Reviewerly/exHarmony huggingface dataset:
RILS-ROLS: robust symbolic regression via iterated local search and ordinary least squares

Abstract In this paper, we solve the well-known symbolic regression problem that has been intensively studied and has a wide range of applications. To solve it, we propose an efficient metaheuristic-based approach, called RILS - ROLS . RILS - ROLS is based on the following two elements: ( i ) iterated local search, which is the method backbone, mainly solving combinatorial and some continuous aspects of the problem; ( ii ) ordinary least squares method, which focuses on the continuous aspect of the search space—it efficiently determines the best—fitting coefficients of linear combinations within solution equations. In addition, we introduce a novel fitness function that combines important model quality measures: $$R^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math> score, RMSE score, size of the model (or model complexity), and carefully designed local search, which allows systematic search in proximity to candidate solution. Experiments are conducted on the two well-known ground-truth benchmark sets from literature: Feynman and Strogatz . RILS - ROLS was compared to 14 other competitors from the literature. Our method outperformed all 14 competitors with respect to the symbolic solution rate under varying levels of noise. We observed the robustness of the method with respect to noise, as the symbolic solution rate decreases relatively slowly with increasing noise. Statistical analysis of the obtained experimental results confirmed that RILS - ROLS is a new state-of-the-art method for solving the problem of symbolic regression on datasets whose target variable is modelled as a closed-form equation with allowed operators. In addition to evaluation on known ground-truth datasets, we introduced a new randomly generated set of problem instances. The goal of this set of instances was to test the sensitivity of our method with respect to incremental equation sizes under different levels of noise. We have also proposed a parallelized extension of RILS-ROLS that has proven adequate in solving several very large instances with 1 million records and up to 15 input variables.
Learning binary and sparse permutation-invariant representations for fast and memory efficient whole slide image search

Considering their gigapixel sizes, the representation of whole slide images (WSIs) for classification and retrieval systems is a non-trivial task. Patch processing and multi-Instance Learning (MIL) are common approaches to analyze WSIs. However, in end-to-end training, these methods require high GPU memory consumption due to the simultaneous processing of multiple sets of patches. Furthermore, compact WSI representations through binary and/or sparse representations are urgently needed for real-time image retrieval within large medical archives. To address these challenges, we propose a novel framework for learning compact WSI representations utilizing deep conditional generative modeling and the Fisher Vector Theory. The training of our method is instance-based, achieving better memory and computational efficiency during the training. To achieve efficient large-scale WSI search, we introduce new loss functions, namely gradient sparsity and gradient quantization losses, for learning sparse and binary permutation-invariant WSI representations called Conditioned Sparse Fisher Vector (C-Deep-SFV), and Conditioned Binary Fisher Vector (C-Deep-BFV). The learned WSI representations are validated on the largest public WSI archive, The Cancer Genomic Atlas (TCGA) and also Liver-Kidney-Stomach (LKS) dataset. For WSI search, the proposed method outperforms Yottixel and Gaussian Mixture Model (GMM)-based Fisher Vector both in terms of retrieval accuracy and speed. For WSI classification, we achieve competitive performance against state-of-art on lung cancer data from TCGA and the public benchmark LKS dataset.
Autonomous Robots for Services—State of the Art, Challenges, and Research Areas

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted.
A multi‐modal clustering method for traditional Chinese medicine clinical data via media convergence

Abstract Media convergence is a media change led by technological innovation. Applying media convergence technology to the study of clustering in Chinese medicine can significantly exploit the advantages of media fusion. Obtaining consistent and complementary information among multiple modalities through media convergence can provide technical support for clustering. This article presents an approach based on Media Convergence and Graph convolution Encoder Clustering (MCGEC) for traditional Chinese medicine (TCM) clinical data. It feeds modal information and graph structure from media information into a multi‐modal graph convolution encoder to obtain the media feature representation learnt from multiple modalities. MCGEC captures latent information from various modalities by fusion and optimises the feature representations and network architecture with learnt clustering labels. The experiment is conducted on real‐world multi‐modal TCM clinical data, including information like images and text. MCGEC has improved clustering results compared to the generic single‐modal clustering methods and the current more advanced multi‐modal clustering methods. MCGEC applied to TCM clinical datasets can achieve better results. Integrating multimedia features into clustering algorithms offers significant benefits compared to single‐modal clustering approaches that simply concatenate features from different modalities. It provides practical technical support for multi‐modal clustering in the TCM field incorporating multimedia features.
Generation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg" display="inline" id="d1e503"><mml:mi>μ</mml:mi></mml:math>CT images from medical CT scans of carbonate rocks using a diffusion-based model

Carbonate rocks are known for their high heterogeneity and textural and compositional complexity. Evaluating their petrophysical properties is thus challenging, especially with limited information. One way to obtain an internal image of such rocks is to scan them with X-ray computed tomography scanners, revealing their internal structures. The problem with this approach is the trade-off between cheap bigger volumes scanned with medical CT, but with lower spatial resolution, and higher spatial resolution with μCT, but higher financial and time costs involved, as well as smaller samples (normally centimeter scales). This work proposes a way to have the volume and cost advantages of the medical CT with the spatial resolution of the μCT using a diffusion probabilistic model to convert medical CT into a μCT. The model was trained with a sample of the Brazilian pre-salt composed by Shrub boundstones and tested with one sample of the same facies and another composed of packstone–grainstones. The results show that the model could replicate both samples well, but worked better when the input sample has the same facies as the training set.
Bringing Monochrome to Life: A GAN-based Approach to Colorizing Black and White Images

The automatic image colorization technique has garnered a lot of attention over the past ten years for a variety of applications, including the restoration of old or damaged photos. Due to the many degrees of freedom in the assignment of color information, this problem is very poorly presented. Recent advances in automatic colorization either use photographs with a recurring theme or need highly processed data, like semantic maps, as input. CNN is highly adept at classifying objects and can quickly identify skies, trees, and other vegetation, faces, and people. Even if flags and famous monuments are represented in the data sets, colorization fails to effectively depict them when applied to these items. In our method, we use a conditional Conditional Generative Adversarial Network (Conditional GAN) to try and fully generalize the colorization process. The network is trained using publicly accessible datasets like CIFAR-10.
Federated Transfer–Ordered–Personalized Learning for Driver Monitoring Application

Federated learning (FL) shines through in the internet of things (IoT) with its ability to realize collaborative learning and improve learning efficiency by sharing client model parameters trained on local data. Although FL has been successfully applied to various domains, including driver monitoring applications (DMAs) on the internet of vehicles (IoV), its usages still face some open issues, such as data and system heterogeneity, large-scale parallelism communication resources, malicious attacks, and data poisoning. This paper proposes a federated transfer-ordered-personalized learning (FedTOP) framework to address the above problems and test on two real-world datasets with and without system heterogeneity. The performance of the three extensions, transfer, ordered, and personalized, is compared by an ablation study and achieves 92.32% and 95.96% accuracy on the test clients of two datasets, respectively. Compared to the baseline, there is a 462% improvement in accuracy and a 37.46% reduction in communication resource consumption. The results demonstrate that the proposed FedTOP can be used as a highly accurate, streamlined, privacy-preserving, cybersecurity-oriented, and personalized framework for DMA.
Achieving food security in Ghana: Does governance matter?

Abstract This study complements the extant literature by assessing the role of governance dynamics in food security in Ghana for the period 1980–2019. The empirical evidence is based on the fully modified ordinary least squares technique and governance is categorized into political (entailing political stability and voice and accountability), economical (consisting of regulatory quality and government effectiveness), and institutional (entailing corruption‐control and the rule of law) governance dynamics. The study finds that the engaged governance dynamics improve food security in Ghana. Policy implications are discussed with specific emphasis on the sustainable development goals. Related Articles Ayanoore, Ishmael, and Sam Hickey. 2022. “Reframing the Politics of Natural Resource Governance in Africa: Insights from the Local Content Legislation Process in Ghana.” Politics &amp; Policy 50(1): 119–36. https://doi.org/10.1111/polp.12449 . Oehmke, James F., Sera L. Young, Godfrey Bahiigwa, Boaz Blackie Keizire, and Lori Ann Post. 2018. “The Behavioral‐Economics Basis of Mutual Accountability to Achieve Food Security.” Politics &amp; Policy 46(1): 32–57. https://doi.org/10.1111/polp.12244 . Pelizzo, Riccardo, and Zim Nwokora. 2018. “Party System Change and the Quality of Democracy in East Africa.” Politics &amp; Policy 46(3): 505–28. https://doi.org/10.1111/polp.12255 .
Implementation of circular blockchain-based approach for food crops supply chain with bitcoin prediction using deep learning

Blockchain technology serves as a framework for addressing the challenge of tracking and marketing materials in distributed networks. Agricultural traceability systems in food production provide farmers with food safety and smart contracts. Smart contracts establish cryptocurrency evidence of delivery with automated bitcoin payments to all parties. In this paper, we propose circular blockchain-based traceability and bitcoin prediction in agricultural food crop system to accomplish transparency and traceability. Bitcoin prediction using LSTM benefits farmers and stakeholders to buy and sell their food crops when the profit is high. Moreover, the proposed system provides safety, consensus, shared ledger, speedy payment, and decentralization. All activities are processed in a distributed shared ledger with connections to a decentralized file system, which makes the supply chain visible and traceable. All the activities that are tied up in the supply chain are transparent to consumers and stakeholders. Traceability of the agricultural product is done efficiently with QR code, which results in a trustable relationship between the farmer and the consumer. Finally, our research shows that the accuracy of LSTM outperforms by 88.67% compared to traditional machine learning algorithms such as SVM and Naive Bayes with accuracy of 62.025 and 75.32% in forecasting the bitcoin price.
Quantum correlations and thermal coherence in a two-superconducting charge qubit system

Abstract Superconducting charge qubits represent a cutting-edge technology in the field of quantum computing, offering a promising platform for quantum processing. This study delves into the behaviors of thermal coherence and quantum correlations within a two-superconducting charge qubit system coupled by a fixed capacitance. Specifically, we investigate the effects of thermal noise on entanglement (measured by concurrence ), nonclassical correlations (quantified by local quantum uncertainty ), and quantum coherence (measured by correlated coherence ) within the two-superconducting charge qubit capacitively coupled. Our analysis takes into account the interplay between the equilibrium temperature of the reservoir and various system parameters. Our findings demonstrate that an increase in temperature leads to a decrease in coherence and quantum correlations within the considered system. However, the behavior of these quantum resources is heavily dependent on the system parameters, and a careful selection of these parameters can help mitigate the negative effects of absolute temperature. Additionally, we observe that local quantum uncertainty and correlated coherence are more resilient than thermal entanglement to rising temperatures. These results provide insight into how a two-superconducting charge qubit system can be optimized for achieving quantum advantages.
Mutual coupling between stock market and cryptocurrencies

We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns--and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.
Computational simulation of the ore-forming processes associated with the Sanshandao-Haiyu gold belt, Jiaodong Peninsula, eastern China: implications for the duration of ore formation

The Sanshandao-Haiyu gold belt, Jiaodong Peninsula, Eastern China accounts for more than 1,400 t gold resources, which is always recognized as a coupled result of structural deformation, pore-fluid flow, and hydrothermal alteration-mineralization processes. However, the ore-forming processes associated with this gold belt has not been fully understood by the coupled perspective. In this paper, we use computational modeling to study the spatial distribution of dilation (positive volumetric strain) and the mineralization rate, which can predict the mineralization patterns from a chemical perspective, so as to solve the fully coupled problem during ore-forming processes. The model was loaded on the FLAC 3D platform to test four scenarios: 1) Syn-compression in the NW-SE direction; 2) syn-compression in the WNW-ESE direction; 3) syn-compression in the NNW-SSE direction, and 4) syn-extension in the NW-SE direction. The simulation results show that only the syn-compressional scenario can produce wide dilation zones with a distribution pattern similar to the known orebodies. The higher dilation zones with the volumetric strain (&amp;gt;0.1%) and the highest pore-fluid flux are closely associated with the fault geometry, particularly for the gentle segments near the steep-flat transition areas. Besides, the dilation zones also occur with very high absolute values of pore-fluid pressure gradient and temperature gradient, indicating significantly perturbed physical-chemical conditions. The receiver operating characteristic (ROC) curves of the mineralization rate show that the area under the curve (AUC = 0.862) of the NW-SE syn-compression model is the highest among these four scenarios, indicating a major paleo compression stress direction of NW-SE during ore-forming period. Furthermore, based on the mineralization rate, we estimated that the mineralization duration of the Sanshandao-Haiyu gold belt would be less than 100,000 years. These simulation results can not only enhance our understanding of the ore-forming processes in the Sanshandao-Haiyu gold belt and Jiaodong Peninsula but also demonstrate that these can be used directly or indirectly for deep mineral exploration.
Adaptation and validation of an instrument for assessing the digital competence of special education teachers

The instruments for the evaluation of teachers’ digital competence are abundant, however, there is still a lack of instruments oriented to the context of Special Education. In this sense, this study presents the validation process of an instrument that aims to determine the level of knowledge and digital competence of Special Education teachers with respect to the use of Information and Communication Technologies (ICT) for the care of students with educational needs. Being an adapted instrument, it was subjected to content validation by means of ‘expert judgement’ in order to determine the coherence, relevance and clarity of the items. The redesigned questionnaire was subjected to a reliability analysis, using Cronbach’s alpha and McDonald’s omega, and the value obtained was considered to indicate that the instrument has an excellent level of reliability, giving scientific validity to the instrument designed to assess the level of digital competence of Special Education teachers to attend to students with educational needs.
A Survey on Identity and Access Management for Cross-Domain Dynamic Users: Issues, Solutions, and Challenges

Access control management in a heterogeneous cloud environment, where the number of users is growing, is a daunting task for service providers.Efficiency is heavily reliant on shared resources in a modern cloud computing culture.Although data or service sharing is highly appreciated for collaborative projects, preserving identity and access management security is challenging in this context.The difficulties encountered are diverse, including a single point of failure, incompatibility, dynamic user groups, trust establishment, and revocation.Despite extensive research, certain obstacles and issues need to be addressed.In this article, challenges in access management in centralized and decentralized identity governance are grouped into different categories and accompanied by background information on the topic.Studies and implemented projects have been evaluated regarding their value and flaws.Traditional approaches, such as centralized and federated identity, as well as more futuristic methods, such as blockchain-based decentralized identity, AI/ML access management, and ABE schema, have been investigated while writing this paper.A comparative evaluation of the proposed studies has been included, where the differences and similarities can be observed.
A MapReduce Based Approach for Secure Batch Satellite Image Encryption

The overarching goal of this research was to examine the state of satellite imagery security in relation to its deteriorating form due to rising demand.The most common approaches to safeguarding satellite images during transmission across transmission networks, which are not protected by standard encryption, are the focus of this investigation.Since satellite imagery can be encrypted both in transit and while stored on a computer's hard drive, we put the suggested Image Encryption System to the test by applying it to a collection of satellite photos.Concurrently encrypting data and running MapReduce jobs is key to the study methodology employed.This will be carried out in the Hadoop ecosystem, where an innovative method of analysing random numbers for use in Image encryption will be put to the test.The encryption was processed using MapReduce in the Hadoop ecosystem.Images were saved as BMP files with added security metadata.The evaluation of experiments was based on four (4) indicators.It was found that the processing time for batch encryption calculations grew in proportion to the amount of computations.All cluster, map, and reduction processes were put to the test using encrypted images, exposing load balancing difficulties and inefficiencies.Histogram analysis, the basis of an image encryption technique, provides evidence that the encrypted pixel values are consistent.Therefore, compared to other methods, such as a histogram or information entropy, this one is superior.Because of how it was crafted, it can withstand even the most sophisticated attacks without being compromised.
Human Face Detection and Recognition using Artificial Intelligence

The method of recognizing and finding a human by their face in an image is called Human Face Detection and Recognition (HFDR). Application of Biometric method in detecting a human's Identity is called as HFD system. HFDR is a verification system designed to determine one's identity. Innovation in science and technology is rapidly achieving the peak level day by day. Artificial Intelligence (AI) and Machine Learning (ML) are the two such innovative inventions that kicked it up a notch and have turned the world to the next phase. This research is intended to detect and recognize faces using HFDR using ML. The consequences of the simulation experiment clearly show that the HFDR using ML method is best known for its accuracy in HFDR compared to the other algorithms used for Human Face Detection (HFD). The exactness of the proposed HFDR using ML method is illustrated through the experimental outcomes.