Reviewerly/exHarmony huggingface dataset:
Background Subtraction With Real-Time Semantic Segmentation

Accurate and fast foreground (FG) object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel BGS framework with the real-time semantic segmentation. Our proposed framework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background (BG) models and segments FG objects. The real-time semantic segmenter S is used to refine the FG segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame It, the BGS segmenter B computes a preliminary FG/BG mask B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> . At the same time, the real-time semantic segmenter S extracts the object-level semantics S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> . Then, some specific rules are applied on B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> and S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> to generate the final detection D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> . Finally, the refined FG/BG mask D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> is fed back to update the BG model. The comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves the state-of-the-art performance among all unsupervised BGS methods while operating at the real-time and even performs better than some deep learning-based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization.
3D Human Pose Machines with Self-supervised Learning

Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests. In fact, completing this task is quite challenging due to the diverse appearances, viewpoints, occlusions and inherently geometric ambiguities inside monocular images. Most of the existing methods focus on designing some elaborate priors /constraints to directly regress 3D human poses based on the corresponding 2D human pose-aware features or 2D pose predictions. However, due to the insufficient 3D pose data for training and the domain gap between 2D space and 3D space, these methods have limited scalabilities for all practical scenarios (e.g., outdoor scene). Attempt to address this issue, this paper proposes a simple yet effective self-supervised correction mechanism to learn all intrinsic structures of human poses from abundant images. Specifically, the proposed mechanism involves two dual learning tasks, i.e., the 2D-to-3D pose transformation and 3D-to-2D pose projection, to serve as a bridge between 3D and 2D human poses in a type of “free” self-supervision for accurate 3D human pose estimation. The 2D-to-3D pose implies to sequentially regress intermediate 3D poses by transforming the pose representation from the 2D domain to the 3D domain under the sequence-dependent temporal context, while the 3D-to-2D pose projection contributes to refining the intermediate 3D poses by maintaining geometric consistency between the 2D projections of 3D poses and the estimated 2D poses. Therefore, these two dual learning tasks enable our model to adaptively learn from 3D human pose data and external large-scale 2D human pose data. We further apply our self-supervised correction mechanism to develop a 3D human pose machine, which jointly integrates the 2D spatial relationship, temporal smoothness of predictions and 3D geometric knowledge. Extensive evaluations on the Human3.6M and HumanEva-I benchmarks demonstrate the superior performance and efficiency of our framework over all the compared competing methods.
DHP19: Dynamic Vision Sensor 3D Human Pose Dataset

Human pose estimation has dramatically improved thanks to the continuous developments in deep learning. However, marker-free human pose estimation based on standard frame-based cameras is still slow and power hungry for real-time feedback interaction because of the huge number of operations necessary for large Convolutional Neural Network (CNN) inference. Event-based cameras such as the Dynamic Vision Sensor (DVS) quickly output sparse moving-edge information. Their sparse and rapid output is ideal for driving low-latency CNNs, thus potentially allowing real-time interaction for human pose estimators. Although the application of CNNs to standard frame-based cameras for human pose estimation is well established, their application to event-based cameras is still under study. This paper proposes a novel benchmark dataset of human body movements, the Dynamic Vision Sensor Human Pose dataset (DHP19). It consists of recordings from 4 synchronized 346x260 pixel DVS cameras, for a set of 33 movements with 17 subjects. DHP19 also includes a 3D pose estimation model that achieves an average 3D pose estimation error of about 8 cm, despite the sparse and reduced input data from the DVS.
Signature verification approach using fusion of hybrid texture features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely discrete wavelet and local quantized patterns (LQP) features, are employed to extract two kinds of transform and statistical-based information from signature images. For each writer, two separate signature models, corresponding to each set of LQP and wavelet features, using one-class support vector machines (SVMs) are created to obtain two different authenticity scores for a given signature. Finally, a score-level classifier fusion based on the average method is performed to integrate the scores obtained from the two one-class SVMs and achieve the final verification score. To train the one-class SVMs in the proposed system, only genuine signatures are considered. The proposed signature verification method was tested using four different publicly available datasets to demonstrate the generality of the proposed method. The evaluation results indicate that the proposed system outperforms other existing systems in the literature.
How Can We Make Gan Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach

Single image super-resolution (SISR) is of great importance as a low-level computer vision task. The fast development of Generative Adversarial Network (GAN) based deep learning architectures realises an efficient and effective SISR to boost the spatial resolution of natural images captured by digital cameras. However, the SISR for medical images is still a very challenging problem. This is due to (1) compared to natural images, in general, medical images have lower signal to noise ratios, (2) GAN based models pre-trained on natural images may synthesise unrealistic patterns in medical images which could affect the clinical interpretation and diagnosis, and (3) the vanilla GAN architecture may suffer from unstable training and collapse mode that can also affect the SISR results. In this paper, we propose a novel lesion focused SR (LFSR) method, which incorporates GAN to achieve perceptually realistic SISR results for brain tumour MRI images. More importantly, we test and make comparison using recently developed GAN variations, e.g., Wasserstein GAN (WGAN) and WGAN with Gradient Penalty (WGAN-GP), and propose a novel multi-scale GAN (MS-GAN), to achieve a more stabilised and efficient training and improved perceptual quality of the super-resolved results. Based on both quantitative evaluations and our designed mean opinion score, the proposed LFSR coupled with MS-GAN has performed better in terms of both perceptual quality and efficiency.
A Blockchain Framework for Containerized Food Supply Chains

Global agricultural trade flows demonstrated a three-fold growth during the past decade particularly in emerging economies. At the same time, global food scandals in conjunction with the spillover effects in economy and society highlight the unreliability of existing food tracking systems and the inefficiency in monitoring food quality and fraud incidents across global food supply chains (SCs). Blockchain technology (BCT), which has already been successfully applied on the financial industry to validate critical transactions, seems to be a promising option. This research investigates a two-stage containerized food SC by implementing a demonstrator application at the Hyperledger Fabric framework. The study findings indicates that on one hand BCT has entered its maturity phase while on the other hand its adoption in food SC operations could add significant value by authenticating critical parameters and providing enhanced traceability . At the same time, BCT enabled by other digital technologies could allow for the optimization of global food SCs. Thus, BCT constitutes a promising digital technology that provides the capability to food SC stakeholders to securely share information, enhance process control and traceability and prevent potential risks.
Blockchain-Based Secure Storage Management with Edge Computing for IoT

As a core technology to manage decentralized systems, blockchain is gaining much popularity to deploy such applications as smart grid and healthcare systems. However, its utilization in resource-constrained mobile devices is limited due to high demands of resources and poor scalability with frequent-intensive transactions. Edge computing can be integrated to facilitate mobile devices in offloading their mining tasks to cloud resources. This integration ensures reliable access, distributed computation and untampered storage for scalable and secure transactions. It is imperative therefore that crucial issues of security, scalability and resources management be addressed to achieve successful integration. Studies have been conducted to explore suitable architectural requirements, and some researchers have applied the integration to deploy some specific applications. Despite these efforts, however, issues of anonymity, adaptability and integrity still need to be investigated further to attain a practical, secure decentralized data storage. We based our study on peer-to-peer and blockchain to achieve an Internet of Things (IoT) design supported by edge computing to acquire security and scalability levels needed for the integration. We investigated existing blockchain and associated technologies to discover solutions that address anonymity, integrity and adaptability issues for successful integration of blockchain in IoT systems. The discovered solutions were then incorporated in our conceptual design of the decentralized application prototype presented for secure storage of IoT data and transactions.
Red Blood Cell Image Generation for Data Augmentation Using Conditional Generative Adversarial Networks

In this paper, we describe how to apply image-to-image translation techniques to medical blood smear data to generate new data samples and meaningfully increase small datasets. Specifically, given the segmentation mask of the microscopy image, we are able to generate photorealistic images of blood cells which are further used alongside real data during the network training for segmentation and object detection tasks. This image data generation approach is based on conditional generative adversarial networks which have proven capabilities to high-quality image synthesis. In addition to synthesizing blood images, we synthesize segmentation mask as well which leads to a diverse variety of generated samples. The effectiveness of the technique is thoroughly analyzed and quantified through a number of experiments on a manually collected and annotated dataset of blood smear taken under a microscope.
High fidelity based reversible data hiding using modified LSB matching and pixel difference

Owing to the inefficiency to hide large volume of secret data for the reversible data hiding (RDH) image steganography approaches, we propose two improved RDH based approaches, such as (1) improved dual image based least significant bit (LSB) matching with reversibility, and (2) n-rightmost bit replacement (n-RBR) and modified pixel value differencing (MPVD). The first approach extends the ability of LSB matching with reversibility using dual images. Whereas the second approach utilizes four identical cover images for secret data embedding using two phases, such as (1) n-rightmost bit replacement (n-RBR) and (2) modified pixel value differencing (MPVD). In the n-RBR phase, n bits of secret data are embedded in the pair of two neighboring pixels of the first two identical images, where 1 ≤ n ≤ 4. Correspondingly, the MPVD phase uses the third and fourth identical images for hiding the secret data. Experimental results with respect to peak signal-to-noise ratio (PSNR), embedding capacity (EC), structural similarity index (SSIM), and the comparative analysis with recently proposed state-of-art approaches exhibit the superiority of the proposed approach. Besides reversibility, the proposed approach ensures high fidelity to salt and pepper (S&P) noise, RS analysis, and pixel difference histogram (PDH) analysis.
BRUSCHETTA: An IoT Blockchain-Based Framework for Certifying Extra Virgin Olive Oil Supply Chain

Urban population is expected to continuously grow in size. The smart city concepts allows to handle the new challenges and issues created by this growth by applying a wide range of technologies that can provide citizens with a better living environment. Smart agriculture will play an important part of smart cities, as a sustainable and high quality food supply chain is crucial to facilitate the grow of human agglomerates. In this context, European laws imposes very strict requirements in the food industry, in order to ensure that food provenance is always guaranteed. Such fine-grained traceability can be only achieved by applying state-of-the-art technologies. In this paper, we present BRUSCHETTA, a blockchain-based application for the traceability and the certification of the Extra Virgin Olive Oil (EVOO) supply chain. EVOO is an emblematic food product for Italy, but it is also one of the most falsified ones. BRUSCHETTA provides a blockchain-based system to enforce the certification of this product by tracing its entire supply chain: from the plantation to the shops. The goal is to enable the final customer to access a tamper-proof history of the product, including the farming, harvesting, production, packaging, conservation, and transportation processes. BRUSCHETTA leverages Internet of Things (IoT) technologies in order to interconnect sensors dedicated to EVOO quality control, and to let them operate on the blockchain. We also provide a support for the correct tailoring of the BRUSCHETTA blockchain system, and we propose a mechanism for its dynamic auto-tuning to optimize it in case of high loads.
Dense Scene Information Estimation Network for Dehazing

Image dehazing continues to be one of the most challenging inverse problems. Deep learning methods have emerged to complement traditional model-based methods and have helped define a new state of the art in achievable dehazed image quality. Yet, practical challenges remain in dehazing of real-world images where the scene is heavily covered with dense haze, even to the extent that no scene information can be observed visually. Many recent dehazing methods have addressed this challenge by designing deep networks that estimate physical parameters in the haze model, i.e. ambient light (A) and transmission map (t). The inverse of the haze model may then be used to estimate the dehazed image. In this work, we develop two novel network architectures to further this line of investigation. Our first model, denoted as At-DH, designs a shared DenseNet based encoder and two distinct DensetNet based decoders to jointly estimate the scene information viz. A and t respectively. This in contrast to most recent efforts (include those published in CVPR'18) that estimate these physical parameters separately. As a natural extension of At-DH, we develop the AtJ-DH network, which adds one more DenseNet based decoder to jointly recreate the haze-free image along with A and t. The knowledge of (ground truth) training dehazed/clean images can be exploited by a custom regularization term that further enhances the estimates of model parameters A and t in AtJ-DH. Experiments performed on challenging benchmark image datasets of NTIRE'19 and NTIRE'18 demonstrate that At-DH and AtJ-DH can outperform state-of-the-art alternatives, especially when recovering images corrupted by dense haze.
T-Net: Parametrizing Fully Convolutional Nets With a Single High-Order Tensor

Recent findings indicate that over-parametrization, while crucial for successfully training deep neural networks, also introduces large amounts of redundancy. Tensor methods have the potential to efficiently parametrize over-complete representations by leveraging this redundancy. In this paper, we propose to fully parametrize Convolutional Neural Networks (CNNs) with a single high-order, low-rank tensor. Previous works on network tensorization have focused on parametrizing individual layers (convolutional or fully connected) only, and perform the tensorization layer-by-layer separately. In contrast, we propose to jointly capture the full structure of a neural network by parametrizing it with a single high-order tensor, the modes of which represent each of the architectural design parameters of the network (e.g. number of convolutional blocks, depth, number of stacks, input features, etc). This parametrization allows to regularize the whole network and drastically reduce the number of parameters. Our model is end-to-end trainable and the low-rank structure imposed on the weight tensor acts as an implicit regularization. We study the case of networks with rich structure, namely Fully Convolutional Networks (FCNs), which we propose to parametrize with a single 8th-order tensor. We show that our approach can achieve superior performance with small compression rates, and attain high compression rates with negligible drop in accuracy for the challenging task of human pose estimation.
A recursive ensemble model for forecasting the power output of photovoltaic systems

Solar power provides a clean and renewable energy source. However, unlike many conventional sources, Photovoltaic (PV) power generation is of high volatility and uncertainty in short terms, which creates great challenges to forecasting and balancing electricity generation with demand. This study investigates the effects of PV solar power variability and proposes a data-driven ensemble modeling technique to improve the prediction accuracy of PV power generation. Three different types of models are integrated within a recursive arithmetic average model on their stand-alone predictions. The proposed methodology is later demonstrated to be of higher accuracy by comparing its prediction performance with each stand-alone forecasting model. Several different training and testing samples have been analyzed with the proposed model. The results show that the ensemble model performs better than the other stand-alone forecasting techniques in general.
Backprop as Functor: A compositional perspective on supervised learning

A supervised learning algorithm searches over a set of functions <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A\rightarrow B$</tex> parametrised by a space <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> to find the best approximation to some ideal function <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f:A\rightarrow B$</tex> . It does this by taking examples <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(a, f(a))\in A\times B$</tex> , and updating the parameter according to some rule. We define a category where these update rules may be composed, and show that gradient descent-with respect to a fixed step size and an error function satisfying a certain property-defines a monoidal functor from a category of parametrised functions to this category of update rules. A key contribution is the notion of request function. This provides a structural perspective on backpropagation, giving a broad generalisation of neural networks and linking it with structures from bidirectional programming and open games.
Generating a hierarchical fuzzy rule-based model

This study proposes a novel methodology for the extraction of a hierarchical Takagi-Sugeno fuzzy rule-based architecture from data. This architecture reduces the number and complexity of involved fuzzy rules, and, in many cases, improves the predictive performance of the model. The proposed hierarchical architecture takes the form of a cascading topology in which the predicted result computed at the previous layer is considered in the output part of the fuzzy rules. We propose a well-defined general methodology for the extraction of this hierarchical topology from data and discuss strategies for feature selection and choosing the number of rules at each level. The performance of the proposed methodology is demonstrated through extensive experiments, including case studies outlining specific behaviors and parameterizations, and comparative experiments showing the performance of the proposed architecture compared to a standard flat fuzzy rule-based system.
ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment

Cloud computing has been one of the most popular distributed computing paradigms. Elasticity is a crucial feature that distinguishes cloud computing from other distributed computing models. It considers the resource provisioning and allocation processes can be implemented automatically and dynamically. Elasticity feature allows cloud platforms to handle different loads efficiently without disrupting the normal behavior of the application. Therefore, providing a resource elasticity analytical model can play a significant role in cloud resource management. This paper presents Controlling Elasticity (ControCity) framework for controlling resources elasticity through using “buffer management ” and “elasticity management ”. In the proposed framework, there are two essential components called buffer manager and elasticity manager in the application layer and middleware layer, respectively. The buffer management controls the input queue of the user's request and the elasticity management controls the elasticity of the cloud platform using learning automata technique. In the application layer, applications are received by cloud applications and, then, placed in the control of the buffer. Buffer manager controls the queue of requests, and elasticity manager of the middleware layer using the learning automata provides a solution for controlling the elasticity of the cloud platform. The experimental results indicate that the ControCity reduces the response time by up to 3.7%, and increases the resource utilization and elasticity by up to 8.4% and 5.4%, respectively, compared with the other approaches.