Liquid State Machine

Model-based selective regression testing promises reduction in cost and labour by selecting a subset of the test suite corresponding to the modifications after system evolution. However, identification of modifications in the systems and selection of corresponding test cases is challenging due to interdependencies among models. State-based testing is an important approach to test the system behaviour. Unfortunately the existing state-based regression testing approaches do not care for dependencies of the state machine with other system models. This paper presents the tool support and evaluation of our statebased selective regression testing methodology for evolving state-based systems. START is an Eclipsebased tool for state-based regression testing compliant with UML 2.1 semantics. START deals with dependencies of state machines with class diagrams to cater for the change propagation. We applied the START on a case study and our results show significant reduction in the test cases ...

Abstract. This paper investigates the potential of recurrent spiking neurons for classification problems. It presents a hybrid approach based on the paradigm of Reservoir Computing. The practical applications based on recurrent spiking neurons are limited due to their ...

This paper describes a process to reverse engineer structural and behavioural formal models of a GUI application by a dynamic technique, mixing manual with automatic exploration. The goal is to diminish the effort required to construct the model and mapping information needed in a model-based GUI testing process. A skeleton of a state machine model of the GUI, represented in a formal pre/post specification language, is generated automatically by the exploration process. Mapping information between the model and the ...

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

In this research, we present a new method, termed F-Sign, for automatic extraction of unique signatures from malware files. F-Sign is primarily intended for high-speed network traffic filtering devices that are based on deep-packet inspection. Malicious executables are analyzed using two approaches: disassembly, utilizing IDA-Pro, and the application of a dedicated state machine in order to obtain the set of functions comprising the executables. The signature extraction process is based on a comparison with a common function repository. By eliminating functions appearing in the common function repository from the signature candidate list, F-Sign can minimize the risk of false-positive detection errors. To minimize false-positive rates even further, F-Sign proposes intelligent candidate selection using an entropy score to generate signatures. Evaluation of F-Sign was conducted under various conditions. The findings suggest that the proposed method can be used for automatically generating signatures that are both specific and sensitive.

Algorithmic State Machine (ASM) charts are useful in the top down design of digital systems. We give ex- amples where the easiest way to transcribe cooperating ASMs into Verilog fails to simulate properly. Although introducing ad-hoc delays might overcome the problem in a particular case, we propose an alternative solution that uses the so called "unknown logic value" x to