Reviewerly/exHarmony huggingface dataset:
Fully homomorphic encryption-based privacy-preserving scheme for cross edge blockchain network

The growing development of blockchain technology has led to the emergence of blockchain applications for various fields. One significant use of blockchain technology is the edge blockchain network, which is a blockchain network made up of connected edge computer nodes. However, on-chain privacy data leaking is a concern on the present edge blockchain network. The demand for collaborative interaction amongst edge computing nodes in many domains is growing more critical as the Internet of Things continues to evolve. In this paper, we propose a data privacy-preserving scheme for cross edge blockchain network, which uses the privacy protection technology of fully homomorphic encryption to encrypt the data on the chain to ensure the “available and invisible” of the on-chain privacy data. First of all, this paper designs a cross edge blockchain model and give details of the interaction and privacy computing process in the cross edge blockchain. a distributed private key sharing scheme is designed in this work to provide the relay chain’s auditable functionality. Subsequently, we describe the security of the scheme in detail. Finally, we implement this scheme using the CKKS algorithm. Through simulation experiments, we evaluate the performance of the instantiation process of the scheme. The test results manifest that the proposed scheme has excellent practicability and security.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si24.svg" display="inline" id="d1e2056"><mml:mrow><mml:mi>L</mml:mi><mml:mi>R</mml:mi><mml:mi>F</mml:mi></mml:mrow></mml:math>: A logically randomized forest algorithm for classification and regression problems

Tree-based ensemble algorithms (TEAs) have made significant advances in recent years due to their simple algorithmic design. However, when the proportion of the ‘most informative’ features is low, the performance of conventional TEAs degrades significantly. The primary rationale for performance degradation is that traditional algorithmic design appears to be biased toward the least informative features, and the sub-space selection procedure contains uninformative features. This paper proposes a logically randomized forest (LRF) algorithm by incorporating two different enhancements into existing TEAs. The first enhancement is made to address the issue of biasness by performing feature-level engineering. The second enhancement is the approach by which individual feature sub-spaces are selected. To derive the first enhancement, we use the graph-theoretic principle of minimal vertex cover to construct a relevant assemblage of features. Following that, the permutation-based feature importance technique is employed to calculate the ‘informativeness’ of the relevant features in order to infuse logical randomness into the individual trees in the forest. For the second enhancement, the stratified sampling method is used to ensure that the most informative features are present in all newly created feature sub-spaces. Consequently, individual trees are generated using the Roulette wheel-based selection (RWS) algorithm. The proposed algorithm has been evaluated on two real-world genomic data sets, ten hybrid-synthetic classification data sets, and twenty multidisciplinary benchmark data sets with varying characteristics. The experimental findings demonstrate that the LRF outperforms the existing benchmark and cutting-edge TEAs.
Evolutionary feature selection on high dimensional data using a search space reduction approach

Feature selection is becoming more and more a challenging task due to the increase of the dimensionality of the data. The complexity of the interactions among features and the size of the search space make it unfeasible to find the optimal subset of features. In order to reduce the search space, feature grouping has arisen as an approach that allows to cluster feature according to the shared information about the class. On the other hand, metaheuristic algorithms have proven to achieve sub-optimal solutions within a reasonable time. In this work we propose a Scatter Search (SS) strategy that uses feature grouping to generate an initial population comprised of diverse and high quality solutions. Solutions are then evolved by applying random mechanisms in combination with the feature group structure, with the objective of maintaining during the search a population of good and, at the same time, as diverse as possible solutions. Not only does the proposed strategy provide the best subset of features found but it also reduces the redundancy structure of the data. We test the strategy on high dimensional data from biomedical and text-mining domains. The results are compared with those obtained by other adaptations of SS and other popular strategies. Results show that the proposed strategy can find, on average, the smallest subsets of features without degrading the performance of the classifier.
On the Success Rate of Side-Channel Attacks on Masked Implementations

This study derives information-theoretical bounds of the success rate (SR) of side-channel attacks on masked implementations. We first develop a communication channel model representing side-channel attacks on masked implementations. We then derive two SR bounds based on the conditional probability distribution and mutual information of shares. The basic idea is to evaluate the upper-bound of the mutual information between the non-masked secret value and the side-channel trace by the conditional probability distribution of shares given its leakage, with a help of the Walsh-Hadamard transform. With the derived theorems, we also prove the security of masking schemes: the SR decreases exponentially with an increase in the number of masking shares, under a much more relaxed condition than the previous proof. To validate and utilize our theorems in practice, we propose a deep-learning-based profiling method for approximating the conditional probability distribution of shares to estimate the SR bound and the number of traces required for attacking a given device. We experimentally confirm that our bounds are much stronger than the conventional bounds on masked implementations, which validates the relevance of our theorems to practice.
META-BTS

Bootstrapping, which enables the full homomorphic encryption scheme that can perform an infinite number of operations by restoring the modulus of the ciphertext with a small modulus, is an essential step in homomorphic encryption. However, bootstrapping is the most time and memory consuming of all homomorphic operations. As we increase the precision of bootstrapping, a large amount of computational resources is required. Specifically, for any of the previous bootstrap designs, the precision of bootstrapping is limited by rescaling precision.
Metallogenesis of the Bakoshi-Kundila gold deposit in northern West Nigerian Subshield: Insights from pyrite chemical and sulfur isotopic compositions and zircon U-Pb geochronology

The Bakoshi-Kundila gold deposit, located in northern West Nigerian Subshield, in the southern trans-Saharan Orogenic Belt of West Africa, is a recent discovery and artisanally mined deposit. Like other Nigerian gold deposits, the mechanism and timing of gold precipitation remains unknown. In this study, we report the results of an integrated study of pyrite chemical and isotopic compositions and U-Pb on zircon geochronology to determine the gold precipitation mechanisms, fluid sources, and formation age of the gold mineralization. Two mineralization stages are recognized: (i) the Au-pyrite-quartz vein-type mineralization, including two generations of pyrite referred to as Py1a and Py1b, and (ii) wall rock disseminated-type Au-pyrite mineralization, comprising three pyrite types, Py2a, Py2b, and Py3. Py1 is Au-rich and As-poor pyrite and contain lattice-bound Au (Au+), nanoparticle Au (Au0), and native Au grains adsorbed on Py1b surface. Associated with Py1 are Ag-Te-Au bearing minerals. In contrast, Py2 and Py3 are As-rich and are less in Au; they contain only lattice-bound Au. Py1 grains have higher contents of Ag, Pb, Bi, Sb, Tl, and Cu which correlate with Au compared to those from Py2 and Py3. Py1 generations has lower δ34SV-CDT from –5.43 to –2.11 ‰ compared to the higher values of +3.63 to +5.47 ‰ obtained for the Py2 and Py3. These sulfur isotopes data are within the δ34S values of 0 ± 5 ‰ of the magmatic reservoir. U-Pb on zircon from Au-pyrite-quartz vein stage yielded older ages of 644.5 ± 2.2 Ma and 681.5 ± 1.8 Ma, compared to younger age of 606. 4 ± 1.8 Ma yielded by zircon grains from wall rock-type mineralization. Additionally, zircon grains from Au-bearing Tourmalinite yielded ages of 679 ± 1.3 Ma and 609.6 ± 7.1 Ma. We propose that Au mineralization at the Bakoshi-Kundila occurred during two periods in the Neoproterozoic: (i) an early ∼ 682 Ma (Cryogenian), possibly related to the emplacement of 683 Ma Yettiti granite and (ii) a second-stage Au disseminated in wall rock of granites and might have experienced 606–610 Ma Ediacaran metamorphism during the late tectonic events in West Nigerian Subshield.
A comparative study on deep‐learning methods for dense image matching of multi‐angle and multi‐date remote sensing stereo‐images

Abstract Deep‐learning (DL) stereomatching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo‐images lacking a systematic evaluation on how robust DL methods are on satellite stereo‐images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereomatching methods through hundreds of multi‐date multi‐site satellite stereopairs with varying geometric configurations, against the traditional well‐practiced Census‐semi‐global matching (SGM), to comprehensively understand their accuracy, robustness, generalisation capabilities, and their practical potential. The DL methods include a learning‐based cost metric through convolutional neural networks (MC‐CNN) followed by SGM, and three end‐to‐end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalise well for unseen data. The learning‐based cost metric and Census‐SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereopairs and are less sensitive in comparison to the Census‐SGM, while learning‐based cost metrics can generalise on satellite images when trained on different datasets (airborne or ground‐view).
Generalized minimum error entropy Kalman filter for non-Gaussian noise

Error entropy is a well-known learning criterion in information theoretic learning (ITL), and it has already been applied to a wide range of fields. However, the shape of error entropy cannot be changed freely since its kernel function is the Gaussian kernel function, which causes the error entropy-based algorithm to handle only some specific kinds of noises. Benefiting from the property that the generalized Gaussian kernel function is free to adjust its shape, a novel Kalman-type filter algorithm based on the generalized minimum error entropy (GMEEKF) criterion is derived. Moreover, the mean error behavior, mean square error behavior, and computational complexity of the GMEEKF algorithm are analyzed. Finally, several simulations and experiments are performed to demonstrate the performance of the GMEEKF algorithm in comparison with the existing Kalman-type filter algorithms.
A data augmentation framework by mining structured features for fake face image detection

For fake face image detection, most existing detectors exploit local artifacts, ignoring the mining of structured forgery clues existed in global images, which greatly constrains detection performance. In this work, we verify the importance of structured forgery clues for fake face image detection, and present a new data augmentation framework called Mining Structured Features (MSF) to promote the convolutional neural network (CNN) based detector. Specifically, MSF generates a position-sensitive artifact map, which is exploited to divide a candidate face image into strong correlation (SC) and weak correlation (WC) regions. By dynamically erasing some positions in SC and WC regions during the training process, MSF can promote the robustness of the detector to the above regions. In essence, MSF expands the attention range of the detector and fully mines the structured features from global images. MSF plays an auxiliary role, which can be seamlessly integrated into existing CNN-based detectors for end-to-end training. Extensive experimental results on four public datasets including HFFD, FF++, DFDC and Celeb-DF show that the detectors trained with the guidance of MSF can mine more useful clues distributed in fake face images to improve detection accuracies, achieving better results than state-of-the-art works. Our code will be available at https://github.com/EricGzq/MSF.
Pure graph-guided multi-view subspace clustering

Multi-view subspace clustering approaches have shown outstanding performance in revealing similarity relationships and complex structures hidden in data. Despite the progress, previous multi-view clustering methods still face two challenges: (1) it is difficult to simultaneously achieve sparsity and connectivity of the affinity graph; (2) existing methods usually separate the graph learning step from the clustering process, which leads to unsatisfactory clustering performance as the final results critically rely on the learned graph. In this paper, we propose to achieve a structured consensus graph for multi-view subspace clustering by leveraging the sparsity and connectivity of each affinity graph. In the proposed method, the pure graph for each view is searched by finding the good neighbors. The multiple pure graphs are further fused into a consensus graph with a block-diagonal structure. That is, the consensus graph is enforced to contain exactly c connected components where c is the number of the clusters. Hence the label to each sample can be directly assigned since each connected component precisely corresponds to an individual cluster. As a result, the proposed model seamlessly accomplishes the subtasks including graph construction, pure graph learning (i.e., good neighbors searching), and cluster label allocation in a mutual reinforcement manner. Extensive experimental results demonstrate the superiority and reliability of our proposed method.
A social smart city for public and private mobility: A real case study

Nowadays, smart city paradigm plays a primary role in the fulfillment of sustainable solutions in the field of urban mobility, both public and private. At the same time, the Internet of Things (IoT) is allowing the development of increasingly advanced solutions for real-time management of collected information related to the management and coexistence of vehicles (i.e., buses, cars, trains, bicycles, etc.) immersed in urban and sub-urban traffic. The Social IoT (SIoT) paradigm adds a relational connotation between objects typical of human relationships. Objects operate as equals and request/provide information among them in the perspective of providing IoT services to users while maintaining their individuality. Social object relationship enables the design of solutions aiming to improve the exchange of information among network nodes in terms of security from malicious attacks external to the so-called social network of objects. In this context, a new SIoT smart city solution is presented in this article: private and public vehicles together with pedestrians are involved in a real-time collection of data to improve the viability of the city in order to suggest new directions and information to citizens to better organize how to live the city. The developed architecture presented in this article is equipped with an artificial intelligence that process collected traffic data and, thanks to machine learning techniques, evaluate the directions and flows undertaken by vehicles and pedestrians on a daily basis. The authors are also presenting an application that allow both citizens to live the city in a better way and municipal authorities to promptly manage traffic flows. The proposed system was installed in a specific area of Cagliari (Italy) and the traffic flows have been compared with daily traffic data monitored before the installation, observing an average gain of up to 35 percent in daily traffic reduction.
Path Planning and Energy Efficiency of Heterogeneous Mobile Robots Using Cuckoo–Beetle Swarm Search Algorithms with Applications in UGV Obstacle Avoidance

In this paper, a new meta-heuristic path planning algorithm, the cuckoo–beetle swarm search (CBSS) algorithm, is introduced to solve the path planning problems of heterogeneous mobile robots. Traditional meta-heuristic algorithms, e.g., genetic algorithms (GA), particle swarm search (PSO), beetle swarm optimization (BSO), and cuckoo search (CS), have problems such as the tenancy to become trapped in local minima because of premature convergence and a weakness in global search capability in path planning. Note that the CBSS algorithm imitates the biological habits of cuckoo and beetle herds and thus has good robustness and global optimization ability. In addition, computer simulations verify the accuracy, search speed, energy efficiency and stability of the CBSS algorithm. The results of the real-world experiment prove that the proposed CBSS algorithm is much better than its counterparts. Finally, the CBSS algorithm is applied to 2D path planning and 3D path planning in heterogeneous mobile robots. In contrast to its counterparts, the CBSS algorithm is guaranteed to find the shortest global optimal path in different sizes and types of maps.
Financial Inclusion, Fintech, and Income Inequality in Africa

Financial inclusion and Fintech have revolutionized the financial sector and fundamentally changed how we store, save, borrow, transfer, and invest money. This paper investigates the impact of financial inclusion and Fintech on income inequality using waves of survey data for 2011, 2014, and 2017 across 39 African countries. By using pooled ordinary least square and two-stage least square (2sls) estimation methods, we obtain three key findings. First, institutional factors such as political stability, control of corruption, and government effectiveness determine Fintech and financial inclusion. Second, Fintech encourages individuals to have a formal bank account, thereby promoting financial inclusion. Third, financial inclusion and Fintech exacerbate income inequality. The direct implication of our findings is that policymakers make tradeoffs whether they seek to achieve higher inclusion and Fintech or to reduce income inequality. We highlight that a pro-poor financial sector development is vital. Easing the bottleneck in obtaining loans, offering agriculture-based Fintech services, and improving digital literacy are important steps to gain the most out of inclusion and Fintech in reducing income inequality.
‘We're now the Walking Dead’: Predatory Policing, Youth Agency and Framing in Nigeria’s #EndSARS Social Activism

The #EndSARS protests swept through Nigeria in October 2020 providing opportunities for Nigerian youths to demonstrate against police violence, poor governance and daunting socio-economic challenges plaguing their country. Drawing on the protesters’ collective sentiments on Twitter, this article interrogates youth agency, ethics of force in policing and collective action of #EndSARS protest actors against predatory policing. It examines the deployment of digital affordances in protest movements and discusses how #EndSARS protesters initiated frames of collective action, agency and injustice to identify problems, ventilate their grievances, attribute blame, mobilise themselves and articulate solutions. Using the cultural theory of social movements, it argues that the protesters enacted frames through discursive strategies to express ideological views and achieve specific purposes. The article further employs Ruth Wodak’s discursive paradigms to investigate the dynamics of in-group and out-group categorisations in #EndSARS protest movement. The discursive mechanisms are also employed to study the creation of stereotypes, legitimisation of protest acts and mobilisation of aggrieved Nigerians against police brutality.
A novel semi-supervised classification approach for evolving data streams

Classification plays a crucial role in mining the evolving data streams. The concept drift and concept evolution are the major issues of data streams classification, which greatly affect the classification performance. Most existing works of concept drift and evolution are supervised in nature, where labeling the data is time and resource consuming. In this paper, for the evolving data streams, a semi-supervised classification approach using partially labeled data is proposed. Firstly, an ensemble model dynamically maintains a series of micro-clusters to capture the concept drift. The ensemble model processes the instances in an online fashion rather than chunk-based. Secondly, the concept evolution detection module is constructed to detect the outliers by the local density. The module examines the current buffer to capture the class emergence in data streams with complex class distribution. For improving the execution efficiency of emerging class detection without compromising performance, several constructive strategies are adopted, including removing part of the buffer and selectively executing sample generation. The extensive experiments are constructed about the popular data streams sets and the processed industry data streams, whose results indicate the practicality and effectiveness of the proposed approach for the classification of evolving data streams.
Distributed minimum error entropy Kalman filter

Recently, the distributed Kalman filter (DKF) has been considered a major method for the applications of Wireless sensor networks (WSNs), for instance, the Internet of Things, and Swarm Intelligence, in which non-Gaussian noise influence is an urgent issue. In this paper, taking any sensor of WSNs as a fusion node, a dynamic Gain Matrix is constructed for its neighbors’ information fusion. Then, the Minimum Error Entropy (MEE) is furtherly introduced into the information fusion process, a modified DKF algorithm called the Distributed Fusion MEE Kalman Filter (DF-MEE-KF) is proposed for eliminating the non-Gaussian noise influence, which improved the estimation accuracy well. Moreover, considering many bad communication conditions of WSNs, such as Communication Denial Environments, and Underwater Acoustic Communication Environments, it is required that the higher estimation accuracy the better, and the lower communication cost the better. Therefore, the diffusion rule is applied for the nodes’ information fusion by constructed fusion weights, thereby an extended DF-MEE-KF algorithm, the Diffusion MEE Kalman filter (Diff-MEE-KF), is obtained. Finally, the convergence of the proposed DF-MEE-KF and Diff-MEE-KF algorithms is proved. Numerical simulation examples also demonstrate that the DF-MEE-KF algorithm performs good estimation accuracy, and the Diff-MEE-KF algorithm achieves a lower communication cost under the same estimation accuracy, when in non-Gaussian noise-influenced WSNs’ applications.