Reviewerly/exHarmony huggingface dataset:
An online continual object detector on VHR remote sensing images with class imbalance

It is a great challenge for traditional offline detectors to learn from continuous data streams, remember previous tasks and adapt to new-coming tasks in dynamic environments. To meet the challenge, online continual learning has recently attracted increasing attention, while the overwhelming majority of works focus only on classification with a balanced class distribution assumption. In this paper, we propose a replay-based approach called an online continual object detector (OCOD) for very-high-resolution (VHR) remote sensing images. First, we find that rehearsal imbalance is ubiquitous, and has more important impact on experimental results than class imbalance, which is contrary to the situation of offline learning (due to the limited memory). Here, rehearsal imbalance refers to significant difference among the number of images pertaining to various classes. Second, entropy is used to measure the degree of rehearsal imbalance in the memory, and an entropy reservoir sampling (ERS) strategy is proposed to maintain rehearsal balance in the online memory. Finally, a rehearsal-balancing priority assignment network (RBPAN) is proposed to adaptively select images from the memory for a rehearsal-balancing replay procedure. The experimental results obtained on three publicly available VHR satellite images from the NWPU VHR-10, DIOR and DOTA datasets, highlight the effectiveness and practicality of developed method.
Enhancing resolution of micro-CT images of reservoir rocks using super resolution

Current hardware configuration of micro-CT detectors puts a lower limit on voxel size that can be acquired while maintaining a sufficiently large field of view. This limits the degree to which rock pores can be resolved in a micro-CT image and thus restricting the application envelope of Digital Rock technology. Super resolution techniques can refine voxel size while maintaining a sufficiently large field of view using pairs of low- and high-resolution images for training. However, for interpretation of quality of Digital Rock results, image quality is not determined by voxel size alone but by the degree to which a feature such as pore throat is resolved, which depends on both the physical size of the feature and voxel size. Furthermore, artificially down-sampling finer voxel size images to obtain images of coarser voxel size for training deep learning networks is not sufficient to capture the mapping between images acquired at different resolutions. This is especially true for reservoir rocks because the noise and artifacts introduced during imaging and reconstruction are more complex than that captured by simple down-sampling operation. We overcome these two limitations, by (1) using the ratio of pore throat size and voxel size (N) to group training dataset instead of voxel size and (2) using pairs of registered micro-CT images acquired using a state-of-the-art detector instead of synthetically down-sampled images. We show that combination of these two techniques produced images with better sharpness and contrast and enabled us to refine voxel size significantly beyond what is possible using the current imaging technology while maintaining the field of view.
Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection

Abstract The sine cosine algorithm (SCA) is a metaheuristic algorithm proposed in recent years that does not resort to nature-related metaphors but explores and exploits the search space with the help of two simple mathematical functions of sine and cosine. SCA has fewer parameters and a simple structure and is widely used in various fields. However, it tends to fall into local optimality because it does not have a well-balanced exploitation and exploration phase. Therefore, in this paper, a new, improved SCA algorithm (QCSCA) is proposed to improve the performance of the algorithm by introducing a quick move mechanism and a crisscross mechanism to SCA and adaptively improving one of the parameters. To verify the effectiveness of QCSCA, comparison experiments with some conventional metaheuristic algorithms, advanced metaheuristic algorithms, and SCA variants are conducted on IEEE CEC2017 and CEC2013. The experimental results show a significant improvement in the convergence speed and the ability to jump out of the local optimum of the QCSCA. The scalability of the algorithm is verified in the benchmark function. In addition, QCSCA is applied to 14 real-world datasets from the UCI machine learning database for selecting a subset of near-optimal features, and the experimental results show that QCSCA is still very competitive in feature selection (FS) compared to similar algorithms. Our experimental results and analysis show that QCSCA is an effective method for solving global optimization problems and FS problems.
Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning

Radar has played an irreplaceable role in modern warfare. A variety of radar-jamming methods have been applied in recent years, which makes the electromagnetic environment more complex. The classification of radar jamming is critical for electronic counter-countermeasures (ECCM). In the field of signal classification, machine learning-based methods take great effort to find proper features as well as classifiers, and deep learning-based methods depend on large training datasets. For the above reasons, an efficient transfer learning-based method is proposed in this paper. Firstly, one-dimensional radar signals were transformed into time–frequency images (TFIs) using linear and bilinear time–frequency analysis, which is inspired by symmetry theory. Secondly, pretrained AlexNet and SqueezeNet networks were modified to classify the processed TFIs. Finally, performance of this method was evaluated and compared using a simulated data set with nine types of radar-jamming signals. The results demonstrate that our proposed classification method performs well in accuracy and efficiency at a 1% training ratio, which is practical for anti-jamming.
Fast subspace clustering by learning projective block diagonal representation

Block Diagonal Representation (BDR) has attracted massive attention in subspace clustering, yet the high computational cost limits its widespread application. To address this issue, we propose a novel approach called Projective Block Diagonal Representation (PBDR), which rapidly pursues a representation matrix with the block diagonal structure. Firstly, an effective sampling strategy is utilized to select a small subset of the original large-scale data. Then, we learn a projection mapping to match the block diagonal representation matrix on the selected subset. After training, we employ the learned projection mapping to quickly generate the representation matrix with an ideal block diagonal structure for the original large-scale data. Additionally, we further extend the proposed PBDR model (i.e., PBDRℓ1 and PBDR*) by capturing the global or local structure of the data to enhance block diagonal coding capability. This paper also proves the effectiveness of the proposed model theoretically. Especially, this is the first work to directly learn a representation matrix with a block diagonal structure to handle the large-scale subspace clustering problem. Finally, experimental results on publicly available datasets show that our approaches achieve faster and more accurate clustering results compared to the state-of-the-art block diagonal-based subspace clustering approaches, which demonstrates its practical usefulness.
Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs

Graph Neural Networks (GNN) show good performance in relational data classification. However, their contribution to concept learning and the validation of their output from an application domain's and user's perspective have not been thoroughly studied. We argue that combining symbolic learning methods, such as Inductive Logic Programming (ILP), with statistical machine learning methods, especially GNNs, is an essential forward-looking step to perform powerful and validatable relational concept learning. In this contribution, we introduce a benchmark for the conceptual validation of GNN classification outputs. It consists of the symbolic representations of symmetric and non-symmetric figures that are taken from a well-known Kandinsky Pattern data set. We further provide a novel validation framework that can be used to generate comprehensible explanations with ILP on top of the relevance output of GNN explainers and human-expected relevance for concepts learned by GNNs. Our experiments conducted on our benchmark data set demonstrate that it is possible to extract symbolic concepts from the most relevant explanations that are representative of what a GNN has learned. Our findings open up a variety of avenues for future research on validatable explanations for GNNs.
The Generals' Scuttlebutt

One of the most successful applications of peer-to-peer communication networks is in the context of blockchain protocols, which-in Satoshi Nakamoto's own words-rely on the "nature of information being easy to spread and hard to stifle." Significant efforts were invested in the last decade into analyzing the security of these protocols, and invariably the security arguments known for longest-chain Nakamoto-style consensus use an idealization of this tenet. Unfortunately, the real-world implementations of peer-topeer gossip-style networks used by blockchain protocols rely on a number of ad-hoc attack mitigation strategies that leave a glaring gap between the idealized communication layer assumed in formal security arguments for blockchains and the real world, where a wide array of attacks have been showcased.
Watch Your Back

Cybercriminals often leverage Bitcoin for their illicit activities. In this work, we propose back-and-forth exploration, a novel automated Bitcoin transaction tracing technique to identify cybercrime financial relationships. Given seed addresses belonging to a cybercrime campaign, it outputs a transaction graph, and identifies paths corresponding to relationships between the campaign under study and external services and other cybercrime campaigns. Back-and-forth exploration provides two key contributions. First, it explores both forward and backwards, instead of only forward as done by prior work, enabling the discovery of relationships that cannot be found by only exploring forward (e.g., deposits from clients of a mixer). Second, it prevents graph explosion by combining a tagging database with a machine learning classifier for identifying addresses belonging to exchanges.
UltraHi-PrNet: An Ultra-High Precision Deep Learning Network for Dense Multi-Scale Target Detection in SAR Images

Multi-scale target detection in synthetic aperture radar (SAR) images is one of the key techniques of SAR image interpretation, which is widely used in national defense and security. However, multi-scale targets include several types. For example, targets with similar-scale, large-scale, and ultra-large-scale differences coexist in SAR images. In particular, it is difficult for existing target detection methods to detect both ultra-large-scale targets and ultra-small-scale targets in SAR images, resulting in poor detection results for these two types of targets. To solve these problems, this paper proposes an ultra-high precision deep learning network (UltraHi-PrNet) to detect dense multi-scale targets. Firstly, a novel scale transfer layer is constructed to transfer the features of targets of different scales from bottom networks to top networks, ensuring that the features of ultra-small-scale, small-scale, and medium-scale targets in SAR images can be extracted more easily. Then, a novel scale expansion layer is constructed to increase the range of the receptive field of feature extraction without increasing the feature resolution, ensuring that the features of large-scale and ultra-large-scale targets in SAR images can be extracted more easily. Next, the scale expansion layers with different expansion rates are densely connected to different stages of the backbone network, and the features of the target with ultra-large-scale differences are extracted. Finally, the classification and regression of targets were achieved based on Faster R-CNN. Based on the SAR ship detection dataset (SSDD), AIR-SARShip-1.0, high-resolution SAR ship detection dataset-2.0 (high-resolution SSDD-2.0), the SAR-ship-dataset, and the Gaofen-3 airport dataset, the experimental results showed that this method can detect similar-scale, large-scale, and ultra-large-scale targets more easily. At the same time, compared with other advanced SAR target detection methods, the proposed method can achieve higher accuracy.
Efficient differentially private kernel support vector classifier for multi-class classification

In this paper, we propose a multi-class classification method using kernel supports and a dynamical system under differential privacy. For small datasets, kernel methods, such as kernel support vector machines (SVMs), show good generalization performance with high-dimensional feature mapping. However, kernel SVMs have a fundamental weakness in achieving differential privacy because they construct decision functions based on a subset of the training data called support vectors. Furthermore, multi-class SVMs must decompose the training data into a binary class, which requires multiple accesses to the same training data. To address these limitations, we develop a two-phase classification algorithm based on support vector data description (SVDD). We first generate and prove a differentially private SVDD (DP-SVDD) by perturbing the sphere center in a high-dimensional feature space. Next, we partition the input space using a dynamical system and classify the partitioned regions using a noisy count. The proposed method results in robust, fast, and user-friendly multi-class classification, even on small-sized datasets, where differential privacy performs poorly.
Improved single shot detection using <scp>DenseNet</scp> for tiny target detection

Summary As the development of deep learning and the continuous improvement of computing power, as well as the needs of social production, target detection has become a research hotspot in recent years. However, target detection algorithm has the problem that it is more sensitive to large targets and does not consider the feature‐feature interrelationship, which leads to a high false detection or missed detection rate of small targets. An small target detection method (C‐SSD) based on improved SSD is proposed, that replaces the backbone network VGG‐16 of the SSD network with the improved dense convolution network (C‐DenseNet) network to achieves further feature fusion through fast connections between dense blocks. The Introduction of residuals in the prediction layer and DIoU‐NMS further improves the detection accuracy. Experimental results demonstrate that C‐SSD outperforms other networks at three different image scales and achieves the best performance of 83. A 8% accuracy on the PASCAL VOC2007 test set, proving the effectiveness of the algorithm. C‐SSD achieves a better balance of speed and accuracy, showing excellent performance in rapid detection of small targets.
All that is solid melts in the Ethereum: the brave new (art) world of NFTs

This article explores non-fungible tokens, better known as NFTs, or blockchain-based certificates of ownership for visual or physical assets, a cultural phenomenon which has come to the media attention following the sale of a non-fungible token photo collage, Everyday: The First 5,000 Days, for more than 69 million USD at a Christie's auction in March 2021.The sale made Mike Winkelman (aka Beeple) the third most valuable living artist, behind Jeff Koons and David Hockney starting off a conversation about NFTs which has covered a wide range of issues, including: the contested relationship between art and the market, the long debated question of collective and/or individual authorship within digital aesthetics, the most recent developments in Artificial Intelligence and their creative potential with regard to NFTsi.e. the appearance of intelligent non-fungible tokens (iNFTs)and, lastly, the role of cryptocurrencies as a tool for artists' empowerment or, conversely, as a selling out, under new technological guises, to the capitalist logic of the market.The article reviews and evaluates such debates with the aim to offer critical pointers to help the reader navigate the emerging world of Crypto art.
Scalable one-stage multi-view subspace clustering with dictionary learning

The clustering of large numbers of heterogeneous features is a hot topic in multi-view communities. Most existing multi-view clustering (MvC) methods employ matrix factorization or anchor strategies to handle large-scale datasets. The former operates on the original data and is, therefore, sensitive to noise and feature redundancy, which is reflected in the final clustering performance. The latter requires post-processing steps to generate the clustering results, which may be suboptimal owing to the isolation steps. To address the above problems, we propose one-stage multi-view subspace clustering with dictionary learning (OSMvSC). Specifically, we integrate dictionary learning, representation coefficient matrix learning, and matrix factorization as a unified learning framework, which directly learns the dictionary and representation coefficient matrix to encode the original multi-view data, and obtains the clustering results with linear time complexity without any postprocessing step. By manipulating the class centroid with the nuclear norm, a more compact and discriminative class centroid representation can be obtained to further improve clustering performance. An effective optimization algorithm with guaranteed convergence is designed to solve the proposed method. Substantial experiments on various real-world multi-view datasets demonstrate the effectiveness and superiority of the proposed method. The source code is available at https://github.com/justcallmewilliam/OSMvSC.
Intelligent Deep Machine Learning Cyber Phishing URL Detection Based on BERT Features Extraction

Recently, phishing attacks have been a crucial threat to cyberspace security. Phishing is a form of fraud that attracts people and businesses to access malicious uniform resource locators (URLs) and submit their sensitive information such as passwords, credit card ids, and personal information. Enormous intelligent attacks are launched dynamically with the aim of tricking users into thinking they are accessing a reliable website or online application to acquire account information. Researchers in cyberspace are motivated to create intelligent models and offer secure services on the web as phishing grows more intelligent and malicious every day. In this paper, a novel URL phishing detection technique based on BERT feature extraction and a deep learning method is introduced. BERT was used to extract the URLs’ text from the Phishing Site Predict dataset. Then, the natural language processing (NLP) algorithm was applied to the unique data column and extracted a huge number of useful data features in terms of meaningful text information. Next, a deep convolutional neural network method was utilised to detect phishing URLs. It was used to constitute words or n-grams in order to extract higher-level features. Then, the data were classified into legitimate and phishing URLs. To evaluate the proposed method, a famous public phishing website URLs dataset was used, with a total of 549,346 entries. However, three scenarios were developed to compare the outcomes of the proposed method by using similar datasets. The feature extraction process depends on natural language processing techniques. The experiments showed that the proposed method had achieved 96.66% accuracy in the results, and then the obtained results were compared to other literature review works. The results showed that the proposed method was efficient and valid in detecting phishing websites’ URLs.
Systematic literature review and analysis for Arabic text steganography method practically

Despite the rapidly growing studies on Arabic text steganography (ATS) noted recently; systematic, in-depth, and critical reviews are in scarcity due to high overlap or low segregation level among the existing review articles linked to this research area. As such, the objective of this paper is to present an extensive systematic literature review (SLR) on the techniques and algorithms used to analyse ATS. Data were retrieved from three primary databases, namely Science direct Journal, IEEE Explore Digital Library, and Scopus Journal. As a result, 214 publications were identified since the past 5 years regarding methods of analysing ATS. A comprehensive SLR was executed to detect a range of unique characteristics from the algorithms, which led to the discovery of a new structure of ATS categories. Essentially, a hybrid method for ATS was identified with other sub-disciplines, especially cryptography, which leads to a new branch in enhancing security for ATS. Other relevant findings included key performance and evaluation criteria used to measure the performance of the algorithms (i.e., capacity, invisibility, robustness, & security). 87 % of the reviewed articles are the capacity measurement performance. Therefore, it disclosed a huge potential for the other two criteria (i.e. invisibility, robustness and security) to set a benchmark for future research endeavour.
DescribeML

Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased predictions. To overcome this situation, the ML community is proposing a data-centric cultural shift, where data issues are given the attention they deserve, for instance, proposing standard descriptions for datasets.
Platypus

Due to the popularity of blockchain-based cryptocurrencies, the increasing digitalization of payments, and the constantly reducing role of cash in society, central banks have shown an increased interest in deploying central bank digital currencies (CBDCs) that could serve as a digital cash-equivalent. While most recent research on CBDCs focuses on blockchain technology, it is not clear that this choice of technology provides the optimal solution. In particular, the centralized trust model of a CBDC offers opportunities for different designs. In this paper, we depart from blockchain designs and instead build on ideas from traditional e-cash schemes. We propose a new style of building digital currencies that combines the transaction processing model of e-cash with an account-based fund management model. We argue that such a style of building digital currencies is especially well-suited to CBDCs. We also design the first such digital currency system, called Platypus, that provides strong privacy, high scalability, and expressive but simple regulation, which are all critical features for a CBDC. Platypus achieves these properties by adapting techniques similar to those used in anonymous blockchain cryptocurrencies like Zcash to fit our account model and applying them to the e-cash context.