Semi-supervised Tuning from Temporal Coherence

Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures. In particular, enforcing smooth output changes while presenting temporally-closed frames from video sequences, proved to be an effective strategy. In this paper we prove the efficacy of temporal coherence for semi-supervised incremental tuning. We show that a deep architecture, just mildly trained in a supervised manner, can progressively improve its classification accuracy, if exposed to video sequences of unlabeled data. The extent to which, in some cases, a semi-supervised tuning allows to improve classification accuracy (approaching the supervised one) is somewhat surprising. A number of control experiments pointed out the fundamental role of temporal coherence.Comment: Under review as a conference paper at ICLR 201

Software integration testing based on communication coverage criteria and partial model generation

This paper considers the problem of integration testing the components of a timed distributed software system. We assume that communication between the components is specified using timed interface automata and use computational tree logic (CTL) to define communication-based coverage criteria that refer to send- and receive-statements and communication paths. The proposed method enables testers to focus during component integration on such parts of the specification, e.g. behaviour specifications or Markovian usage models, that are involved in the communication between components to be integrated. A more specific application area of this approach is the integration of test-models, e.g. a transmission gear can be tested based on separated models for the driver behaviour, the engine condition, and the mechanical and hydraulical transmission states. Given such a state-based specification of a distributed system and a concrete coverage goal, a model checker is used in order to determine the coverage or generate test sequences that achieve the goal. Given the generated test sequences we derive a partial test-model of the components from which the test sequences are derived. The partial model can be used to drive further testing and can also be used as the basis for producing additional partial models in incremental integration testing. While the process of deriving the test sequences could suffer from a combinatorial explosion, the effort required to generate the partial model is polynomial in the number of test sequences and their length. Thus, where it is not feasible to produce test sequences that achieve a given type of coverage it is still possible to produce a partial model on the basis of test sequences generated to achieve some other criterion. As a result, the process of generating a partial model has the potential to scale to large industrial software systems. While a particular model checker, UPPAAL, was used, it should be relatively straightforward to adapt the approach for use with other CTL based model checkers. A potential additional benefit of the approach is that it provides a visual description of the state-based testing of distributed systems, which may be beneficial in other contexts such as education and comprehension

On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs

Modern networks are of huge sizes as well as high dynamics, which challenges the efficiency of community detection algorithms. In this paper, we study the problem of overlapping community detection on distributed and dynamic graphs. Given a distributed, undirected and unweighted graph, the goal is to detect overlapping communities incrementally as the graph is dynamically changing. We propose an efficient algorithm, called \textit{randomized Speaker-Listener Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of label propagation. Besides detecting high-quality communities, rSLPA can incrementally update the detected communities after a batch of edge insertion and deletion operations. To the best of our knowledge, rSLPA is the first algorithm that can incrementally capture the same communities as those obtained by applying the detection algorithm from the scratch on the updated graph. Extensive experiments are conducted on both synthetic and real-world datasets, and the results show that our algorithm can achieve high accuracy and efficiency at the same time.Comment: A short version of this paper will be published as ICDE'2018 poste