a b s t r a c tHaving a clear view of events that occurred over time is a difficult objective to achieve in digital investigations (DI). Event reconstruction, which allows investigators to understand the timeline of a crime, is one of the most important step of a DI process. This complex task requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. For this purpose, we propose a new methodology, supported by theoretical concepts, that can assist investigators through the whole process including the construction and the interpretation of the events describing the case. The proposed approach is based on a model which integrates knowledge of experts from the fields of digital forensics and software development to allow a semantically rich representation of events related to the incident. The main purpose of this model is to allow the analysis of these events in an automatic and efficient way. This paper describes the approach and then focuses on the main conceptual and formal aspects: a formal incident modelization and operators for timeline reconstruction and analysis. AbstractHaving a clear view of events that occurred over time is a difficult objective to achieve in digital investigations (DI). Event reconstruction, which allows investigators to understand the timeline of a crime, is one of the most important step of a DI process. This complex task requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. For this purpose, we propose a new methodology, supported by theoretical concepts, that can assist investigators through the whole process including the construction and the interpretation of the events describing the case. The proposed approach is based on a model which integrates knowledge of experts from the fields of digital forensics and software development to allow a semantically rich representation of events related to the incident. The main purpose of this model is to allow the analysis of these events in an automatic and efficient way. This paper describes the approach and then focuses on the main conceptual and formal aspects: a formal incident modelization and operators for timeline reconstruction and analysis.

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International audienceOne of the biggest challenges in Big Data is the exploitation of Value from large volume of data. To exploit value one must focus on extracting knowledge from Big Data sources. In this paper we present a new simple but highly scalable process to automatically learn the label hierarchy from huge sets of unstructured text. We aim to extract knowledge from these sources using a Hierarchical Multi-Label Classification process called Semantic HMC. Five steps compose the Semantic HMC: Indexation, Vectorization, Hierarchization, Resolution and Realization. The first three steps construct the label hierarchy from data sources. The last two steps classify new items according to the hierarchy labels. To perform the classification without heavily relying on the user, the process is unsupervised, where no thesaurus or label examples are required. The process is implemented in a scalable and distributed platform to process Big Data

Increasing complexity in both the software and the underlying hardware, and ever tighter time-to-market pressures are some of the key challenges faced when designing multimedia embedded systems. Optimizing the debugging phase can help to reduce development time significantly. A powerful approach used extensively during this phase is the analysis of execution traces. However, huge trace volumes make manual trace analysis unmanageable. In such situations, Data Mining can help by automatically discovering interesting patterns in large amounts of data. In this paper, we are interested in discovering periodic behaviors in multimedia applications. Therefore, we propose a new pattern mining approach for automatically discovering all periodic patterns occurring in a multimedia application execution trace.Furthermore, gaps in the periodicity are of special interest since they can correspond to cracks or drop-outs in the stream. Existing periodic pattern definitions are too restrictive regarding the size of the gaps in the periodicity. So, in this paper, we specify a new definition of frequent periodic patterns that removes this limitation. Moreover, in order to simplify the analysis of the set of frequent periodic patterns we propose two complementary approaches: (a) a lossless representation that reduces the size of the set and facilitates its analysis, and (b) a tool to identify pairs of "competitors" where a pattern breaks the periodicity of another pattern. Several experiments were carried out on embedded video and audio decoding application traces, demonstrating that using these new patterns it is possible to identify abnormal behaviors.

Agricultural land use may influence macroinvertebrate communities by way of pesticide contamination associated with agricultural runoff. However, information about the relation between runoff-related pesticides and communities of benthic macroinvertebrates in stormwater wetland that receive agricultural runoff does not currently exist. Here we show changes in macroinvertebrates communities of a stormwater wetland that collects pesticide-contaminated runoff from a vineyard catchment. Sixteen runoff-associated pesticides, including the insecticide flufenoxuron, were continuously quantified at the inlet of the stormwater wetland from April to September (period of pesticide application). In parallel, benthic macroinvertebrate communities, pesticide concentrations, and physicochemical parameters in the wetland were assessed twice a month. Twenty-eight contaminated runoffs ranging from 1.1 to 114 m3 entered the wetland during the study period. Flufenoxuron concentrations in runoff-suspended solids ranged from 1.5 to 18.5 μg kg(-1) and reached 6 μg kg(-1) in the wetland sediments. However, flufenoxuron could not be detected in water. The density, diversity, and abundance of macroinvertebrates largely varied over time. Redundancy and formal concept analyses showed that concentrations of flufenoxuron, vegetation cover, and flow conditions significantly determine the community structures of stormwater wetland macroinvertebrates. This study shows that flow conditions, vegetation cover, and runoff-related pesticides jointly affect communities of benthic macroinvertebrates in stormwater wetlands.