Large-scale protein analysis and reliable annotations are particularly helpful for scholars in biology and medicine community. Understanding the functional characterizations of protein sequences has been a major challenge in recent years. Extensive computer based prediction systems have been developed to support the need since many proteins' sub cellular localizations are still unknown. In this work, numerous protein description methods and three common classifiers are used in our experiments. These protein description methods are classified into two categories: protein composition based and position-specific scoring matrix based. A better prediction is achieved upon widely used data sets. Through these experiments, it is expected to give a comparison between protein description methods for protein sub cellular localization and their classification characteristics.

Invisible bacteria are found almost everywhere, and having a great impact on our everyday life. Particularly, many species of gram-negative bacteria are pathogenic and cause a wide variety of diseases in humans and animals. It is crucial in drug design to cure diseases brought by gram-negative bacteria. Unfortunately, a new drug discovery can be expensive and time-consuming even with the advance of biotechnology. Designing a highly effective and efficient computational system, especially for identifying protein subcellular localization for gram-negative bacteria, is an important research field.In this paper, we propose a new computational system which combines a well-known classifier, support vector machines (SVMs), a protein descriptor, DP-PSSM (Directional Property-PSSM), and an optimal tool for system tuning. In addition, an evolutionary computation based feature selection technique is applied to further improve the performance of our computational system. Our computational system, EF-SVM-PSL, had been tested through 10 fold cross validation on predicting subcellular localizations of three gram-negative bacteria protein datasets, PS1444, NR828, and EV243. Our EF-SVM-PSL has a relative simple architecture and performs competitively with the best alternative systems.

In this paper, we present a new compositional verification methodology for efficiently verifying high-assurance properties such as reachability and deadlock freedom of real-time systems. In this methodology, each component of real-time systems is initially specified as a timed automaton and it communicates with other components via synchronous and/or asynchronous communication channels. Then, each component is analyzed by a generation of its state-space graph which is formalized as a new state-space representation model called Multiset Labeled Transition Systems (MLTSs). Afterward, the state spaces of the components are hierarchically composed and simplified through a composition algorithm and a set of condensation rules, respectively, to get a condensed state space of the system. The simplified state spaces preserve equivalence with respect to deadlock and reachable states. Such equivalence is assured by our reduction theories called IOT-failure equivalence and IOT-state equivalence. To show the performance of our methodology, we developed a verification tool RT-IOTA and carried out experiments on some benchmarks such as CSMA/CD protocol, a rail-road crossing, an alternating bit-protocol, etc. Specifically, we look at the time taken to generate the statespace, the size of the state space, and the amount of reduction achieved by our condensation rules. The results demonstrate the strength of our new technique in dealing with the state-explosion problem.

This paper presents a methodology to reduce the high analysis complexity in modeling and verifying high-assurance properties of safety-critical systems. Our new methodology includes a state-space construction mechanism, multiset labeled transition systems, two congruence theorems and a set of condensation rules for the analysis of deadlock states and reachable states. In the new state-space construction mechanism, the state space can hierarchically be generated and simplified for both synchronous and asynchronous processes. In our multiset labeled transition systems, several actions can be performed as an atomic instruction. Our congruence theorems and condensation ules are much more powerful than current compositional-verification techniques since the high interleaved behaviors of asynchronous as well as invisible actions can be eliminated efficiently. Our experimental results on the analysis of the deadlock property show a significant improvement on many systems such as asynchronous-communication models and resource-competition problems.

We present a new net-reduction methodology to facilitate the analysis of real-time systems using Delay Time Petri Nets (DTPNs). Net reduction is one of the most important techniques for reducing the state-explosion problem of Petri nets. However, the application of net reduction to current timed-extensions of Petri nets (such as Merlin's Time PNs) is very limited due to the difficulty faced in the preservation of timing constraints. To overcome this problem, we introduce DTPNs which are inspired by Merlin's (1976) Time PNs, Senac's (1994) Hierarchical Time Stream PNs, and Little's (1991) Timed PNs. We show that DTPNs are much more suitable for net reduction. Then, we present a new set of DTPN reduction rules for the analysis of schedule and deadlock analysis. Our work is distinct from the others since our goal is to analyze real-time systems and the reduction methods we propose preserve both timing properties (schedule) and deadlock. To evaluate our framework, we have implemented an automated analysis tool whose main functions include net reduction and class-graph generation. The experimental results show that our net-reduction methodology leads to a significant contribution to the efficient analysis of real-time systems.

The concept of knowledge-based software architecture has recently emerged as a new way to improve our ability to effectively construct and maintain complex, large-scale software systems. Under this new paradigm, software engineers are able to do evolutionary design of complex systems through architecture specification, design rationale capture, architecture validation and verification, and architecture transformation. This paper surveys some of the important techniques that have been developed to support these activities. In particular, we are interested in knowledge/requirements acquisition and analysis. We survey some tools that use the knowledge-based approach to solve these problems. We also discuss various software architecture styles, architecture description languages (ADLs) and features of ADLs that help build better software systems. We then compare various ADLs based on these features. The efficient methods that were developed for verification, validation and high assurance of architectures are also discussed. Based on our survey results, we give a basis for comparing the various knowledge-based systems and list these comparisons in the form of a table.

The state-explosion problem of formal verification has obstructed its application to large-scale software systems. In this article, we introduce a set of new condensation theories: IOT-failure equivalence, IOT-state equivalence, and firing-dependence theory to cope with this problem. Our condensation theories are much weaker than current theories used for the compositional verification of Petri nets. More significantly, our new condensation theories can eliminate the interleaved behaviors caused by asynchronously sending actions. Therefore, our technique provides a much more powerful means for the compositional verification of asynchronous processes. Our technique can efficiently analyze several state-based properties: boundedness, reachable markings, reachable submarkings, and deadlock states. Based on the notion of our new theories, we develop a set of condensation rules for efficient verification of large-scale software systems. The experimental results show a significant improvement in the analysis large-scale concurrent systems.

Timing analysis is essential to the development of valid computer systems especially for a distributed real-time system. We present both static and dynamic analysis procedures to verify timing properties of distributed real-time systems using timing constraint Petri nets (TCPNs). With both derived static information and collected dynamic data, we can locate a faulty task in a distributed real-time system using a timing behavior cross reference task. After a faulty task has been pinpointed the causes of a timing constraint violation in a faulty task can be identified.