38545491
Defining Re-usable Composite Aspect Patterns: An FDAF Based Approach
Architecting secure systems is an important and challenging problem. Solutions to model individual, or atomic, security capabilities have been proposed, including security patterns, component based, aspect-oriented, and service-oriented approaches. However, little work is available on how to model reusable compositions of security capabilities, where the security capabilities interact with each other and other parts of the system design. Here, an aspect-oriented approach to modeling composite aspects is presented. The approach is defined as an extension to the Formal Design Analysis Framework (FDAF). The FDAF metamodel is extended to support the static representation of composite aspects and an approach to defining the compositions is introduced. A composite aspect that provides Account Lockout with Selective Event Logging (ALSEL) capabilities is used as an example.
29100810
An Expert System Based Approach to Modeling and Selecting Requirement Engineering Techniques
The importance of requirements engineering (RE) has been raised numerous times in literatures. To choose suitable RE techniques for a particular project in a given situation is a challenging task, requiring substantial expertise and efforts. To help solving this problem, an expert system based approach is proposed. This expert system uses the knowledge from domain experts to model the causal factors of RE techniques. It can select suitable RE techniques for a software project. A web-based questionnaire is created in the first place to collect the expertise available in the community. The information collected by the questionnaire is analyzed and transformed into a new dataset for constructing a Bayesian Belief Network (BBN). The resulting BBN integrated with a GUI forms an expert system for RE techniques modeling and selection. Empirical study validates the transformed dataset and shows that the expert system outperforms other predictors in selecting suitable RE techniques in different RE phases.
16895768
A Novel Stroke Extraction Model for Chinese Characters based on Steerable Filters
The technology of stroke extraction plays a key role in the off-line Chinese character recognition field. The accuracy and computing complexity of classifying depend on the output of stroke extraction. Based on the analysis of the existing methods, this paper proposed a novel stroke extraction model with steerable filters. The experiments indicate that this model can be used to extract strokes from both printed and handwritten Chinese characters. Meanwhile, it also can be applied to eliminate the redundant stroke connections impressively with considerable low computing complexity
40790501
A Practical Peer-Performance-Aware DHT
How to Build an efficient Distributed Hash Table (DHT) is a fundamental issue in Peer-to-Peer research field. Previous solutions ignore the heterogeneity of the large scale network. However, in practice, the fact is that the resource held by each peer in the Internet is extremely diverse. And the the willing to share local resources of each peer is also diverse. Therefore, the contribution for the system of a peer should depend on the resources it holds or how many resources it want to share, and should not be uniform. In this paper, we propose a Peer-Performance-Aware Distributed Hash Table (PPADHT) which aims to exploit the heterogeneity. It takes the performance difference of peers into consideration to construct a dynamic variation of wrapped butterfly to achieve the goal. We also show how to optimize the performance of PPADHT in the view of hop counts by random graphs. Our simulation results show that the average lookup hop counts of the PPADHT is approximately a log scale with constant out degrees. And it can achieve loadbalance in two ways: both the document load and message routing load, without introducing any additional load on the peer. Here, the load balance means the load is proportion to the performance of peer.
17461739
The design and implementation of LilyTask in shared memory
Constructing high performance computing system and providing easy-to-use programming model for users are two main parts of parallel computing, but the latter has been in a sorry state for a long time. LilyTask programming model is based on tasks which can be easily mapped to the decomposition of computation. Without explict synchronization and mutual exclusion, users will concentrate on the inherent parallelism in a problem instead of lock/unlock programming techniques. Most existing implementations of the task pool, a flexible data structure for task parallel, lack basic representation of relations among tasks. LilyTask introduces task group and task relation to help users easily map subproblems to tasks. The kernel of LilyTask system, a distributed task pool, automatically exploits potential parallelism among tasks at runtime. With runtime task assignment and task stealing, LilyTask system achieves dynamic load balancing. Our performance evaluation shows that LilyTask system with task pool outperforms sequential programs and BSPlib in solving both regular and irregular problems.
1361502
Model Ontological Commitments Using ORM+ in T-Lex
When designing and developing ontology based applications, we semantically ground them by ontologically committing the application rules to their respective domain. These rules can be, for instance decision rules for a decision support system. For the DOGMA framework we have introduced ORM + , a novel extension of ORM for modeling, visualizing and interchanging ontological commitments. In this paper, we illustrate our ongoing research on ORM + and T-Lex as its supporting tool. We demonstrate in the field of on-line customer management.
1436959
Towards Semantically Grounded Decision Rules Using ORM +
Recently, ontologies are proposed for many purposes to assist decision making, such as representing terminology and categorizing information. Current ontology-based decision support systems mainly contain semantically rich decision rules. In order to ground the semantics, we formalize those rules by committing them to domain ontologies. Those semantically grounded decision rules can represent the semantics precisely, thus improve the functionalities of many available rule engines. We model and visualize the rules by means of a novel extension of ORM. These rules are further stored in an XML-based markup language, ORM+ ML, which is a hybrid language of Rule-ML and ORM-ML. We demonstrate in the field of on-line customer management.