Conventional analysis of time series signals representing earthquakes does not provide any clue about the vulnerability of such disastrous events. Time series signals contain P and S waves, which can detect earthquake epicentres. Due to the failure of the old method for determining earthquake susceptibility over time, decision-making is needed. This research suggests a multi-criteria decision-making method to determine earthquake signal risk time zones. This study used TOPSIS for this job. TOPSIS ranks greatest and worst resemblance to positive and negative ideal solutions. Alternatives and criteria constitute the decision matrix. Segmenting the earthquake's duration creates alternate time zones, and seismic signal dynamics are used to set criteria. Statistical mean and standard deviation are two criteria among many. Other criteria include Hurst exponent, power spectrum maximum amplitude, and segmented signal anomaly (assumed as alternate). The proposed approach was tested using Indian Meteorological Department Bhuj earthquake data. The paper describes how to evaluate criteria for a time zone alternative. To simplify computation, earthquake incidence is separated into 14 equal-length time segments. Results demonstrate that the proposed method accurately detects earthquake time series signal sensitive time zones.

High performance computing (HPC) systems often require substantial resources, and can take up to several hours or days to execute. Upon a failure, it is important to loose as little computation as possible. In this work we present an algorithm-based fault tolerance (ABFT) strategy for hypercube-based parallel algorithms. The strategy assumes the virtual VCube topology, which has several logarithmic properties that are preserved even as nodes fail. The strategy guarantees that the algorithm does not halt even after up to (N - 1) nodes crash, in a system of N nodes. We use parallel sorting as a case study, describing how to make a fault-tolerant version of three parallel sorting algorithms: HyperQuickSort, QuickMerge and Bitonic Sort. The algorithms were implemented in MPI using ULMF to handle faults. Experimental results are presented showing the performance and robustness of the solution for sorting up to a billion integers in scenarios with faults.

Metamorphic malware is new and one of the most advanced malwares recently discovered. This malware can bypass anti-viruses and are much harder to detect if present in any computer system or network. This research paper intends to develop a better classification method for this metamorphic malware using the latest malware API calls dataset. The multi-class malware classification used in this study is gated recurrent units (GRU). Another non-conventional multi-class malware classification method used is convolution neural network with long short-term memory (CNN + LSTM). The multi-classification results obtained by GRU are 55% with a 0.56 F1-score, and CNN + LSTM is 60% with a 0.61 F1-score, which is quite good. Moreover, the performance of the proposed deep learning models is compared against different classifiers and existing models to show their effectiveness in categorising malware.

We present work towards a compositional design approach that will lead designers to develop safe reactive systems. To this end, we extend the theory of I/O-automata that is widely used for modelling reactive systems with composition operator required for dealing with a specific assembly of such systems: systems that consist of a chain of components arranged so that the output of each component is the input of the next, and behave like pipelines. We show that the proposed composition operator ensures semantics preserving of reactive components models. The paper presents a general result on correct-by-construction approach for reactive systems design.

The composition of web services in cloud computing allows making them cooperative in order to satisfy a client request. However, the substitution of one of these services by another must ensure the proper functioning of the new composition. Hence there is a need to develop adequate methods to verify the services substitution. In this aim, we propose, in the present paper, two verification methods. The first one deals with context-independent substitution and sets if a web service can substitute another one. The second one focuses on context-aware substitution and decides the substitutability of web services in a specific composition. These methods use open coloured Petri net (OCNets) as a formal framework for modelling web services and their composition in cloud computing. This model allows the structural analysis of web services' interfaces. For the behavioural verification, we use services automata, which allow capturing OCNets' behaviour and checking its preservation.

Fault diagnosis of discrete-event systems (DESs) has received a lot of attention in industry and academia during the last two decades. In DES based diagnosis, the two main discussed topics are offline diagnosability analysis and online diagnosis. A pioneering approach that led to the development of various techniques is based on the so-called diagnose. However, this approach suffers from the combinatorial explosion problem due to the exponential complexity of construction. To partially overcome this problem, an efficient approach to construct a symbolic diagnoser is proposed in this paper. The proposed approach consists in constructing a diagnoser based on the symbolic observation graph (SOG), which combines symbolic and enumarative representations. The construction of the diagnoser as well as the verification of diagnosability are performed simultaneously on the fly, which can considerably reduce the state space of the diagnoser and thus the overall running time. To evaluate the efficiency and the scalability of the approach, some experimental results are presented and discussed based on a DES benchmark.

Fault diagnosis of discrete-event systems (DESs) has received a lot of attention in industry and academia during the last two decades. In DES based diagnosis, the two main discussed topics are offline diagnosability analysis and online diagnosis. A pioneering approach that led to the development of various techniques is based on the so-called diagnose. However, this approach suffers from the combinatorial explosion problem due to the exponential complexity of construction. To partially overcome this problem, an efficient approach to construct a symbolic diagnoser is proposed in this paper. The proposed approach consists in constructing a diagnoser based on the symbolic observation graph (SOG), which combines symbolic and enumarative representations. The construction of the diagnoser as well as the verification of diagnosability are performed simultaneously on the fly, which can considerably reduce the state space of the diagnoser and thus the overall running time. To evaluate the efficiency and the scalability of the approach, some experimental results are presented and discussed based on a DES benchmark.

International audience

Test automation saves time and cost by digitising test generation and execution. The existing techniques fail to produce effective and compliable test scripts for not considering both syntactic and semantic information. The proposed three-layer architecture incorporates these information for generation of proper test scripts. It analyses the source code for extracting syntax and UML diagrams for obtaining semantics. Class methods and sequence of calls are extracted from UMLs, and syntax for class instantiations and method calls are accumulated from source code to generate unit and integration tests. A case study as well as experiments, conducted on sample Java projects, conform the competence of the generation process along with the generated test scripts.

We introduce and study a prototyping language for real-time distributed systems involving bounded-time migration and communication. The time constraints of this language are expressed as bounded intervals given by real numbers used to model faithfully the uncertainty of the delay in migration and communication of processes placed at explicit locations of a distributed system. We provide the operational semantics of this language and illustrate the new language by an example. Due to a translation of the bounded real-time distributed systems described in rTIMO into a network of timed safety automata, we can use an existing software tool for analysing their qualitative and quantitative properties.