9,848 research outputs found

    Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

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    There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks. While these methods can be applied to classification or semantic segmentation tasks by simply averaging samples, this is not the case for object detection, where detection sample bounding boxes must be accurately associated and merged. A weak merging strategy can significantly degrade the performance of the detector and yield an unreliable uncertainty measure. This paper provides the first in-depth investigation of the effect of different association and merging strategies. We compare different combinations of three spatial and two semantic affinity measures with four clustering methods for MC Dropout with a Single Shot Multi-Box Detector. Our results show that the correct choice of affinity-clustering combination can greatly improve the effectiveness of the classification and spatial uncertainty estimation and the resulting object detection performance. We base our evaluation on a new mix of datasets that emulate near open-set conditions (semantically similar unknown classes), distant open-set conditions (semantically dissimilar unknown classes) and the common closed-set conditions (only known classes).Comment: to appear in IEEE International Conference on Robotics and Automation 2019 (ICRA 2019

    Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

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    A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data is arranged into sets, called bags, that are weakly labeled. Most AL methods focus on single instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance active learning (MIAL). The \textit{aggregated informativeness} method identifies the most informative instances based on classifier uncertainty, and queries bags incorporating the most information. The other proposed method, called \textit{cluster-based aggregative sampling}, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single instance AL methods

    A multilabel fuzzy relevance clustering system for malware attack attribution in the edge layer of cyber-physical networks

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    The rapid increase in the number of malicious programs has made malware forensics a daunting task and caused users’ systems to become in danger. Timely identification of malware characteristics including its origin and the malware sample family would significantly limit the potential damage of malware. This is a more profound risk in Cyber-Physical Systems (CPSs), where a malware attack may cause significant physical damage to the infrastructure. Due to limited on-device available memory and processing power in CPS devices, most of the efforts for protecting CPS networks are focused on the edge layer, where the majority of security mechanisms are deployed. Since the majority of advanced and sophisticated malware programs are combining features from different families, these malicious programs are not similar enough to any existing malware family and easily evade binary classifier detection. Therefore, in this article, we propose a novel multilabel fuzzy clustering system for malware attack attribution. Our system is deployed on the edge layer to provide insight into applicable malware threats to the CPS network. We leverage static analysis by utilizing Opcode frequencies as the feature space to classify malware families. We observed that a multilabel classifier does not classify a part of samples. We named this problem the instance coverage problem. To overcome this problem, we developed an ensemble-based multilabel fuzzy classification method to suggest the relevance of a malware instance to the stricken families. This classifier identified samples of VirusShare, RansomwareTracker, and BIG2015 with an accuracy of 94.66%, 94.26%, and 97.56%, respectively

    Visual Integration of Data and Model Space in Ensemble Learning

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    Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture

    Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways

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    Diverse classes of proteins function through large-scale conformational changes; sophisticated enhanced sampling methods have been proposed to generate these macromolecular transition paths. As such paths are curves in a high-dimensional space, they have been difficult to compare quantitatively, a prerequisite to, for instance, assess the quality of different sampling algorithms. The Path Similarity Analysis (PSA) approach alleviates these difficulties by utilizing the full information in 3N-dimensional trajectories in configuration space. PSA employs the Hausdorff or Fr\'echet path metrics---adopted from computational geometry---enabling us to quantify path (dis)similarity, while the new concept of a Hausdorff-pair map permits the extraction of atomic-scale determinants responsible for path differences. Combined with clustering techniques, PSA facilitates the comparison of many paths, including collections of transition ensembles. We use the closed-to-open transition of the enzyme adenylate kinase (AdK)---a commonly used testbed for the assessment enhanced sampling algorithms---to examine multiple microsecond equilibrium molecular dynamics (MD) transitions of AdK in its substrate-free form alongside transition ensembles from the MD-based dynamic importance sampling (DIMS-MD) and targeted MD (TMD) methods, and a geometrical targeting algorithm (FRODA). A Hausdorff pairs analysis of these ensembles revealed, for instance, that differences in DIMS-MD and FRODA paths were mediated by a set of conserved salt bridges whose charge-charge interactions are fully modeled in DIMS-MD but not in FRODA. We also demonstrate how existing trajectory analysis methods relying on pre-defined collective variables, such as native contacts or geometric quantities, can be used synergistically with PSA, as well as the application of PSA to more complex systems such as membrane transporter proteins.Comment: 9 figures, 3 tables in the main manuscript; supplementary information includes 7 texts (S1 Text - S7 Text) and 11 figures (S1 Fig - S11 Fig) (also available from journal site

    Fair comparison of skin detection approaches on publicly available datasets

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    Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann
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