Lucid programs are data-flow programs and can be visually represented as data flow graphs (DFGs) and composed visually. Forensic Lucid, a Lucid dialect, is a language to specify and reason about cyberforensic cases. I...
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Lucid programs are data-flow programs and can be visually represented as data flow graphs (DFGs) and composed visually. Forensic Lucid, a Lucid dialect, is a language to specify and reason about cyberforensic cases. It includes the encoding of the evidence (representing the context of evaluation) and the crime scene modeling in order to validate claims against the model and perform event reconstruction, potentially within large swaths of digital evidence. To aid investigators to model the scene and evaluate it, instead of typing a Forensic Lucid program, we propose to expand the design and implementation of the Lucid DFG programming onto Forensic Lucid case modeling and specification to enhance the usability of the language and the system. We briefly discuss the related work on visual programming and DFG modeling in an attempt to define and select one approach or a composition of approaches for Forensic Lucid based on various criteria such as previous implementation, wide use, formal backing in terms of semantics and translation. In the end, we solicit the readers' constructive, opinions, feedback, comments, and recommendations within the context of this short discussion.
Extensive data generated by peers of nodes in wireless sensor networks (WSNs) needs to be analysed and processed in order to extract information that is meaningful to the user. Data processing techniques that achieve ...
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Nowadays, wireless sensor networks are widely used to monitor real time events and answer the ad hoc queries from a certain member node. However, computing and maintaining the information of aggregate queries in event...
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Automated random testing is efficient at detecting faults but it is certainly not an optimal testing strategy for every given program. For example, an automated random testing tool ignores that some routines have stro...
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Automated random testing is efficient at detecting faults but it is certainly not an optimal testing strategy for every given program. For example, an automated random testing tool ignores that some routines have stronger preconditions, they use certain literal values, or they are more error-prone. Taking into account such characteristics may increase testing effectiveness. In this article, we present Evotec, an enhancement of random testing which relies on genetic algorithms to evolve a best testing strategy for contract-equipped programs. The resulting strategy is optimized for detecting more faults, satisfying more routine preconditions and establishing more object states on a given set of classes to test. Our experiment tested 92 classes over 1710 hours. It shows that Evotec detected 29% more faults than random+ and 18% more faults than the precondition-satisfaction strategy.
Specification mining methods are used to extract candidate specifications from system execution traces. A major challenge for specification mining is succinctness. That is, in addition to the soundness, completeness, ...
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Specification mining methods are used to extract candidate specifications from system execution traces. A major challenge for specification mining is succinctness. That is, in addition to the soundness, completeness, and scalable performance of the specification mining method, one is interested in producing a succinct result, which conveys a lot of information about the system under investigation but uses a short, machine and human-readable representation. In this paper we address the succinctness challenge in the context of scenario-based specification mining, whose target formalism is live sequence charts (LSC), an expressive extension of classical sequence diagrams. We do this by adapting three classical notions: a definition of an equivalence relation over LSCs, a definition of a redundancy and inclusion relation based on isomorphic embeddings among LSCs, and a delta-discriminative measure based on an information gain metric on a sorted set of LSCs. These are applied on top of the commonly used statistical metrics of support and confidence. A number of case studies show the utility of our approach towards succinct mined specifications.
Real-time code clone search is an emerging family of clone detection research that aims at finding clone pairs matching an input code fragment in fractions of a second. For these techniques to meet actual real world r...
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Real-time code clone search is an emerging family of clone detection research that aims at finding clone pairs matching an input code fragment in fractions of a second. For these techniques to meet actual real world requirements, they have to be scalable and provide a short response time. Our research presents a hybrid clone search approach using source code pattern indexing, information retrieval clustering, and Semantic Web reasoning to respectively achieve short response time, handle false positives, and support automated grouping/querying.
With the growth of Cloud Computing, more and more companies are offering different cloud services. From the customer's point of view, it is always difficult to decide whose services they should use, based on users...
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With the growth of Cloud Computing, more and more companies are offering different cloud services. From the customer's point of view, it is always difficult to decide whose services they should use, based on users' requirements. Currently there is no software framework which can automatically index cloud providers based on their needs. In this work, we propose a framework and a mechanism, which measure the quality and prioritize Cloud services. Such framework can make significant impact and will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their Quality of Services (QoS).
Automatic layout can be a crucial support feature for complex diagramming tools. Adding suitable layout algorithms to diagramming tools is a complex task and meta-tools should incorporate these for reuse. We present M...
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Automatic layout can be a crucial support feature for complex diagramming tools. Adding suitable layout algorithms to diagramming tools is a complex task and meta-tools should incorporate these for reuse. We present MaramaALM, a generalised set of automatic layout mechanisms. This has been incorporated in the Eclipse-based Marama meta-toolset to support automatic layout in Marama diagrams. It provides an easy-to-use mechanism for tool developers to add such layouts to their generated tools. We describe our motivation for MaramaALM, our approach to its implementation and an example case study of using these tool extensions.
Automatic speech recognition tasks whose domains involve recognizing proper names are usually challenging due to the infinite number of possible lexical items. In some tasks such as automated directories, to avoid unc...
Automatic speech recognition tasks whose domains involve recognizing proper names are usually challenging due to the infinite number of possible lexical items. In some tasks such as automated directories, to avoid unconstrained recognitions, one could design the system to handle a finite number of proper names at a time. However, the recognition grammars need to be generated in unsupervised fashions. In this paper, a recognition system capable of selecting a Thai proper name from dynamic name lists was proposed and evaluated upon name lists of various sizes. The system decomposes names to sequences of Grams, lexical units defined with specific pronunciations and spellings, upon which pronunciation grammars are produced and utilized as recognition constraints. Recognition accuracies of above 85% to approximately 91% were obtained on lists containing 10 to 80 names.
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