Reviewerly/exHarmony huggingface dataset:
A Novel Violent Video Detection Scheme Based on Modified 3D Convolutional Neural Networks

Violent video constitutes a threat to public security, and effective detection algorithms are in urgent need. In order to improve the detection accuracy of 3D convolutional neural networks (3D ConvNet), a novel violent video detection scheme based on the modified 3D ConvNet is proposed. In this paper, the preprocessing method of data is improved, and a new sampling method by using the key frame as dividing nodes is designed. Then, a random sampling method is adapted to produce the input frame sequence. With experimental evaluations on the crowd violence dataset, the results demonstrate the effectiveness of the proposed new sampling method. For three public violent detection datasets: hockey fight, movies, and crowd violence, individualized strategies are implemented to suit the varied clip length. For the short clips, the 3D ConvNet is constructed by using the uniform sampling method. For the longer clips, the new frame sampling strategy is adopted. The proposed scheme obtains competitive results: 99.62% on hockey fight, 99.97% on movies, and 94.3% on crowd violence. The experimental results show that our method is simple and effective.
Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction

Genetic Algorithm (GA) is one of the most well-regarded evolutionary algorithms in the history. This algorithm mimics Darwinian theory of survival of the fittest in nature. This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm. The most popular improvements in the main component of this algorithm (selection, crossover, and mutation) are given too. The chapter also investigates the application of this technique in the field of image processing. In fact, the GA algorithm is employed to reconstruct a binary image from a completely random image.
A PV power interval forecasting based on seasonal model and nonparametric estimation algorithm

With the continuous increase of grid-connected photovoltaic (PV), high-precision PV power prediction is increasingly important. Extant deterministic forecasting methods do not facilitate fully effective dispatching decisions or power grid risk analysis. Furthermore, the single model also has insufficient generalization ability and unstable forecasting performance in PV power forecasting. This paper proposes an alternative multi-model PV power interval forecasting method which takes into account the seasonal distribution of power fluctuation characteristics. PV output power, absolute power deviation, and relative variation rate are first analyzed for the seasonal distribution characteristics of PV output as they fluctuate over time. Seasonal multi-models for deterministic forecasting of PV power are then built based on an extreme learning machine (ELM). Deterministic forecasting error is fitted by kernel density estimation to complete the PV power interval forecast. The effectiveness of the proposed method is validated by comparison against other methods.
Enhancing Resolution of Digital Rock Images with Super Resolution Convolutional Neural Networks

Single Image Super Resolution (SISR) techniques based on Super Resolution Convolutional Neural Networks (SRCNN) are applied to micro-computed tomography ({\mu}CT) images of sandstone and carbonate rocks. Digital rock imaging is limited by the capability of the scanning device resulting in trade-offs between resolution and field of view, and super resolution methods tested in this study aim to compensate for these limits. SRCNN models SR-Resnet, Enhanced Deep SR (EDSR), and Wide-Activation Deep SR (WDSR) are used on the Digital Rock Super Resolution 1 (DRSRD1) Dataset of 4x downsampled images, comprising of 2000 high resolution (800x800) raw micro-CT images of Bentheimer sandstone and Estaillades carbonate. The trained models are applied to the validation and test data within the dataset and show a 3-5 dB rise in image quality compared to bicubic interpolation, with all tested models performing within a 0.1 dB range. Difference maps indicate that edge sharpness is completely recovered in images within the scope of the trained model, with only high frequency noise related detail loss. We find that aside from generation of high-resolution images, a beneficial side effect of super resolution methods applied to synthetically downgraded images is the removal of image noise while recovering edgewise sharpness which is beneficial for the segmentation process. The model is also tested against real low-resolution images of Bentheimer rock with image augmentation to account for natural noise and blur. The SRCNN method is shown to act as a preconditioner for image segmentation under these circumstances which naturally leads to further future development and training of models that segment an image directly. Image restoration by SRCNN on the rock images is of significantly higher quality than traditional methods and suggests SRCNN methods are a viable processing step in a digital rock workflow.
Shock transmission in the cryptocurrency market. Is Bitcoin the most influential?

The growing cryptocurrency market has attracted the attention from many investors worldwide, mainly due to the ease of entering the market and its extremely volatile character. The main aim of this paper is to examine interdependencies between log-returns of cryptocurrencies, with the special focus on Bitcoin. Based on implications from the literature, we use methods dedicated for studying the stock market and apply the two-step analysis, comparing results between two subsequent periods. Results obtained using Minimum Spanning Tree (MST) method show that cryptocurrencies form hierarchical clusters, consistently over two separate periods, indicating potential topological properties of the cryptocurrency market. Then, using Vector Autoregression (VAR) model, we study the transmission of demand shocks within clusters. Results show that changes in Bitcoin price do not affect and are not affected by changes in prices of other cryptocurrencies. However, results indicate that findings obtained for Bitcoin shall not be generalized to the entire cryptocurrency market.
A Cross-Chain Solution to Integrating Multiple Blockchains for IoT Data Management

With the rapid development of the internet of things (IoT), traditional industries are setting off a massive wave of digitization. In the era of the Internet of Everything, millions of devices and links in IoT pose more significant challenges to data management. Most existing solutions employ centralized systems to control IoT devices, which brings about the privacy and security issues in IoT data management. Recently, blockchain has attracted much attention in the field of IoT due to its decentralization, traceability, and non-tamperability. However, it is non-trivial to apply the current blockchain techniques to IoT due to the lack of scalability and high resource costs. Different blockchain platforms have their particular advantages in the scenario of IoT data management. In this paper, we propose a cross-chain framework to integrate multiple blockchains for efficient and secure IoT data management. Our solution builds an interactive decentralized access model which employs a consortium blockchain as the control station. Other blockchain platforms customized for specific IoT scenarios run as the backbone of all IoT devices. It is equivalent to opening the off-chain channels on the consortium blockchain. Our model merges transactions in these channels for confirmation based on the notary mechanism. Finally, we implement a prototype of the proposed model based on hyperledge Fabric and IOTA Tangle. We evaluate the performance of our method through extensive experiments. The results demonstrate the effectiveness and efficiency of our framework.
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition

Deep Neural Networks (DNNs) have achieved remarkable successes in large-scale visual recognition. However, they often suffer from overfitting under noisy labels. To alleviate this problem, we propose a conceptually simple but effective MetaCleaner, which can learn to hallucinate a clean representation of an object category, according to a small noisy subset from the same category. Specially, MetaCleaner consists of two flexible submodules. The first submodule, namely Noisy Weighting, can estimate the confidence scores of all the images in the noisy subset, by analyzing their deep features jointly. The second submodule, namely Clean Hallucinating, can generate a clean representation from the noisy subset, by summarizing the noisy images with their confidence scores. Via MetaCleaner, DNNs can strengthen its robustness to noisy labels, as well as enhance its generalization capacity with richer data diversity. Moreover, MetaCleaner can be easily integrated into the standard training procedure of DNNs, which promotes its value for real-life applications. We conduct extensive experiments on two popular benchmarks in noisy-labeled recognition, i.e., Food-101N and Clothing1M. For both datasets, our MetaCleaner significantly outperforms baselines, and achieves the state-of-the-art performance.
GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation

To bridge source and target domains for domain adaptation, there are three important types of information including data structure, domain label, and class label. Most existing domain adaptation approaches exploit only one or two types of this information and cannot make them complement and enhance each other. Different from existing methods, we propose an end-to-end Graph Convolutional Adversarial Network (GCAN) for unsupervised domain adaptation by jointly modeling data structure, domain label, and class label in a unified deep framework. The proposed GCAN model enjoys several merits. First, to the best of our knowledge, this is the first work to model the three kinds of information jointly in a deep model for unsupervised domain adaptation. Second, the proposed model has designed three effective alignment mechanisms including structure-aware alignment, domain alignment, and class centroid alignment, which can learn domain-invariant and semantic representations effectively to reduce the domain discrepancy for domain adaptation. Extensive experimental results on five standard benchmarks demonstrate that the proposed GCAN algorithm performs favorably against state-of-the-art unsupervised domain adaptation methods.
A Neural Micro-Expression Recognizer

Recognizing micro-expressions underpins significant and critical research and significant application. We speculate that this problem requires the understanding of the subtle face movement, integration of face structures and a solution of limited training data. In this paper, we build an effective micro-expression recognition system that leverages techniques stemming from these speculations. First, we introduce an optical flow method based on the onset frame and the apex frame to encode the subtle face motion. This has already been validated by prior research. Second, to obtain discriminative representations from the rigid face structures, part-based average pooling is proposed to inject structure priors to the network. Finally, because the system suffers from small training sets, we propose to transfer domain knowledge from macro-expression recognition tasks to micro-expression recognition. Specifically, we adopt two domain adaptation techniques including adversarial training and expression magnification and reduction (EMR). Through experiment, we show that the proposed system achieves very competitive results on the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> Micro-Expression Grand Challenge (MEGC).
An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation

Segmentation is an important method for MRI medical image analysis as it can provide the radiologists with noninvasive information about a patient that is crucial to the diagnostic process. The efficiency of such a computer-aided diagnosis system relies on the accuracy of an adopted image segmentation method. Multi-level thresholding is a segmentation method that has been widely adopted in medical image analysis in recent studies, where selecting the optimal thresholds has a pivotal role in determining the efficiency and the accuracy of the segmentation algorithm. While some well-known methods, such as Kapur’s and Otsu’s, are proven effective for bi-level thresholding, multi-level thresholding remains a challenge as it is computationally expensive. Evolutionary algorithms, such as Differential Evolution (DE), have the potential to address this problem, as they can find sufficiently good solutions with manageable computational effort. While a number of DE solutions have been proposed for multi-level thresholding, they are not stable, in that, when the number of thresholds increases, the algorithm efficiency decreases due to the imbalance between exploration and exploitation. In this paper, we propose a DE solution that achieves a good balance between exploration and exploitation through a new adaptive approach and new mutation strategies. The new adaptive approach can generate optimal solutions in assigning populations by measuring the quality of candidate solutions to evaluate the efficiency of different parts of the proposed DE algorithm. The new mutation methods harness Mantegna Lévy and Cauchy distributions, as well as Cotes’ Spiral to improve global search, and to further balance between exploitation and exploration. We further experimentally compare the proposed DE algorithm, referred to as Adaptive Differential Evolution with Lévy Distribution (ALDE), against three DE benchmark algorithms on T2 weighted MRI brain images. Our results show that ALDE can, not only obtain optimal thresholds at a reasonable computational cost, but more importantly, clearly outperforms the benchmark algorithms.
3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images

Accurate and reliable segmentation of the prostate gland using magnetic resonance (MR) imaging has critical importance for the diagnosis and treatment of prostate diseases, especially prostate cancer. Although many automated segmentation approaches, including those based on deep learning have been proposed, the segmentation performance still has room for improvement due to the large variability in image appearance, imaging interference, and anisotropic spatial resolution. In this paper, we propose the 3D adversarial pyramid anisotropic convolutional deep neural network (3D APA-Net) for prostate segmentation in MR images. This model is composed of a generator (i.e., 3D PA-Net) that performs image segmentation and a discriminator (i.e., a six-layer convolutional neural network) that differentiates between a segmentation result and its corresponding ground truth. The 3D PA-Net has an encoder-decoder architecture, which consists of a 3D ResNet encoder, an anisotropic convolutional decoder, and multi-level pyramid convolutional skip connections. The anisotropic convolutional blocks can exploit the 3D context information of the MR images with anisotropic resolution, the pyramid convolutional blocks address both voxel classification and gland localization issues, and the adversarial training regularizes 3D PA-Net and thus enables it to generate spatially consistent and continuous segmentation results. We evaluated the proposed 3D APA-Net against several state-of-the-art deep learning-based segmentation approaches on two public databases and the hybrid of the two. Our results suggest that the proposed model outperforms the compared approaches on three databases and could be used in a routine clinical workflow.
Unsupervised Face Normalization With Extreme Pose and Expression in the Wild

Face recognition achieves great success thanks to the emergence of deep learning. However, many contemporary face recognition models still have limited invariance to strong intra-personal variations such as large pose changes. Face normalization provides an effective and cheap way to distil face identity and dispel face variances for recognition. We focus on face generation in the wild with unpaired data. To this end, we propose a Face Normalization Model (FNM) to generate a frontal, neutral expression, photorealistic face image for face recognition. FNM is a well-designed Generative Adversarial Network (GAN) with three distinct novelties. First, a face expert network is introduced to construct generator and provide the ability of retaining face identity. Second, with the reconstruction of normal face, pixel-wise loss is applied to stabilize optimization process. Third, we present a series of face attention discriminators to refine local textures. FNM could recover canonical-view, expression-free image and directly improve the performance of face recognition model. Extensive qualitative and quantitative experiments on both controlled and in-the-wild databases demonstrate the superiority of our face normalization method.
Coarse-to-Fine Semantic Segmentation From Image-Level Labels

Deep neural network-based semantic segmentation generally requires large-scale cost extensive annotations for training to obtain better performance. To avoid pixel-wise segmentation annotations that are needed for most methods, recently some researchers attempted to use object-level labels (e.g., bounding boxes) or image-level labels (e.g., image categories). In this paper, we propose a novel recursive coarse-to-fine semantic segmentation framework based on only image-level category labels. For each image, an initial coarse mask is first generated by a convolutional neural network-based unsupervised foreground segmentation model and then is enhanced by a graph model. The enhanced coarse mask is fed to a fully convolutional neural network to be recursively refined. Unlike the existing image-level label-based semantic segmentation methods, which require labeling of all categories for images that contain multiple types of objects, our framework only needs one label for each image and can handle images that contain multi-category objects. Only trained on ImageNet, our framework achieves comparable performance on the PASCAL VOC dataset with other image-level label-based state-of-the-art methods of semantic segmentation. Furthermore, our framework can be easily extended to foreground object segmentation task and achieves comparable performance with the state-of-the-art supervised methods on the Internet object dataset.
Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency

Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become a must-have property of all new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient.
Tracking in Aerial Hyperspectral Videos Using Deep Kernelized Correlation Filters

Hyperspectral imaging holds enormous potential to improve the state of the art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multimodal hyperspectral sensors have attracted growing interest due to their ability to record extended data quickly from aerial platforms. In this paper, we apply popular concepts from traditional object tracking, namely, kernelized correlation filters (KCFs) and deep convolutional neural network features to aerial tracking in the hyperspectral domain. We propose the deep hyperspectral KCF-based tracker (DeepHKCF) to efficiently track aerial vehicles using an adaptive multimodal hyperspectral sensor. We address low temporal resolution by designing a single KCF-in-multiple regions-of-interest (ROIs) approach to cover a reasonably large area. To increase the speed of deep convolutional features extraction from multiple ROIs, we design an effective ROI mapping strategy. The proposed tracker also provides flexibility to couple with the more advanced correlation filter trackers. The DeepHKCF tracker performs exceptionally well with deep features set up in a synthetic hyperspectral video generated by the digital imaging and remote sensing image generation (DIRSIG) software. In addition, we generate a large, synthetic, single-channel data set using DIRSIG to perform vehicle classification in the wide-area motion imagery (WAMI) platform. This way, the high fidelity of the DIRSIG software is proven, and a large-scale aerial vehicle classification data set is released to support studies on vehicle detection and tracking in the WAMI platform.
Factor Graph Attention

Dialog is an effective way to exchange information, but subtle details and nuances are extremely important. While significant progress has paved a path to address visual dialog with algorithms, details and nuances remain a challenge. Attention mechanisms have demonstrated compelling results to extract details in visual question answering and also provide a convincing framework for visual dialog due to their interpretability and effectiveness. However, the many data utilities that accompany visual dialog challenge existing attention techniques. We address this issue and develop a general attention mechanism for visual dialog which operates on any number of data utilities. To this end, we design a factor graph based attention mechanism which combines any number of utility representations. We illustrate the applicability of the proposed approach on the challenging and recently introduced VisDial datasets, outperforming recent state-of-the-art methods by 1.1% for VisDial0.9 and by 2% for VisDial1.0 on MRR. Our ensemble model improved the MRR score on VisDial1.0 by more than 6%.