reducing the synchronization error. In addition, the protocol is efficient in terms of processing and storage costs at the mobile devices, as well as in the overhead attached per message across the wired and wireless channels.

• Journal of Microbiology
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• v.43 no.5
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• pp.411-416
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• 2005

The high-throughput sequencing of microbial genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable research attention in recent years. This paper addresses the development of a predictive model, known as the dependence decomposition weight matrix model (DDWMM), which was designed to detect the core promoter region, including the -10 region and the transcription start sites (TSSs), in prokaryotic genomic DNA sequences. This is an issue of some importance with regard to genome annotation efforts. Our predictive model captures the most significant dependencies between positions (allowing for non­adjacent as well as adjacent dependencies) via the maximal dependence decomposition (MDD) procedure, which iteratively decomposes data sets into subsets, based on the significant dependence between positions in the promoter region to be modeled. Such dependencies may be intimately related to biological and structural concerns, since promoter elements are present in a variety of combinations, which are separated by various distances. In this respect, the DDWMM may prove to be appropriate with regard to the detection of core promoter regions and TSSs in long microbial genomic contigs. In order to demonstrate the effectiveness of our predictive model, we applied 10-fold cross-validation experiments on the 607 experimentally-verified promoter sequences, which evidenced good performance in terms of sensitivity.

• Asia pacific journal of information systems
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• v.10 no.4
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• pp.133-152
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• 2000

Successful Electronic Commerce(EC) requires not only a transformation of the supply chain but a redesign of organizational processes as well. Just as the organizations in a supply chain must cooperate with each other to achieve a collective goal, so too do the departments and groups that comprise an organization depend on each other to achieve the organization's goals. A lack of coordination at either of these levels can result in poor performance and high coordination costs. In addition, changes in inter-organizational dependencies can affect the dependencies within the affected organizations. The lack of coordination across two levels results in a transition gap in transforming the inter-organizational design to the intro-organizational design in EC deployments. The purpose of this research is to develop a modeling method for coordination, managing the linkage between inter- and intra-organizations in EC deployments. The method was applied to the EC development project of a cable TV home-shopping company. The coordination approach enabled us to manage dependencies among the coordination elements and identify the effect of a change on other elements, which became the basis for effective EC deployments. We will discuss the method and compare it with other dependency management methods.

• Asian Journal of Business Environment
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• v.11 no.4
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• pp.31-38
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• 2021

Purpose: This paper uses the resource-dependency theory to present the case of the Pakistan sugar industry to highlight how the industry uses a strategic alliance to gain a powerful bargaining position over its critical dependencies. The case of the Pakistan sugar industry is well-known and it is common knowledge that the alliance or the cartel within it is responsible for frequent price hikes and sugar supply shortages in the country. Research design, data and methodology: We use a case study, qualitative document analysis design to trace how the alliance overcomes its various dependencies, and in doing so, how does it harm various stakeholder interests. Results: This paper finds that the sugar industry alliance maintains its bargaining power by manipulating sugar supply through horizontal alliances, political affiliations, underselling and under-reporting sugar stocks, purchasing sugarcane from the black market, and by gaining billions of rupees in export subsidies by hoarding stock and using its political connections. Conclusion: The paper concludes by providing a summary of the measures which the government has taken to curb this anticompetitive conduct; the most important of which is the removal of protectionist measures for sugar trade and allowing market forces to control the demand and supply of sugar in the local market.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2897-2904
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• 2023

The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method is a widely used method in human reliability analysis (HRA). Performance shaping factors (PSFs) refer to the factors that may influence human performance and are used to adjust nominal human error probabilities (HEPs) in SPAR-H. However, the PSFs are assumed to be independent, which is unrealistic and can lead to unreasonable estimation of HEPs. In this paper, a new method is proposed to handle the dependencies among PSFs in SPAR-H to obtain more reasonable results. Firstly, the dependencies among PSFs are analyzed by using decision-making trial and evaluation laboratory (DEMATEL) method. Then, PSFs are assigned different weights according to their dependent relationships. Finally, multipliers of PSFs are modified based on the relative weights of PSFs. A case study is illustrated that the proposed method is effective in handling the dependent PSFs in SPAR-H, where the duplicate calculations of the dependent part can be reduced. The proposed method can deal with a more general situation that PSFs are dependent, and can provide more reasonable results.

• Korean Journal of Cognitive Science
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• v.4 no.2
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• pp.115-143
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• 1994

In this paper,I present an extended two-level model using LR parsing techniques.The LR-based two-level model not only guarantees effcient morphological processing but also achieves a higher degree of descriptive adequacy than Koskenniemi's original model.The two-level model is augmented with an independent morphosyntactic module based on feature-based CF word grammar.By adopting a CF word grammar,our model is capable of dealing with complex words with discontinuous dependencies without having duplicate lexicons.It is shown how LR predictions manifested in the parsing table can help the morphological processor to minimize the dictionary lookup process.

• Knowledge Management Research
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• v.10 no.2
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• pp.35-48
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• 2009

The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.