Reviewerly/exHarmony huggingface dataset:
Long-time protection of thermal correlations in a hybrid-spin system under random telegraph noise

The engineering features of transmitting mediums and their impact on different characteristics of a quantum system play a significant role in the efficient performance of nonlocal protocols. For this purpose, the dynamics of open quantum systems and coupling mediums remain a pathway. In this work, we investigate the dynamics of quantum correlations using negativity, uncertainty-induced nonlocality, and local quantum Fisher information in a hybrid qubit-qutrit thermal state when coupled with a magnetic field and influenced by random telegraph noise. Different features of the system parameters are taken into account while designing longer preservation of qubit-qutrit correlations. We show that the temperature has an inverse impact on the initial values of negativity, uncertainty-induced nonlocality, and local quantum Fisher information. When the magnetic field is characterized by different features, the entanglement, nonlocality, and Fisher information show a variety of dynamical maps, assuring their distinct nature. In addition, the qubit-qutrit correlations undergo repeated revivals when the configuration is restricted to the non-Markovian regime. On the other hand, an exponential drop with a single minimum is observed in the Markovian regime of the coupled field. Most importantly, our findings reveal that the present coupled fields have several advantages that can be leveraged to generate the optimal degree of entanglement, nonlocality, and local quantum Fisher information preservation in quantum dynamical maps.
Towards hybrid <scp>human‐AI</scp> learning technologies

Abstract Education is a unique area for application of artificial intelligence (AI). In this article, the augmentation perspective and the concept of hybrid intelligence are introduced to frame our thinking about AI in education. The involvement of quadruple helix stakeholders (i.e., researchers, education professionals, entrepreneurs, and policymakers) is necessary to understand the opportunities and challenges of different educational use cases from an integrated point of view. To facilitate a meaningful dialogue, a common language about AI in education is needed. This article outlines elements of such a common language. The detect‐diagnose‐act framework is used to describe the core functions of AI in education. The six levels of automation model is introduced to develop our thinking about the roles of AI, learners, and teachers in educational arrangements. In this model, the transition of control between teacher and technology is articulated at different levels and related to the augmentation perspective. Finally, the future of AI in education is discussed using self‐regulated learning as an example. The proposed common language will help to support a coordinated development of an interdisciplinary dialogue between quadruple helix stakeholders to strengthen meaningful application of AI for learning and teaching.
Learning Granularity-Unified Representations for Text-to-Image Person Re-identification

Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps. Existing works usually ignore the difference in feature granularity between the two modalities, i.e., the visual features are usually fine-grained while textual features are coarse, which is mainly responsible for the large inter-modal gaps. In this paper, we propose an end-to-end framework based on transformers to learn granularity-unified representations for both modalities, denoted as LGUR. LGUR framework contains two modules: a Dictionary-based Granularity Alignment (DGA) module and a Prototype-based Granularity Unification (PGU) module. In DGA, in order to align the granularities of two modalities, we introduce a Multi-modality Shared Dictionary (MSD) to reconstruct both visual and textual features. Besides, DGA has two important factors, i.e., the cross-modality guidance and the foreground-centric reconstruction, to facilitate the optimization of MSD. In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance. Comprehensive experiments show that our LGUR consistently outperforms state-of-the-arts by large margins on both CUHK-PEDES and ICFG-PEDES datasets. Code will be released at https://github.com/ZhiyinShao-H/LGUR.
Target Detection Method of UAV Aerial Imagery Based on Improved YOLOv5

Due to the advantages of small size, lightweight, and simple operation, the unmanned aerial vehicle (UAV) has been widely used, and it is also becoming increasingly convenient to capture high-resolution aerial images in a variety of environments. Existing target-detection methods for UAV aerial images lack outstanding performance in the face of challenges such as small targets, dense arrangement, sparse distribution, and a complex background. In response to the above problems, some improvements on the basis of YOLOv5l have been made by us. Specifically, three feature-extraction modules are proposed, using asymmetric convolutions. They are named the Asymmetric ResNet (ASResNet) module, Asymmetric Enhanced Feature Extraction (AEFE) module, and Asymmetric Res2Net (ASRes2Net) module, respectively. According to the respective characteristics of the above three modules, the residual blocks in different positions in the backbone of YOLOv5 were replaced accordingly. An Improved Efficient Channel Attention (IECA) module was added after Focus, and Group Spatial Pyramid Pooling (GSPP) was used to replace the Spatial Pyramid Pooling (SPP) module. In addition, the K-Means++ algorithm was used to obtain more accurate anchor boxes, and the new EIOU-NMS method was used to improve the postprocessing ability of the model. Finally, ablation experiments, comparative experiments, and visualization of results were performed on five datasets, namely CIFAR-10, PASCAL VOC, VEDAI, VisDrone 2019, and Forklift. The effectiveness of the improved strategies and the superiority of the proposed method (YOLO-UAV) were verified. Compared with YOLOv5l, the backbone of the proposed method increased the top-one accuracy of the classification task by 7.20% on the CIFAR-10 dataset. The mean average precision (mAP) of the proposed method on the four object-detection datasets was improved by 5.39%, 5.79%, 4.46%, and 8.90%, respectively.
Exploring the application scenarios and issues facing Metaverse technology in education

This study aims to explore how the Metaverse platform could be applied to the field of online education, the benefits it brings, and the challenges posed by the application of Metaverse to online education. The study analyzes the benefits of immersive learning brought about by Metaverse and its potential to save education costs, enhance efficiency and students’ interest in learning, and explores the application of Metaverse technology in education in relation to the characteristics of online education. The results show that Metaverse technology can support several aspects of online classrooms with realistic senses, personalized teaching models, realistic 3D identities, interactive communication, virtual reality (VR) technology, and gamified learning. This research also presents the problems faced by the Edu-Metaverse, including technology development difficulties, interaction issues, content production, game addiction, privacy, and ethics.
Integrating external knowledge into aspect-based sentiment analysis using graph neural network

Aspect-based sentiment analysis aims to analyze the sentiment polarity of a given aspect. The graph convolutional neural network model is widely used. However, most existing research focuses on mining the context-word-to-aspect-word dependencies of dependency trees based on the sentence itself without using much text-related external knowledge. In addition, the problem of reasonably capturing words outside the multihop grammatical distance and edge label hinders the effect of GCN. This paper proposes a graph convolutional network that fuses external knowledge (sentiment lexicon and part-of-speech information) (EK-GCN). Specifically, we conduct a statistical study on part-of-speech and construct a part-of-speech matrix to fully consider the influence of denying words, degree words, and other words that affect sentiment expression in sentences on sentiment classification. Then, an external sentiment lexicon is used to assign sentiment scores to each word in the sentence to construct a sentiment score matrix to highlight the weight of sentiment words, which to a certain extent, compensates for the fact that the syntactic dependency tree cannot capture edge labels. In addition, we design a Word–Sentence Interaction Network (WSIN), which can fully consider the information of the current aspect word and interact with the context information of the reviews to filter useful sentence information. We conduct experiments on four benchmark datasets, and the excellent experimental results demonstrate the effectiveness of our model. The results also verify that fully integrating external knowledge can assist in completing aspect-based sentiment analysis tasks.
A Platform-Free Proof of Federated Learning Consensus Mechanism for Sustainable Blockchains

Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners' self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.
A comprehensive review of object detection with deep learning

In the realm of computer vision, Deep Convolutional Neural Networks (DCNNs) have demonstrated excellent performance. Video Processing, Object Detection, Image Segmentation, Image Classification, Speech Recognition and Natural Language Processing are some of the application areas of CNN. Object detection is the most crucial and challenging task of computer vision. It has numerous applications in the field of security, military, transportation and medical sciences. In this review, object detection and its different aspects have been covered in detail. With the gradual increase in the evolution of deep learning algorithms for detecting objects, a significant improvement in the performance of object detection models has been observed. However, this does not imply that the conventional object detection methods, which had been evolving for decades prior to the emergence of deep learning, had become outdated. There are some cases where conventional methods with global features are superior choice. This review paper starts with a quick overview of object detection followed by object detection frameworks, backbone convolutional neural network, and an overview of common datasets along with the evaluation metrics. Object detection problems and applications are also studied in detail. Some future research challenges in designing deep neural networks are discussed. Lastly, the performance of object detection models on PASCAL VOC and MS COCO datasets is compared and conclusions are drawn.
Benefits of pairing floating solar photovoltaics with hydropower reservoirs in Europe

Achieving carbon-neutrality is increasing the demand of renewable electricity which is raising the competition for land and associated acquisition costs. Installation of floating photovoltaic (FPV) on existing hydropower reservoirs offers one solution to limited land availability while providing solar electricity, leveraging water bodies, and reducing water evaporation losses. This work assesses the potential electricity output of FPVs at regional and national levels on 337 hydropower reservoirs in the EU27 considering four scenarios and two types of floaters. Evaporation, water losses and water savings due to FPVs installation are also estimated using climatic parameters for the year 2018. The reservoirs' total water losses are estimated at 9380 mcm. The installation of FPVs of equal installed capacity as the hydropower plants, has the potential to generate 42.31 TWh covering 2.3% of the total reservoir area. In this case, up to 557 mcm could be saved by installing FPV. The FPVs' multiple benefits and the potential offered by existing hydropower reservoirs are compatible with the EU's goals for net zero emissions and more autonomy from imported fossil fuels and energy transformation.
Artificial intelligence and machine learning

Abstract Within the last decade, the application of “artificial intelligence” and “machine learning” has become popular across multiple disciplines, especially in information systems. The two terms are still used inconsistently in academia and industry—sometimes as synonyms, sometimes with different meanings. With this work, we try to clarify the relationship between these concepts. We review the relevant literature and develop a conceptual framework to specify the role of machine learning in building (artificial) intelligent agents. Additionally, we propose a consistent typology for AI-based information systems. We contribute to a deeper understanding of the nature of both concepts and to more terminological clarity and guidance—as a starting point for interdisciplinary discussions and future research.
Machine Learning in Earthquake Seismology

Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data mining techniques can significantly improve our capability for seismic data processing. In this review we provide a comprehensive overview of ML applications in earthquake seismology, discuss progress and challenges, and offer suggestions for future work. ▪ Conceptual, algorithmic, and computational advances have enabled rapid progress in the development of machine learning approaches to earthquake seismology. ▪ The impact of that progress is most clearly evident in earthquake monitoring and is leading to a new generation of much more comprehensive earthquake catalogs. ▪ Application of unsupervised approaches for exploratory analysis of these high-dimensional catalogs may reveal new understanding of seismicity. ▪ Machine learning methods are proving to be effective across a broad range of other seismological tasks, but systematic benchmarking through open source frameworks and benchmark data sets are important to ensure continuing progress.
Evolving chimp optimization algorithm by weighted opposition-based technique and greedy search for multimodal engineering problems

This paper presents an evolved chimp optimization algorithm (ChOA) that uses greedy search (GS) and opposition-based learning (OBL) to respectively increase the ChOA’s capabilities for exploration and exploitation in addressing real practical engineering-constrained problems. In order to investigate the efficiency of the GSOBL-ChOA, its performance is evaluated by twenty-three standard benchmark functions, 10 benchmark functions from CEC06-2019, a randomly generated landscape, and 12 real practical Constrained Optimization Problems (COPs-2020) from a wide variety of engineering fields, including power system design, synthesis and process design, industrial chemical producer, power-electronic design, mechanical design, and animal feed ratio. The findings are compared to those obtained using benchmark optimizers such as CMA-ES and SHADE as state-of-the-art optimization techniques and CEC competition winners; standard ChOA; OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. In order to perform a comprehensive assessment, three non-parametric statistical tests, including the Wilcoxon rank-sum, Bonferroni–Dunn and Holm, and Friedman average rank tests, are utilized. The top two algorithms are GSOBL-ChOA and CMA-ES, with scores of forty and eleven, respectively, among 27 mathematical functions. jDE100 obtained the highest score of 100 in the 100-digit challenge, followed closely by DISHchain1e+12, which achieved the highest possible score of 97, and GSOBL-ChOA obtained the fourth-highest score of 93. Finally, GSOBL-ChOA and CMA-ES outperform other benchmarks in five and four real practical COPs, respectively. The source code of the paper can be downloaded using the following link: https://se.mathworks.com/matlabcentral/fileexchange/119108-evolving-chimp-optimization-algorithm-by-weighted-opposition.
IEGQO-AOA: Information-Exchanged Gaussian Arithmetic Optimization Algorithm with Quasi-opposition learning

Arithmetic optimization algorithm (AOA) is a math optimizer proposed to solve optimization challenges. Its capability to find the global solution comes from the behavior of four arithmetic operators: multiplication, division, subtraction and addition. Local minima stagnation and sluggish convergence are the major concerns of AOA. To handle these issues, three effective modifications are proposed. Information exchange is introduced among the search agents first. Then, promising solutions around the best and current solutions are visited by a plausible way based on the Gaussian distribution. Finally, quasi-opposition of the best solution is obtained to have a higher chance of approaching the global solution. The proposed approach is named as Information-Exchanged Gaussian AOA with Quasi-Opposition learning (IEGQO-AOA). 23 standard benchmark functions, 10 CEC2020 test functions and 1 real-life engineering design problem are solved by the proposed IEGQO-AOA and its competing peers such as the original and modified versions of AOA, dwarf mongoose optimization, reptile search algorithm, aquila optimizer, bat algorithm, sine cosine algorithm, original and enhanced version of salp swarm algorithm, dragonfly search algorithm, LSHADE-EpSin, stochastic fractal search, improved jaya and moth-flame optimization, perturbed stochastic fractal search and nelder-mead simplex orthogonal learning moth-flame optimization algorithm. Comparative results based on the statistical tests ratify the potential of IEGQO-AOA in solving problems concerning accuracy and convergence without compromising on the algorithm’s simplicity much.
A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction

The clarity of medical images is crucial for doctors to identify and diagnose different diseases. High-resolution images have more detailed information and clearer content than low-resolution images. It is well known that medical images can frequently have some blurred object boundaries, and that traditional deep learning models cannot adequately describe the uncertainty of these blurred boundaries. This paper proposes a new fuzzy metric to characterize the uncertainty of pixels and designs a fuzzy hierarchical fusion attention neural network based on multiscale guided learning. Specifically, a fuzzy neural information-processing block is proposed, which converts an input image into a fuzzy domain using fuzzy membership functions. The uncertainty of the pixels is processed using the proposed fuzzy rules, and then the output of the fuzzy rule layer is fused with the result of the convolution in the neural network. Simultaneously, a multiscale guided-learning dense residual block and pyramidal hierarchical attention module are designed to extract more effective hierarchical image information. Finally, a recurrent memory module with a residual structure is used to process the output features of the hierarchical attention modules. A recursive sub-pixel reconstruction module is used at the tail of the network to reconstruct the images. Compared with existing super-resolution methods using the public COVID-CT dataset, the proposed method demonstrated superior performance in high-resolution medical image reconstruction and reduced the number of parameters and analysis time of the models.
Cryptocurrencies on the road to sustainability: Ethereum paving the way for Bitcoin

Amid the current climate emergency and global energy crisis, regulators have started to consider their options to limit the power demand of cryptocurrency networks. One specific way crypto-asset communities can limit their environmental impact is by avoiding or replacing the energy-intensive proof-of-work (PoW) mining mechanism. Ethereum, the second largest crypto-asset by market capitalization, had its PoW replaced with an alternative known as proof-of-stake during an event called The Merge on September 15, 2022. In this perspective, the likely range of electricity saved due to this change is estimated, while the limitations in assessing these figures are highlighted. Lastly, the challenges and opportunities in replicating The Merge on other cryptocurrencies such as Bitcoin are discussed.
Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution

Magnetic resonance imaging (MRI) can present multicontrast images of the same anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared with SR reconstruction using a single-contrast, multicontrast SR reconstruction is promising to yield SR images with higher quality by leveraging diverse yet complementary information embedded in different imaging modalities. However, existing methods still have two shortcomings: (1) they neglect that the multi-contrast features at different scales contain different anatomical details and hence lack effective mechanisms to match and fuse these features for better reconstruction; and (2) they are still deficient in capturing long-range dependencies, which are essential for the regions with complicated anatomical structures. We propose a novel network to comprehensively address these problems by developing a set of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques; we call it McMRSR. Firstly, we tame transformers to model long-range dependencies in both reference and target images. Then, a new multi-scale contextual matching method is proposed to capture corresponding contexts from reference features at different scales. Furthermore, we introduce a multi-scale aggregation mechanism to gradually and interactively aggregate multi-scale matched features for reconstructing the target SR MR image. Extensive experiments demonstrate that our network outperforms state-of-the-art approaches and has great potential to be applied in clinical practice. Codes are available at https://github.com/XAIMI-Lab/McMRSR.