Summary form only given, as follows. We study the development of integrated fault-tolerant scheduling algorithms. The proposed algorithms ensure ultra-reliable execution of tasks where both hardware and software failu...
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Summary form only given, as follows. We study the development of integrated fault-tolerant scheduling algorithms. The proposed algorithms ensure ultra-reliable execution of tasks where both hardware and software failures are considered, and system performance improvement. Also, the proposed algorithms have the capability for on-line system-level fault diagnosis.
Cryptol is a programming language designed for specifying cryptographic algorithms. Despite its high-level modeling nature, Cryptol programs are fully executable. Further, a large subset of Cryptol can be automaticall...
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Cryptol is a programming language designed for specifying cryptographic algorithms. Despite its high-level modeling nature, Cryptol programs are fully executable. Further, a large subset of Cryptol can be automatically synthesized to hardware. To meet the inherent high-assurance requirements of cryptographic systems, Cryptol comes with a suite of formal-methods based tools that enable users to perform various program verification tasks. In this paper, we provide an overview of Cryptol and its verification toolset, especially focusing on the co-verification of third-party VHDL implementations against highlevel Cryptol specifications. As a case study, we demonstrate the technique on two hand-written VHDL implementations of the Skein hash algorithm.
The purpose of this paper is to give an overview of recent developments in algorithms and software for linear matrix inequality (LMI) problems. We review the definition and some basic properties of the semidefinite pr...
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The purpose of this paper is to give an overview of recent developments in algorithms and software for linear matrix inequality (LMI) problems. We review the definition and some basic properties of the semidefinite programming (SDP) problem, and describe recent developments in interior point algorithms and available software. We conclude with some extensions of the SDP.
There are many large system problems that are hard to model exactly or in a computationally tractable fashion. Examples include the mapping of human DNA, speech recognition, and automated learning in computer chess. T...
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There are many large system problems that are hard to model exactly or in a computationally tractable fashion. Examples include the mapping of human DNA, speech recognition, and automated learning in computer chess. Traditional artificial intelligence solution techniques for such problems rely on a combination of custom encoding of expert knowledge and heuristic search. They take much time to hand craft and then often are unable to take advantage of faster computers as they become available. In this context, the authors explore the advantage of using statistical search techniques in which the knowledge is encoded in some form of statistical model whose parameters are automatically adjusted or trained with domain data. The benefits are faster development times, greater solution accuracy (compared to hand crafted solutions) and the ability to allow the problem size and desired solution accuracy to be scaled up with computational resources. They apply this approach to certain critical computational problems in mapping the human genome. They use a Bayesian model to provide the best solution accuracy as a function of the number of parameters. Heuristic search techniques derived from artificial intelligence are used to search the model space in an efficient manner in the average case.
Iterative decoding has emerged as one of the most promising techniques for improving receivers performance. This paper is focused on exploring the feasibility of software implementation of iterative decoding algorithm...
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Iterative decoding has emerged as one of the most promising techniques for improving receivers performance. This paper is focused on exploring the feasibility of software implementation of iterative decoding algorithms for the 2nd and 3rd generations of the cellular communications standards. Two examples for iterative decoding algorithms are described: turbo decoding for the 3G cellular standards and turbo equalization for a GSM receiver. The principles described can be used to efficiently construct and implement other iterative decoding algorithms.
Effective and efficient Program Increment (PI) planning plays a pivotal role in enhancing collaboration and productivity within software development teams following the Agile way of working. This paper investigates th...
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ISBN:
(数字)9798350350982
ISBN:
(纸本)9798350350999
Effective and efficient Program Increment (PI) planning plays a pivotal role in enhancing collaboration and productivity within software development teams following the Agile way of working. This paper investigates the integration of Genetic algorithms (GAs) into the PI planning process to address the challenge of optimizing resource allocation. PI planning can be a complex and time-consuming process, requiring teams to balance numerous interdependent factors such as resource constraints, task urgency and importance, as well as team member availability. Genetic algorithms, inspired by the principles of natural selection, provide an alternative optimization technique that can explore a vast solution space and propose a number of possible and more efficient PI plans. By modeling the PI planning problem as a genetic algorithm, teams can leverage the algorithm's ability to rapidly converge on near-optimal solutions, leading to improved resource utilization, reduced planning time, and enhanced team alignment.
In the domain of software architecture recovery, classical clustering algorithms have been used to recover module views, while new ones have been proposed to tackle specific software architecture issues. Nonetheless, ...
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In the domain of software architecture recovery, classical clustering algorithms have been used to recover module views, while new ones have been proposed to tackle specific software architecture issues. Nonetheless, little information concerning their empirical evaluation in this context is presently available. This paper presents an empirical study that evaluates four clustering algorithms according to three previously proposed criteria: extremity of cluster distribution, authoritativeness, and stability, which were measured against consecutive releases of four different systems. Our results suggest that the k-means algorithm performs best in terms of authoritativeness and extremity and that the modularization quality algorithm produces more stable clusters. They also point out that fully automated clustering techniques alone cannot recover module views in a sensible way, but may provide a reasonable first step to speed up an expert-assisted architecture recovery process.
An overview of the software package LVQPAK, which has been developed for convenient and effective application of learning vector quantization algorithms, is presented. Two new features are included: fast conflict-free...
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An overview of the software package LVQPAK, which has been developed for convenient and effective application of learning vector quantization algorithms, is presented. Two new features are included: fast conflict-free initial distribution of codebook vectors into the class zones and the optimized-learning-rate algorithm OLVQ1.< >
Development of software change prediction models, based on the change histories of a software, are valuable for early identification of change prone classes. Classification of these change prone classes is vital to yi...
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Development of software change prediction models, based on the change histories of a software, are valuable for early identification of change prone classes. Classification of these change prone classes is vital to yield competent use of limited resources in an organization. This paper validates Artificial Immune System (AIS) algorithms for development of change prediction models using six open source data sets. It also compares the performance of AIS algorithms with other machine learning and statistical algorithms. The results of the study indicate, that the models developed, are effective means of predicting change prone classes in the future versions of the software. However, AIS algorithms do not perform better that machine learning and other statistical algorithms. The study provides conclusive results about the capabilities of AIS algorithms and reports whether there are any significant differences in the performance of different algorithms using a statistical test.
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