Reviewerly/exHarmony huggingface dataset:
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement

Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named document enhancement generative adversarial networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017, and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.
Deep Learning-Based Model Predictive Control for Continuous Stirred-Tank Reactor System

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. The proposed DeepMPC consists of a growing deep belief network (GDBN) and an optimal controller. First, GDBN can automatically determine its size with transfer learning to achieve high performance in system identification, and it serves just as a predictive model of a controlled system. The model can accurately approximate the dynamics of the controlled system with a uniformly ultimately bounded error. Second, quadratic optimization is conducted to obtain an optimal controller. This work analyzes the convergence and stability of DeepMPC. Finally, the DeepMPC is used to model and control a second-order CSTR system. In the experiments, DeepMPC shows a better performance in modeling, tracking, and antidisturbance than the other state-of-the-art methods.
SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors

Medical image segmentation is an essential task in computer-aided diagnosis. Despite their prevalence and success, deep convolutional neural networks (DCNNs) still need to be improved to produce accurate and robust enough segmentation results for clinical use. In this paper, we propose a novel and generic framework called Segmentation-Emendation-reSegmentation-Verification (SESV) to improve the accuracy of existing DCNNs in medical image segmentation, instead of designing a more accurate segmentation model. Our idea is to predict the segmentation errors produced by an existing model and then correct them. Since predicting segmentation errors is challenging, we design two ways to tolerate the mistakes in the error prediction. First, rather than using a predicted segmentation error map to correct the segmentation mask directly, we only treat the error map as the prior that indicates the locations where segmentation errors are prone to occur, and then concatenate the error map with the image and segmentation mask as the input of a re-segmentation network. Second, we introduce a verification network to determine whether to accept or reject the refined mask produced by the re-segmentation network on a region-by-region basis. The experimental results on the CRAG, ISIC, and IDRiD datasets suggest that using our SESV framework can improve the accuracy of DeepLabv3+ substantially and achieve advanced performance in the segmentation of gland cells, skin lesions, and retinal microaneurysms. Consistent conclusions can also be drawn when using PSPNet, U-Net, and FPN as the segmentation network, respectively. Therefore, our SESV framework is capable of improving the accuracy of different DCNNs on different medical image segmentation tasks.
NEMO

The demand for mobile video streaming has experienced tremendous growth over the last decade. However, existing methods of video delivery fall short of delivering high-quality video. Recent advances in neural super-resolution have opened up the possibility of enhancing video quality by leveraging client-side computation. Unfortunately, mobile devices cannot benefit from this because it is too expensive in computation and power-hungry.
BlockMaze: An Efficient Privacy-Preserving Account-Model Blockchain Based on zk-SNARKs

The disruptive blockchain technology is expected to have broad applications in many areas due to its advantages of transparency, fault tolerance, and decentralization, but the open nature of blockchain also introduces severe privacy issues. Since anyone can deduce private information about relevant accounts, different privacy-preserving techniques have been proposed for cryptocurrencies under the UTXO model, e.g., Zerocash and Monero. However, it is more challenging to protect privacy for account-model blockchains (e.g., Ethereum) since it is much easier to link accounts in the account-model blockchain. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlockMaze</i> , an efficient privacy-preserving account-model blockchain based on zk-SNARKs. Along with dual-balance model, BlockMaze achieves strong privacy guarantees by hiding account balances, transaction amounts, and linkage between senders and recipients. Moreover, we provide formal security definitions and prove the security of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlockMaze</i> . Finally, we implement a prototype of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlockMaze</i> based on Libsnark and Go-Ethereum, and conduct extensive experiments to evaluate its performance. Our 300-node experiment results show that BlockMaze has high efficiency in computation and transaction throughput: one transaction verification takes about 14.2 ms, one transaction generation takes 6.1-18.6 seconds, and its throughput is around 20 TPS.
COVID-19 Networking Demand: An Auction-Based Mechanism for Automated Selection of Edge Computing Services

Network and cloud service providers are facing an unprecedented challenge to meet the demand of end-users during the COVID-19 pandemic.Currently, billions of people around the world are ordered to stay at home and use remote connection technologies to prevent the spread of the disease.The COVID-19 crisis brought a new reality to network service providers that will eventually accelerate the deployment of edge computing resources to attract the massive influx of users' traffic.The user can elect to procure its resource needs from any edge computing provider based on a variety of attributes such as price and quality.The main challenge for the user is how to choose between the price and multiple quality of service deals when such offerings are changing continually.This problem falls under multi-attribute decision-making.This paper investigates and proposes a novel auction mechanism by which network service brokers would be able to automate the selection of edge computing offers to support their end-users.We also propose a multi-attribute decision-making model that allows the broker to maximize its utility when several bids from edge-network providers are present.The evaluation and experimentation show the practicality and robustness of the proposed model.
Detecting Prohibited Items in X-Ray Images: a Contour Proposal Learning Approach

X-ray baggage screening plays a vital role in aviation security. Manual inspection of potentially anomalous items is challenging due to the clutter and occlusion within Xray scans. Here, we address this issue by presenting an object-boundaries driven framework for the automated detection of suspicious items from X-ray baggage scans. Rather than recognizing objects directly from the X-ray images, our two-stage detection approach first extracts contour-based proposals using a novel cascaded structure tensor technique and subsequently passes the candidate proposals to a single feed-forward convolutional neural network for recognition. Thorough experimentation on GDXray and SIXray datasets demonstrates that the proposed model achieves a mean area under the curve of 0.9878, outperforming the existing renown state-of-the-art object detection frameworks.
A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the relative efficiencies of different pooling methods on noreference (blind) VQA. Here we conduct a large-scale comparative evaluation to assess the capabilities and limitations of multiple temporal pooling strategies on blind VQA of usergenerated videos. The study yields insights and general guidance regarding the application and selection of temporal pooling models. In addition, we also propose an ensemble pooling model built on top of high-performing temporal pooling models. Our experimental results demonstrate the relative efficacies of the evaluated temporal pooling models, using several popular VQA algorithms evaluated on two recent largescale natural video quality databases. Conclusively, we also provide an empirical recipe for applying temporal pooling of frame-based quality predictions.
Privacy-Preserving Multi-Keyword Searchable Encryption for Distributed Systems

As cloud storage has been widely adopted in various applications, how to protect data privacy while allowing efficient data search and retrieval in a distributed environment remains a challenging research problem. Existing searchable encryption schemes are still inadequate on desired functionality and security/privacy perspectives. Specifically, supporting multi-keyword search under the multi-user setting, hiding search pattern and access pattern, and resisting keyword guessing attacks (KGA) are the most challenging tasks. In this article, we present a new searchable encryption scheme that addresses the above problems simultaneously, which makes it practical to be adopted in distributed systems. It not only enables multi-keyword search over encrypted data under a multi-writer/multi-reader setting but also guarantees the data and search pattern privacy. To prevent KGA, our scheme adopts a multi-server architecture, which accelerates search response, shares the workload, and lowers the key leakage risk by allowing only authorized servers to jointly test whether a search token matches a stored ciphertext. A novel subset decision mechanism is also designed as the core technique underlying our scheme and can be further used in applications other than keyword search. Finally, we prove the security and evaluate the computational and communication efficiency of our scheme to demonstrate its practicality.
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions

High quality AI solutions require joint optimization of AI algorithms and their hardware implementations. In this work, we are the first to propose a fully simultaneous, Efficient Differentiable DNN (deep neural network) architecture and implementation co-search (EDD) methodology. We formulate the co-search problem by fusing DNN search variables and hardware implementation variables into one solution space, and maximize both algorithm accuracy and hardware implementation quality. The formulation is differentiable with respect to the fused variables, so that gradient descent algorithm can be applied to greatly reduce the search time. The formulation is also applicable for various devices with different objectives. In the experiments, we demonstrate the effectiveness of our EDD methodology by searching for three representative DNNs, targeting low-latency GPU implementation and FPGA implementations with both recursive and pipelined architectures. Each model produced by EDD achieves similar accuracy as the best existing DNN models searched by neural architecture search (NAS) methods on ImageNet, but with superior performance obtained within 12 GPU-hour searches. Our DNN targeting GPU is 1.40× faster than the state-of-the-art solution reported in Proxyless [1], and our DNN targeting FPGA delivers 1.45× higher throughput than the state-of-the-art solution reported in DNNBuilder [2].
ShuttleSpace: Exploring and Analyzing Movement Trajectory in Immersive Visualization

We present ShuttleSpace, an immersive analytics system to assist experts in analyzing trajectory data in badminton. Trajectories in sports, such as the movement of players and balls, contain rich information on player behavior and thus have been widely analyzed by coaches and analysts to improve the players' performance. However, existing visual analytics systems often present the trajectories in court diagrams that are abstractions of reality, thereby causing difficulty for the experts to imagine the situation on the court and understand why the player acted in a certain way. With recent developments in immersive technologies, such as virtual reality (VR), experts gradually have the opportunity to see, feel, explore, and understand these 3D trajectories from the player's perspective. Yet, few research has studied how to support immersive analysis of sports data from such a perspective. Specific challenges are rooted in data presentation (e.g., how to seamlessly combine 2D and 3D visualizations) and interaction (e.g., how to naturally interact with data without keyboard and mouse) in VR. To address these challenges, we have worked closely with domain experts who have worked for a top national badminton team to design ShuttleSpace. Our system leverages 1) the peripheral vision to combine the 2D and 3D visualizations and 2) the VR controller to support natural interactions via a stroke metaphor. We demonstrate the effectiveness of ShuttleSpace through three case studies conducted by the experts with useful insights. We further conduct interviews with the experts whose feedback confirms that our first-person immersive analytics system is suitable and useful for analyzing badminton data.
Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis

In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.
An efficient and outsourcing-supported attribute-based access control scheme for edge-enabled smart healthcare

In recent years, the continuous development of smart healthcare has brought substantial convenience to our lives, especially for emerging edge-enabled smart healthcare systems that transmit and store a large amount of medical data. However, if private health data of users are leaked or tampered with, it will cause enormous damage to users’ rights, even threatening the safety of their lives. Traditionally, attribute-based encryption (ABE) is used to encrypt data and implement fine-grained access control for such sensitive information. However, the computing overhead of traditional ABE is relatively large, and in an edge-enabled environment, the outsourcing part of encryption and decryption to the edge node can reduce the computing cost of resource-constrained devices for edge-enabled smart healthcare. However, current schemes are not efficient for resource-constrained devices and edge nodes. Therefore, an efficient ABE scheme is proposed that outsources part of the encryption and decryption to the edge nodes as well as supports attribute updates, enabling flexible right control. A formal security proof is provided, verifying that our scheme is secure under the Decisional Bilinear Diffie–Hellman assumption. The performance of our scheme is evaluated at different security levels, and the experimental results demonstrate that our scheme is more efficient for resource-constrained devices than the traditional ABE.
Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification

Person re-identification has seen significant advancement in recent years. However, the ability of learned models to generalize to unknown target domains still remains limited. One possible reason for this is the lack of large-scale and diverse source training data, since manually labeling such a dataset is very expensive and privacy sensitive. To address this, we propose to automatically synthesize a large-scale person re-identification dataset following a set-up similar to real surveillance but with virtual environments, and then use the synthesized person images to train a generalizable person re-identification model. Specifically, we design a method to generate a large number of random UV texture maps and use them to create different 3D clothing models. Then, an automatic code is developed to randomly generate various different 3D characters with diverse clothes, races and attributes. Next, we simulate a number of different virtual environments using Unity3D, with customized camera networks similar to real surveillance systems, and import multiple 3D characters at the same time, with various movements and interactions along different paths through the camera networks. As a result, we obtain a virtual dataset, called RandPerson, with 1,801,816 person images of 8,000 identities. By training person re-identification models on these synthesized person images, we demonstrate, for the first time, that models trained on virtual data can generalize well to unseen target images, surpassing the models trained on various real-world datasets, including CUHK03, Market-1501, DukeMTMC-reID, and almost MSMT17. The RandPerson dataset is available at https://github.com/VideoObjectSearch/RandPerson.
Machine learning techniques for hate speech classification of twitter data: State-of-the-art, future challenges and research directions

Twitter is a microblogging tool that allow the creation of big data through short digital contents. This study provides a survey of machine learning techniques for hate speech classification from Twitter data streams. Hate speech classification in Twitter data streams has remain a vibrant research focus, but little research efforts have been devoted to the design of a generic metadata architecture, threshold settings and fragmentation issues. Hate speech classification techniques presented in literature address some of the challenges inherent in Twitter data streams but limited in the aforementioned issues. This study presented collection of hate speech benchmarks datasets suitable for testing the efficiency of classification models. This study also presented the pros and cons for single and hybrid machine learning methods in hate speech classification. The summary of performance evaluation for the surveyed machine learning methods was also presented. The study also presented a generic metadata architecture for hate speech classification in Twitter to tackle issues with Twitter data streams. The developed generic metadata architecture was observed to performed better across all evaluation metrics for hate speech detection having 0.95, 0.93, 0.92 and 0.93 for accuracy, precision, recall and F1-score respectively, when compared to similar methods. Similarly, the developed generic metadata architecture for hate speech sentiment classification performed better with F1-score of 91.5% compared to related methods. The developed generic metadata architecture also indicates a more perfect test having an AUC of 0.97, when compared to similar methods. The statistical validation of results points out the efficiency of the developed system. Finally, the results also showed that the developed system is very good for automatic topic detection and categorization.
Dual Attention GANs for Semantic Image Synthesis

In this paper, we focus on the semantic image synthesis task that aims at transferring semantic label maps to photo-realistic images. Existing methods lack effective semantic constraints to preserve the semantic information and ignore the structural correlations in both spatial and channel dimensions, leading to unsatisfactory blurry and artifact-prone results. To address these limitations, we propose a novel Dual Attention GAN (DAGAN) to synthesize photo-realistic and semantically-consistent images with fine details from the input layouts without imposing extra training overhead or modifying the network architectures of existing methods. We also propose two novel modules, i.e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively. Specifically, SAM selectively correlates the pixels at each position by a spatial attention map, leading to pixels with the same semantic label being related to each other regardless of their spatial distances. Meanwhile, CAM selectively emphasizes the scale-wise features at each channel by a channel attention map, which integrates associated features among all channel maps regardless of their scales. We finally sum the outputs of SAM and CAM to further improve feature representation. Extensive experiments on four challenging datasets show that DAGAN achieves remarkably better results than state-of-the-art methods, while using fewer model parameters.