Reviewerly/exHarmony huggingface dataset:
MTRNet++: One-stage mask-based scene text eraser

A precise, controllable, interpretable and easily trainable text removal approach is necessary for both user-specific and large-scale text removal applications. To achieve this, we propose a one-stage mask-based text inpainting network, MTRNet++. It has a novel architecture that includes mask-refine, coarse-inpainting and fine-inpainting branches, and attention blocks. With this architecture, MTRNet++ can remove text either with or without an external mask. It achieves state-of-the-art results on both the Oxford and SCUT datasets without using external ground-truth masks. The results of ablation studies demonstrate that the proposed multi-branch architecture with attention blocks is effective and essential. It also demonstrates controllability and interpretability.
Orientation guided anchoring for geospatial object detection from remote sensing imagery

Object detection from remote sensing imagery plays a significant role in a wide range of applications, including urban planning, intelligent transportation systems, ecology and environment analysis, etc. However, scale variations, orientation variations, illumination changes, and partial occlusions, as well as image qualities, bring great challenges for accurate geospatial object detection. In this paper, we propose an efficient orientation guided anchoring based geospatial object detection network based on convolutional neural networks. To handle objects of varying sizes, the feature extraction subnetwork extracts a pyramid of semantically strong features at different scales. Based on orientation guided anchoring, the anchor generation subnetwork generates a small set of high-quality, oriented anchors as object proposals. After orientation region of interest pooling, objects of interest are detected from the object proposals through the object detection subnetwork. The proposed method has been tested on a large geospatial object detection dataset. Quantitative evaluations show that an overall completeness, correctness, quality, and F1-measure of 0.9232, 0.9648, 0.8931, and 0.9435, respectively, are obtained. In addition, the proposed method achieves a processing speed of 8 images per second on a GPU on the cloud computing platform. Comparative studies with the existing object detection methods also demonstrate the advantageous detection accuracy and computational efficiency of our proposed method.
A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments

This paper presents a multi-population differential evolution algorithm to address dynamic optimization problems. In the proposed approach, a cellular learning automaton adjusts the behavior of each subpopulation by adaptively controlling its updating schemes. As the environment changes over time, an evolving population may go through a quite number of state transitions. Each state demands specific characteristics from an optimizer; hence, an adapted evolutionary scheme for one state may be unsuitable for the upcoming ones. Additionally, a learning approach may have limited time to adapt to a newly encountered state due to the frequentness of the environmental changes. Hence, it is infeasible for a dynamic optimizer to unlearn its existing beliefs to accommodate the practices required to embrace newly encountered states. In order to address this issue, we introduce a context dependent learning approach, which can adapt the behavior of each subpopulation according to the contexts of its different states. The performance of the proposed approach is compared with several state-of-the-art dynamic optimizers over the GDBG benchmark set. Comparison results indicate that the proposed method can achieve statistically superior performances on a wide range of tested instances.
Adversarially robust transfer learning

Transfer learning, in which a network is trained on one task and re-purposed on another, is often used to produce neural network classifiers when data is scarce or full-scale training is too costly. When the goal is to produce a model that is not only accurate but also adversarially robust, data scarcity and computational limitations become even more cumbersome. We consider robust transfer learning, in which we transfer not only performance but also robustness from a source model to a target domain. We start by observing that robust networks contain robust feature extractors. By training classifiers on top of these feature extractors, we produce new models that inherit the robustness of their parent networks. We then consider the case of fine tuning a network by re-training end-to-end in the target domain. When using lifelong learning strategies, this process preserves the robustness of the source network while achieving high accuracy. By using such strategies, it is possible to produce accurate and robust models with little data, and without the cost of adversarial training. Additionally, we can improve the generalization of adversarially trained models, while maintaining their robustness.
Binarization of degraded document images with global-local U-Nets

Document binarization plays a significant role in the document analysis to extract foreground text from the background. Traditional Convolutional Neural Networks (CNNs) focus only on local textual features and ignore global context, which are both important for pixel classification in segmentation based document binarization. In this paper, we propose a local-global combined approach for document binarization. This model is composed of a global branch and a local branch, taking the global patches from downsampled image and cropped local patches from source image as respective inputs. The final binary prediction is achieved via combining the results of this two branches. The experimental results on several DIBCO datasets present that our method outperforms many traditional and state-of-the-art document binarization algorithms.
Understanding solar resource variability: An in-depth analysis, using Chile as a case of study

Short-term solar radiation variability is a key parameter to understand the solar resource, as it is concerned with describing the dynamic characteristics of irradiance, that have significant transient effects in solar system performance. Worldwide installed solar capacity is consistently increasing and it can considerably affect the dynamics of electric systems. An understanding of resource dynamics and how it affects power forecasts will become paramount for appropriate handling of the solar resource. Chile showcases this scenario, as solar installed capacity is close to 10% of the nationwide total and energy curtailment is occurring mainly due to transmission restrictions. Although several metrics are available in the literature to assess solar resource variability, consensus needs to be stablished, as well as a common framework to evaluate results obtained through the different metrics. This work analyses 1-min resolution data for several years at eight locations in Chile to assess short-term solar radiation variability using 6 well-stablished metrics, compares the information provided by each one and develops correlations to compare results among them. Additionally, high-speed transient phenomena are analyzed and a new metric is defined to assess the solar resource with respect to its quality in static and dynamic terms. Finally, four day classification schemes are developed and evaluated, and results for Chile are shown in the context of its geography and climatological classification.
A Virtual-Real Interaction Approach to Object Instance Segmentation in Traffic Scenes

Object instance segmentation in traffic scenes is an important research topic. For training instance segmentation models, synthetic data can potentially complement real data, alleviating manual effort on annotating real images. However, the data distribution discrepancy between synthetic data and real data hampers the wide applications of synthetic data. In light of that, we propose a virtual-real interaction method for object instance segmentation. This method works over synthetic images with accurate annotations and real images without any labels. The virtual-real interaction guides the model to learn useful information from synthetic data while keeping consistent with real data. We first analyze the data distribution discrepancy from a probabilistic perspective, and divide it into image-level and instance-level discrepancies. Then, we design two components to align these discrepancies, i.e., global-level alignment and local-level alignment. Furthermore, a consistency alignment component is proposed to encourage the consistency between the global-level and the local-level alignment components. We evaluate the proposed approach on the real Cityscapes dataset by adapting from virtual SYNTHIA, Virtual KITTI, and VIPER datasets. The experimental results demonstrate that it achieves significantly better performance than state-of-the-art methods.
Dynamic Autonomous Cross Consortium Chain Mechanism in e-Healthcare

Safe and scalable dynamic autonomous data interaction between medical institutions can increase the number of clinical trial records, which is of great significance for improving the level of medical trial collaboration, especially for clinical decision-making with regard to rare diseases. Through a preset authorization access and consensus mechanism, consortium chain provides integrity and traceability management for medical clinical data. However, how to enable users have ownership of their own medical data and share their medical data safely and dynamically between different medical institutions remains an area of particular concern. To achieve dynamic communication between medical consortium chains, this paper proposes (i) a cross-chain communication mechanism by simplifying the heterogeneous node communication topology and (ii) the construction rules of the node identity credibility path-proof to carry out dynamic construction and verification of the path-proof for cross-chain transactions. In addition, the consensus of the cross-chain transaction is modeled as a threshold digital signature process with multiple privileged subgroups; thus, the intra-chain consortium consensus based on the verification node list is extended to the cross-chain consensus. A smart contract deployment and execution scheme based on rational node value transfer mechanism is proposed by analyzing the value transfer game between nodes. Experimental results showed that the proposed scheme can not only enable patients to share their records safely and autonomously in an authorized medical consortium chain within milliseconds but also realize dynamic adaptive interaction among heterogeneous consortium chains.
METEOR: Measurable Energy Map Toward the Estimation of Resampling Rate via a Convolutional Neural Network

In recent years, with the improvements in machine learning, image forensics has made considerable progress in detecting editing manipulations. This progress also raises more questions in image forensics research, such as can the parameters applied in a manipulation be estimated. Many parameter estimation works have already been performed. However, most of these works are based on mathematical analyses. In this paper, we attempt to solve a particular parameter estimation problem from a different aspect. Specifically, a new convolutional neural network (CNN) model is proposed to estimate the resampling rate for resampled images regardless of whether the image is upscaled or downscaled. This model features an original layer to generate a measurable energy map toward the estimation of resampling rate (METEOR). The METEOR layer is demonstrated to be an outstanding method that can assist in enhancing the estimation performance of the CNN. Furthermore, the METEOR layer can also increase the robustness of the CNN against JPEG compression, which makes it extremely important in realistic application scenarios. Our work has verified that machine learning, particularly CNNs, with proper optimization can also be refined to adapt to parameter estimation in digital forensics with excellent performance and robustness.
Action Recognition Using Attention-Joints Graph Convolutional Neural Networks

Human skeleton contains significant information about actions, therefore, it is quite intuitive to incorporate skeletons in human action recognition.Human skeleton resembles to a graph where body joints and bones mimic to graph nodes and edges.This resemblance of human skeleton to graph structure is the main motivation to apply graph convolutional neural network for human action recognition.Results show that the discriminant contribution of different joints is not equal for different actions.Therefore, we propose to use attention-joints that correspond to joints significantly contributing to the specific actions.Features corresponding to only these attention-joints are computed and assigned as node features of the graph.In our method, node features (also termed as attention-joint features) include the i) distances of attention-joints from the center-of-gravity of human body, ii) distances between adjacent attention-joints and iii) joints flow features.The proposed method gives a simple but more efficient representation of skeleton sequences by concatenating more relative distances and relative coordinates to other joints.The proposed methodology has been evaluated on single image Stanford 40-Actions dataset, as well as on temporal skeleton-based action recognition PKU-MDD and NTU-RGBD datasets.Results show that this framework outperforms existing state-of-the-art methods.
A predictive model for phishing detection

Nowadays, many anti-phishing systems are being developed to identify phishing contents in online communication systems. Despite the availability of myriads anti-phishing systems, phishing continues unabated due to inadequate detection of a zero-day attack, superfluous computational overhead and high false rates. Although Machine Learning approaches have achieved promising accuracy rate, the choice and the performance of the feature vector limit their effective detection. In this work, an enhanced machine learning-based predictive model is proposed to improve the efficiency of anti-phishing schemes. The predictive model consists of Feature Selection Module which is used for the construction of an effective feature vector. These features are extracted from the URL, webpage properties and webpage behaviour using the incremental component-based system to present the resultant feature vector to the predictive model. The proposed system uses Support Vector Machine and Naïve Bayes which have been trained on a 15-dimensional feature set. The experiments were based on datasets consisting of 2541 phishing instances and 2500 benign instances. Using 10-fold cross-validation, the experimental results indicate a remarkable performance with 0.04% False Positive and 99.96% accuracy for both SVM and NB predictive models.
Supervised and Semi-Supervised Learning for MIMO Blind Detection With Low-Resolution ADCs

The use of low-resolution analog-to-digital converters (ADCs) is considered to be an effective technique to reduce the power consumption and hardware complexity of wireless transceivers. However, in systems with low-resolution ADCs, obtaining channel state information (CSI) is difficult due to significant distortions in the received signals. The primary motivation of this paper is to show that learning techniques can mitigate the impact of CSI unavailability. We study the blind detection problem in multiple-input-multiple-output (MIMO) systems with low-resolution ADCs using learning approaches. Two methods, which employ a sequence of pilot symbol vectors as the initial training data, are proposed. The first method exploits the use of a cyclic redundancy check (CRC) to obtain more training data, which helps improve the detection accuracy. The second method is based on the perspective that the to-be-decoded data can itself assist the learning process, so no further training information is required except the pilot sequence. For the case of 1-bit ADCs, we provide a performance analysis of the vector error rate for the proposed methods. Based on the analytical results, a criterion for designing transmitted signals is also presented. Simulation results show that the proposed methods outperform existing techniques and are also more robust.
Single image dehazing based on learning of haze layers

This paper proposes a new haze layer based single image dehazing algorithm. The residual images, which exists between the hazy images and the clear images, will be firstly obtained by haze layers through an end-to-end mapping from the original hazy images. Then the designed convolutional neural network can remove the residual image from the given hazy image to obtain a recovered dehazed image. In order to remove the halos and block artefacts in the dehazed images, a guided filter is applied before generating the final dehazed images, which will also increase the reality of the result images. Since the proposed haze layer based dehazing algorithm directly learns the residual images, it enjoys a relatively high learning rate and low computation. We tested and compared the proposed algorithm with other dehazing methods in various variables, such as the density of the fog and the testing scenes, e.g. real-world images or synthetic images. Experimental results showed the advancement of the proposed method in many metrics.
Sliding Window Blockchain Architecture for Internet of Things

Internet of Things (IoT) refers to the concept of enabling Internet connectivity and associated services to nontraditional computers formed by integrating essential computing and communication capability to physical things for everyday usage. Security and privacy are two of the major challenges in IoT. The essential security requirements of IoT cannot be ensured by the existing security frameworks due to the constraints in CPU, memory, and energy resources of the IoT devices. Also, the centralized security architectures are not suitable for IoT because they are subjected to single point of attacks. Defending against targeted attacks on centralized resources is expensive. Therefore, the security architecture for IoT needs to be decentralized and designed to meet the limitations in resources. Blockchain is a decentralized security framework suitable for a variety of applications. However, blockchain in its original form is not suitable for IoT, due to its high-computational complexity and low scalability. In this article, we propose a sliding window blockchain (SWBC) architecture that modifies the traditional blockchain architecture to suit IoT applications. The proposed SWBC uses previous $(n-1)$ blocks to form the next block hash with limited difficulty in proof-of-work (PoW). The performance of SWBC is analyzed on a real-time data stream generated from a smart home testbed. The results show that the proposed blockchain architecture increases security and minimizes memory overhead while consuming fewer resources.
Why do users not report spear phishing emails?

Cyber security training programs encourage users to report suspicious spear phishing emails, and most antiphishing software provide interfaces to assist in the reporting. Evidence, however, suggests that reporting is scarce. This research examined why this is the case. To this end, Social Cognitive Theory (SCT) was used to examine the influence of the triadic factors of perceived self-efficacy toward antiphishing behaviors, expected negative outcomes from reporting spear phishing emails, and cyber security self-monitoring, on individuals' likelihood of reporting spear phishing emails. Based on recent research on phishing victims, the present study also incorporated cyber risk beliefs (CRBs) into the SCT framework. The model, tested using survey data (N = 386), revealed that the likelihood of reporting spear phishing emails is increased by perceived self-efficacy, expected negative outcomes, and cyber security self-monitoring. Furthermore, the CRBs directly influenced the three SCT factors and indirectly the individuals' likelihood of reporting spear phishing emails. The findings add to our understanding of SCT and the science of cyber security.
The requirements of cryptocurrency for money, an Islamic view

This research aims to evaluate the suitability of cryptocurrency as money from the Islamic perspective. Money, in the Islamic perspective, has specific characteristics and requirements, such as stability and is based on assets. Cryptocurrency may not fulfil this as it has queries as money from the Islamic perspective. The research method applied data of 23 cryptocurrency prices and related information. The result shows that cryptocurrency is hugely volatile and has limits to being called 'money,' as it is limited and used for speculation, which is prohibited in Islam. The research implies that Muslims would be reluctant to use cryptocurrency as money, as a currency of transaction. This reason raise an expectation that the cryptocurrency will not develop rapidly in Muslim countries.
Mining correlated high-utility itemsets using various measures

Abstract Discovering high-utility itemsets (HUIs) consists of finding sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining (HUIM) is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding several itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in HUIM to find profitable itemsets that are highly correlated, using the all-confidence and bond measures. An efficient algorithm named FCHM (fast correlated high-utility itemset miner) is proposed to efficiently discover correlated high-utility itemsets (CHIs). Two versions of the algorithm are proposed: FCHM$_{all\text{-}confidence}$ and FCHM$_{bond}$, which are based on the all-confidence and bond measures, respectively. An experimental evaluation was done using four real-life benchmark datasets from the HUIM literature: mushroom, retail, kosarak and foodmart. Results show that FCHM is efficient and can prune a huge amount of weakly CHIs.
A Lightweight Privacy-Preserving Fair Meeting Location Determination Scheme

Equipped with mobile devices, people relied on location-based services (LBSs) can expediently and reasonably organize their activities. But location information may disclose people's sensitive information, such as interests and health status. Besides, the limited resources of mobile devices restrict the further development of LBSs. In this article, aiming at the fair meeting position determination service, we design a lightweight privacy-preserving solution. In our scheme, mobile users only need to submit service requests. A cloud server and a location services provider are responsible for service response, where the cloud server achieves most of the calculation, and the location services provider determines the fair meeting location based on the computational results of the cloud server and broadcasts it to mobile users. The proposed scheme adopts homomorphic encryptions and random permutation methods to preserve the location privacy of mobile users. The security analyses show that the proposed scheme is privacy preserving under our defined threat models. Besides, the presented solution only needs to calculate $n$ Euclidean distances, and hence, our scheme has linear computation and communication complexity.