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Full-Text Articles in Theory and Algorithms
On The Sequential Massart Algorithm For Statistical Model Checking, Cyrille Jegourel, Jun Sun, Jin Song Dong
On The Sequential Massart Algorithm For Statistical Model Checking, Cyrille Jegourel, Jun Sun, Jin Song Dong
Research Collection School Of Computing and Information Systems
Several schemes have been provided in Statistical Model Checking (SMC) for the estimation of property occurrence based on predefined confidence and absolute or relative error. Simulations might be however costly if many samples are required and the usual algorithms implemented in statistical model checkers tend to be conservative. Bayesian and rare event techniques can be used to reduce the sample size but they can not be applied without prerequisite or knowledge about the system under scrutiny. Recently, sequential algorithms based on Monte Carlo estimations and Massart bounds have been proposed to reduce the sample size while providing guarantees on error …
Using Finite-State Models For Log Differencing, Hen Amar, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Using Finite-State Models For Log Differencing, Hen Amar, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Much work has been published on extracting various kinds of models from logs that document the execution of running systems. In many cases, however, for example in the context of evolution, testing, or malware analysis, engineers are interested not only in a single log but in a set of several logs, each of which originated from a different set of runs of the system at hand. Then, the difference between the logs is the main target of interest. In this work we investigate the use of finite-state models for log differencing. Rather than comparing the logs directly, we generate concise …
Cross-Language Learning For Program Classification Using Bilateral Tree-Based Convolutional Neural Networks, Duy Quoc Nghi Bui, Lingxiao Jiang, Yijun Yu
Cross-Language Learning For Program Classification Using Bilateral Tree-Based Convolutional Neural Networks, Duy Quoc Nghi Bui, Lingxiao Jiang, Yijun Yu
Research Collection School Of Computing and Information Systems
Towards the vision of translating code that implements an algorithm from one programming language into another, this paper proposes an approach for automated program classification using bilateral tree-based convolutional neural networks (BiTBCNNs). It is layered on top of two tree-based convolutional neural networks (TBCNNs), each of which recognizes the algorithm of code written in an individual programming language. The combination layer of the networks recognizes the similarities and differences among code in different programming languages. The BiTBCNNs are trained using the source code in different languages but known to implement the same algorithms and/or functionalities. For a preliminary evaluation, we …
An Iterated Local Search Algorithm For The Team Orienteering Problem With Variable Profits, Aldy Gunawan, Kien Ming Ng, Graham Kendall, Junhan Lai
An Iterated Local Search Algorithm For The Team Orienteering Problem With Variable Profits, Aldy Gunawan, Kien Ming Ng, Graham Kendall, Junhan Lai
Research Collection School Of Computing and Information Systems
The orienteering problem (OP) is a routing problem that has numerous applications in various domains such as logistics and tourism. The objective is to determine a subset of vertices to visit for a vehicle so that the total collected score is maximized and a given time budget is not exceeded. The extensive application of the OP has led to many different variants, including the team orienteering problem (TOP) and the team orienteering problem with time windows. The TOP extends the OP by considering multiple vehicles. In this article, the team orienteering problem with variable profits (TOPVP) is studied. The main …