Biblio

Here we explore the applicability of traditional sliding window based convolutional neural network (CNN) detection pipeline and region based object detection techniques such as Faster Region-based CNN (R-CNN) and Region-based Fully Convolutional Networks (R-FCN) on the problem of object detection in X-ray security imagery. Within this context, with limited dataset availability, we employ a transfer learning paradigm for network training tackling both single and multiple object detection problems over a number of R-CNN/R-FCN variants. The use of first-stage region proposal within the Faster RCNN and R-FCN provide superior results than traditional sliding window driven CNN (SWCNN) approach. With the use of Faster RCNN with VGG16, pretrained on the ImageNet dataset, we achieve 88.3 mAP for a six object class X-ray detection problem. The use of R-FCN with ResNet-101, yields 96.3 mAP for the two class firearm detection problem requiring 0.1 second computation per image. Overall we illustrate the comparative performance of these techniques as object localization strategies within cluttered X-ray security imagery.

Fog computing provides a new architecture for the implementation of the Internet of Things (IoT), which can connect sensor nodes to the cloud using the edge of the network. This structure has improved the latency and energy consumption in the cloud. In this heterogeneous and distributed environment, resource allocation is very important. Hence, scheduling will be a challenge to increase productivity and allocate resources appropriately to the tasks. Programs that run in this environment should be protected from intruders. We consider three parameters as authentication, integrity, and confidentiality to maintain security in fog devices. These parameters have time and computational overhead. In the proposed approach, we schedule the modules for the run in fog devices by heuristic algorithms based on data mining technique. The objective function is included CPU utilization, bandwidth, and security overhead. We compare the proposed algorithm with several heuristic algorithms. The results show that our proposed algorithm improved the average energy consumption of 63.27%, cost 44.71% relative to the PSO, ACO, SA algorithms.

Vulnerability detection is an import issue in information system security. In this work, we propose the deep learning method for vulnerability detection. We present three deep learning models, namely, convolution neural network (CNN), long short term memory (LSTM) and convolution neural network — long short term memory (CNN-LSTM). In order to test the performance of our approach, we collected 9872 sequences of function calls as features to represent the patterns of binary programs during their execution. We apply our deep learning models to predict the vulnerabilities of these binary programs based on the collected data. The experimental results show that the prediction accuracy of our proposed method reaches 83.6%, which is superior to that of traditional method like multi-layer perceptron (MLP).

Machine learning and data mining algorithms typically assume that the training and testing data are sampled from the same fixed probability distribution; however, this violation is often violated in practice. The field of domain adaptation addresses the situation where this assumption of a fixed probability between the two domains is violated; however, the difference between the two domains (training/source and testing/target) may not be known a priori. There has been a recent thrust in addressing the problem of learning in the presence of an adversary, which we formulate as a problem of domain adaption to build a more robust classifier. This is because the overall security of classifiers and their preprocessing stages have been called into question with the recent findings of adversaries in a learning setting. Adversarial training (and testing) data pose a serious threat to scenarios where an attacker has the opportunity to ``poison'' the training or ``evade'' on the testing data set(s) in order to achieve something that is not in the best interest of the classifier. Recent work has begun to show the impact of adversarial data on several classifiers; however, the impact of the adversary on aspects related to preprocessing of data (i.e., dimensionality reduction or feature selection) has widely been ignored in the revamp of adversarial learning research. Furthermore, variable selection, which is a vital component to any data analysis, has been shown to be particularly susceptible under an attacker that has knowledge of the task. In this work, we explore avenues for learning resilient classification models in the adversarial learning setting by considering the effects of adversarial data and how to mitigate its effects through optimization. Our model forms a single convex optimization problem that uses the labeled training data from the source domain and known- weaknesses of the model for an adversarial component. We benchmark the proposed approach on synthetic data and show the trade-off between classification accuracy and skew-insensitive statistics.

Visual object tracking is challenging when the object appearances occur significant changes, such as scale change, background clutter, occlusion, and so on. In this paper, we crop different sizes of multiscale templates around object and input these multiscale templates into network to pretrain the network adaptive the size change of tracking object. Different from previous the tracking method based on deep convolutional neural network (CNN), we exploit deep Residual Network (ResNet) to offline train a multiscale object appearance model on the ImageNet, and then the features from pretrained network are transferred into tracking tasks. Meanwhile, the proposed method combines the multilayer convolutional features, it is robust to disturbance, scale change, and occlusion. In addition, we fuse multiscale search strategy into three kernelized correlation filter, which strengthens the ability of adaptive scale change of object. Unlike the previous methods, we directly learn object appearance change by integrating multiscale templates into the ResNet. We compared our method with other CNN-based or correlation filter tracking methods, the experimental results show that our tracking method is superior to the existing state-of-the-art tracking method on Object Tracking Benchmark (OTB-2015) and Visual Object Tracking Benchmark (VOT-2015).

Organisers of large-scale crowdsourcing initiatives need to consider how to produce outcomes with their projects, but also how to build volunteer capacity. The initial project experience of contributors plays an important role in this, particularly when the contribution process requires some degree of expertise. We propose three analytical dimensions to assess first-time contributor engagement based on readily available public data: cohort analysis, task analysis, and observation of contributor performance. We apply these to a large-scale study of remote mapping activities coordinated by the Humanitarian OpenStreetMap Team, a global volunteer effort with thousands of contributors. Our study shows that different coordination practices can have a marked impact on contributor retention, and that complex task designs can be a deterrent for certain contributor groups. We close by providing recommendations about how to build and sustain volunteer capacity in these and comparable crowdsourcing systems.