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With the rise of short video and the development of self-media, video has become a type of frequently used data, thus the protection of video data is particularly important. Digital watermarking is a very effective digital authentication and copyright protection technology that hides a piece of data called watermark into digital multimedia content. However, most existing video watermark algorithms fail to deal with the videos under geometric distortions, and they only focus on copyright protection, ignoring the task of tamper detection. This paper proposes a multipurpose video watermarking algorithm that is resistant to rotation and scaling attacks while providing both copyright protection and tamper detection. We design a DFT template embedding method to make the watermarking algorithm resistant to rotation and scaling attacks efficiently. This method can detect the rotation and scaling that the video has undergone during the watermark extraction process, and then extract the copyright watermark information based on geometric correction. In addition, we propose a robust watermark embedding method with tamper detection function, which embeds copyright watermark information with validation bits in the DCT coefficients of I frames. By embedding the copyright watermark block by block, the algorithm can not only improve the robustness, but also enable the system to detect tampering. Experiments show that the proposed algorithm has good robustness to various attacks such as compression, filtering, rotation, scaling, frame rate changing and other inter-frame operations, and it outperforms previous geometrically robust algorithm by an average of 12.8% under geometric attacks on the normalized correlation values. Also, we show that our algorithm is effective in tamper detection by localizing the distorted areas in the video frames. [ABSTRACT FROM AUTHOR]

In this paper, we focus on how to better represent video data in the field of action recognition. We propose a new image feature named phase spectrum reconstruction map to facilitate action recognition, which extracts the contour features of RGB frame images from a video clip beneficial for action recognition. We demonstrate the effectiveness of such a feature with ablation experiments using the channel-based feature fusion method and two-stream method. Also, we verify that the reconstructed map does contain motion-related features and can be learned by convolutional neural networks only using the reconstructed map as input features. Our method is trained and evaluated using the benchmark datasets HMDB-51 and UCF-101, and both show significant improvements over other methods without adding the reconstructed map features. [ABSTRACT FROM AUTHOR]

For covert communication in lossy channels, it is necessary to consider that the carrier of the hidden watermark will undergo multiple image-processing attacks. In order to ensure that secret information can be extracted without distortion from the watermarked images that have undergone attacks, in this paper, we design a novel fragmented secure communication system. The sender will fragment the secret data to be transmitted and redundantly hide it in a large number of multimodal carriers of messenger accounts on multiple social platforms. The receiver receives enough covert carriers, extracts each fragment, and concatenates the transmitted secret data. This article uses the image carrier as an example to fragment the text file intended for transmission and embeds it into a large number of images, with each fragment being redundant and embedded into multiple images. In this way, at the receiving end, only enough stego images need to be received to extract the information in each image, and then concatenate the final secret file. In order to resist various possible attacks during image transmission, we propose a strong robust image watermarking method. This method adopts a watermark layer based on DFT, which has high embedding and detection efficiency and good invisibility. Secondly, a watermark layer based on DCT is adopted, which can resist translation attacks, JPEG attacks, and other common attacks. Experiments have shown that our watermarking method is very fast; both the embedding time and the extraction time are less than 0.15 s for images not larger than 2000 × 2000 . Our watermarking method has very good invisibility with 41 dB PSNR on average. And our watermarking method is more robust than existing schemes and robust to nearly all kinds of attacks. Based on this strong robust image watermarking method, the scheme of fragmenting and hiding redundant transmission content into a large number of images is effective and practical. Our scheme can 100% restore the secret file completely under different RST or hybrid attacks, such as rotation by 1 degree and 5 degrees, scaling by 1.25 and 0.8, and cropping by 10% and 25%. Our scheme can successfully restore the secret file completely even if 30% of received images are lost. When 80% of received images are lost, our scheme can still restore 61.1% of the secret file. If all stego images can be obtained, the original text file can be completely restored. [ABSTRACT FROM AUTHOR]

Watermarking is an excellent solution to protect multimedia privacy but will be damaged by attacks such as noise adding, image filtering, compression, and especially scaling and cutting. In this paper, we propose a watermarking scheme to embed the watermark in the DWT-DCT composite transform coefficients, which is robust against normal image processing operations and geometric attacks. To make our scheme robust to scaling operations, a resampling detection network is trained to detect the scaling factor and then rescale the scaling-attacked image before watermark detection. To make our scheme robust to cutting operations, a template watermark is embedded in the Y channel to locate the cutting position. Experiments for various low- and high-resolution images reveal that our scheme has excellent performance in terms of imperceptibility and robustness. [ABSTRACT FROM AUTHOR]

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print–scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print–scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print–scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution. [ABSTRACT FROM AUTHOR]

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from −20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities. [ABSTRACT FROM AUTHOR]

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB. [ABSTRACT FROM AUTHOR]

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer–Convolution fusion dehazing network (TCFDN), which uses Transformer's global modeling ability and convolutional neural network's local modeling ability to improve the dehazing ability. In the Transformer–Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer–Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer–Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends. [ABSTRACT FROM AUTHOR]

At many construction sites, whether to wear a helmet is directly related to the safety of the workers. Therefore, the detection of helmet use has become a crucial monitoring tool for construction safety. However, most of the current helmet wearing detection algorithms are only dedicated to distinguishing pedestrians who wear helmets from those who do not. In order to further enrich the detection in construction scenes, this paper builds a dataset with six cases: not wearing a helmet, wearing a helmet, just wearing a hat, having a helmet, but not wearing it, wearing a helmet correctly, and wearing a helmet without wearing the chin strap. On this basis, this paper proposes a practical algorithm for detecting helmet wearing states based on the improved YOLOv5s algorithm. Firstly, according to the characteristics of the label of the dataset constructed by us, the K-means method is used to redesign the size of the prior box and match it to the corresponding feature layer to increase the accuracy of the feature extraction of the model; secondly, an additional layer is added to the algorithm to improve the ability of the model to recognize small targets; finally, the attention mechanism is introduced in the algorithm, and the CIOU_Loss function in the YOLOv5 method is replaced by the EIOU_Loss function. The experimental results indicate that the improved algorithm is more accurate than the original YOLOv5s algorithm. In addition, the finer classification also significantly enhances the detection performance of the model. [ABSTRACT FROM AUTHOR]

Inventory is the basis of business activities; inventory management helps industries keep their inventories stocked with reasonable quantities, which ensures consumers demand while minimizing storage costs. The traditional manual inventory management has low efficiency and a high labor cost. In this paper, we used improved YOLOv3 to detect the cups stored on the warehouse shelves and counted their numbers to realize automated inventory management. The warehouse images are collected by the camera and transmitted to the industrial computer, which runs the YOLOv3 network. There are three feature maps in YOLOv3, the two smaller feature maps and the structure behind them are removed, and the k-means algorithm is used to optimize the default anchor size. Moreover, the detection range is limited to a specified area. Experiments show that, by eliminating those two feature maps, the network parameter is reduced from 235 MB to 212 MB, and detection FPS is improved from 48.15 to 54.88 while mAP is improved from 95.65% to 96.65% on our test dataset. The new anchors obtained by the k-means algorithm further improve the mAP to 96.82%. With those improvements, the average error rate of detection is reduced to 1.61%. Restricted detection areas eliminate irrelevant items to ensure the high accuracy of the detection result. The accurately counted number of cups and its change provide significant data for inventory management. [ABSTRACT FROM AUTHOR]

The major obstacle in semantic segmentation is that it requires a large number of pixel-level labeled data to train an effective model. In order to reduce the cost of annotation, weakly-supervised methods use weaker labels to overcome the need for per-pixel labels, while zero-shot methods transfer the knowledge learned from seen classes to unseen classes to reduce the number of classes that need to be labeled. To further alleviate the burden of annotation, we introduce a more challenging task of Weakly-supervised Zero-shot Semantic Segmentation (WZSS): learning models which only utilize image-level annotation of seen classes to segment images containing unseen objects. To this end, we propose a Dual Semantic-Guided (DSG) model which is double guided by semantic embeddings of classes to obtain classification scores and localization maps. By ignoring the localization maps with low classification scores, our proposed framework can generate prediction segmentation masks. To improve our model's performance, we propose a simple stochastic selection on semantic embeddings during inference, which explores the difference between image-level class embeddings and pixel-level class embeddings. This simple approach increases our model's performance in terms of hIoU from 25.9 to 31.8. In addition, compared with some zero-shot semantic segmentation methods, our method delivers better results in terms of hIoU (31.8) and mIoU u (22.0) on the PASCAL VOC 2012 dataset with less supervision information. [ABSTRACT FROM AUTHOR]

Esophageal cancer is an exceedingly aggressive and malignant cancer that imposes a substantial burden on patients and their families. It is usually treated with surgery, chemotherapy, radiotherapy, and molecular-targeted therapy. Immunotherapy is a novel treatment modality for esophageal cancer wherein genetically engineered adoptive cell therapy is utilized, which modifies immune cells to attack cancer cells. Using chimeric antigen receptor (CAR) or T cell receptor (TCR) modified T cells yielded demonstrably encouraging efficacy in patients. CAR-T cell therapy has shown robust clinical results for malignant hematological diseases, particularly in B cell-derived malignancies. Natural killer (NK) cells could serve as another reliable and safe CAR engineering platform, and CAR-NK cell therapy could be a more generalized approach for cancer immunotherapy because NK cells are histocompatibility-independent. TCR-T cells can detect a broad range of targeted antigens within subcellular compartments and hold great potential for use in cancer therapy. Numerous studies have been conducted to evaluate the efficacy and feasibility of CAR and TCR based adoptive cell therapies (ACT). A comprehensive understanding of genetically-modified T cell technologies can facilitate the clinical translation of these adoptive cell-based immunotherapies. Here, we systematically review the state-of-the-art knowledge on genetically-modified T-cell therapy and provide a summary of preclinical and clinical trials of CAR and TCR-transgenic ACT. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor T (CAR-T) cell therapy has exhibited a substantial clinical response in hematological malignancies, including B-cell leukemia, lymphoma, and multiple myeloma. Therefore, the feasibility of using CAR-T cells to treat solid tumors is actively evaluated. Currently, multiple basic research projects and clinical trials are being conducted to treat lung cancer with CAR-T cell therapy. Although numerous advances in CAR-T cell therapy have been made in hematological tumors, the technology still entails considerable challenges in treating lung cancer, such as on−target, of−tumor toxicity, paucity of tumor-specific antigen targets, T cell exhaustion in the tumor microenvironment, and low infiltration level of immune cells into solid tumor niches, which are even more complicated than their application in hematological tumors. Thus, progress in the scientific understanding of tumor immunology and improvements in the manufacture of cell products are advancing the clinical translation of these important cellular immunotherapies. This review focused on the latest research progress of CAR-T cell therapy in lung cancer treatment and for the first time, demonstrated the underlying challenges and future engineering strategies for the clinical application of CAR-T cell therapy against lung cancer. [ABSTRACT FROM AUTHOR]

The development of the Internet, together with the progress of multimedia processing techniques, has led to the problems of data piracy, data tampering and illegal dissemination. Digital watermarking is an effective approach to data authentication and copyright protection. This paper proposes a geometrically robust multi-bit video watermarking algorithm based on 2-D DFT (two-dimensional discrete Fourier transform). While most of the existing video watermarking schemes require synchronization to extract the watermark from rotated or scaled videos, which is time-consuming and affects the accuracy, the proposed method can do direct extraction without performing synchronization for videos attacked by rotation, scaling or cropping. For embedding the watermark, circular templates in DFT domain are transformed into spatial masks and added to the video frames in spatial domain. A perceptual model based on local contrast is applied to keep the fidelity of the watermarked video. We also propose an accurate and efficient extraction method which is based on the cross-correlation between the Wiener-filtered DFT magnitude and the stretched template sequence in polar coordinates. Experimental results show that the proposed algorithm is robust against various kinds of attacks, such as compression, filtering, rotation, scaling, cropping, frame averaging and frame rate changing. [ABSTRACT FROM AUTHOR]

Recent advance on highly realistic AI‐synthesised video approaches makes it hard to distinguish whether a speaker in video is real. Many existing DeepFakes detection approaches can be invalidated by using them as supervision in adversarial training, which in turn improves the performance of DeepFakes. This makes the problem a dilemma. However, human can recognise the identity of familiar persons by observing their motion styles. Inspired by this, the paper proposes a novel method to recognise original speaker's identity clues in DeepFakes videos by learning individual motion styles. As a biological signature, motion styles can neither be used in the training process of DeepFakes nor be modified to deceive detection methods without reducing realistic performance. Also, this paper proposes a novel pipeline to continuously restore the original frames from DeepFakes videos without knowing the DeepFakes approach in advance. Based on above ideas, our scheme makes it possible to restore DeepFakes videos. Through training the cross‐modal transfer module, the appearance embedding can be inferred from the identity code. Extensive experiments are conducted to reveal the effectiveness of the method, it shows for the first time that the original persons can be identified automatically and the original video from DeepFakes videos can be generated. [ABSTRACT FROM AUTHOR]

As one of the most fundamental tasks in computer vision, semantic segmentation assigns per pixel prediction of object categories. Training a robust model for semantic segmentation is challenging since pixel-level annotations are expensive to obtain. To alleviate the burden of annotations, the authors propose a weakly-supervised framework for zero-shot semantic segmentation, which can segment images having target classes without any pixel-level labelled instances. Under the assumption that the accessibility to image-level annotations of target classes does not violate the principle of zero pixel-level label in zero-shot semantic segmentation, we utilised image-level annotations to improve the proposed model's ability to extract pixel-level features. Furthermore, unlike existing zero-shot semantic segmentation methods, which use semantic embeddings as class embeddings to transfer knowledge from source classes to target classes, we use image-level features as their class embeddings to transfer knowledge since the distribution of pixel-level features is more similar to the distribution of image-level features rather than the distribution of semantic embeddings. Experimental results on the PASCAL-VOC data set under different data splits demonstrate that the proposed model achieves promising results. [ABSTRACT FROM AUTHOR]

Multi‐task cascaded convolutional neural network (MTCNN) is a human face detection architecture which uses a cascaded structure with three stages (P‐Net, R‐Net and O‐Net). The authors intend to reduce the computation time of the whole process of the MTCNN. They find that the non‐maximum suppression (NMS) processes after the P‐Net occupy over half of the computation time. Therefore, the authors propose a self‐fine‐tuning method which makes the control of computation time for the NMS process easier. Self‐fine‐tuning is a training trick which uses hard samples generated by P‐Net to retrain P‐Net. After self‐fine‐tuning, the distribution of human face probabilities generated by P‐Net is changed, and the tail of distribution becomes thinner. The control of the number of NMS input boxes can be made easier when the distribution has a thinner tail, and choosing a suitable threshold to filter the face boxes will generate less boxes. So the computation time can be reduced. In order to keep the performance of MTCNN, the authors still propose a landmark data set augmentation, which can enhance the performance of the self‐fine‐tuned MTCNN. From the experiments, it is found that the proposed scheme can significantly reduce the computation time of MTCNN. [ABSTRACT FROM AUTHOR]

It is well established that 5-methylcytosine (5mC) in genomic DNA of mammalian cells can be oxidized into 5-hydroxymethylcytosine (5hmC) and other derivates by DNA dioxygenase TETs. While conversion of 5mC to 5hmC plays an important role in active DNA demethylation through further oxidation steps, a certain proportion of 5hmCs remain in the genome. Although 5hmCs contribute to the flexibility of chromatin and protect bivalent promoters from hypermethylation, the direct effect of 5hmCs on gene transcription is unknown. In this present study, we have engineered a zinc-finger protein-based P16-specific DNA dioxygenase (P16-TET) to induce P16 hydroxymethylation and demethylation in cancer cells. Our results demonstrate, for the first time, that although the hydroxymethylated P16 alleles retain transcriptionally inactive, hydroxymethylation could increase the susceptibility of reactivation of methylated P16 alleles. [ABSTRACT FROM AUTHOR]

Action recognition plays a central role in intelligent surveillance system, game-control, human-computer interaction, and so on. In this work, we design a multi-task framework that improves the recent Spatial-Temporal Graph Convolutional Networks (ST-GCN) for skeleton-based action recognition by introducing the attention mechanism and co-occurrence feature learning. Specifically, we use an attentional branch to pay more attention to more discriminating features and aggregates co-occurrence features from all joints globally in another branch. Additionally, our multi-task framework exploits the inherent correlation between branches to further enhance the classification accuracy and convergence speed. Experiments have been carried out on NTURGB+D and Kinetics human action dataset. The results clearly show that the accuracy of the proposed multi-task framework are distinguishably higher than ST-GCN and other mainstream methods for 3D action recognition. [ABSTRACT FROM AUTHOR]

The robust watermarking scheme has very broad application scenarios. For robust watermarking methods, the transform domain based watermarking algorithms are popular. Indeed, these algorithms have strong robustness, but the visual effects may be affected. In order to balance the robustness and imperceptibility, a histogram based watermarking scheme is proposed. In this scheme, in order to enhance the robustness and imperceptibility, the low-frequency construction method (LFCM) is proposed. In LFCM, the most suitable pixels for modifications are selected by calculating the weights of pixels at different positions, and these pixels will be modified in the histogram based embedding process. After the modification, the low-frequency of the image will enhance, and the robustness of the watermarking will enhance at the same time. Experiment results show that the proposed scheme can resist compression, scaling, noise, and median blur attacks, and can resist rotation attacks to a certain extent. The proposed scheme is a non-blind watermarking scheme, and non-blind watermarking scheme is feasible when this scheme has good performance. [ABSTRACT FROM AUTHOR]

During the image acquisition process of face recognition, the obtained face images are affected inevitably by varied illumination and position in different environment. Local Binary Pattern (LBP) operator is used to decrease illumination effectiveness. Improved Pairwise-constrained Multiple Metric Learning method (IPMML) is proposed as a classification metric in our prior work, which solves the misalignment problem in a better way compared with PMML. To solve the high computation complexity of IPMML, Linear Discriminant Analysis (LDA) is performed before IPMML. Thus, a face recognition method based on LBP and IPMML is proposed, which can overcome the illumination and misalignment problems. LBP is selected to extract texture features of face images firstly. Second, LDA is applied to reduce the dimension. Then the fisher features are divided into sub-blocks according to the dimension of features and every block is a column vector. Fourth, a classification metric -- IPMML is used to obtain the optimum Mahalanobis matrix. Fifth, the Mahalanobis matrix is used to compute the final discriminative distance. Finally, the Nearest Neighborhood Classifier (NNC) is applied to classify face images. The experimental results show that the proposed method can achieve high recognition rates and is robust to illumination and facial expression variation, especially for misaligned face images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel face feature extraction approach based on Local Binary Pattern (LBP) and Two Dimensional Locality Preserving Projections (2DLPP) to enhance the texture features and preserve the space structure properties of a face image. LBP is firstly used to remove the effect of illumination and noise, which would enhance the detailed texture characteristics of face images. Then 2DLPP is performed to extract some prominent features and decrease the image dimension with space structure information. The Nearest Neighborhood Classifier (NNC) is used to recognize a face image at the end. In addition, the rule for dimension selection is studied from the results of experiments about choosing an appropriate feature dimension by 2DLPP computation. The experimental results on the Yale, the extended Yale B and CMU PIE C09 benchmark datasets showed that the proposed face feature extraction and recognition method achieves a better performance in comparison with similar techniques, and the proposed dimension selection rule can give an appropriate feature dimension in 2DLPP. [ABSTRACT FROM AUTHOR]

In this study, the problem of event-triggered-based adaptive control (ETAC) for a class of discrete-time nonlinear systems with unknown parameters and nonlinear uncertainties is considered. Both neural network (NN) based and linear identifiers are used to approximate the unknown system dynamics. The feedback output signals are transmitted, and the parameters and the NN weights of the identifiers are tuned in an aperiodic manner at the event sample instants. A switching mechanism is provided to evaluate the approximate performance of each identifier and decide which estimated output is utilised for the event-triggered controller design, during any two events. The linear identifier with an auxiliary output and an improved adaptive law is introduced so that the nonlinear uncertainties are no longer assumed to be Lipschitz. The number of transmission times are significantly reduced by incorporating multiple model schemes into ETAC. The boundedness of both the parameters of identifiers and the system outputs is demonstrated though the Lyapunov approach. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Vector quantisation (VQ) is a widely used method for data compression or data clustering. This study proposes a fast codebook design method for image VQ. General particle swarm optimisation (PSO)‐K‐means hybrid methods for codebook design take too much time. To deal with this problem, the authors present a new clustering method, which takes a very short time and generates a pretty good codebook. This method contains four parts, i.e. pooling, PSO algorithm, reverse cast, and K‐means algorithm. Pooled images are used for the PSO process while original images are used for K‐means fine‐tuning, and these two processes connected by the reverse cast process. In the authors' experiment, this method can dramatically reduce the computation time by using big sizes of pooling window or enhance the codebook quality by using small sizes of pooling window. They can make the calculation time almost one‐tenth of that of the PSO‐K‐means hybrid method, and the scheme is also faster than the K‐means algorithm. Experimental results demonstrate that the main advantages of the proposed algorithm lie in the fact that it can reduce the computation time and enhance the quality of codebooks. [ABSTRACT FROM AUTHOR]

Frame insertion, deletion and duplication are common inter-frame tampering operations in digital videos. In this paper, based on similarity analysis, a passive-blind forensics scheme for video shots is proposed to detect inter-frame forgeries. This method is composed of two parts: HSV (Hue-Saturation-Value) color histogram comparison and SURF (Speeded Up Robust Features) feature extraction together with FLANN (Fast Library for Approximate Nearest Neighbors) matching for double-checking. We mainly calculate H-S and S-V color histograms of every frame in a video shot and compare the similarity between histograms to detect and locate tampered frames in the shot. Then we utilize SURF feature extraction and FLANN matching to further confirm the forgery types in the tampered locations. Experimental results demonstrate that the proposed detection method is efficient and accurate in terms of forgery identification and localization. In contrast to other inter-frame forgery detection methods, our scheme can detect three kinds of forgery operations and has its own superiority and applicability as a passive-blind detection method. [ABSTRACT FROM AUTHOR]

While promising progress has been achieved in the zero-shot learning (ZSL) task. The existing approaches still suffer from the strong bias problem between the unseen and seen classes. This Letter presents a unified feature generating framework equipped with a boundary decision loss to tackle this issue in ZSL. Specifically, the hybrid semantic and visual classification strategy is proposed, which can effectively align the bidirectional visual-semantic interactions. Furthermore, this Letter introduces a decision loss that optimises the decision boundary of seen and unseen classes to further alleviate the confusion of generated features. Extensive experiments on three popular datasets animals with attributes, Caltech-UCSD-Birds 200-2011, and SUN show that the proposed approach outperforms previous state-of-the-art works under both traditional ZSL and challenging generalised ZSL settings. [ABSTRACT FROM AUTHOR]

In this paper, a new blind watermarking for copyright protection of color images based on the U matrix through Quaternion Singular Value Decomposition (QSVD) is proposed. The proposed method represents the color image with a quaternion matrix, so that it can deal with the multichannel information in a holistic way. Then the array of pure quaternion is divided into non-overlapping blocks and we perform QSVD on each block to get its U matrix. The watermark is inserted into the optimally selected coefficients of the quaternion elements in the first column of the U matrix. Besides, in the procedures of watermark insertion and extraction, ensuring higher fidelity and robustness to several possible image attacks have been considered. The experimental results show that the proposed method outperforms existing schemes in terms of robustness and invisibility. [ABSTRACT FROM AUTHOR]

This study presents a hierarchical palmprint feature extraction and recognition approach based on multi‐wavelet and complex network (CN) since they can effectively decrease redundant information and enhance key points of main lines and wrinkles. The palmprint is first pre‐filtered and decomposed once using multi‐wavelet. Three components (LL1,2,3) corresponding to the pre‐filter except for diagonal component are extracted as the elementary features. Second, binary images (BLL1,2,3) are obtained by the average window method using different thresholds. Third, three series of dynamic evolution CN models (the 1st, 2nd, 3rd CNs) are constructed from global to local, which is based on the mosaiced images obtained from BLL1,2,3, BLL1 and four equally divided sub‐images of BLL1, respectively. Fourth, statistical features are extracted from complex networks, in which average degree and standard deviation of the degrees are extracted for the 1st CNs and average degrees are extracted for the 2nd and 3rd CNs. Fifth, the fisher feature is extracted using the linear discriminate analysis method. Finally, the nearest neighbourhood classifier is used to recognise palmprint. Based on the CASIA Palmprint Image Database, experimental results show that the proposed method can effectively recognise palmprint with good robustness and overcome the problem of small training samples number. [ABSTRACT FROM AUTHOR]

Many real-world complex systems consist of a set of basic units that are connected by different kinds of relationships. All types of such systems can be described by a multilayer network, where each link represents different types of interaction among the same set of nodes. In this paper, we present a general framework to characterize the influences (centrality) of layers. Furthermore, we propose two measures for layer centrality in terms of network connectivity under this framework. The basic idea of our measures consists in assigning more centrality value to layers that contribute more connectivity in a multilayer network. In other words, layers are more influential if more centrality values of links are assigned to them. We validate the measures on a real-world dataset of air transportation multilayer network and find that the measures are able to extract novel and useful information from the dataset. [ABSTRACT FROM AUTHOR]

Vector quantization (VQ) has been successfully used in data compression and feature extraction areas. Codebook design is the essential step of VQ. The K-means algorithm is a famous data clustering technique which is also an efficient codebook design scheme. The main disadvantages of K-means algorithm lie in that the initial cluster centroids greatly affect the convergence speed and the final clustering performance. In the past two decades, many codebook initialization techniques have been proposed. However, most of these techniques do not make full use of the features of the training vectors, and some techniques require high extra computational load. This paper presents an efficient and simple technique for the conventional K-means algorithm based on feature classification and grouping. Firstly, all training vectors are classified into sixteen categories based on a two-level classifier including an edge classifier and a contrast classifier. Then, the training vectors in each category are sorted based on their norm values and divided into groups. Each group has the same size, and the centroid vector of each group is calculated as an initial codeword. Experimental results show that, compared with several typical initialization techniques, our technique can obtain a better codebook along with a faster convergence speed in a shorter time. [ABSTRACT FROM AUTHOR]

Complex network response to node loss is a central question in different fields of science ranging from physics, sociology, biology to ecology. Previous studies considered binary networks where the weight of the links is not accounted for. However, in real-world networks the weights of connections can be widely different. Here, we analyzed the response of real-world road traffic complex network of Beijing, the most prosperous city in China. We produced nodes removal attack simulations using classic binary node features and we introduced weighted ranks for node importance. We measured the network functioning during nodes removal with three different parameters: the size of the largest connected cluster (LCC), the binary network efficiency (Bin EFF) and the weighted network efficiency (Weg EFF). We find that removing nodes according to weighted rank, i.e. considering the weight of the links as a number of taxi flows along the roads, produced in general the highest damage in the system. Our results show that: (i) in order to model Beijing road complex networks response to nodes (intersections) failure, it is necessary to consider the weight of the links; (ii) to discover the best attack strategy, it is important to use nodes rank accounting links weight. [ABSTRACT FROM AUTHOR]

A novel approach to detect and localise anomalous events in crowed scenes by processing surveillance videos is introduced in this study. Unusual events are those that significantly differ from current dominated behaviours. The proposed approach both detects pixel‐level and block‐level anomalies. In pixel level, Gaussian mixture models are used to detect abnormalities. Block‐level detection segments the crowd into blocks according to pedestrian detection, and then anomalies are spotted and localised with a social force model. Experimental results using the USCD datasets Ped1 and Ped2 show that the proposed method performs favourably against state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

How to predict the future popularity of a message or video on online social media (OSM) has long been an attractive problem for researchers. Although many difficulties are still ahead, recent studies suggest that temporal and topological features of early adopters generally play a very important role. However, with the increase of the adopters, the feature space will grow explosively. How to select the most effective features is still an open issue. In this work, we investigate several feature extraction methods over the Twitter platform and find that most predictive power concentrates on the second half of the propagation period, and that not only a model trained on one platform generalizes well to others as previous works observed, but also a model trained on one dataset performs well on predicting the popularity for other datasets with different number of observed early adopters. According to these findings, at least for the best features by far, the data used to extract features can be halved without loss of evident accuracy and we provide a way to roughly predict the growth trend of a social-media item in real-time. [ABSTRACT FROM AUTHOR]

Vector quantisation (VQ) shows a good performance for image coding with high‐compression ratios. However, there are many difficulties for image coding with VQ, especially the edge degradation and high‐computational complexity. To resolve these two problems, the authors propose a new coding method based on edge orientation patterns (EOPs) by classifying image blocks into nine classes according to their edge orientations. For colour image coding, 27 codebooks (nine for each colour component) are pre‐designed based on a series training images. In the encoding stage, an input colour image is decomposed into Y, Cb, and Cr components, and each component image is divided into non‐overlapping 4 × 4 blocks. For each block, eight edge orientation templates of size 4 × 4 are performed to determine its edge orientation. According to the edge orientation, each block is compressed by using the corresponding codebook. Essentially, the authors' scheme is a kind of classified VC (CVQ). Simulation results show that, their EOP‐based CVQ can largely improve the compression efficiency as well as speeding up the encoding process and it is sufficient to establish effectiveness of the authors' algorithm as compared with the existing techniques. [ABSTRACT FROM AUTHOR]

Recent advances in convolutional neural networks have achieved prominent results for object counting or locating in images. In these networks, some adopt detection‐based methods which require providing bounding‐box annotations for training and these methods usually cannot deal with the crowded cases well. Other networks adopt density regression‐based methods which require only point annotations for training but these methods can only predict the total count without providing any location information. In this Letter, the authors propose an attention guided convolutional neural network (AGNet) for both crowded object locating and counting using point annotation as supervision without annotated bounding boxes which are far more time‐consuming to label. Accordingly, they define a multi‐task objective loss function to lead the training of AGNet to achieve the goal of crowded object locating. The experimental results on four challenging benchmark datasets for crowded objects counting demonstrate the superiority of the proposed AGNet over recent state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability. [ABSTRACT FROM AUTHOR]

The intellectual property (IP) reuse‐based design model is vulnerable to misappropriation and fraud. The situation requires new IP protection mechanisms to guarantee the rights for both IP producers and IP users. In this study, the authors study the constraints used in circuit partitioning and hereby introduce two fingerprinting methods by cell perturbations. They evaluate the proposed methods on ISPD98 benchmark suits by the state‐of‐the‐art partitioner hMetis and cell manipulation. By tuning the fingerprinting strategies, the performance of fingerprinted designs is well maintained. Experimental result shows that the overhead of fingerprinting methods is trivial. The relative Hamming distance of cell fixing method is high, which indicates the method could yield distinct fingerprinting solutions. The relative Hamming distance of cell exchange method is low which is due to the low percentage of cell being fixed. [ABSTRACT FROM AUTHOR]

Scale-free networks in which the degree displays a power-law distribution can be classified into assortative, disassortative, and neutral networks according to their degree-degree correlation. The second-order centrality proposed in a distributed computation manner is quick-calculated and accurate to identify critical nodes. We explore the second-order centrality correlation (SOC) for each type of the scale-free networks. The SOC-SOC correlation in assortative network and neutral network behaves similarly to the degree-degree correlation, while it behaves an apparent difference in disassortative networks. Experiments show that the invulnerability of most of scale-free networks behaves similarly under the node removal ordering by SOC centrality and degree centrality, respectively. The netscience network and the Yeast network behave a little differently because they are native disconnecting networks. [ABSTRACT FROM AUTHOR]

Recently, the controllability of complex networks has attracted wide attention of researchers. The main contribution comes from Liu et al. who proposed the structural controllability as an analytical framework for making predictions regarding the control of directed networks in Nature. Since then, the controllability of many model and real networks has been deeply investigated except deterministic complex networks. In this paper, we focus on studying the controllability of deterministic complex networks. We examine six typical deterministic networks, the simulation results show that the minimum number of driver nodes grows linearly with network size. When the network size is large enough, the controllability approximates to a constant not more than 0.4, indicating that the deterministic networks are relatively easy to control. Furthermore, we investigate the characteristics of driver nodes in deterministic complex networks, finding that the driver nodes tend to avoid high degree nodes but to have high clustering coefficients. [ABSTRACT FROM AUTHOR]

This paper proposes an improved community model for social networks based on social mobility. The relationship between the group distribution and the community size is investigated in terms of communication rate and turnover rate. The degree distributions, clustering coefficients, average distances and diameters of networks are analyzed. Experimental results demonstrate that the proposed model possesses the small-world property and can reproduce social networks effectively and efficiently. [ABSTRACT FROM AUTHOR]