Industrial Internet of Things (IIoT) is experiencing rapid developments in the era of Industry 4.0. However, the ever-increasing applications put forward higher requirements for authentication. Facing such a problem, researchers combine two cutting-edge techniques, i.e., physical unclonable function (PUF) and blockchain. In detail, PUF can generate multiple challenge–response pairs (CRPs) for IIoT devices by leveraging their unique physical features. Moreover, blockchain platforms are employed for storing/synchronizing CRPs, thereby resisting the single-point failure. Although realizing the unclonable authentications, the existing works ignore the device heterogeneity of IIoT and fail to develop the specified blockchain platform for supporting PUF. In this article, we present a hybrid-PUF-based consortium blockchain for IIoT authentication, named HPCchain. Specifically, we first present the notion of hybrid-PUF, which assigns different devices to generate different types of PUFs, and then employs them to play different roles in HPCchain. In this way, we can overcome the IIoT heterogeneity. Moreover, we propose the PUF-empowered credit scheme for HPCchain and realize the dynamic endorsement with which we develop a PUF-based consensus mechanism for HPCchain. Finally, we design the registration and authentication schemes for IIoT nodes, atop HPCchain. Extensive experiments demonstrate the validity of our proposals.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that the use of entropy-minimization-only may lead to collapsed trivial solutions for UDA. In this article, we try to seek possible close-to-ideal UDA solutions by focusing on some intuitive properties of the ideal domain adaptation solution. In particular, we propose to introduce diversity maximization for further regulating entropy minimization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without the use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.

Summary Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks. A capsule groups data into vectors or matrices as poses rather than conventional scalars to represent specific properties of target instance.Based on pose,a capsule should be attached to a probability (often denoted as activation) for its presence. The dynamic routing helps capsule network achieve more generalization capacity with fewer model parameters. However, the bottleneck, which prevents widespread applications of capsule, is the expense of computation during routing. To address this problem, we generalize existing routing methods within the framework of weighted kernel density estimation, proposing two fast routing methods with different optimization strategies. Our methods prompt the time efficiency of routing by nearly 40% with negligible performance degradation. By stacking a hybrid of convolutional layers and capsule layers, we construct a network architecture to handle inputs at a resolution of 64 × 64 pixels. The proposed models achieve a parallel performance with other leading methods in multiple benchmarks.

Learning part-based features with multiple branches has been proven as an effective way to deliver high performance person re-identification. Existing works mostly exploit extra constraints on different branches to ensure the diversity of extracted features, which may lead to the increased complexity in network architecture and the difficulty for training. In this letter, we propose a quite simple multi-branch structure consisting of a global branch as well as a part branch in an asymmetrical way. We empirically demonstrate that such simple architecture can provide surprisingly high performance without imposing any extra constraint. On top of this, we further prompt the performance with a lightweight implementation of attention module. Extensive experimental results prove that the proposed method, termed Asymmetrical Network (AsNet), outperforms state-of-the-art methods with obvious margin on standard benchmark datasets such as Market1501, DukeMTMC, CUHK03. We believe that AsNet can serve as a strong baseline for related research and the source code is publicly available at https://github.com/www0wwwjs1/asnet.git.

Recent progress in deep convolutional neural networks has considerably changed the landscape of speech recognition, computer vision, natural language processing and so on. However, limited by the large number of parameters, the memory space and high computational complexity, it is a challenging task to deploy the deep neural network model in embedded system. To solve this problem, we propose Equal Trained Ternary Quantization (ETTQ), a ternary quantization method by improving Trained Ternary Quantization, which uses only a full-precision scaling coefficient for each layer, and quantize the weights to three levels. These positive and negative weights have same absolute values that are trainable parameters. Experiments show that the performance of ETTQ is only slightly worse than TTQ, but can converge faster and more stable during the training over CIFAR-10 and CIFAR-100 datasets.

Fully convolutional neural networks (FCNs) have shown great success in dense estimation tasks. One key pillar of such progress is mining multi-scale context cues from features in different convolutional layers. This paper introduces contextual aggregating network(CAN), a generic convolutional feature ensembling framework for semantic segmentation. Our framework first captures multi-scale contextual clues by concatenating multi-level feature representation, which carries both coarse semantics and fine details. Then it adaptively integrates stacked features to perform dense pixel estimation. The proposed CAN is trainable end-to-end, and allows us to fully investigate multi-scale context information embedded in images. The experiments show the promising results of our method on PASCAL VOC 2012 and Cityscapes dataset.

The grayscale-thermal tracking has attracted increasing attention due to the fact that it can make thermal information complement with grayscale information. Since there exists a large gap between the grayscale and the thermal video sequences, how to exploit the intrinsic relation between the grayscale and the thermal targets has become the key point. To address this issue, in this paper, we propose an inverse sparse representation based framework for the grayscale-thermal tracking, in which a canonical correlation analysis based inverse sparse representation model is adopted to jointly encode the target candidates in the grayscale and the thermal video sequences. The target coding process can explore the similarity between the grayscale and the thermal appearance in a common subspace, which can highlight the useful and discriminative information in both grayscale and thermal targets. The experiments on OSU-CT dataset can illustrate the promising performance of our tracking framework.

Open set domain adaptation by back propagation (OSDA-BP) was recently proposed as a novel end-to-end training approach for tackling the open set scenario, where only a few categories of interest are shared between source and target data. This paper provides an insightful understanding of the binary cross entropy loss employed in OSDA-BP for picking up the potential unknown samples. With this new understanding, we propose to replace the binary cross entropy loss with a symmetrical Kullback Leibler(KL) distance based loss. This improved OSDA-BP method is extensively evaluated over Office-31 dataset and a considerable performance improvement is observed.

Recently, exploring multiple feature maps from different layers in fully convolutional networks (FCNs) has gained substantial attention to capture context information for semantic segmentation. This paper presents a novel encoder-decoder architecture, called dense deconvolutional network (DDN), for semantic segmentation, where the feature maps of deeper convolutional layers are densely upsampled for the shallow deconvolutional layers. The proposed DDN is trainable end-to-end, and allows us to fully investigate multiple scale context cues embedded in images. The experimental results show that our DDN outperforms previous FCNs and encoder-decoder networks (EDNs) on PASCAL VOC 2012 dataset.

Recently, self-normalizing neural networks have been proposed with a scaled version of exponential linear units (SELUs), which can force neuron activations automatically converge towards zero mean and unit variance without use of batch normalization. As the negative part of SELUs is an exponential function, it is computationally intensive. In this paper, we introduce self-normalizing piecewise linear units (SPeLUs) for fast approximation of SELUs, adopting piecewise linear functions instead of the exponential part. Various possible shapes are discussed for piecewise linear units with stable self-normalizing properties. Experiments show that SPeLUs can provide an efficient and fast alternative to SELUs, with almost similar classification performance over MNIST, CIFAR-10 and CIFAR-10 datasets. With SPeLUs, we also show that batch normalization can be simply neglected for constructing deep neural nets, which could be advantageous for fast implementation of deep neural networks.

In traditional sparse representation based visual tracking, the particles are densely sampled, the appearance of some candidates may be very similar, hence the particle observations can be divided into disjointed groups. Existing methods only exploit the group similarity in a certain feature space. In this paper we propose a multi-view discriminant based multitask sparse representation method to exploit the group similarity in a multi-feature space. The proposed method can discriminate the reliability of observation groups and achieve a proper multi-view fusion by using a multi-view discriminant matrix to project multi-feature observation groups into a common subspace. Experiment results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.

Despite the success of many advanced tracking methods in this area, tracking targets with drastic variation of appearance such as deformation, view change and partial occlusion in video sequences is still a challenge in practical applications. In this paper, we take these serious tracking problems into account simultaneously, proposing a dynamic graph based model to track object and its deformable parts at multiple resolutions. The method introduces well learned structural object detection models into object tracking applications as prior knowledge to deal with deformation and view change. Meanwhile, it explicitly formulates partial occlusion by integrating spatial potentials and temporal potentials with an unparameterized occlusion handling mechanism in the dynamic conditional random field framework. Empirical results demonstrate that the method outperforms state-of-the-art trackers on different challenging video sequences.

This paper explores the performance of Neural Network (NN) for human action recognition and proposes a novel hierarchical and boosting-based action recognition system. Specifically, the main contributions of our work are three-fold: (1) A boosted NN based scheme is applied to the human action recognition task for the first time, during which we extend the standard binary AdaBoost algorithm to a multiclass version; (2) A novel hierarchical recognition framework with pre-decision and post-decision modules is proposed, which can significantly enhance the training efficiency as well as the frame-based recognition accuracy; (3) Numerous modified features (both motion and shape features) are utilized and combined in this paper. Experiments on the Weizmann dataset show promising results of our approach in comparison with other state-of-the-art methods.

This paper addresses the problem of small scale target tracking. The divided-by-zero problem in the weight computation of mean shift algorithm and its associated tracking interrupt problem are presented. To tackle these problems, the Parzen window density estimation method is modified to interpolate the histogram of the target candidate. Then the Kullback-Leibler distance is employed as a new similarity measure between the target model and the target candidate. Its corresponding weight computation and new location expressions are derived. On the basis of these works, we propose a new small target tracking algorithm using mean shift framework. The tracking experiments for real world video sequences show that the proposed algorithm can track the target successively and accurately. It can successfully track very small targets with only 6×12 pixels.