Reviewerly/exHarmony huggingface dataset:
Plaintext-Related Image Encryption Algorithm Based on Block Structure and Five-Dimensional Chaotic Map

In this paper, a five-dimensional discrete hyperchaotic map is constructed by combining Logistic map and 3D discrete Lorenz map. Based on this chaotic system, a block-based image encryption scheme related to plaintext is designed. The encryption scheme includes scrambling operation and diffusion operation. In the scrambling operation, the scrambling sequence is related to the pixels of the plaintext image, while in the diffusion operation, two rounds of diffusion are carried out for intra-block diffusion, and the generation of random diffusion sequence is related to a pixel value in the block in each round of diffusion, which makes the algorithm resist chosen-plaintext attack. When the next block is diffused, the generation of the diffusion sequence is related to the first and last pixels of the previous block, which makes the block and block interact with each other and enhances the sensitivity of the algorithm to plaintext. Theoretical analysis and experimental results demonstrate the security and practicability of the cryptosystem.
Web service API recommendation for automated mashup creation using multi-objective evolutionary search

Modern software development builds on external Web services reuse as a promising way that allows developers delivering feature-rich software by composing existing Web service Application Programming Interfaces, known as APIs. With the overwhelming number of Web services that are available on the Internet, finding the appropriate Web services for automatic service composition, i.e., mashup creation, has become a time-consuming, difficult, and error-prone task for software designers and developers when done manually. To help developers, a number of approaches and techniques have been proposed to automatically recommend Web services. However, they mostly focus on recommending individual services. Nevertheless, in practice, service APIs are intended to be used together forming a social network between different APIs, thus should be recommended collectively. In this paper, we introduce a novel automated approach, called SerFinder, to recommend service sets for automatic mashup creation. We formulate the service set recommendation as a multi-objective combinatorial problem and use the non-dominated sorting genetic algorithm (NSGA-II) as a search method to extract an optimal set of services to create a given mashup. We aim at guiding the search process towards generating the adequate compromise among three objectives to be optimized (i) maximize services historical co-usage, (ii) maximize services functional matching with the mashup requirements, and (iii) maximize services functional diversity. We perform a large-scale empirical experiment to evaluate SerFinder on a benchmark of real-world mashups and services. The obtained results demonstrate the effectiveness of SerFinder in comparison with recent existing approaches for mashup creation and services recommendation. The statistical analysis results provide an empirical evidence that SerFinder, significantly outperforms four state-of-the-art widely-used multi-objective search-based algorithms as well as random search.
A novel approach for learning label correlation with application to feature selection of multi-label data

Each example of multi-label data is represented in an object with its feature vector (i.e. an instance) while being related to multiple labels. Learning label correlation can effectively reduce the labels needed to be predicted and optimize the classification performance. For this reason, label correlation plays a crucial role in multi-label learning and has been explored by many existing algorithms. Generally, every label has its own indispensable features, and it is reasonable to assume that a higher repetitiveness of indispensable features represents higher correlations among labels. Inspired by this fact, the essential elements for each label, which are composed of indispensable features, are constructed in this paper. A method is proposed for learning label correlation and applying it to feature selection of multi-label data based on the overlap of different families of essential elements related to label. Firstly, the essential elements of each label are defined and characterized to reflect the internal connection between features and label. In addition, a process for calculating the essential elements of a single label is provided. Secondly, by considering the overlap of the essential element collections that are determined by the different labels, relevancy of label and corresponding relevance judgement matrix with the label set are described. Therefore, several labels with strong relationships are assigned to a relevant group of labels. Meanwhile, local and global label correlations can be computed. Thus a novel multi-label learning algorithm, called CLSF, is presented to select a compact subset of indispensable features for each relevant group of labels by applying local label correlation to feature selection of multi-label data. Finally, comprehensive experiments on eleven benchmark data sets clearly illustrate the effectiveness and efficiency of CLSF against five other multi-label learning algorithms.
Illumination-Invariant Person Re-Identification

Due to the effect of weak illumination, person images captured by surveillance cameras usually contain various degradations such as color shift, low contrast and noise. These degradations result in severe discriminant information loss, which makes the person re-identification (re-id) more challenging. However, existing person re-identification approaches are designed based on the assumption that the pedestrians images are under well lighting conditions, which is impractical in real-world scenarios. Inspired by the Retinex theory, we propose a illumination-invariant person re-identification framework which is able to simultaneously achieve Retinex illumination decomposition and person re-identification. We first verify that directly using weak illuminated images can greatly reduce the performance of person re-id. We then design a bottom-up attention network to remove the effect of weak illumination and obtain the enhanced image without introducing over-enhancement. To effectively connect low-level and high-level vision tasks, a joint training strategy is further introduced to boost the performance of person re-id under weak illumination conditions. Experiments have demonstrated the advantages of our method on benchmarks with severe lighting changes and low light conditions.
Learnable Aggregating Net with Diversity Learning for Video Question Answering

Video visual question answering (V-VQA) remains challenging at the intersection of vision and language, where it requires joint comprehension of video and natural language question. Image-Question co-attention mechanism, which aims at generating a spatial map highlighting image regions relevant to answering the question and vice versa, has obtained impressive results. Despite the success, simply applying co-attention to video visual question answering results in unsatisfactory performance due to the complexity and temporal nature of videos. In this paper, we proposed a novel architecture, namely Learnable Aggregating Net with Diversity learning (LAD-Net), for V-VQA. In the proposed method, we address two central problems: 1) how to deploy co-attention to V-VQA task considering the complex and diverse content of videos; and 2) how to aggregate the frame-level features without destroying the feature distributions and temporal information. To solve these problems, our LAD-Net first extends single-path based co-attention mechanism to a multi-path pyramid co-attention structure with a novel diversity learning to explicitly encourage attention diversity. For video-level (or question-level) descriptor, instead of taking a simple temporal pooling (i.e., average pooling), we propose a new learnable aggregation method with a set of evidence gates. It automatically aggregates adaptively-weighted frame-level features (or word-level features) to extract rich video (or question) context semantic information by imitating Bags-of-Words (BoW) quantization. With evidence gates, it then further chooses the most related signals representing the evidence information to predict the answer.Extensive validations on the two challenging video visual question answering datasets TGIF-QA and TVQA show that LAD-Net achieves the state-of-the-art performance under various settings and metrics. Our proposed strategies are of particular importance for improving the performance of the baseline co-attention V-VQA.
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification

Deep neural networks (DNNs) have been shown lack of robustness for the vulnerability of their classification to small perturbations on the inputs. This has led to safety concerns of applying DNNs to safety-critical domains. Several verification approaches have been developed to automatically prove or disprove safety properties of DNNs. However, these approaches suffer from either the scalability problem, i.e., only small DNNs can be handled, or the precision problem, i.e., the obtained bounds are loose. This paper improves on a recent proposal of analyzing DNNs through the classic abstract interpretation technique, by a novel symbolic propagation technique. More specifically, the values of neurons are represented symbolically and propagated forwardly from the input layer to the output layer, on top of abstract domains. We show that our approach can achieve significantly higher precision and thus can prove more properties than using only abstract domains. Moreover, we show that the bounds derived from our approach on the hidden neurons, when applied to a state-of-the-art SMT based verification tool, can improve its performance. We implement our approach into a software tool and validate it over a few DNNs trained on benchmark datasets such as MNIST, etc.
Beyond Cartesian Representations for Local Descriptors

The dominant approach for learning local patch descriptors relies on small image regions whose scale must be properly estimated a priori by a keypoint detector. In other words, if two patches are not in correspondence, their descriptors will not match. A strategy often used to alleviate this problem is to “pool” the pixel-wise features over log-polar regions, rather than regularly spaced ones. By contrast, we propose to extract the “support region” directly with a log-polar sampling scheme. We show that this provides us with a better representation by simultaneously oversampling the immediate neighbourhood of the point and undersampling regions far away from it. We demonstrate that this representation is particularly amenable to learning descriptors with deep networks. Our models can match descriptors across a much wider range of scales than was possible before, and also leverage much larger support regions without suffering from occlusions. We report state-of-the-art results on three different datasets.
Optimizing the F-Measure for Threshold-Free Salient Object Detection

Current CNN-based solutions to salient object detection (SOD) mainly rely on the optimization of cross-entropy loss (CELoss). Then the quality of detected saliency maps is often evaluated in terms of F-measure. In this paper, we investigate an interesting issue: can we consistently use the F-measure formulation in both training and evaluation for SOD? By reformulating the standard F-measure we propose the relaxed F-measure which is differentiable w.r.t the posterior and can be easily appended to the back of CNNs as the loss function. Compared to the conventional cross-entropy loss of which the gradients decrease dramatically in the saturated area, our loss function, named FLoss, holds considerable gradients even when the activation approaches the target. Consequently, the FLoss can continuously force the network to produce polarized activations. Comprehensive benchmarks on several popular datasets show that FLoss outperforms the state-of-the-art with a considerable margin. More specifically, due to the polarized predictions, our method is able to obtain high-quality saliency maps without carefully tuning the optimal threshold, showing significant advantages in real-world applications.
Selfish Mining in Ethereum

As the second largest cryptocurrency by market capitalization and today's biggest decentralized platform that runs smart contracts, Ethereum has received much attention from both academia and industry. Nevertheless, there exist very few studies about the security of its mining strategies, especially from the selfish mining perspective. In this paper, we fill this research gap by analyzing selfish mining in Ethereum and understanding its potential threat. First, we introduce a 2-dimensional Markov process to model the behavior of a selfish mining strategy inspired by a Bitcoin mining strategy proposed by Eyal and Sirer. Second, we derive the stationary distribution of our Markov model and compute long-term average mining rewards. This allows us to determine the threshold of computational power which makes selfish mining profitable in Ethereum. We find that this threshold is lower than that in Bitcoin mining (which is 25% as discovered by Eyal and Sirer), suggesting that Ethereum is more vulnerable to selfish mining than Bitcoin.
ParBlockchain: Leveraging Transaction Parallelism in Permissioned Blockchain Systems

Many existing blockchains do not adequately address all the characteristics of distributed system applications and suffer from serious architectural limitations resulting in performance and confidentiality issues. While recent permissioned blockchain systems, have tried to overcome these limitations, their focus has mainly been on workloads with no-contention, i.e., no conflicting transactions. In this paper, we introduce OXII, a new paradigm for permissioned blockchains to support distributed applications that execute concurrently. OXII is designed for workloads with (different degrees of) contention. We then present ParBlockchain, a permissioned blockchain designed specifically in the OXII paradigm. The evaluation of ParBlockchain using a series of benchmarks reveals that its performance in workloads with any degree of contention is better than the state of the art permissioned blockchain systems.
Utilization of blockchain for mitigating the distributed denial of service attacks

Abstract Distributed Denial of Service (DDoS) attacks cause devastating effects on the web services and hence harm the digital availability. The DDoS attackers use vulnerabilities exposed through new networking technologies like wireless, mobile, IoT, and associated protocol weaknesses for bringing down the networks and servers. Owing to availability of easily available tools and botnet armies, the DDoS attack incidences in the internet world are increasing day by day. Several techniques have been proposed by the researchers against DDoS attacks. This paper concentrates on the utilization of one of the latest and most promising technologies, that is, blockchain technology against DDoS attacks. The blockchain technology is rapidly finding use in various applications ranging from financial to gaming; this is because of its stable, decentralized, and secure architecture. The DDoS solutions based on blockchain are still in infancy and some solutions provide only architectural details without bothering about the implementation details. This paper presents a study of the blockchain‐based DDoS solutions. It also compares the existing blockchain‐based techniques against DDoS attacks and analyses them. This paper facilitates the development of future research proposals in this emerging area of blockchain technology.
Multi-View Stereo by Temporal Nonparametric Fusion

We propose a novel idea for depth estimation from multi-view image-pose pairs, where the model has capability to leverage information from previous latent-space encodings of the scene. This model uses pairs of images and poses, which are passed through an encoder-decoder model for disparity estimation. The novelty lies in soft-constraining the bottleneck layer by a nonparametric Gaussian process prior. We propose a pose-kernel structure that encourages similar poses to have resembling latent spaces. The flexibility of the Gaussian process (GP) prior provides adapting memory for fusing information from nearby views. We train the encoder-decoder and the GP hyperparameters jointly end-to-end. In addition to a batch method, we derive a lightweight estimation scheme that circumvents standard pitfalls in scaling Gaussian process inference, and demonstrate how our scheme can run in real-time on smart devices.
MonoFENet: Monocular 3D Object Detection With Feature Enhancement Networks

Monocular 3D object detection has the merit of low cost and can be served as an auxiliary module for autonomous driving system, becoming a growing concern in recent years. In this paper, we present a monocular 3D object detection method with feature enhancement networks, which we call <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MonoFENet</i> . Specifically, with the estimated disparity from the input monocular image, the features of both the 2D and 3D streams can be enhanced and utilized for accurate 3D localization. For the 2D stream, the input image is used to generate 2D region proposals as well as to extract appearance features. For the 3D stream, the estimated disparity is transformed into 3D dense point cloud, which is then enhanced by the associated front view maps. With the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoI Mean Pooling</i> layer, 3D geometric features of RoI point clouds are further enhanced by the proposed point feature enhancement ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PointFE</i> ) network. The region-wise features of image and point cloud are fused for the final 2D and 3D bounding boxes regression. The experimental results on the KITTI benchmark reveal that our method can achieve state-of-the-art performance for monocular 3D object detection.
RILOD

Object detection models shipped with camera-equipped edge devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, RILOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of RILOD is a novel incremental learning algorithm that trains end-to-end for one-stage deep object detection models only using training data of new object classes. Specifically to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented RILOD under both edge-cloud and edge-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.
Deep Floor Plan Recognition Using a Multi-Task Network With Room-Boundary-Guided Attention

This paper presents a new approach to recognize elements in floor plan layouts. Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and different types of rooms, in the floor layouts. To this end, we model a hierarchy of floor plan elements and design a deep multi-task neural network with two tasks: one to learn to predict room-boundary elements, and the other to predict rooms with types. More importantly, we formulate the room-boundary-guided attention mechanism in our spatial contextual module to carefully take room-boundary features into account to enhance the room-type predictions. Furthermore, we design a cross-and-within-task weighted loss to balance the multi-label tasks and prepare two new datasets for floor plan recognition. Experimental results demonstrate the superiority and effectiveness of our network over the state-of-the-art methods.
EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation

Assigning standard medical codes (e.g., ICD-9-CM) representing diagnoses or procedures to electronic health record (EHR) is an important task in the medical domain. However, automatic coding is difficult since the clinical note is composed of multiple long and heterogeneous textual narratives (e.g., discharge diagnosis, pathology reports, surgical procedure notes). Furthermore, the code label space is large and the label distribution is extremely unbalanced. The state-of-the-art methods mainly regard EHR coding as a multi-label text classification task and use shallow convolution neural network with fixed window size, which is incapable of learning variable n-gram features and the ontology structure between codes. In this paper, we leverage a densely connected convolutional neural network which is able to produce variable n-gram features for clinical note feature learning. We also incorporate a multi-scale feature attention to adaptively select multi-scale features since the most informative n-grams in clinical notes for each word can vary in length according to the neighborhood. Furthermore, we leverage graph convolutional neural network to capture both the hierarchical relationships among medical codes and the semantics of each code. Finally, We validate our method on the public dataset, and the evaluation results indicate that our method can significantly outperform other state-of-the-art models.
An efficient privacy‐enhanced attribute‐based access control mechanism

Summary Owing to the rapid progress of network researching, attribute‐based access control (ABAC) has attracted more and more attention due to its appreciable expressiveness, flexibility, and scalability. Unfortunately, collecting user attributes is necessary to complete the standard ABAC decision process, which increases the risk of privacy disclosure. This problem increases public doubts about ABAC and hinders its popularization. In this paper, a privacy‐protected and efficient attribute‐based access control (EPABAC) scheme is proposed to prevent the privacy leakage of access subject in the decision‐making process of ABAC by introducing a novel hash‐based binary search tree. The analyses and experimental evaluations show that the EPABAC achieves user privacy protection in the decision‐making process with acceptable additional computing overhead.