Reviewerly/exHarmony huggingface dataset:
RMA-CPABE : A multi-authority CPABE scheme with reduced ciphertext size for IoT devices

Internet of Things (IoT) generates a massive amount of data, which can be prone to huge data and privacy leaks unless given a proper access control. Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic mechanism that provides confidentiality and fine-grained access to stored data. In CP-ABE, a single trusted authority primarily handles attribute management and its revocation. However, in an environment like IoT, due to its heterogeneous nature, the attributes from multiple domains are evidently inherited by IoT devices and hence develop a need for a multi-authority scheme. Therefore, this paper proposes a novel multi-authority CP-ABE scheme with reduced-size ciphertext (RMA-CPABE) to provide secure access in fog-enabled IoT applications with attributes from multiple domains. This scheme considers the resource-constrained nature of IoT devices and reduces the ciphertext size in the sense that the ciphertext size does not increase linearly with the number of attributes; instead, it increases linearly with the number of multi-domain attribute authorities involved in the policy building. RMA-CPABE supports highly required functionalities like attribute update and outsourced decryption to reduce the computation load on the end devices. Further, the proposed scheme is the first to eliminate the most expensive ciphertext update operation during attribute revocation and addition, which is the biggest drawback of most of the existing schemes. The end-user in RMA-CPABE requires holding only a constant size key to access the ciphertexts securely. The security analysis proves that the proposed scheme is secure against Chosen-Ciphertext Attack (CCA) under multiple augmented Multi-Sequence of Exponents Decisional Diffie Hellman (maMSE-DDH) assumptions. The performance analysis shows that RMA-CPABE is suitable for IoT applications.
SiaTrans: Siamese transformer network for RGB-D salient object detection with depth image classification

RGB-D SOD uses depth information to handle challenging scenes and obtain high-quality saliency maps. Existing state-of-the-art RGB-D saliency detection methods overwhelmingly rely on the strategy of directly fusing depth information. Although these methods improve the accuracy of saliency prediction through various cross-modality fusion strategies, misinformation provided by some poor-quality depth images can affect the saliency prediction result. To address this issue, a novel RGB-D salient object detection model (SiaTrans) is proposed in this paper, which allows training on depth image quality classification at the same time as training on SOD. In light of the common information between RGB and depth images on salient objects, SiaTrans uses a Siamese transformer network with shared weight parameters as the encoder and extracts RGB and depth features concatenated on the batch dimension, saving space resources without compromising performance. SiaTrans uses the class token in the backbone network (T2T-ViT) to classify the quality of depth images without preventing the token sequence from going on with the saliency detection task. The greatest benefit of our cross-modality fusion (CMF) and decoder is that they maintain consistency between RGB and RGB-D information decoding. In the test, SiaTrans decides whether to perform an RGB-D or RGB saliency detection task according to the quality classification signal of the depth image. Comprehensive experiments on nine RGB-D SOD benchmark datasets show that SiaTrans has the best overall performance and the least computation compared with recent state-of-the-art methods.
Is my stance the same as your stance? A cross validation study of stance detection datasets

Stance detection identifies a person’s evaluation of a subject, and is a crucial component for many downstream applications. In application, stance detection requires training a machine learning model on an annotated dataset and applying the model on another to predict stances of text snippets. This cross-dataset model generalization poses three central questions, which we investigate using stance classification models on 7 publicly available English Twitter datasets ranging from 297 to 48,284 instances. (1) Are stance classification models generalizable across datasets? We construct a single dataset model to train/test dataset-against-dataset, finding models do not generalize well (avg F1=0.33). (2) Can we improve the generalizability by aggregating datasets? We find a multi dataset model built on the aggregation of datasets has an improved performance (avg F1=0.69). (3) Given a model built on multiple datasets, how much additional data is required to fine-tune it? We find it challenging to ascertain a minimum number of data points due to the lack of pattern in performance. Investigating possible reasons for the choppy model performance we find that texts are not easily differentiable by stances, nor are annotations consistent within and across datasets. Our observations emphasize the need for an aggregated dataset as well as consistent labels for the generalizability of models.
Laplace crossover and random replacement strategy boosted Harris hawks optimization: performance optimization and analysis

Abstract Harris hawks optimization has been a popular swarm intelligence algorithm in recent years. In order to improve the local exploitation ability of the algorithm and improve the problem of slow convergence speed, an enhanced Harris hawks optimization algorithm based on Laplace crossover and random replacement strategy is proposed. This variant uses two optimization mechanisms. Firstly, Laplace crossover is added to enhance the exploitation ability of the algorithm. At the same time, the random replacement strategy is introduced into the original algorithm, which accelerates the convergence speed. The basic functions, IEEE CEC2011 and IEEE CEC2017 functions are used for algorithms comparison, balance diversity analysis, and high-dimensional experiments to verify the superiority of the algorithm proposed in this paper. The experimental results show that the improved algorithm has the advantages of strong optimization ability, high convergence accuracy, and fast convergence speed. The algorithm has solved five engineering design problems using these advantages and can effectively deal with constraint problems.
Legal case document similarity: You need both network and text

Estimating the similarity between two legal case documents is an important and challenging problem, having various downstream applications such as prior-case retrieval and citation recommendation. There are two broad approaches for the task — citation network-based and text-based. Prior citation network-based approaches consider citations only to prior-cases (also called precedents) (PCNet). This approach misses important signals inherent in Statutes (written laws of a jurisdiction). In this work, we propose Hier-SPCNet that augments PCNet with a heterogeneous network of Statutes. We incorporate domain knowledge for legal document similarity into Hier-SPCNet, thereby obtaining state-of-the-art results for network-based legal document similarity. Both textual and network similarity provide important signals for legal case similarity; but till now, only trivial attempts have been made to unify the two signals. In this work, we apply several methods for combining textual and network information for estimating legal case similarity. We perform extensive experiments over legal case documents from the Indian judiciary, where the gold standard similarity between document-pairs is judged by law experts from two reputed Law institutes in India. Our experiments establish that our proposed network-based methods significantly improve the correlation with domain experts' opinion when compared to the existing methods for network-based legal document similarity. Our best-performing combination method (that combines network-based and text-based similarity) improves the correlation with domain experts' opinion by 11.8% over the best text-based method and 20.6% over the best network-based method. We also establish that our best-performing method can be used to recommend/retrieve citable and similar cases for a source (query) case, which are well appreciated by legal experts.
Object Detection Using Structure-Preserving Wavelet Pyramid Reflection Removal Network

Images shot through glass will produce reflections. However, these reflections will cause the objects in the images to be unrecognizable. Hence, to improve the recognition rate, the reflections in the images should be removed. Reflection removal has been widely used in the field of deep learning. Although these methods have good results, they all assume that reflection removal is performed in a specific situation, and provide their own datasets for research, such as strong reflections in some local areas, this limitation will lead to the inability to effectively remove the reflections in the real world, which will affect the recognition of objects. To address this issue, we propose a novel structure-preserving wavelet pyramid reflection removal network (SpWPRRNet) to achieve effective background structure preservation and reflection removal to further improve the object recognition rate in images. After wavelet decomposition, we put the high and low frequency images of each level into reflection layer removal and detail enhancement subnetwork (RDSn) and structure preservation subnetwork (SPSn) respectively, and then use structure level fusion (SLF) and inverse wavelet transform (ISWT) to restore clean images recursively. In addition, to further separate the reflection layer and the transmission layer, we also propose the reflection layer information transmission (RLIT), through which the reflection layer features of the high-frequency images can be extracted to help the SPSn to effectively separate these two layers to achieve the results of reflection removal and improve object recognition rate. The experimental results indicate that the proposed method can greatly improve the object recognition rate in images.
An Efficient Two-Stream Network for Isolated Sign Language Recognition Using Accumulative Video Motion

Sign language is the primary communication medium for persons with hearing impairments. This language depends mainly on hand articulations accompanied by nonmanual gestures. Recently, there has been a growing interest in sign language recognition. In this paper, we propose a trainable deep learning network for isolated sign language recognition, which can effectively capture the spatiotemporal information using a small number of signs’ frames. We propose a hierarchical sign learning module that comprises three networks: dynamic motion network (DMN), accumulative motion network (AMN), and sign recognition network (SRN). Additionally, we propose a technique to extract key postures for handling the variations in the sign samples performed by different signers. The DMN stream uses these key postures to learn the spatiotemporal information pertaining to the signs. We also propose a novel technique to represent the statical and dynamic information of sign gestures into a single frame. This approach preserves the spatial and temporal information of the sign by fusing the sign’s key postures in the forward and backward directions to generate an accumulative video motion frame. This frame was used as an input to the AMN stream, and the extracted features were fused with the DMN features to be fed into the SRN for the learning and classification of signs. The proposed approach is efficient for isolated sign language recognition, especially for recognizing static signs. We evaluated this approach on the KArSL-190 and KArSL-502 Arabic sign language datasets, and the obtained results on KArSL-190 outperformed other techniques by 15% in the signer-independent mode. Additionally, the proposed approach outperformed the state-of-the-art techniques on the Argentinian sign language dataset LSA64. The code is available at https://github.com/Hamzah-Luqman/SLR_AMN.
Binary Black Widow Optimization Approach for Feature Selection

Feature selection is a process of reduction of irrelevant, negligible, noisy features from data sets so as to obtain better performance measurements with fewer features.Throughout the literature, various methods are presented that use different approaches to get through this difficult problem, prevalently.In this study, a binary variant of the Black Widow Optimization (BWO) is proposed in a wrapper mode for the purpose of feature selection.The BWO algorithm has early convergence ability on continuous problems and that characteristic is also effective for finding an optimum solution in feature selection problem.The proposed approach compared with state-of-the-art and widely used approaches such as Binary Particle Swarm Optimization (BPSO and VPSO), Binary Grey Wolf Optimization (BGWO1 and BGWO2).The performance of these algorithms is assessed over 20 benchmark data sets from the UCI repository.The results show that the proposed binary method can be utilized effectively in discrete problems such as feature selection.
Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence.To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper.Firstly, KF online estimation of the Jacobian matrix.We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error.Next, an FL controller is designed for gain adaptation.This approach addresses the problem of the slow convergence of the IBVS system with the KF.Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM).Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560.Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information.We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments.And the proposed method achieves the best results under the three evaluation metrics.
Co-precipitation of gold and base metal sulfides during fluid boiling triggered by fault-valve processes in orogenic gold deposits

The extreme fluctuations in pressure during earthquakes are widely regarded as responsible for gold mineralization in quartz-vein-hosted gold deposits. However, it is barely noticed that base metal sulfides can precipitate together with gold during these processes. Here we present the phenomenon and unravel the mechanism of the co-precipitation of Au and base metal sulfides during the fluid boiling via geological study and thermodynamic modeling, respectively. The Bangbu deposit, a lode-type orogenic gold deposit in Tibet shows two mineralization substages of pyrite. The pyrite formed in the later substage is hosted in the wall rock selvages of laminated crack-seal quartz veins formed by the fault-valve processes. The pyrite grains are characterized by anhedral small crystals with porous textures and abundant native gold, chalcopyrite, galena, and sphalerite inclusions. They also contain higher concentrations of Cu, Pb, Ag, Sb, and Au, and lower concentrations of Co and Ni, compared to the euhedral large-grained pyrite formed in the initial substage. The textural and trace element characteristics of the pyrite indicate that vigorous boiling occurred during fault-valve behavior, which decreased the solubilities of Au, Cu, Pb, and Zn in the ore fluid in Bangbu. The co-precipitation of Au and base metal sulfides triggered by fluid boiling has also been reported in other orogenic gold deposits worldwide. Thermodynamic models are designed to acquire the predominant species and solubility of Au, Cu, Pb, and Zn in orogenic ore fluids under varied chemical conditions. The results show that Au, Cu, Pb, and Zn are dominantly transported as hydrosulfide complexes under lower mesozonal to epizonal deposit conditions. At temperatures of about above 350 °C, Au hydrosulfide species still predominates, but the predominant Cu, Pb, and Zn species change from hydrosulfide to chloride complexes. A sudden decrease in the reduced sulfur concentration during fluid boiling may be the most important mechanism controlling the precipitation of Au, Cu, Pb, and Zn in lower mesozonal to epizonal deposits. The decrease of logfS2 during fluid boiling can increase the fluid pH, resulting in the decrease of solubilities of base metal chloride complexes and thus explain the co-precipitation of Au and base metal sulfides in hypozonal deposits.
Estimation of rock copper content based on Fractional-order derivative and visible Near-infrared–Shortwave infrared spectroscopy

Rock geochemistry can accurately and effectively delineate geological anomalies, providing clear instructions for geological prospecting and playing a crucial role in mineral resource exploration. However, traditional geochemical exploration methods are based on ground sampling and laboratory chemical analysis, which are time-consuming, laborious, and inefficient. This study collected 191 rock samples, and the Cu content and spectral data were obtained using chemical analysis and an analytical spectral device (fieldspec3, portable spectrometer) in the laboratory. However, spectral preprocessing and estimation models are critical for estimation accuracy. Therefore, this study discusses the potential of fractional derivative spectroscopy and a model method for estimating rock Cu content. The results show the following. (1) Compared with the original spectrum and the integer-order derivative spectrum (first and second derivative), the fractional-order derivatives (FODs) can better highlight the spectral details. With the increased order of derivatives, the characteristic band becomes more obvious. (2) The combined FOD and random forest (RF) model performs best for the full sample data. The coefficient of determination (R2) is as high as 0.766, the mean absolute error (MAE), and the ratio of the performance to deviation (RPD) are 9.09 and 2.067, respectively, significantly better than partial least squares (PLS) models (R2 = 0.598, MAE = 13.389, RPD = 1.577) and the K-nearest neighbor (KNN) algorithm (R2 = 0.686, MAE = 10.905, RPD = 1.786). (3) For the samples with Cu content less than 45 mg/kg, using the PLS method and samples with Cu content greater than 45 mg/kg and the RF method to build the estimation model achieved better overall results (R2 = 0.881, MAE = 6.697, RPD = 2.899). Therefore, combining FOD technology and the segmental modeling method can effectively and quickly estimate the Cu content in rocks, providing technical support for estimating Cu content in rocks from aerial imaging hyperspectral images.
Analysis and Optimization of Network Properties for Bionic Topology Hopfield Neural Network Using Gaussian-Distributed Small-World Rewiring Method

The fully connected topology, which coordinates the connection of each neuron with all other neurons, remains the most commonly used structure in Hopfield-type neural networks.However, fully connected neurons may form a highly complex network, resulting in a high training cost and making the network biologically unrealistic.Biologists have observed a small-world topology with sparse connections in the actual brain cortex.The bionic small-world neural network structure has inspired various application scenarios.However, in previous studies, the long-range wirings in the small-world network have been found to cause network instability.In this study, we investigate the influence of neural network training on the small-world topology.The role of the path length and clustering coefficient of neurons is expounded in the neural network training process.We employ Watt and Strogatz's small-world model as the topology for the Hopfield neural network and conduct computer simulations.We observe that the random existence of neuron connections may cause unstable network energies and generate oscillations during the training process.A new method is proposed to mitigate the instability of small-world networks.The proposed method starts with a neuron as the pattern centroid along the radial, which arranges its wirings in compliance with the Gaussian distribution.The new method is tested on the MNIST handwritten digit dataset.The simulation confirms that the new small-world series has higher stability in terms of the learning accuracy and a higher convergence speed compared with Watt and Strogatz's small-world model.
An Improved DE Algorithm to Optimise the Learning Process of a BERT-based Plagiarism Detection Model

Plagiarism detection is a challenging task, aiming to identify similar items in two documents. In this paper, we present a novel approach to automatic plagiarism detection that combines BERT (bidirectional encoder representations from transformers) word embedding, attention mechanism-based long short-term memory (LSTM) networks, and an improved differential evolution (DE) algorithm for weight initialisation. BERT is used to pretrain deep bidirectional representations in all layers, while the pre-trained BERT model can be fine-tuned with only one extra output layer without significant changes in architecture. Deep learning algorithms often use the random weighting method for initialisation, followed by gradient-based optimisation algorithms such as back-propagation for training, making them susceptible to getting trapped in local optima. To address this, population- based metaheuristic algorithms such as DE can be used. We propose an improved DE algorithm with a clustering-based mutation operator, where first a winning cluster of candidate solutions is identified and a new updating strategy is then applied to include new candidate solutions in the current population. The proposed DE algorithm is used in LSTM, attention mechanism, and feed- forward neural networks to yield the initial seeds for subsequent gradient-based optimisation. We compare our proposed model with conventional and population-based approaches on three datasets (SNLI, MSRP and SemEval2014) and demonstrate it to give superior plagiarism detection performance.
Resource scheduling methods for cloud computing environment: The role of meta-heuristics and artificial intelligence

The growth and development of scientific applications have demanded the creation of efficient resource management systems. Resource provisioning and scheduling are two core components of cloud resource management systems. Cloud resource scheduling is the most critical problem to solve efficiently due to the heterogeneity of resources, their inter-dependencies, and unpredictability of load in the cloud environment. In this paper, we review the background of scheduling and state-of-the-art scheduling techniques in cloud computing. We first introduce the general background, and phases of scheduling. A comprehensive survey of existing resource scheduling problems proposed so far is presented considering high-level taxonomy. This high-level taxonomy considers Virtual Machine (VM) placement, Quality of Service (QoS) parameters, heuristic methods, and other miscellaneous techniques for resource scheduling. This study also discusses scheduling in Infrastructure as a Service (IaaS) clouds and comparison based on important parameters is also investigated. The importance of meta-heuristic methods and artificial intelligence for resource scheduling methods in cloud computing is discussed thoroughly. The objective of this work is to help the researchers to understand the basic concepts related to scheduling and facilitate the process of designing new scheduling methods by addressing issues raised in the scheduling and studying the existing methodologies.