A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion

Abstract: In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results.


Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation

Abstract: Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsupervised domain adaptation mean-teacher segmentation framework. In this method, we use a “teacher-assistant” model to provide semantic pseudo-labels, which will fill in the holes in the fine spatial structure given by SAM and generate pseudo-labels close to the ground truth, which then guide the student model for learning. Here, the “teacher-assistant” model helps to distill knowledge. During testing, only the student model is used, thus greatly improving efficiency. We tested SAM-EDA on mainstream segmentation benchmarks in adverse weather conditions and obtained a more-robust segmentation model.


DMCVS: Decomposed motion compensation‐based video stabilization

Abstract: Abstract With the popularity of handheld devices, video stabilization is becoming increasingly important. In previous studies, many methods have been proposed to stabilize shaky videos. However, these methods fail to balance between image content integrity and stability. Some methods sacrifice image content for better stability. Other methods ignore the subtle jitters, which leads to poor stability. This work innovatively proposes a video stabilization method based on decomposed motion compensation. First, a grid‐based motion statistics method is adopted for motion estimation, which obtains more accurate motion vectors according to matched likelihood estimates. Then, the motion compensation is inherently decomposed into two parts: linear motion compensation and auxiliary motion compensation. Linear motion compensation removes complex jitter by constructing linear path constraints to obtain a more stable camera path. Auxiliary motion compensation uses a moving average filter to remove the high‐frequency jitter as a supplement and preserve more image content. The two components are combined with individual weights to derive the final transform matrix and warp the original frames. Experimental results show that our method outperforms the previous methods on NUS and DeepStab datasets qualitatively and quantitatively.


Structural performance‐based anomaly detection for velocity pulse

Abstract: Abstract Pulse‐like ground motion can cause extreme damage to long‐period structures. An automatic algorithm is proposed to identify pulse‐like ground motions, in which improved anomaly detection is applied and the structural performance is considered. To characterize the intrinsic pulse‐like features, the distance‐based anomaly detection algorithm is improved, and the relative cumulative energy is added to the Manhattan distance. The first application of the anomaly detection algorithm performs well in pulse‐like motion identification. The pulse‐like ground motions are identified according to the performance of typical long‐period engineering structures in Hazus by the improved capacity spectrum method. The method is applied to 214 strong ground motions in the Next Generation Attenuation West 2 database. Comprehensive comparisons of the proposed approach with the wavelet analysis and the energy method are presented to illustrate the significance of the structural performance on the pulse‐like identification method.


Coin dimensionality as a resource in quantum metrology involving discrete-time quantum walks

Abstract: We address metrological problems where the parameter of interest is encoded in the internal degree of freedom of a discrete-time quantum walker, and provide evidence that coin dimensionality is a potential resource to enhance precision. In particular, we consider estimation problems where the coin parameter governs rotations around a given axis and show that the corresponding quantum Fisher information (QFI) may increase with the dimension of the coin. We determine the optimal initial state of the walker to maximize the QFI and discuss whether, and to what extent, precision enhancement may be achieved by measuring only the position of the walker. Finally, we consider Grover-like encoding of the parameter and compare results with those obtained from rotation encoding.


Learning from artificial intelligence researchers about international business implications

Abstract: Abstract Artificial intelligence is a dynamic and emerging form of technological innovation that has numerous ramifications for international business managers. The aim of this article is to obtain commentary from researchers about the role artificial intelligence will play in the global arena. This includes asking questions about how it will affect internationalization processes and whether it will lead to more international collaboration. Well‐known researchers provide advice on what international business managers should do in terms of staying competitive but also how they can integrate learning from artificial intelligence into their business operations. Lastly, suggestions for future research regarding the interplay between international business and artificial intelligence are provided.


Conflict-free joint decision by lag and zero-lag synchronization in laser network

Abstract: Abstract With the end of Moore’s Law and the increasing demand for computing, photonic accelerators are garnering considerable attention. This is due to the physical characteristics of light, such as high bandwidth and multiplicity, and the various synchronization phenomena that emerge in the realm of laser physics. These factors come into play as computer performance approaches its limits. In this study, we explore the application of a laser network, acting as a photonic accelerator, to the competitive multi-armed bandit problem. In this context, conflict avoidance is key to maximizing environmental rewards. We experimentally demonstrate cooperative decision-making using zero-lag and lag synchronization within a network of four semiconductor lasers. Lag synchronization of chaos realizes effective decision-making and zero-lag synchronization is responsible for the realization of the collision avoidance function. We experimentally verified a low collision rate and high reward in a fundamental 2-player, 2-slot scenario, and showed the scalability of this system. This system architecture opens up new possibilities for intelligent functionalities in laser dynamics.


Fast elementary gates for universal quantum computation with Kerr parametric oscillator qubits

Abstract: Kerr parametric oscillators (KPOs) can stabilize the superpositions of coherent states, which can be utilized as qubits, and are promising candidates for realizing hardware-efficient quantum computers. Although elementary gates for universal quantum computation with KPO qubits have been proposed, these gates are usually based on adiabatic operations and thus need long gate times, which result in errors caused by photon loss in KPOs realized by, e.g., superconducting circuits. In this work we accelerate the elementary gates by experimentally feasible control methods, which are based on numerical optimization of pulse shapes for shortcuts to adiabaticity. By numerical simulations, we show that the proposed methods can achieve speedups compared to adiabatic ones by up to six times with high gate fidelities of 99.9%. These methods are thus expected to be useful for quantum computers with KPOs.


Automatic segmentation of skeletal muscles from MR images using modified U-Net and a novel data augmentation approach

Abstract: Rapid and accurate muscle segmentation is essential for the diagnosis and monitoring of many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive labor and high inter-operator reproducibility errors. In this study, deep learning (DL) based automatic muscle segmentation from MR scans is investigated for post-menopausal women, who normally experience a decline in muscle volume. The performance of four Deep Learning (DL) models was evaluated: U-Net and UNet++ and two modified U-Net networks, which combined feature fusion and attention mechanisms (Feature-Fusion-UNet, FFU, and Attention-Feature-Fusion-UNet, AFFU). The models were tested for automatic segmentation of 16-lower limb muscles from MRI scans of two cohorts of post-menopausal women (11 subjects in PMW-1, 8 subjects in PMW-2; from two different studies so considered independent datasets) and 10 obese post-menopausal women (PMW-OB). Furthermore, a novel data augmentation approach is proposed to enlarge the training dataset. The results were assessed and compared by using the Dice similarity coefficient (DSC), relative volume error (RVE), and Hausdorff distance (HD). The best performance among all four DL models was achieved by AFFU (PMW-1: DSC 0.828 ± 0.079, 1-RVE 0.859 ± 0.122, HD 29.9 mm ± 26.5 mm; PMW-2: DSC 0.833 ± 0.065, 1-RVE 0.873 ± 0.105, HD 25.9 mm ± 27.9 mm; PMW-OB: DSC 0.862 ± 0.048, 1-RVE 0.919 ± 0.076, HD 34.8 mm ± 46.8 mm). Furthermore, the augmentation of data significantly improved the DSC scores of U-Net and AFFU for all 16 tested muscles (between 0.23% and 2.17% (DSC), 1.6%–1.93% (1-RVE), and 9.6%–19.8% (HD) improvement). These findings highlight the feasibility of utilizing DL models for automatic segmentation of muscles in post-menopausal women and indicate that the proposed augmentation method can enhance the performance of models trained on small datasets.


Exceptional Entanglement and Quantum Sensing with a Parity‐Time‐Symmetric Two‐Qubit System

Abstract: Abstract Parity‐time (PT) symmetric systems with gain‐loss balanced structures can have entirely real eigenenergy spectra despite their non‐Hermitian nature, leading to peculiar discoveries such as single‐mode lasers, wireless power transfer, and non‐reciprocal light propagation. Recently, the study of PT‐symmetry has extended to the quantum realm, inspiring a search for PT‐symmetric quantum effects as well as their unique applications. Here, quantum entanglement and sensing with a PT‐symmetric system of two qubits are studied with Ising‐type interaction. It is shown that exceptional points (EPs) can induce exceptional entanglement phenomena such as collapse‐revival and steady‐state entanglement on the two sides of EPs. The entanglement in the PT‐symmetric system is also found to be generated more quickly than that in the Hermitian counterpart. In addition, a scheme for realizing EP‐enhanced quantum sensing is proposed by calculating the quantum Fisher information to estimate the parameters of the system. The work provides new insights into EP‐engineered quantum effects and can benefit a wide range of applications based on non‐Hermitian quantum devices.


On a Uniqueness Problem in Discrete Tomography

Abstract: Let A be a subset of the integer lattice Zn contained inside a sphere S in Rn. Suppose that for each u∈S we know the number of points of A visible from u. Does this information determine the set A uniquely? In this note we answer this question affirmatively, provided the center of the sphere is not a rational point.


Matrix Factorization Recommendation Algorithm Based on Attention Interaction

Abstract: Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item.


Text-Vision Relationship Alignment for Referring Image Segmentation

Abstract: Abstract Referring image segmentation aims to segment object in an image based on a referring expression. Its difficulty lies in aligning expression semantics with visual instances. The existing methods based on semantic reasoning are limited by the performance of external syntax parser and do not explicitly explore the relationships between visual instances. This article proposes an end-to-end method for referring image segmentation by aligning ’linguistic relationship’ with ’visual relationships’. This method does not rely on external syntax parser for expression parsing. In this paper, the expression is adaptively and structurally parsed into three components: ’subject’, ’object’, and ’linguistic relationship’ by the Semantic Component Parser (SCP) in a learnable manner. Instances Activation Map Module (IAM) locates multiple visual instances based on the subject and object. In addition, the Relationship Based Visual Localization Module (RBVL) firstly enables each instance of the image to learn global knowledge, then decodes the visual relationships between these visual instances, and finally aligns the visual relationships with the linguistic relationships to further accurately locate the target object. The experimental results show that the proposed method improves performance by 4– 9% compared with baseline method on multiple referring image segmentation datasets.


Statistical evaluation of hard-to-measure surfaces

Abstract: Abstract The main goal of this article will be to find ways to evaluate hard-to-measure surfaces statistically. First, the basic characteristics and rules of surface quality will be described according to the standards ČSN EN 4287, ČSN EN 4288 and ČSN EN ISO 2517-2. Subsequently, the measured values of the roughness parameter Sa (arithmetic average of the height of the measured surface) and Sz (the maximum height of the measured surface) will be compared and evaluated which is the best. These parameters will be described and measured on aluminium plates on which the test surfaces were laser engraved. To evaluate the best surface, statistical methods will be used, such as the EDA methodology (exploratory data analysis), hypothesis testing with normality and outlier tests, and last but not least, cluster or cluster analysis, which compares the similarity of the measured data. This article aims to show the possibilities of surface quality assessment using 3D surface roughness parameters, which are not often used in practice.


Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques

Abstract: Abstract The flourishing realm of advanced driver-assistance systems (ADAS) as well as autonomous vehicles (AVs) presents exceptional opportunities to enhance safe driving. An essential aspect of this transformation involves monitoring driver behavior through observable physiological indicators, including the driver’s facial expressions, hand placement on the wheels, and the driver’s body postures. An artificial intelligence (AI) system under consideration alerts drivers about potentially unsafe behaviors using real-time voice notifications. This paper offers an all-embracing survey of neural network-based methodologies for studying these driver bio-metrics, presenting an exhaustive examination of their advantages and drawbacks. The evaluation includes two relevant datasets, separately categorizing ten different in-cabinet behaviors, providing a systematic classification for driver behaviors detection. The ultimate aim is to inform the development of driver behavior monitoring systems. This survey is a valuable guide for those dedicated to enhancing vehicle safety and preventing accidents caused by careless driving. The paper’s structure encompasses sections on autonomous vehicles, neural networks, driver behavior analysis methods, dataset utilization, and final findings and future suggestions, ensuring accessibility for audiences with diverse levels of understanding regarding the subject matter.


An Estimation of the Available Spatial Intensity of Solar Energy in Urban Blocks in Wuhan, China

Abstract: Urban form is an important factor affecting urban energy. However, the design of urban form and energy mostly belong to two separate disciplines and fields, and urban energy planning research rarely considers their mutual relationship. The available space intensity (ASI) of solar energy is formed on the basis of energy planning and urban design; the objective of this research is to evaluate the impact of urban form on the ASI of solar energy and to propose strategies for planning of the space that is available for solar energy so as to improve the efficiency of urban energy utilization and achieve sustainable urban development. Methodologically, this study firstly proposes a model to quantify the ASI of solar energy using three indicators: solar radiation intensity (SRI), solar installation intensity (SII), and solar generation intensity (SEGI). Then, we quantitatively calculate the solar ASI of nine types of typical urban blocks in a sub-center of Wuhan City, Nanhu. Correlation analysis and multiple linear regression analysis are then used to analyze the correlation between the form indicators and solar ASI, as well as the degree of influence. The results show that the differences in SRI, SII, and SEGI amongst the nine types of city blocks were as high as 114.61%, 162.50%, and 61.01%. The solar ASI was mainly affected by three form indicators: the building coverage ratio, the average building height, and the volume-to-area ratio. Reducing the building coverage ratio and increasing vertical development at the same time can effectively improve the ASI of solar energy. The results of this study and the established method provide an important reference and rapid calculation tool for urban energy planning and design, reducing the data and time usually required for solar analysis at the block scale.


Finding Regions of Counterfactual Explanations via Robust Optimization

Abstract: Counterfactual explanations (CEs) play an important role in detecting bias and improving the explainability of data-driven classification models. A CE is a minimal perturbed data point for which the decision of the model changes. Most of the existing methods can only provide one CE, which may not be achievable for the user. In this work, we derive an iterative method to calculate robust CEs (i.e., CEs that remain valid even after the features are slightly perturbed). To this end, our method provides a whole region of CEs, allowing the user to choose a suitable recourse to obtain a desired outcome. We use algorithmic ideas from robust optimization and prove convergence results for the most common machine learning methods, including decision trees, tree ensembles, and neural networks. Our experiments show that our method can efficiently generate globally optimal robust CEs for a variety of common data sets and classification models. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant OCENW.GROOT.2019.015, Optimization for and with Machine Learning (OPTIMAL)]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2023.0153 .


The Temporal and Spatial Characteristics of Wind–Photovoltaic–Hydro Hybrid Power Output Based on a Cloud Model and Copula Function

Abstract: In a high proportion of wind–photovoltaic–hydro hybrid power systems, fluctuation and dispersion make it difficult to accurately quantify the output characteristics. Therefore, in this study, a cloud model and copula correlation coefficient matrix were constructed for a hybrid power generation system based on the output data. Multiple backward cloud transformation based on the sampling-with-replacement method was proposed to calculate the improved entropy and hyperentropy to analyze the fluctuation range and dispersion degree quantitatively. A similarity index was proposed to evaluate the similarity between wind power, PV power, and hydropower. A suitable copula function was selected, and the Kendall and Spearman coefficients show the correlation relationships of the hybrid systems. The temporal and spatial characteristics of the hybrid systems were analyzed based on the two models. A typical example in Qinghai proved the effectiveness and applicability of the method. The results show that the correlation between photovoltaic power and hydropower is better and that, in summer, hydropower can be used to adjust the output of renewable energy.


Parameter instance learning with enhanced vision transformers for aerial person re‐identification

Abstract: Summary In an agnostic space environment, aerial person re‐identification (Re‐ID) is a task that the query person may not occur in the gallery set, it is considered a subordinate task within the domain of open‐world person Re‐ID, and is a more challenging and practical application research. The aerial person images, captured by unmanned aerial vehicles, present more significant challenges such as weak appearance features, fewer individual person samples and occlusion due to variations in camera height and viewing angles compared to ground‐level images. Most state‐of‐the‐arts person Re‐ID methods developed for open‐world datasets rely heavily on local convolutional neural networks but exhibit suboptimal performance when directly applied to aerial person Re‐ID tasks. In this article, a parameter instance learning based on vision transformers (ViT) model is introduced for the design of aerial person Re‐ID. Initially, we employ a self‐supervised paradigm grounded in parameter instance discrimination, aiming to capture feature alignment and instance similarity. Subsequently, using labeled training data, we optimize the network model through the calculation of two types of loss functions. Finally, we employ a feature enhancement strategy utilizing zero‐padding and displacement techniques. This strategy effectively and directly enhances the robustness of the ViT model against issues such as occlusion and misalignment. We conducted experiments on a Re‐ID dataset to validate the effectiveness of the method. Our approach achieves a mean average precision of 57.31% and a Rank‐1 accuracy of 65.29% on the aerial person Re‐ID dataset PRAI‐1581.


Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Abstract: Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF.


Knowledge distillation representation and <scp>DCNMIX</scp> quality prediction‐based Web service recommendation

Abstract: Summary Web service recommendation as an emerging topic attracts increasing attention due to its important practical significance. As the number of available Web services continues to grow, users face the challenge of searching the most suitable services that meet their specific needs. Quality of service (QoS)‐based service recommendation becomes a popular approach to address this issue. However, existing QoS‐based service recommendation methods are inability to effectively capture valuable content and structural information from services. These methods often rely solely on low‐order explicit feature intersections in QoS information, do not fully utilize the high‐order implicit feature intersections, and ignore the rich semantic information existing in service descriptions and user preferences. To address this problem, this paper proposes a Web service recommendation method via combining knowledge distillation representation and DCNMIX quality prediction. This method combines content‐based and structure‐based service classification and service prediction based on multi‐dimensional service quality information. First, it builds a service relationship network using semantic features extracted from service descriptions. Second, it designs a graph neural network knowledge distillation framework. The teacher model extracts the knowledge of the graph neural network model, and the student model learns the structure‐based and feature‐based prior knowledge of the service relationship network. Then the student model is used to learn the knowledge of the teacher model, classify Web services, and obtain service representations. Finally, based on service representations and multi‐dimensional QoS information, it exploits the DCNMIX model to learn the explicit and implicit features intersections of Web services and obtain the prediction score and ranking of Web services. The experimental results on the ProgrammableWeb dataset show that the proposed method outperforms the state‐of‐the‐art baselines in terms of Recall, F1, Logloss, and AUC_ROC.


Analogue Quantum Simulation with Fixed-Frequency Transmon Qubits

Abstract: We experimentally assess the suitability of transmon qubits with fixed frequencies and fixed interactions for the realization of analogue quantum simulations of spin systems. We test a set of necessary criteria for this goal on a commercial quantum processor using full quantum process tomography and more efficient Hamiltonian tomography. Significant single qubit errors at low amplitudes are identified as a limiting factor preventing the realization of analogue simulations on currently available devices. We additionally find spurious dynamics in the absence of drive pulses, which we identify with coherent coupling between the qubit and a low dimensional environment. With moderate improvements, analogue simulation of a rich family of time-dependent many-body spin Hamiltonians may be possible.


New independent daily global solar radiation estimation models based on the day number of the year

Abstract: Solar radiation data are necessary information for the analysis and prediction of certain environmental processes and the planning of related energy projects. Many models have been proposed in the literature for their estimation due to their unavailability in some locations, especially in developing countries. However, peoples are currently finding more accurate and simple models. One type of these models is the day of the year-based (DYB) model, which has as merit over others, the fact that it does not depend on any environmental parameter. In this work, nine existing DYB models are calibrated with Levenberg–Marquardt nonlinear curve fitting technique for the estimation of solar radiation in the regional cities of Cameroon. In addition, two new DYB models are proposed. The results show that one of the two proposed models (M11) best estimates the solar radiation in nine sites and is ranked second in one site with the root mean square error in the range [0.2365; 0.3039], the mean absolute percentage error in the range [0.1873; 0.2348], and the correlation coefficient in the range [0.8332; 0.9164]. The new model M11 is therefore recommended for estimating solar radiation of some sites of Cameroon and in regions with similar climate.


A Machine Learning-Based Pipeline for the Extraction of Insights from Customer Reviews

Abstract: The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clustering. The elements of our model have been integrated and tailored to better meet the requirements of efficient information extraction and topic modeling of the extracted information for opinion mining. Our approach was validated and compared with other state-of-the-art methods using publicly available benchmark datasets. The results show that our system performs better than existing topic modeling and keyword extraction methods in this task.


AGCN-Domain: Detecting Malicious Domains with Graph Convolutional Network and Attention Mechanism

Abstract: Domain Name System (DNS) plays an infrastructure role in providing the directory service for mapping domains to IPs on the Internet. Considering the foundation and openness of DNS, it is not surprising that adversaries register massive domains to enable multiple malicious activities, such as spam, command and control (C&C), malware distribution, click fraud, etc. Therefore, detecting malicious domains is a significant topic in security research. Although a substantial quantity of research has been conducted, previous work has failed to fuse multiple relationship features to uncover the deep underlying relationships between domains, thus largely limiting their level of performance. In this paper, we proposed AGCN-Domain to detect malicious domains by combining various relations. The core concept behind our work is to analyze relations between domains according to their behaviors in multiple perspectives and fuse them intelligently. The AGCN-Domain model utilizes three relationships (client relation, resolution relation, and cname relation) to construct three relationship feature graphs to extract features and intelligently fuse the features extracted from the graphs through an attention mechanism. After the relationship features are extracted from the domain names, they are put into the trained classifier to be processed. Through our experiments, we have demonstrated the performance of our proposed AGCN-Domain model. With 10% initialized labels in the dataset, our AGCN-Domain model achieved an accuracy of 94.27% and the F1 score of 87.93%, significantly outperforming other methods in the comparative experiments.


Cyberbullying Text Identification based on Deep Learning and Transformer-based Language Models

Abstract: In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The intelligent BullyFilterNeT system devised overcomes Out-of-Vocabulary (OOV) challenges associated with non-contextual embeddings and addresses the limitations of context-aware feature representations. To facilitate a comprehensive understanding, three non-contextual embedding models GloVe, FastText, and Word2Vec are developed for feature extraction in Bengali. These embedding models are utilized in the classification models, employing three statistical models (SVM, SGD, Libsvm), and four deep learning models (CNN, VDCNN, LSTM, GRU). Additionally, the study employs six transformer-based language models: mBERT, bELECTRA, IndicBERT, XML-RoBERTa, DistilBERT, and BanglaBERT, respectively to overcome the limitations of earlier models. Remarkably, BanglaBERT-based BullyFilterNeT achieves the highest accuracy of 88.04% in our test set, underscoring its effectiveness in cyberbullying text identification in the Bengali language.


Customer engagement and intention to purchase attitudes of generation Z consumers toward emojis in digital marketing communications

Abstract: Purpose Emojis are quickly becoming a popular new language in social media and marketing. The capability to express emotions and make message understanding easier is one of the primary reasons for using emojis. The aim of this research was to determine the influence of perceived usefulness, perceived ease of use, trust, and involvement on customer engagement due to emojis used in digital marketing communications among Generation Z (Gen Z) in South Africa. Design/methodology/approach Following the descriptive research approach, quantitative research was used in this study. A questionnaire (self-administered) was utilized to test the effectiveness of using emojis among 1,000 young consumers. Structural equation modeling was used to test the hypotheses. Findings The findings of the study yielded positive relationships between the variables, namely between trust and involvement; involvement and the perceived ease of use; involvement and perceived usefulness; perceived ease of use and perceived usefulness; trust and customer engagement; perceived usefulness and customer engagement; involvement and customer engagement; customer engagement and intention to purchase; trust and intention to purchase; and perceived usefulness and intention to purchase. Practical implications This study can help organizations in emerging markets use emojis in their digital marketing communications to engage customers and stimulate intention to purchase among young people, especially the Gen Z cohort, who seek organizations and brands that understand and connect with them. Originality/value By investigating the effects of emojis in digital marketing communications, this study contributes to the customer-centric process and the literature on emoji usage while also involving a credible digital language when communicating with members of Gen Z. By extending TAM, the findings of this study contribute to the TAM literature by demonstrating that emoji usage in digital marketing communications positively influences various attitudinal associations among Gen Z consumers.


Automatic pipeline fault detection using one-dimensional convolutional bidirectional long short-term memory networks with wide first-layer kernels

Abstract: Pipeline networks are crucial components of modern infrastructure, and ensuring their reliable operation is essential for sustainable development. The percussion-based methods are considered promising for detecting pipeline faults due to their avoidance of constant-contact sensors and ease of implementation. However, the majority of existing percussion-based methods suffer from limitations such as the requirement for manual feature extraction, as well as subpar noise resilience and adaptability. This paper introduces a one-dimensional convolutional bidirectional long short-term memory network with wide first-layer kernels for the classification of percussion-induced acoustic signals, thus achieving automatic identification of pipeline leakage and water deposit conditions. This approach directly extracts features from audio signals using wide first-layer convolutional kernels, eliminating the need for manual feature extraction. Additionally, it employs bidirectional long short-term memory to effectively capture long-term signal dependencies from both past and future contexts. To validate the effectiveness of the method, two case studies were conducted on three groups of pipes. The results show that the proposed method demonstrates superior noise resistance and adaptability compared to other methods, and it also exhibits strong applicability to other percussion signal datasets. Additionally, the impact of different first convolutional kernel sizes on the noise resistance and adaptive performance of the model was investigated, which provides robust guidance for the effective processing of percussion-induced acoustic signals.


Sensorimotor adaptation in virtual reality: Do instructions and body representation influence aftereffects?

Abstract: Abstract Perturbations in virtual reality (VR) lead to sensorimotor adaptation during exposure, but also to aftereffects once the perturbation is no longer present. An experiment was conducted to investigate the impact of different task instructions and body representation on the magnitude and the persistence of these aftereffects. Participants completed the paradigm of sensorimotor adaptation in VR. They were assigned to one of three groups: control group, misinformation group or arrow group. The misinformation group and the arrow group were each compared to the control group to examine the effects of instruction and body representation. The misinformation group was given the incorrect instruction that in addition to the perturbation, a random error component was also built into the movement. The arrow group was presented a virtual arrow instead of a virtual hand. It was hypothesised that both would lead to a lower magnitude and persistence of the aftereffect because the object identity between hand and virtual representation would be reduced, and errors would be more strongly attributed to external causes. Misinformation led to lower persistence, while the arrow group showed no significant differences compared to the control group. The results suggest that information about the accuracy of the VR system can influence the aftereffects, which should be considered when developing VR instructions. No effects of body representation were found. One possible explanation is that the manipulated difference between abstract and realistic body representation was too small in terms of object identity.


AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Abstract: Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.


Structure of Professional Competence Development of Future Fine Art Teachers: Integration of Technology, Method and Competence

Abstract: The article gives the data about the structure of proficient competence of advancement: integration of innovation, strategy and competence. It was noted that within the development of professional competence, via the proficient characteristics, educational support of the instructive prepare based on prerequisites of qualification, and the determination of personal approaches to the execution of the educational process are vital. This research investigates the structure of professional competence development in future fine art teachers with a focus on the integration of technology, teaching methods, and subject competence. Qualitative research methods were employed to gain an in-depth understanding of how these factors interact to shape professional competence. Data were collected through classroom observations, interviews with faculty and students, and document analysis. Findings indicate that the integration of technology not only enhances the effectiveness of learning but also enriches students' artistic experiences. Innovative teaching methods, such as project-based learning and student collaboration, also play a crucial role in developing pedagogical and artistic skills. Furthermore, a strong subject competence in fine arts was identified as a key factor in shaping quality art teachers. Implications for curriculum development and art teacher training are discussed. This study provides valuable insights into integrating technology, methods, and subject competence in the formation of professional competence in future fine art teachers.


Disrupting Central Banking or Shadow Central Banking

Abstract: Abstract Private entities have recently attempted to invade the realm of central banking. This chapter identifies such activity as ‘shadow central banking’, and draws analogies to the previous emergence of ‘shadow banking’ more generally. The similarities between shadow banking and shadow central banking are clear when analysing the circumstances of their emergence, their pathologies, and the regulatory response. This chapter focuses on the evolving roles of central banks and private initiatives, as their activities have led to the rethinking of central banking activities and the form of money in modern capitalism. In doing this, it proposes that this disruption of traditional central banking activity stems from a new interpretation of the concept of money, one that defies the classic dichotomy of ‘central bank money’ versus ‘commercial bank money’. This chapter then analyses the main characteristics of shadow central banking and identifies specific commonalities not only with the shadow banking system, but also with historical systems that pre-existed the modern central banking framework. Finally, it considers the systemic risks, systemic conflicts of interest and opaque corporate governance mechanisms associated with shadow central banking and posits some policy considerations to address such risks.


Starting from the Basics

Abstract: Abstract This chapter explores the problem of systematization entailed in smart contracts. From a commercial law perspective, it discusses whether the rules provided in the commercial context for the conclusion of contracts between two ‘human’ parties may be safely extended to blockchain smart contracts. After analysing smart contracts and their main properties, this chapter surveys the relevant North American and European regulations. The chapter suggests that applying the existing regulatory framework to the new emerging commercial practices is the best solution and emphasizes the need for uniform principles as a precondition for commercial practices to prosper. It takes into consideration important historical precedents, such as the Law of Merchants (lex mercatoria), and its new form, the so-called lex cryptographia (or cryptographica), as the correct paradigms to be applied to the globalized cryptographic-based context. Finally, this chapter emphasizes the need for both public regulation and self-regulation as two complementary sources in commercial law, and the role that self-regulatory organizations and industry representatives should play in the shift towards a new technologic area.


Enhancing Disruption

Abstract: Abstract This chapter provides an analysis of initial coin offerings, their development in the market, and their role in shaping the development of the cryptoeconomy. It identifies the initial ICO ‘structural’ pattern and its subsequent evolutions. It then characterizes ICOs in three major jurisdictions—the United States, Europe, and Switzerland. Furthermore, this chapter considers specific unsolved issues as they emerged in the markets and legal systems. In doing so, it focuses on the lack of a definitive approach for regulating ICOs, as well as on specific promises that ICOs breached, in particular with respect to financial inclusion, disintermediation, and infrastructural transformations. Finally, this chapter identifies the main consequences brought by ICOs, with an emphasis on capital market structures and digital securities, which triggered multiple transformations at multiple levels, including corporate governance, development of digital assets, and non-fungible tokens.


Video deepfake detection using Particle Swarm Optimization improved deep neural networks

Abstract: Abstract As complexity and capabilities of Artificial Intelligence technologies increase, so does its potential for misuse. Deepfake videos are an example. They are created with generative models which produce media that replicates the voices and faces of real people. Deepfake videos may be entertaining, but they may also put privacy and security at risk. A criminal may forge a video of a politician or another notable person in order to affect public opinions or deceive others. Approaches for detecting and protecting against these types of forgery must evolve as well as the methods of generation to ensure that proper information is supplied and to mitigate the risks associated with the fast evolution of deepfakes. This research exploits the effectiveness of deepfake detection algorithms with the application of a Particle Swarm Optimization (PSO) variant for hyperparameter selection. Since Convolutional Neural Networks excel in recognizing objects and patterns in visual data while Recurrent Neural Networks are proficient at handling sequential data, in this research, we propose a hybrid EfficientNet-Gated Recurrent Unit (GRU) network as well as EfficientNet-B0-based transfer learning for video forgery classification. A new PSO algorithm is proposed for hyperparameter search, which incorporates composite leaders and reinforcement learning-based search strategy allocation to mitigate premature convergence. To assess whether an image or a video is manipulated, both models are trained on datasets containing deepfake and genuine photographs and videos. The empirical results indicate that the proposed PSO-based EfficientNet-GRU and EfficientNet-B0 networks outperform the counterparts with manual and optimal learning configurations yielded by other search methods for several deepfake datasets.


Determination of the bilingualism coefficient among future subject teachers by the method of associative experiment

Abstract: The paper describes the essence of the phenomenon of bilingualism, types of bilingualism, approaches to its quantitative assessment, as well as the results of an associative experiment aimed at determining the level of bilingualism of future teachers studying at Kazan Federal University on a bilingual basis using Russian and Tatar languages. The experiment involved 40 students – future mathematics teachers in the 2nd and 3rd years of study. The scientific novelty of the research lies in determining that the level of the academic component of bilingual communicative competence among future bilingual teachers is higher than the level of their general communicative competence. As a result, it was found that at this stage of the implementation of the multilingual education model at Kazan Federal University, most future subject teachers have formed the coordinate and the compound type of Tatar-Russian bilingualism both in the field of general communication and academic language in the sphere of mathematics on the topic “Function”.


Digital Therapeutic Cultures and Their New Regime of Psychological Truth

Abstract: The article focuses on contemporary digital therapeutic cultures’ new regime of truth. This entails describing and critically analysing the sociomaterial apparatuses that distinguish truth from false and produce specific modes of subjectivation. The article shows that the digital regime of psychological truth is heir to the behavioural mistrust of subjectivity and the epistemological shift from the causal-comprehensive model towards the probabilistic-predictive one. However, the psychological subject has not been excluded, but her role has changed. The article introduces the distinction between valuable and spurious truths to analyse this shift. Algorithms do not need the psychological subject to produce true outcomes, but they depend on her and psy-knowledges to distinguish significative from irrelevant truths. Following Maurizio Lazzarato, the article concludes that digital therapeutic cultures share one of the main features of contemporary capitalism: to produce value at the intersection of processes of subjectivation and de-subjectivation.


The significance of an authentic author’s fairy tale in developing the emotive competence of middle school students in foreign language classes in the additional education system

Abstract: The purpose of the study is to identify didactic possibilities and provide theoretical justification for the feasibility of using an authentic literary fairy tale in the formation of emotive competence when teaching schoolchildren a foreign language in the system of additional education. The article analyzes the various didactic possibilities of literary fairy tales when teaching a foreign language to middle-level students. The scientific novelty lies in the development and testing of a set of exercises based on the literary fairy tale “A Bear Called Paddington” by Michael Bond, involving students in the process of foreign language communication and ensuring the formation of elements of emotive competence. As a result of working with fiction, children develop not only language skills, but also emotional intelligence. Students learn how to determine the emotional nature of a text and later use emotionally charged vocabulary in monologue speech, which contributes to the formation of emotive competence.


Research on dual robot collaboration method based on improved double ant colony algorithm

Abstract: Purpose This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations. Design/methodology/approach A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy. Findings The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems. Originality/value This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.


Enhancing Central Model Performance: Leveraging Federated Learning Across Virtual Machine Networks for Distributed Training and Synchronization

Abstract: This project takes a closer look at federated learning as a way of achieving superior machine learning models in a distributed manner while preserving privacy in the datasets that contribute. We have modelled a network of cooperating virtual machines working collectively without explicit sharing of data. Rather than distributing the complete big dataset to each system, we have split it into chunks of 10,000, 5,000, 40,000, 5,000 entries. These systems would then work on their data with learning rates of their model's making and in the decision-making processes to modify their settings, so that the data that systems would work on could allow for building their respective models by them. What this means is that the high point in the project is the combination of these models into one overarching model. The overarching model then gets better because of the small models learning from it without having to access the data associated with the models in a direct sense. This way, a better model can be built, which will intimately understand the data and thereby predict more accurately. Taken as a whole, we have shown how federated learning can improve the models of machine learning in a significantly private manner, and thus the methodology is positively postured with respect to future related work


Modernizing and Revolutionizing Land Record Management with Blockchain

Abstract: In the modern era, access to land records has become essential, enabling individuals to verify land ownership for both personal and business purposes. In India, managing land records has proven to be a formidable challenge given the country's vast population. The current system has suffered from incomplete registries, which, in turn, has facilitated fraudulent activities. Moreover, the existing government system is plagued by inconsistencies and involves numerous intermediaries with access to the records, raising concerns about unauthorized alterations. With the increasing acceptance of blockchain technology, the government is actively exploring its implementation to address these issues. In a world moving towards Web 3.0, integrating land records into a blockchain offers significant advantages, primarily through enhanced transparency. This approach ensures that citizens have unhindered access to land records, complete with visual representations on maps, promoting greater inclusivity and accountability


A Blockchain-based federated learning framework for secure aggregation and fair incentives

Abstract: Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that integrates federated learning with blockchain technology to facilitate secure model aggregation and fair incentives in untrustworthy environments. The framework designs a reputation evaluation method using quality as an indicator, and a consensus method based on reputation feedback. The trustworthiness of nodes is dynamically assessed to achieve an efficient and trustworthy model aggregation process while avoiding reputation monopolisation. Furthermore, the paper defines a tailored contribution calculation process for nodes in different roles in an untrusted environment. A reward and punishment scheme based on the joint constraints of contribution and reputation is proposed to attract highly qualified workers to actively participate in federated learning tasks. Theoretical analysis and simulation experiments demonstrate the framework's ability to maintain efficient and secure aggregation under a certain degree of attack while achieving fair incentives for each role node with significantly reduced consensus consumption.


The technology of creative collective project “Illustrated Book” as a means of developing professional competencies of students – future preschool and elementary education teachers

Abstract: The research aim is to justify the use of the technology of organizing a creative collective project “Illustrated Book” as a means of enlightenment, popularizing cultural heritage, and enhancing students’ cognitive, communicative, managerial, methodological, creative, and reflexive components of a teacher’s professional competence. The article addresses the issue of developing professional competencies of pedagogical university students (future preschool educators and elementary school teachers). The novelty of the study is determined by presenting an author’s step-by-step technology for familiarizing students with works of art and organizing creative collective projects, giving students the opportunity to develop organizational, pedagogical, and artistic skills necessary for their future professional activities. As a result of the research, the structure, stages, conditions, organization options, and implementation of collective student projects “Illustrated Book” as a means of developing professional competencies of pedagogical university students have been developed and substantiated.


Transformation of Elliptic Curve Discrete Logarithm Problem to QUBO Using Direct Method in Quantum Annealing Applications

Abstract: This paper investigates how to reduce the elliptic curve discrete logarithm problem over prime fields to the quadratic unconstrained binary optimization (QUBO) problem in order to obtain as few logical qubits as possible. In the best case scenario, if n is the bitlength of a characteristic of prime field Fp, approximately 3n³ logical qubits are required for such a reduction in the Edwards curve case. We present a practical attack on an elliptic curve discrete logarithm problem over the 3-bit prime field F7 for an elliptic curve with the subgroup of order 8. We solved this problem using the D-Wave Advantage QPU. To the best of the authors' knowledge, no one has made, so far, a practical attack on the elliptic curve discrete logarithm over a prime field using the direct quantum method.


Flipover outperforms dropout in deep learning

Abstract: Abstract Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons and reverts their outputs using a negative multiplier during training. This approach offers stronger regularization than conventional dropout, refining model performance by (1) mitigating overfitting, matching or even exceeding the efficacy of dropout; (2) amplifying robustness to noise; and (3) enhancing resilience against adversarial attacks. Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.


Self-attention Guidance Based Crowd Localization and Counting

Abstract: Most existing studies on crowd analysis are limited to the level of counting, which cannot provide the exact location of individuals. This paper proposes a self-attention guidance based crowd localization and counting network (SA-CLCN), which can simultaneously locate and count crowds. We take the form of object detection, using the original point annotations of crowd datasets as supervision to train the network. Ultimately, the center point coordinate of each head as well as the number of crowds are predicted. Specifically, to cope with the spatial and positional variations of the crowd, the proposed method introduces transformer to construct a globallocal feature extractor (GLFE) together with the convolutional structure. It establishes the near-to-far dependency between elements so that the global context and local detail features of the crowd image can be extracted simultaneously. Then, this paper designs a pyramid feature fusion module (PFFM) to fuse the global and local information from high level to low level to obtain a multiscale feature representation. In downstream tasks, this paper predicts candidate point offsets and confidence scores by a simple regression header and classification header. In addition, the Hungarian algorithm is used to match the predicted point set and the labelled point set to facilitate the calculation of losses. The proposed network avoids the errors or higher costs associated with using traditional density maps or bounding box annotations. Importantly, we have conducted extensive experiments on several crowd datasets, and the proposed method has produced competitive results in both counting and localization.


Impact of temperature and solar irradiance in shadow covering scenarios via two-way sensitivity analysis for rooftop solar photovoltaics

Abstract: Solar irradiance and temperature are two primary factors that affect the energy generation efficiency of solar photovoltaic (PV) systems, meaning that climate change may significantly impact the production of solar energy in the future.In this study, a two-way sensitivity analysis is carried out to determine the energy generation potential under future climate change conditions, and conditions of shadow covering are also considered.The simulation results indicate that the impact of solar irradiance causes the most variation in energy generation, and that shadow covering has a weak impact on the output.In the one-way sensitivity analysis, solar irradiance makes a ± 18% difference in energy generation, whereas shadow covering represents about ± 2%.In the future trend of climate change in the world and Taiwan, the energy generation potential can decrease up to 21% and increase 8%, respectively, mainly corresponded by solar irradiance.We suggest that compared with the issue of temperature, the efficiency of solar irradiance usage is more important, especially in view of the global trend toward solar irradiance loss.Taiwan also suffers the issue, but the different trend of climate change impact may make the challenge different.


Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

Abstract: Since requirement dependency extraction is a cognitively challenging and error-prone task, this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies. In this paper, the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles: uncertainty probability, text similarity difference degree and active learning variant prediction divergence degree. By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs, the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples. As the optimal samples are continuously added into the initial training set, the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved. Therefore, compared with other active learning strategies, a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples. Finally, the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%, the weight-recall by 2.45%, and the weight-precision by 2.64% in comparison with other strategies, saving approximately 46% of the labelled data compared with the machine learning approach.


The AI empathy effect: a mechanism of emotional contagion

Abstract: While advances in technology have led to the widespread use of artificial intelligence (AI) in frontline services, the AI empathic effect is often overlooked. Based on emotional contagion theory, this research explores the impacts of AI emotional mimicry and empathic concern in service success and failure via experimental designs, respectively. Results indicate that AI with high emotional mimicry and empathic concern resulted in higher arousal and pleasure, increasing tourists’ continuous usage intention. Arousal and pleasure played significant mediating roles in service success. However, while empathic concern significantly affected continuous usage intention through pleasure, arousal did not have a similar effect. The joint effects of extrinsic and intrinsic anthropomorphism (emotional mimicry) were significant in a successful service context, but not in service failure. These results have important implications for the development and promotion of AI and enriches theoretical applications of emotional contagion theory and empathy.


Mask-Pyramid Network: A Novel Panoptic Segmentation Method

Abstract: In this paper, we introduce a novel panoptic segmentation method called the Mask-Pyramid Network. Existing Mask RCNN-based methods first generate a large number of box proposals and then filter them at each feature level, which requires a lot of computational resources, while most of the box proposals are suppressed and discarded in the Non-Maximum Suppression process. Additionally, for panoptic segmentation, it is a problem to properly fuse the semantic segmentation results with the Mask RCNN-produced instance segmentation results. To address these issues, we propose a new mask pyramid mechanism to distinguish objects and generate much fewer proposals by referring to existing segmented masks, so as to reduce computing resource consumption. The Mask-Pyramid Network generates object proposals and predicts masks from larger to smaller sizes. It records the pixel area occupied by the larger object masks, and then only generates proposals on the unoccupied areas. Each object mask is represented as a H × W × 1 logit, which fits well in format with the semantic segmentation logits. By applying SoftMax to the concatenated semantic and instance segmentation logits, it is easy and natural to fuse both segmentation results. We empirically demonstrate that the proposed Mask-Pyramid Network achieves comparable accuracy performance on the Cityscapes and COCO datasets. Furthermore, we demonstrate the computational efficiency of the proposed method and obtain competitive results.


Dynamic educational recommender system based on Improved LSTM neural network

Abstract: Nowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human-computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system according to the individual's interests in educational resources. This system is evaluated based on clicking or downloading the source with the help of the user so that the appropriate resources can be suggested to users. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of being aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: (1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and (2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes. An average accuracy of 0.9978 and a Loss of 0.0051 offer more appropriate recommendations than similar works.


Artificial Neural Networks for Determining the Empirical Relationship between Meteorological Parameters and High-Level Cloud Characteristics

Abstract: The special features of the applicability of artificial neural networks to the task of identifying relationships between meteorological parameters of the atmosphere and optical and geometric characteristics of high-level clouds (HLCs) containing ice crystals are investigated. The existing models describing such relationships do not take into account a number of atmospheric effects, in particular, the orientation of crystalline ice particles due to the simplified physical description of the medium, or within the framework of these models, accounting for such dependencies becomes a highly nontrivial task. Neural networks are able to take into account the complex interaction of meteorological parameters with each other, as well as reconstruct almost any dependence of the HLC characteristics on these parameters. In the process of prototyping the software product, the greatest difficulty was in determining the network architecture, the loss function, and the method of supplying the input parameters (attributes). Each of these problems affected the most important issue of neural networks—the overtraining problem, which occurs when the neural network stops summarizing data and starts to tune to them. Dependence on meteorological parameters was revealed for the following quantities: the altitude of the cloud center; elements m22 and m44 of the backscattering phase matrix (BSPM); and the m33 element of BSPM requires further investigation and expansion of the analyzed dataset. Significantly, the result is not affected by the compression method chosen to reduce the data dimensionality. In almost all cases, the random forest method gave a better result than a simple multilayer perceptron.


Task-Adaptive Neuromorphic Computing Using Reconfigurable Organic Neuristors with Tunable Plasticity and Logic-in-Memory Operations

Abstract: The brain's function can be dynamically reconfigured through a unified neuron–synapse architecture, enabling task-adaptive network-level topology for energy-efficient learning and inferencing. Here, we demonstrate an organic neuristor utilizing a ferroelectric–electrolyte dielectric interface. This neuristor enables tunable short- to long-term plasticity and reconfigurable logic-in-memory functions by controlling the interfacial interaction between electrolyte ions and ferroelectric dipoles. Notably, the short-term plasticity of the organic neuristor allows for power-efficient reservoir computing in edge-computing scenarios, exhibiting impressive recognition accuracy, including images (90.6%) and acoustic signals (97.7%). For high-performance computing tasks, the neuristor based on long-term plasticity and logic-in-memory operations can construct all of the hardware circuits of a binarized neural network (BNN) within a unified framework. The BNN demonstrates excellent noise tolerance, achieving high recognition accuracies of 99.2% and 86.4% on the MNIST and CIFAR-10 data sets, respectively. Consequently, our research sheds light on the development of power-efficient artificial intelligence systems.


MM-NeRF: Large-Scale Scene Representation with Multi-Resolution Hash Grid and Multi-View Priors Features

Abstract: Reconstructing large-scale scenes using Neural Radiance Fields (NeRFs) is a research hotspot in 3D computer vision. Existing MLP (multi-layer perception)-based methods often suffer from issues of underfitting and a lack of fine details in rendering large-scale scenes. Popular solutions are to divide the scene into small areas for separate modeling or to increase the layer scale of the MLP network. However, the subsequent problem is that the training cost increases. Moreover, reconstructing large scenes, unlike object-scale reconstruction, involves a geometrically considerable increase in the quantity of view data if the prior information of the scene is not effectively utilized. In this paper, we propose an innovative method named MM-NeRF, which integrates efficient hybrid features into the NeRF framework to enhance the reconstruction of large-scale scenes. We propose employing a dual-branch feature capture structure, comprising a multi-resolution 3D hash grid feature branch and a multi-view 2D prior feature branch. The 3D hash grid feature models geometric details, while the 2D prior feature supplements local texture information. Our experimental results show that such integration is sufficient to render realistic novel views with fine details, forming a more accurate geometric representation. Compared with representative methods in the field, our method significantly improves the PSNR (Peak Signal-to-Noise Ratio) by approximately 5%. This remarkable progress underscores the outstanding contribution of our method in the field of large-scene radiance field reconstruction.


Prostate cancer grading framework based on deep transfer learning and Aquila optimizer

Abstract: Abstract Prostate cancer is the one of the most dominant cancer among males. It represents one of the leading cancer death causes worldwide. Due to the current evolution of artificial intelligence in medical imaging, deep learning has been successfully applied in diseases diagnosis. However, most of the recent studies in prostate cancer classification suffers from either low accuracy or lack of data. Therefore, the present work introduces a hybrid framework for early and accurate classification and segmentation of prostate cancer using deep learning. The proposed framework consists of two stages, namely classification stage and segmentation stage. In the classification stage, 8 pretrained convolutional neural networks were fine-tuned using Aquila optimizer and used to classify patients of prostate cancer from normal ones. If the patient is diagnosed with prostate cancer, segmenting the cancerous spot from the overall image using U-Net can help in accurate diagnosis, and here comes the importance of the segmentation stage. The proposed framework is trained on 3 different datasets in order to generalize the framework. The best reported classification accuracies of the proposed framework are 88.91% using MobileNet for the “ISUP Grade-wise Prostate Cancer” dataset and 100% using MobileNet and ResNet152 for the “Transverse Plane Prostate Dataset” dataset with precisions 89.22% and 100%, respectively. U-Net model gives an average segmentation accuracy and AUC of 98.46% and 0.9778, respectively, using the “PANDA: Resized Train Data (512 × 512)” dataset. The results give an indicator of the acceptable performance of the proposed framework.


Hierarchical matching and reasoning for Multi-Query Image Retrieval

Abstract: As a promising field, Multi-Query Image Retrieval (MQIR) aims at searching for the semantically relevant image given multiple region-specific text queries. Existing works mainly focus on a single-level similarity between image regions and text queries, which neglect the hierarchical guidance of multi-level similarities and result in incomplete alignments. Besides, the high-level semantic correlations that intrinsically connect different region-query pairs are rarely considered. To address above limitations, we propose a novel Hierarchical Matching and Reasoning Network (HMRN) for MQIR. It disentangles MQIR into three hierarchical semantic representations, which is responsible to capture fine-grained local details, contextual global scopes, and high-level inherent correlations. HMRN consists of two modules: Scalar-based Matching (SM) module and Vector-based Reasoning (VR) module. Specifically, the SM module characterizes the multi-level alignment similarity, which consists of a fine-grained local-level similarity and a context-aware global-level similarity. Afterwards, the VR module is developed to excavate the potential semantic correlations among multiple region-query pairs, which further explores the high-level reasoning similarity. Finally, these three-level similarities are aggregated into a joint similarity space to form the ultimate similarity. Extensive experiments on the benchmark dataset demonstrate that our HMRN substantially surpasses the current state-of-the-art methods. For instance, compared with the existing best method Drill-down, the metric R@1 in the last round is improved by 23.4%. Our source codes will be released at https://github.com/LZH-053/HMRN.


A Structured Sentiment Analysis Dataset Based on Public Comments from Various Domains

Abstract: A structured sentiment analysis dataset, derived from social media comments, is introduced in this paper. The dataset spans 22 diverse domains and comprises over 200,000 reviews, providing a rich resource for sentiment analysis tasks in the Chinese language context. Each comment within the dataset has been manually annotated with a sentiment label, either positive, negative, or neutral, and grouped by topic. This meticulous annotation process ensures the dataset's reliability for training, validating, and testing sentiment analysis models. The construction of the dataset involved a three-step process. Initially, data was collected from the topics that garnered high attention and discussion rates, thereby reflecting the authentic opinions of users. Following data collection, preprocessing was undertaken to remove extraneous elements, while preserving emoticons that are crucial for sentiment analysis. The final step involved manual annotation by researchers, who assigned sentiment labels to each comment based on various factors. The dataset stands as a valuable contribution to the field of natural language processing, particularly for sentiment analysis tasks in the Chinese language context.


Spatial super-resolution of finite-time Lyapunov exponent fields with generative adversarial networks

Abstract: In this article, two generative adversarial networks that enhance the spatial resolution of two- and three-dimensional finite-time Lyapunov fields by a factor of 4 in each dimension are presented. Using these models, high-resolution distributions can be approximated based on low-resolution input, the latter of which is associated with a computational cost and storage reduced by factors of about 42 and 43, respectively. Evaluating various test cases, the performance and generalizability of this approach are assessed. Shortcomings are only observed in the case of high-frequency spatial fluctuations where no sufficient statistical information is available in the low-resolution space. The major flow structures, however, are adequately rendered, giving rise to Lagrangian analyses of complex flow configurations that may otherwise remain elusive due to an excessive computational cost.


A Generative Method for a Laryngeal Biosignal

Abstract: The Glottal Area Waveform (GAW) is an important component in quantitative clinical voice assessment, providing valuable insights into vocal fold function. In this study, we introduce a novel method employing Variational Autoencoders (VAEs) to generate synthetic GAWs. Our approach enables the creation of synthetic GAWs that closely replicate real-world data, offering a versatile tool for researchers and clinicians. We elucidate the process of manipulating the VAE latent space using the Glottal Opening Vector (GlOVe). The GlOVe allows precise control over the synthetic closure and opening of the vocal folds. By utilizing the GlOVe, we generate synthetic laryngeal biosignals. These biosignals accurately reflect vocal fold behavior, allowing for the emulation of realistic glottal opening changes. This manipulation extends to the introduction of arbitrary oscillations in the vocal folds, closely resembling real vocal fold oscillations. The range of factor coefficient values enables the generation of diverse biosignals with varying frequencies and amplitudes. Our results demonstrate that this approach yields highly accurate laryngeal biosignals, with the Normalized Mean Absolute Error values for various frequencies ranging from 9.6 ⋅ 10-3 to 1.20 ⋅ 10-2 for different experimented frequencies, alongside a remarkable training effectiveness, reflected in reductions of up to approximately 89.52% in key loss components. This proposed method may have implications for downstream speech synthesis and phonetics research, offering the potential for advanced and natural-sounding speech technologies.


Smart home system using blockchain technology in green lighting environment in rural areas

Abstract: Currently, with the rapid development of smart home technology, the demand for establishing efficient and sustainable smart home systems in rural areas is increasing. However, in rural environments, the effective management and intelligent control of green energy face many challenges. To address these issues, this work aims to design a smart home system based on blockchain technology to achieve efficient energy management and intelligent control in a green lighting environment in rural areas. The main goals include improving the performance and safety of the system to meet the lighting needs of rural areas and promote sustainable development. The system comprises two primary components: the home gateway and cloud services. These components encompass functions like data monitoring and transmission, cloud storage, and remote control. The work also introduces the structural interaction, user node interaction, and the data security transmission scheme of the smart home system. Ultimately, the system's effectiveness is confirmed through simulation experiments. The results demonstrate that the system achieves the lowest latency when the transaction arrival rate is 40tps and the block size is 10. Additionally, the access control scheme based on the Hyperledger Fabric consortium chain can efficiently handle access requests for smart home resources and meet the practical application requirements within an appropriate range of security parameters. The main research conclusion is that the designed smart home system based on blockchain technology has achieved significant results in improving performance and security. This not only provides reliable lighting solutions for rural areas, but also provides important theoretical and practical guidance for the future development of smart home systems. The direction of future work includes further optimizing system performance, expanding the scope of application, and exploring more advanced blockchain technology applications in the field of smart homes. This will provide more possibilities and innovative directions for the development of future smart home systems.


Reimagining learning through AI art: the promise of DALL-E and MidJourney for education and libraries

Abstract: Purpose This paper aims to explore the potential impact of artificial intelligence (AI) image generators, specifically MidJourney and DALL-E, on education and library services. The study aims to understand how these tools can revolutionize learning experiences and library resources while also addressing the ethical considerations surrounding their use. Design/methodology/approach This study investigates the technical foundations of MidJourney and DALL-E, highlighting their neural network architectures. It also traces the iterative refinement of these models and examines cost, accessibility and the unique prompt-guided capabilities of DALL-E 3. Findings MidJourney and DALL-E show remarkable progress in generating high-quality, photorealistic images from text prompts. The iterative refinement of these models demonstrates a trend toward improved creative output and user accessibility. DALL-E 3, in particular, allows users to guide image generation through prompt modifications, offering unprecedented control over the creative process. The study identifies potential applications in personalized learning, visual communication and research support in libraries, while recognizing challenges such as cost and accessibility. Originality/value This research innovatively explores AI's impact on education and libraries, detailing applications in personalized learning and research while addressing legal and ethical considerations.


Usability Testing of Mobile Applications: A Methodological Framework

Abstract: Less than five percent of all mobile applications have become successful throughout 2023. The success of a new mobile application depends on a variety of factors ranging from business understanding, customer value, and perceived quality of use. In this sense, the topic of usability testing of mobile applications is relevant from the point of view of user satisfaction and acceptance. However, the current knowledge seems to be fragmented, scattered across many papers and reports, and sometimes poorly documented. This paper attempts to fill this gap by investigating the current state of knowledge by reviewing the previous literature relevant to the research topic and developing a unified view. In particular, the methodological framework is outlined and discussed, including the discourse on settings for laboratory and field studies, data collection techniques, experimental designs for mobile usability testing, and a generic research framework. Therefore, the paper contributes to both the theory and practice of human–computer interaction by providing methodological foundations for usability testing of mobile applications, paving the way for further studies in this area. Moreover, the paper provides a better understanding of the related topics, in particular shedding light on methodological foundations, key concepts, challenges, and issues, equipping readers with a comprehensive knowledge base to navigate and contribute to the advancement of the field of mobile usability.


Organizational and pedagogical conditions for the formation of the readiness for industry practice-oriented activities among professional training teachers

Abstract: The aim of the study is to provide a theoretical substantiation of the organizational and pedagogical conditions for the formation of the readiness for industry practice-oriented activities among future teachers of the secondary vocational education system to increase the effectiveness of training in higher vocational pedagogical education and ensure that the results of this training meet the current requirements of employers. The paper presents a set of organizational-methodological, content-activity-related, technological-instrumental and personnel-related conditions that contribute to the development of industry-specific professional skills and abilities among future teachers of the secondary vocational education system. The scientific novelty of the study lies in a detailed description of the content of organizational and pedagogical conditions for the training of undergraduate students in the programme 44.03.04 Vocational Training (Economics and Management) during industry operational practice in order to include them in the process of industrial training and development of industry-specific professional skills that contribute to the formation of the readiness to perform industry practice-oriented activities in organizations of the secondary vocational education system. The author believes that such a set of conditions will contribute to improving the quality of training not only for teachers of professional educational organizations, but also, as a result, workers and middle-level specialists who are needed by the labor market. Following the study, the results of testing organizational and pedagogical conditions have been presented and their effectiveness for practical application has been proved.


Flexible preparation of the W state via the GRAPE algorithm in a nanoring-quantum-dot system

Abstract: Abstract In this paper, we put forward a scheme for flexibly preparing W states in the presence of detunings and asymmetric couplings in a nanoring-quantum-dot (nanoring-QD) system. Based on the gradient ascent pulse engineering algorithm, the coupling strength modulation (CSM) and the energy level modulation (ELM) methods are proposed, respectively. In the CSM method, the waveforms of the coupling strengths are properly designed to compensate for detunings, thus the decline on the fidelity is effectively suppressed. For the asymmetric couplings, a high-fidelity W state can be generated by the ELM method, which alters the energy levels of QDs in a desired manner. Finally, we generalize the current scheme to realize the preparation of N -particle W states in nanoring-QD systems.


Research Communities and the Collective Investment in Data Infrastructure

Abstract: Abstract The field of comparative law and politics requires further investment in data infrastructure, including databases of judicial opinions, codes of various types, many features of the litigation process, which can promote coordination in scholarship at scale. The resource costs associated with producing these data are likely to be prohibitive for single researchers and even many small groups of scholars. Producing what the field needs is likely to require considerable collaboration among a relatively large, methodologically diverse set of scholars. In addition to providing vital infrastructure, this kind of collaboration can also help promote a healthy research community by encouraging learning about and coordination on good scientific standards. The approach will address communication problems that inhibit parallel work on shared research problems. Doing this well is no simple task, though. Organizing collaborative teams depends on ensuring that the incentives of team members are consistent with group goals, a challenge complicated by standard team production concerns, but especially problematic in light of disciplinary incentives that discourage infrastructural investment. Drawing on the experiences of the Varieties of Democracy Project, this chapter discusses how this kind of collaboration might manifest in the field. It highlights a decentralized team approach that balances individual incentives for academic freedom against group needs for consistency.


A quantum blind signature scheme based on dense coding for non-entangled states

Abstract: Abstract Some schemes where quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. Considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non entangled quantum blind signature scheme based on dense encoding.The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources.After receiving particles,the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature, and the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification. Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection, higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature.Simultaneously utilizing the secondary distribution and rearrangement of non entangled quantum states, a non entangled quantum state representation of three bits of classical information was achieved,reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency, while at the same time, this scheme meets the requirements of unforgeability, non repudiation, and prevention of information leakage.


Hybrid deep learning models for time series forecasting of solar power

Abstract: Abstract Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns present in solar power data. In this study, all of the possible combinations of convolutional neural network (CNN), long short-term memory (LSTM), and transformer (TF) models are experimented. These hybrid models also compared with the single CNN, LSTM and TF models with respect to different kinds of optimizers. Three different evaluation metrics are also employed for performance analysis. Results show that the CNN–LSTM–TF hybrid model outperforms the other models, with a mean absolute error (MAE) of 0.551% when using the Nadam optimizer. However, the TF–LSTM model has relatively low performance, with an MAE of 16.17%, highlighting the difficulties in making reliable predictions of solar power. This result provides valuable insights for optimizing and planning renewable energy systems, highlighting the significance of selecting appropriate models and optimizers for accurate solar power forecasting. This is the first time such a comprehensive work presented that also involves transformer networks in hybrid models for solar power forecasting.


Absolute-separability witnesses for symmetric multiqubit states

Abstract: The persistent separability of certain quantum states, known as symmetric absolutely separable (SAS), under symmetry-preserving global unitary transformations is of key significance in the context of quantum resources for bosonic systems.In this work, we develop criteria for detecting SAS states of any number of qubits.Our approach is based on the Glauber-Sudarshan P representation for finite-dimensional quantum systems.We introduce three families of SAS witnesses, one linear and two nonlinear in the eigenvalues of the state, formulated respectively as an algebraic inequality or a quadratic optimization problem.These witnesses are capable of identifying more SAS states than previously known counterparts.We also explore the geometric properties of the subsets of SAS states detected by our witnesses, shedding light on their distinctions.


İnsansız Hava Araçları (İHA) İçin Küresel Navigasyon Uydu Sistemi (GNSS) Bağımsız Navigasyon

Abstract: The ability of Unmanned Aerial Vehicles (UAV) to perform autonomous navigation depends on the accurate determination of their positions provided by the Global Navigation Satellite System (GNSS). For position determination and environmental orientation during flight, UAVs are usually equipped with electronic equipment such as GNSS, Inertial Measurement Unit (IMU), gyroscope and accelerometer. However, the GNSS signal may be lost or distorted due to poor weather, obstacles or terrain, the unfavorable position of satellites, spoofing and jamming. In such cases of GNSS signal loss or deterioration, the IMU alone becomes unable to provide reliable UAV location information. Especially in cases where there is not enough visibility and the UAV cannot be brought to the take-off point by manual operation, the loss of the GNSS signal causes great losses. In this paper, GNSS independent flight and navigation studies are included. It is seen that the use of hybrid navigation solutions has great importance in GNSS independent UAV flights.