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Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, we propose a real-time object detection algorithm for videos based on the YOLO network. We eliminate the influence of the image background by image preprocessing, and then we train the Fast YOLO model for object detection to obtain the object information. Based on the Google Inception Net (GoogLeNet) architecture, we improve the YOLO network by using a small convolution operation to replace the original convolution operation, which can reduce the number of parameters and greatly shorten the time for object detection. Our Fast YOLO algorithm can be applied to real-time object detection in video. [ABSTRACT FROM AUTHOR]

Relocation is one of the most common problems in Simultaneous Localization and Mapping (SLAM). This paper presents a novel relocation method, using unsupervised deep learning algorithm to extract the feature of Light Detection and Ranging (LiDAR) data, and narrows the scope of relocation by classifying these features to reduce the randomness of the relocation. Compared with the other methods which is based on matching the manual feature points, this method avoids some limitations of manual features. We modify the Particle Filter SLAM (PF-SLAM), and use our relocation method to replace the original method for experimentation. The experimental results demonstrate that this method can be relocation whit high success rate only use a small amount of computational resource in a short time. Training neural network will consume a lot of computing resources, we also propose a cloud computing framework to the implementation of this method for the mobile robot which computational resources are limited. [ABSTRACT FROM AUTHOR]

1 Creating a dataset for estimating depth from target foreground and background scene variations. 2 Creation of relationships between depth of person, foreground and background scene field. 3 Dealing with perspective errors in depth predictions while capturing still photographs of person along camera axial line. This paper proposes a new technique to achieve a person's depth from a single image by considering the target foreground and background scene variations in extreme weather conditions within 40 meters range. For this purpose series of images are captured on each person at successive intervals. The height, distance, foreground, and background features are extracted using an object detection deep learning framework. The obtained features are then subsequently trained by the Gradient Booster Regressor to predict the depth information. Furthermore, the algorithm is tested on various images and is validated with ground truth depth data. The findings presented in this paper attest to the reliability of the methodology used for depth estimation. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, a novel intrusion detection method combining a deep learning-based method and a feature-based method is proposed for smart grid. Specifically, long short-term memory and extreme gradient boosting are adopted for intrusion detection, and the results are fused based on the accuracies of these two models. As the XGBoost method is sensitive to its parameters and unsuitable selections greatly degrade its performance, in this paper, a Bayesian method is proposed to optimize these parameters. Moreover, a crossover scheme in a genetic algorithm is introduced to reduce the impact of falling into a local optimum of Bayesian optimization. Extensive experimental results show the effectiveness of the proposed algorithm. [Display omitted] • An intrusion detection method is proposed combining two classifiers adaptively for smart grid. • A Bayesian based method is proposed to optimize the parameters of eXtreme Gradient Boosting. • A method is proposed to reduce the impact of falling into local optimum of Bayesian optimization. [ABSTRACT FROM AUTHOR]

While deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These vulnerabilities make it challenging to deploy Deep Neural Network (DNN) models in security-critical areas. Recently, many research studies have been conducted to develop defense techniques enabling more robust models. In this paper, we target detecting adversarial samples by differentiating them from their clean equivalents. We investigate various metrics for detecting adversarial samples. We first leverage moment-based predictive uncertainty estimates of DNN classifiers derived through Monte-Carlo (MC) Dropout Sampling. We also introduce a new method that operates in the subspace of deep features obtained by the model. We verified the effectiveness of our approach on different datasets. Our experiments show that these approaches complement each other, and combined usage of all metrics yields 99 % ROC-AUC adversarial detection score for well-known attack algorithms. [ABSTRACT FROM AUTHOR]

Today, incorporating advanced machine learning techniques into intrusion detection systems (IDSs) plays a crucial role in securing mobile edge computing systems. However, the mobility demands of our modern society require more advanced IDSs to make a good trade-off between coping with the rapid growth of traffic data and responding to attacks. Thus, in this paper, we propose a lightweight distributed IDS that exploits the advantages of centralized platforms to train and learn from large amounts of data. We investigate the benefits of two promising bio-inspired optimization algorithms, namely Ant Lion Optimization and Ant Colony Optimization, to find the optimal data representation for the classification process. We use Deep Forest as a classifier to detect intrusive actions more robustly and generate as few false positives as possible. The experiment results show that the proposed approach can enhance the reliability of lightweight intrusion detection systems in terms of accuracy and execution time. [Display omitted] • Hybrid learning model has benefits in supporting intrusion detection in mobile edge computing. • Deep Forest is effective in strengthening attack detection predictability of IDSs in mobile edge computing. • Bio-inspired optimization algorithms improve the classification process. • The employed strategy enhances attack recognition of IDSs to deal with different threats in mobile edge computing. [ABSTRACT FROM AUTHOR]

• The combination of mobile devices and edge computing reduces the total computing overhead and makes resource allocation reasonable. • Evaluate system performance by the weighted sum of delay and energy consumption. • Change the parameter update method to accelerate the convergence speed of the deep reinforcement learning algorithm. • A neural network is used to approximate the behavior value function, and a target q network is used to update the target. With the arrival of the 5th generation mobile networks (5 G) era, the data needed by mobile devices (MDs) is explosively growing. High-consumption, low-latency applications are huge challenges for resource-constrained Internet of things (IoT) devices. Mobile edge computing overcomes the limitations of computing resources on MDs by offloading tasks generated by MDs and assigning them to nearby MEC servers. Therefore, mobile edge computing (MEC) becomes important. This paper presents a task offloading strategy for the multi-device multi-server system. To meet the task requirements of different MDs, we formulate an overhead minimization problem to optimize the delay and energy consumption of the system. We propose the Double Deep Q Network (Double-DQN) algorithm to perform location selection strategies for tasks generated on the mobile devices and allocate respective computing resources. Simulation results show that the algorithm can allocate resources reasonably and reduce the overhead of the entire system. [Display omitted] [ABSTRACT FROM AUTHOR]

As an essential subproblem in the face recognition field, occluded face recognition has received considerable attention in recent years. However, satisfactory recognition accuracy for occluded faces has not been achieved. Therefore, in order to solve this problem, this paper proposes a convolutional neural network that has multidimensional serial feature extraction modules for occluded faces and uses the deep learning method to improve the recognition rate. In order to improve the expression ability of the network, the method extracts features from two dimensions — space and channel — and built a multidimensional serial feature extraction module. By "multiscale" and "dependence" processing features, the generalization ability was significantly improved. Through the experimental test, the recognition accuracy of the multidimensional feature network (MFNet) reached 90.35%. This accuracy is 17.89% (72.36%) better than that of the traditional algorithm. Meanwhile, compared with other convolutional neural networks, this network has different degrees of improvement, which are 0.68% (ArcFace), 2.14% (Visual Geometry Group Face), and 4.83% (DeepID3). [ABSTRACT FROM AUTHOR]

Neural network-based decoding algorithms have potential value to be researched in the field of polar codes due to their low decoding latency. The neural successive cancellation (NSC) algorithm, combining deep learning and successive cancellation (SC) algorithm of polar codes, was proposed to reduce the latency of decoding. In terms of overall latency, the NSC algorithm does not fully consider the parallel decoding of special nodes in SC decoding tree, which limits the reduction of system delay to a certain extent. In this paper, we propose a multi-in-one neural simplified successive cancellation (MIO-NSSC) decoding algorithm for polar codes based on deep learning. The proposed MIO-NSSC algorithm, which is suitable for general nodes, mainly improves the existing fast simplified successive cancellation (FSSC) and the NSC algorithms to obtain a multi-in-one neural network instead of multiple neural networks in the NSC algorithm by using a new training strategy. Through applying the FSSC algorithm to a special node, the decoding delay of the proposed algorithm is further reduced. The experimental results demonstrate that the proposed MIO-NSSC algorithm can achieve significant latency reduction and resource consumption efficiency improvement compared with the NSC algorithm. The latency of the proposed MIO-NSSC decoding algorithm is about 21% lower than that of the NSC algorithm, and approximately seven neural networks are saved compared with the NSC algorithm. Furthermore, the MIO-NSSC algorithm can reduce the computational complexity without loss of performance. [ABSTRACT FROM AUTHOR]