Reviewerly/exHarmony huggingface dataset:
Double image encryption scheme for iris template protection using 3D Lorenz system and modified equal modulus decomposition in hybrid transform domain

In this paper, a double image encryption mechanism for iris template protection using 3D Lorenz chaotic system with modified equal modulus decomposition in the hybrid transform domain is proposed. The hybrid transform is produced by the combination of Walsh transform, Kekre transform and fractional Fourier transform of various orders. Here the fractional orders of the fractional Fourier transform enlarge the key-space of the proposed scheme and improve robustness against brute-force attack. The proposed scheme is asymmetric and non-linear in nature. To validate and authenticate the proposed cryptosystem, the numerical simulations have been performed on grayscale images. Results demonstrate that the proposed cryptosystem has higher robustness against noise and special attacks.
Learning Optimality Theory for Accuracy-Based Learning Classifier Systems

Evolutionary computation has brought great progress to rule-based learning but this progress is often blind to the optimality of the system design. This article theoretically reveals an optimal learning scheme on the most popular evolutionary rule-based learning approach-the accuracy-based classifier system (or XCS). XCS seeks to form accurate, maximally general rules that together classify the state space of a given domain. Previously, setting up the system to perform well has been a “blackart” as no systematic approach to XCS parameter tuning existed. We derive a theoretical approach that mathematically guarantees that XCS identifies the accurate rules, which also returns a theoretically valid XCS parameter setting. Then, we demonstrate our theoretical setting derives the maximum correctness of rule-identification in the fewest iterations possible. We also experimentally show that our theoretical setting enables XCS to easily solve several challenging problems where it had previously struggled.
A Precision-Scalable Energy-Efficient Convolutional Neural Network Accelerator

Quantization is a promising technique to compress the size of Convolutional Neural Network (CNN) models. Recently, various precision-scalable designs have been presented to reduce the computational complexity in CNNs. However, most of them adopt straightforward calculation scheme to implement the CNN, which causes high bandwidth requirement and low hardware utilization efficiency. This paper proposes a new precision-scalable architecture which can fully reduce the computational complexity in CNN inference and meanwhile has a finely simplified calculation scheme. Based on the proposed scheme, a well-optimized multiplier called Compositional Processing Element (C-PE) is devised. Compared with the previous multipliers, the new C-PE requires less area and power. Furthermore, two levels of optimization are introduced to the design to relieve the bandwidth problem and increase the hardware utilization efficiency. Implemented under the TSMC 90nm CMOS technology, the whole design achieves 6-68.1 fps in various precisions on VGG16 benchmark and a 49.8TOPS/W energy efficiency at 500MHz when scaled to 28nm, which is much better than previous precision-scalable ones.
Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

Underwater images suffer from low visibility and contrast caused by absorption and scattering, which leads to haze and some further limitations. The existing underwater single image dehazing methods cannot achieve a balance between the performance and computational complexity, and are difficult to produce satisfactory results in the regions with large distance. To overcome these problems, we propose a new underwater single image dehazing method, which includes an improved background light estimation based on the quad-tree subdivision iteration algorithm, and a novel transmission estimation method. For the background light estimation, we introduce a robust score for each region of the image, which can evaluate the region from both smoothness and color. For the transmission estimation, we propose the color space dimensionality reduction prior (CSDRP), which allows conversing an image from the three-dimensional RGB color space to a 2D color space, namely the UV color space. In the UV color space, by clustering the pixels into mounts of haze-lines and carefully setting the haze-free boundary, the transmission map can be figured out and used to produce an excellent dehazed image. Experimental results show that our method has competitive effects compared with mainstream underwater single image dehazing methods.
Bayesian Reinforcement Learning and Bayesian Deep Learning for Blockchains With Mobile Edge Computing

We present a novel game-theoretic, Bayesian reinforcement learning (RL) and deep learning (DL) framework to represent interactions of miners in public and consortium blockchains with mobile edge computing (MEC). Within the framework, we formulate a stochastic game played by miners under incomplete information. Each miner can offload its block operations to one of the base stations (BSs) equipped with the MEC server. The miners select their offloading BSs and block processing rates simultaneously and independently, without informing other miners about their actions. As such, no miner knows the past and current actions of others and, hence, constructs its belief about these actions. Accordingly, we devise a Bayesian RL algorithm based on the partially-observable Markov decision process for miner's decision making that allows each miner to dynamically adjust its strategy and update its beliefs through repeated interactions with each other and with the mobile environment. We also propose a novel unsupervised Bayesian deep learning algorithm where the uncertainties about unobservable states are approximated with Bayesian neural networks. We show that the proposed Bayesian RL and DL algorithms converge to the stable states where the miners' actions and beliefs form the perfect Bayesian equilibrium (PBE) and myopic PBE, respectively.
Breast Mass Tumor Classification using Deep Learning

This study presents pre-trained Convolutional Neural Network (CNN) models to classify pre-segmented mammogram mass tumors as benign or malignant. Transfer learning, particular pre-processing and data augmentation were applied to overcome the limitation of the scarcity of available training dataset. The proposed models are based on modified versions of Inception V3 and ResNet50 to tackle the classification problem mentioned above. The proposed architectures have been tested on the Digital Database for Screening Mammography (DDSM) dataset, and it achieved an accuracy of 0.796, precision of 0.754, and a recall of 0.891 on InceptionV3-like CNN model. On the other hand, an accuracy of 0.857, precision of 0.857, and a recall rate of 0.873 have been achieved with the ResNet50-like CNN network. Overall, the proposed ResNet50-like model achieved a ~5% improvement in accuracy compared to the existing state-of-the-art method for this dataset.
Deep Image Sensing and Retrieval Using Suppression, Scale Spacing and Division, Interpolation and Spatial Color Coordinates With Bag of Words for Large and Complex Datasets

Intelligent and efficient image retrieval from versatile image datasets is an inevitable requirement of the current era. Primitive image signatures are vital to reflect the visual attributes for content based image retrieval (CBIR). Algorithmically descriptive and well identified visual contents form the image signatures to correctly index and retrieve similar results. Hence feature vectors should contain ample image information with color, shape, objects, spatial information perspectives to distinguish image category as a qualifying candidate. This contribution presents a novel features detector by locating the interest points by applying non-maximum suppression to productive sum of derivative of pixels computed from differential of corner scores. The interest points are described by applying scale space interpolation to scale space division produced from Hessian blob detector resulted after Gaussian smoothing. The computed shape and object information is fused with color features extracted from the spatially arranged L2 normalized coefficients. High variance coefficients are selected for object based feature vectors to reduce the massive data which in fuse form transformed to bag-of-words (BoW) for efficient retrieval and ranking. To check the competitiveness of the presented approach it is experimented on nine well-known image datasets Caltech-101, ImageNet, Corel-10000, 17-Flowers, Columbia object image library (COIL), Corel-1000, Caltech-256, tropical fruits and Amsterdam library of textures (ALOT) belong to shape, color, texture, and spatial & complex objects categories. Extensive experimentation is conducted for seven benchmark descriptors including maximally stable extremal region (MSER), speeded up robust features (SURF), difference of Gaussian (DoG), red green blue local binary pattern (RGBLBP), histogram of oriented gradients (HOG), scale invariant feature transform (SIFT), and local binary pattern (LBP). Remarkable outcomes reported that the presented technique has significant precision rates, recall rates, average retrieval precision & recall, mean average precision & recall rates for many image semantic groups of the challenging datasets. Results comparison is presented with research techniques and reported improved results.
An improved image steganography technique using edge based data hiding in DCT domain

Abstract Steganography is a type of secret communication which is used to hide the confidential information from the intruder. The security during information communication is a key concern which makes image steganography a popular technique. Image steganography techniques may be broadly classified into two domains i.e., spatial domain and frequency domain. This paper proposes an improved image steganography technique in which data is embedded in the edge pixels of the carrier image. An intruder has less suspicion about the existence of data bits in edges, because edge pixels appear to be different than their neighbours and thus ensures better security. The proposed technique uses different types of edge detection filters like Prewitt, Sobel, Laplacian and Canny in the existing image steganography using edge based data hiding in DCT domain algorithm. Furthermore, the proposed technique results in excellent performance with PSNR of 99% and SNR of 96%. However, the proposed steganography technique surprisingly results in reduction of image size mainly because of image compression.
Controls on cobalt and nickel distribution in hydrothermal sulphide deposits in Bergslagen, Sweden - constraints from solubility modelling

We address controls on Co and Ni distribution, based on their solubility in hydrothermal fluids as functions of pH, ƒO2 and T, in two end-member types of sulphide deposits in Bergslagen, Sweden. Oxidized hydrothermal fluids, as have been suggested for the formation of the Zinkgruvan deposit, would efficiently transport Co and Ni in solution, even at 150 °C. Formation of Co and Ni sulphides and sulphosalts supersedes or overlaps the precipitation of other sulphides along a reduction or H2S-mixing path. This is consistent with the presence of Co and Ni sulphides and sulphosalts in vent-proximal Cu-Zn mineralization at Zinkgruvan. Reduced, acidic and hot (≥250 °C) hydrothermal fluids that have been invoked for the formation of deposits like Falun and Stollberg could also transport Co and Ni in solution. However, their solubility is strongly dependent on high T and low pH. Cooling and neutralization are here proposed as likely key triggers for the deposition of Co and Ni, yet, unlike in the Zinkgruvan scenario, saturation will occur within the pyrite stability field, whereby these metals may be sequestered as stoichiometric lattice substitutions in pyrite and other sulphides rather than forming minerals of their own. We conclude that at any T or realistic pH, hydrothermal systems involving oxidized brines have a greater ability to traverse and leach large rock volumes of Co and Ni. Consequently, areas hosting deposits that formed from such brines have a significant exploration potential for these metals, even in areas where Co-enriched source rocks are lacking or subordinate.
Error Detection Architectures for Ring Polynomial Multiplication and Modular Reduction of Ring-LWE in $\boldsymbol{\frac{\mathbb{Z}/p\mathbb{Z}[x]}{x^{n}+1}}$ Benchmarked on ASIC

Ring learning with error (ring-LWE) within lattice-based cryptography is a promising cryptographic scheme for the post-quantum era. In this article, we explore efficient error detection approaches for implementing ring-LWE encryption. For achieving accurate operation of the ring-LWE problem and thwarting active side-channel attacks, error detection schemes need to be devised so that the induced overhead is not a burden to deeply embedded and constrained applications. This article, for the first time, investigates error detection schemes for both stages of the ring-LWE encryption operation, i.e., ring polynomial multiplication and modular reduction. Our schemes exploit recomputing with encoded operands, which successfully counter both natural faults (for the stuck-at model). We implement our schemes on an application-specific integrated circuit. As performance metrics show hardware overhead, our schemes prove to be low complexity with high error coverage. The proposed efficient architectures can be tailored and utilized for post-quantum cryptographic schemes in different usage models with diverse constraints.
An Attention-Based BILSTM-CRF for Chinese Named Entity Recognition

Named entity recognition (NER) is a very basic task in natural language processing (NLP). Compared with English, the task of Chinese NER faces more challenges. One of the reasons is the blurring of Chinese entity boundaries, which is closely related to the segmentation results. Previous research on this task can be roughly divided into two categories, word-based methods and character-based methods, but both methods have their own shortcomings. In this article, we combine character information with word information, and introduce the attention mechanism into a bidirectional long short-term memory network-conditional random field (BILSTM-CRF) model. First, we utilizes a bidirectional long short-term memory network to obtain more complete contextual information. Then the model utilizes the attention mechanism to learn more about the context grammar of Chinese named entities, and finally, our model uses the conditional random field model to obtain the predicted label sequence. Experimental results T prove that our method can effectively improve the effectiveness of named entity recognition.
Mass detection in mammograms by bilateral analysis using convolution neural network

Automatic detection of the masses in mammograms is a big challenge and plays a crucial role to assist radiologists for accurate diagnosis. In this paper, a bilateral image analysis method based on Convolution Neural Network (CNN) is developed for mass detection in mammograms. The proposed bilateral mass detection method consists of two networks: a registration network for registering bilateral mammograms and a Siamese-Faster-RCNN network for mass detection using a pair of registered mammograms. In the first step, self-supervised learning network is built to learn the spatial transformation between bilateral mammograms. This network can directly estimate spatial transformation by maximizing an image-wise similarity metric and corresponding points labeling is not needed. In the second step, an end-to-end network combining the Region Proposal Network (RPN) and a Siamese Fully Connected (Siamese-FC) network is designed. Different from existing methods, the designed network integrates mass detection on single image with registered bilateral images comparison. The proposed method is evaluated on three datasets (publicly available dataset INbreast and private dataset BCPKUPH and TXMD). For INbreast dataset, the proposed method achieves 0.88 true positive rate (TPR) with 1.12 false positives per image (FPs/I). For BCPKUPH dataset, the proposed method achieves 0.85 TPR with 1.86 FPs/I. For TXMD dataset, the proposed method achieves 0.85 TPR with 2.70 FPs/I. Registration experimental result shows that the proposed method is suitable for bilateral mass detection. Mass detection experimental results show that the proposed method performs better than unilateral mass detection method, different bilateral connection schemes and image level fusion bilateral schemes.
Clustered coverage orienteering problem of unmanned surface vehicles for water sampling

Abstract This study investigates a clustered coverage orienteering problem (CCOP), which is a generalization of the classical orienteering problem. The problem is widely motivated by the emerging unmanned techniques (eg, unmanned surface vehicles and drones) applied to environmental monitoring. Specifically, the unmanned surface vehicles (USVs) are used to monitor reservoir water quality by collecting samples. In the CCOP, the water sampling sites (ie, the nodes) are grouped into clusters, and a minimum number of nodes must be visited in each cluster. With each node representing a certain coverage area of the water, the objective of the CCOP is to monitor as much as possible the total coverage area in one tour of the USV, considering that overlapping areas provide no additional information. An integer programming model is first formulated through a linearization procedure that captures the overlapping feature. A two‐stage exact algorithm is proposed to obtain an optimal solution to the problem. The efficiency and effectiveness of the two‐stage exact algorithm are demonstrated through experiments on randomly generated instances. The algorithm can effectively solve instances with up to 60 sampling sites.
Full Reference Image Quality Assessment Based on Visual Salience With Color Appearance and Gradient Similarity

Image quality assessment (IQA) model is designed to measure the image quality in consistent with subjective ratings by computational models. In this research, a reliable full reference color IQA model is proposed by combining the Visual saliency with Color appearance (VC) similarity, gradient similarity and chrominance similarity. Two new color appearance indices, vividness and depth, are selected to build the visual saliency similarity map. The structure and chrominance features are characterized by different channels of chosen color space. VC map plays two roles in the proposed model. One is utilized as feature to compute the local quality of distorted image, and the other is as a weight part to reflect the importance of local domain. The novel model is called visual saliency with color appearance and gradient similarity (VCGS). To quantify the specific parameters of VCGS, some experiments are conducted based on the statistical correlation indices. Massive experiments are performed on the publicly available benchmark single and multiple distortion databases, and the commonly evaluation criteria results prove that VCGS works with higher consistency with the subjective evaluations than the other state-of-the-art IQA models for the prediction accuracy. Besides, VCGS maintain a moderate computational complexity. The MATLAB source code of VCGS is publicly available online at https://github.com/AlAlien/VCGS.
A survey of pattern mining in dynamic graphs

Abstract Graph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities. This article is categorized under: Algorithmic Development &gt; Spatial and Temporal Data Mining Algorithmic Development &gt; Association Rules
Evolving LSSVM and ELM models to predict solubility of non-hydrocarbon gases in aqueous electrolyte systems

Due to lack of a comprehensive and accurate predictive model for estimation of non-hydrocarbon (N2 and CO2) solubility in the aqueous system, the main aim of the present study is the suggestion of new estimation tools for non-hydrocarbons solubility in the different aqueous solutions. To this end, two computational-based models, including extreme learning machine (ELM) and least-squares support vector machine (LSSVM), have been implemented. These models have excellent backgrounds in the estimation of behaviors of fluids. The non-hydrocarbon solubility values of LSSVM and ELM algorithms have been compared with this dataset in visual and mathematical methods. Furthermore, the sensitivity analysis has been employed to identify the amount of impacts of aforementioned parameters on solubility of non-hydrocarbons. These reliable investigations can help researchers to successfully estimate the main thermodynamic parameters which have important roles in optimization of design of industrial plants such as natural gas processing units.
A Detection Approach Using LSTM-CNN for Object Removal Caused by Exemplar-Based Image Inpainting

Exemplar-based image inpainting technology is a “double-edged sword”. It can not only restore the integrity of image by inpainting damaged or removed regions, but can also tamper with the image by using the pixels around the object region to fill in the gaps left by object removal. Through the research and analysis, it is found that the existing exemplar-based image inpainting forensics methods generally have the following disadvantages: the abnormal similar patches are time-consuming and inaccurate to search, have a high false alarm rate and a lack of robustness to multiple post-processing combined operations. In view of the above shortcomings, a detection method based on long short-term memory (LSTM)-convolutional neural network (CNN) for image object removal is proposed. In this method, CNN is used to search for abnormal similar patches. Because of CNN’s strong learning ability, it improves the speed and accuracy of the search. The LSTM network is used to eliminate the influence of false alarm patches on detection results and reduce the false alarm rate. A filtering module is designed to eliminate the attack of post-processing operation. Experimental results show that the method has a high accuracy, and can resist the attack of post-processing combination operations. It can achieve a better performance than the state-of-the-art approaches.