<scp>TLPGNN</scp> : A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on Single and Multiple GPUs

Abstract: Graph Neural Networks (GNNs) are an emerging class of deep learning models specifically designed for graph-structured data. They have been effectively employed in a variety of real-world applications, including recommendation systems, drug development, and analysis of social networks. The GNN computation includes regular neural network operations and general graph convolution operations, which take most of the total computation time. Though several recent works have been proposed to accelerate the computation for GNNs, they face the limitations of heavy pre-processing, low efficiency atomic operations, and unnecessary kernel launches. In this paper, we design TLPGNN , a lightweight two-level parallelism paradigm for GNN computation. First, we conduct a systematic analysis of the hardware resource usage of GNN workloads to understand the characteristics of GNN workloads deeply. With the insightful observations, we then divide the GNN computation into two levels, i.e., vertex parallelism for the first level and feature parallelism for the second. Next, we employ a novel hybrid dynamic workload assignment to address the imbalanced workload distribution. Furthermore, we fuse the kernels to reduce the number of kernel launches and cache the frequently accessed data into registers to avoid unnecessary memory traffic. To scale TLPGNN to multi-GPU environments, we propose an edge-aware row-wise 1-D partition method to ensure a balanced workload distribution across different GPU devices. Experimental results on various benchmark datasets demonstrate the superiority of our approach, achieving substantial performance improvement over state-of-the-art GNN computation systems, including Deep Graph Library (DGL), GNNAdvisor, and FeatGraph, with speedups of 6.1 ×, 7.7 ×, and 3.0 ×, respectively, on average. Evaluations of multiple-GPU TLPGNN also demonstrate that our solution achieves both linear scalability and a well-balanced workload distribution.


GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception

Abstract: Existing algorithms for enhancing low-light images predominantly focus on the low-light region, which leads to over-enhancement of the glare region, and the high complexity of the algorithm makes it difficult to apply it to embedded devices. In this paper, a GS-AGC algorithm based on regional luminance perception is proposed. The indirect perception of the human eye’s luminance vision was taken into account. All similar luminance pixels that satisfied the luminance region were extracted, and adaptive adjustment processing was performed for the different luminance regions of low-light images. The proposed method was evaluated experimentally on real images, and objective evidence was provided to show that its processing effect surpasses that of other comparable methods. Furthermore, the potential practical value of GS-AGC was highlighted through its effective application in road pedestrian detection and face detection. The algorithm in this paper not only effectively suppressed glare but also achieved the effect of overall image quality enhancement. It can be easily combined with the embedded hardware FPGA for acceleration to improve real-time image processing.


Statistical Inference of Normal Distribution Based on Several Divergence Measures: A Comparative Study

Abstract: Statistical predictive analysis is a very useful tool for predicting future observations. Previous literature has addressed both Bayesian and non-Bayesian predictive distributions of future statistics based on past sufficient statistics. This study focused on evaluating Bayesian and Wald predictive-density functions of a future statistic V based on a past sufficient statistic W obtained from a normal distribution. Several divergence measures were used to assess the closeness of the predictive densities to the future density. The difference between these divergence measures was investigated, using a simulation study. A comparison between the two predictive densities was examined, based on the power of a test. The application of a real data set was used to illustrate the results in this article.


Named Entity Recognition in Government Audit Texts Based on ChineseBERT and Character-Word Fusion

Abstract: Named entity recognition of government audit text is a key task of intelligent auditing. Aiming at the problems of scarcity of corpus in the field of governmental auditing, insufficient utilization of traditional character vector word-level information features, and insufficient capturing of auditing entity features, this study builds its own dataset in the field of auditing and proposes the model CW-CBGC for recognizing named entities in governmental auditing text based on ChineseBERT and character-word fusion. First, the ChineseBERT pre-training model is used to extract the character vector that integrates the features of glyph and pinyin, combining with word vectors dynamically constructed by the BERT pre-training model; then, the sequences of character-word fusion vectors are input into the bi-directional gated recurrent neural network (BiGRU) to learn the textual features. Finally, the global optimal sequence label is generated by Conditional Random Field (CRF), and the GHM classification loss function is used in the model training to solve the problem of error evaluation under the conditions of noisy entities and unbalanced number of entities. The F1 value of this study’s model on the audit dataset is 97.23%, which is 3.64% higher than the baseline model’s F1 value; the F1 value of the model on the public dataset Resume is 96.26%, which is 0.73–2.78% higher than the mainstream model. The experimental results show that the model proposed in this paper can effectively recognize the entities in government audit texts and has certain generalization ability.


Digital Challenges: The Need to Improve the Use of Information Technologies in Teaching

Abstract: In the post-pandemic scenario, a study was conducted at I.E. 50499 Justo Barrionuevo Álvarez in Cusco, Peru, to investigate the relationship between the use of information technologies and digital competencies among teachers. With a sample of 54 teachers, a structured questionnaire was administered to assess their competencies. The results revealed a direct positive correlation between the use of technologies and digital competencies, with a Spearman's Rho coefficient of 0.877, indicating a significant relationship. Correlations between the use of information technologies and the dimensions of digital competencies ranged from moderate to high. Significant correlations were observed in areas such as problem-solving (Rho=0.457), information and digital literacy (Rho=0.633), and security (Rho=0.743), among others. These findings suggest that, despite limited experience and limited knowledge of digital technologies among teachers in the institution, there is a notable relationship between the use of these technologies and their digital competencies. This study underscores the need for further training in information technologies for teachers in non-modernized urban contexts and for those who are older adults with limited prior experience in the digital domain. Enhancing digital competencies is crucial for adapting to the educational challenges in this new era of education


Improving Breast Tumor Multi-Classification from High-Resolution Histological Images with the Integration of Feature Space Data Augmentation

Abstract: To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and the scarcity of representative training data. To tackle these limitations, we propose a deep learning-based breast tumor gigapixel histological image multi-classifier integrated with a high-resolution data augmentation model to process the entire slide by exploring its local and global information and generating its different synthetic versions. The key idea is to perform the classification and augmentation in feature latent space, reducing the computational cost while preserving the class label of the input. We adopt a deep learning-based multi-classification method and evaluate the contribution given by a conditional generative adversarial network-based data augmentation model on the classifier’s performance for three tumor classes in the BRIGHT Challenge dataset. The proposed method has allowed us to achieve an average F1 equal to 69.5, considering only the WSI dataset of the Challenge. The results are comparable to those obtained by the Challenge winning method (71.6), also trained on the annotated tumor region dataset of the Challenge.


Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity

Abstract: Semantic segmentation of human images is a research hotspot in the field of computer vision. At present, the semantic segmentation models based on U-net generally lack the ability to capture the spatial information of images. At the same time, semantic incompatibility exists because the feature maps of encoder and decoder are directly connected in the skip connection stage. In addition, in low light scenes such as at night, it is easy for false segmentation and segmentation accuracy to appear. To solve the above problems, a portrait semantic segmentation method based on dual-modal information complementarity is proposed. The encoder adopts a double branch structure, and uses a SK-ASSP module that can adaptively adjust the convolution weights of different receptor fields to extract features in RGB and gray image modes respectively, and carries out cross-modal information complementarity and feature fusion. A hybrid attention mechanism is used in the jump connection phase to capture both the channel and coordinate context information of the image. Experiments on human matting dataset show that the PA and MIoU coefficients of this algorithm model reach 96.58% and 94.48% respectively, which is better than U-net benchmark model and other mainstream semantic segmentation models.


The role of complexity in the ability to notice incoherent uses of connectives for L2

Abstract: Abstract When learning a second language, some connectives are more difficult to acquire and to master than others. While previous research has assessed different factors responsible for this difficulty by using offline tasks, little is known about the extent to which L2-readers are sensitive to different connectives while reading . In our study, we compared self-paced reading times of native and non-native speakers of French for sentences that were correctly or incorrectly marked with either the infrequent connective cependant (‘however’) or the more frequent mais (‘but’). Results showed that incorrect uses only produced longer reading times when mais was used. Yet, in a sentence-evaluation-task using the same set of sentences, L2-speakers were able to discriminate incorrectly marked sentences with cependant from correctly marked ones. We conclude that a good theoretical understanding of connectives for L2 (Experiment 2) does not always warrant a quick activation of their meaning while reading (Experiment 1).


Detection of Pumping Unit in Complex Scenes by YOLOv7 with Switched Atrous Convolution

Abstract: The petroleum and natural gas industries exhibit a high dependency on lifting equipment for oil and gas. Any malfunction in these devices can lead to severe economic losses. Therefore, continuous and timely monitoring of the status of pumping equipment is of paramount importance to proactively prevent potential issues. In an effort to enhance this monitoring process, this study delves into multi-source data images at the well site and extends traditional information analysis methods. It introduces an improved YOLOv7 method based on switchable atrous convolution. While the YOLOv7 algorithm achieves a balance between speed and accuracy, its robustness in non-standard environments is suboptimal. To address this limitation, we propose the utilization of a switchable atrous convolution method for enhancement, thereby augmenting the adaptability of the model. Images of pumping units from diverse scenarios are actively collected and utilized to construct training, validation, and test sets. Different models, including YOLOv7SAC, YOLOv7, and YOLOv5-n, undergo testing, and their detection performances are systematically compared in complex environments. Experimental findings demonstrate that YOLOv7SAC consistently attains optimal detection results across various scenes. In conclusion, the study suggests that the combination of the YOLOv7 model with switchable atrous convolution proves effective for detecting pumping unit equipment in complex scenarios. This provides robust theoretical support for the detection and identification of pumping equipment issues under challenging conditions.


A Unified Spatial-Pressure Sensitivity Partitioning and Leakage Detection Method within a Deep Learning Framework

Abstract: This study introduces an innovative approach for leak detection in water distribution systems (WDSs), integrating three-order embedding, k-means clustering, and long short-term memory (LSTM) networks, with pressure-sensitive analysis techniques. This comprehensive methodology segments the network into distinct partitions, utilizes simulated leak events to train the deep learning networks, and establishes a sophisticated model for accurately identifying leak partitions. This approach generates a leak dataset by adjusting water demands, which could effectively pinpoint the leaks in a specific partition by leveraging both the pressure sensitivity and spatial coordinates of nodes, allowing for the elimination of the need for manual work and precise identification of leaks in targeted areas. Through the analysis of two case studies, the model demonstrates its ability to effectively pinpoint potential leak partitions, significantly enhancing operational efficiency and reliability in managing the complex problems of urban water resource management. This approach not only optimizes leak detection but also paves the way for advanced, data-driven strategies in WDSs, ensuring sustainable and secure water distribution in urban settings.


Nonequilibrium quantum probing through linear response

Abstract: The formalism of linear response theory can be extended to encompass physical situations where an open quantum system evolves toward a nonequilibrium steady state. Here, we use the framework put forward by Konopik and Lutz [Phys. Rev. Res. 1, 033156 (2019)] to go beyond unitary perturbations of the dynamics. Considering an open system comprised of two coupled quantum harmonic oscillators, we study the system's response to unitary perturbations, affecting the Hamiltonian dynamics, as well as nonunitary perturbations, affecting the properties of the environment, e.g., its temperature and squeezing. We show that linear response, combined with a quantum probing approach, can effectively provide valuable quantitative information about the perturbation and characteristics of the environment, even in cases of nonunitary dynamics.


SEDD: Robust Blind Image Watermarking With Single Encoder And Dual Decoders

Abstract: Abstract Blind image watermarking is regarded as a vital technology to provide copyright of digital images. Due to the rapid growth of deep neural networks, deep learning-based watermarking methods have been widely studied. However, most existing methods which adopt simple embedding and extraction structures cannot fully utilize the image features. In this paper, we propose a novel Single-Encoder-Dual-Decoder (SEDD) watermarking architecture to achieve high imperceptibility and strong robustness. Precisely, the single encoder utilizes normalizing flow to realize watermark embedding, which can effectively fuse the watermark and cover image. For watermark extraction, we introduce a parallel dual-decoder to improve the imperceptibility and extracting ability. Extensive experiments demonstrate that better watermark robustness and imperceptibility are obtained by SEDD architecture. Our method achieves a bit error rate less than 0.1% under most attacks such as JPEG compression, Gaussian blur and crop. Besides, the proposed method also obtains strong robustness under combined attacks and social platform processing.


Quantum Dicke battery supercharging in the bound-luminosity state

Abstract: Quantum batteries, which are quantum systems to be used for the storage and transformation of energy, have been recently attracting research interest. A promising candidate for their investigation is the Dicke model, which describes an ensemble of two-level systems interacting with a single-mode electromagnetic wave in a resonator cavity. In order to charge the battery, a coupling between the ensemble of two-level systems and resonator cavity should be turned off at a certain moment of time. This moment of time is chosen in such a way that the energy gets fully stored in an ensemble of two-level systems. In our previous works we have investigated a bound-luminosity superradiant state of the extended Dicke model and found analytical expressions for the dynamics of coherent energy transfer between the superradiant condensate and the ensemble of two-level systems. Here, using our previous results, we have derived analytically the superlinear law for the quantum battery charging power $P\ensuremath{\sim}{N}^{3/2}$ as a function of the number $N$ of two-level systems in the battery, and also the $N$ dependence for the charging time ${t}_{c}\ensuremath{\sim}{N}^{\ensuremath{-}1/2}$. The $N$ exponent $3/2$ of the charging power is in quantitative correspondence with the recent result $1.541$ obtained numerically by other authors. The physics of Dicke quantum battery charging is considered in detail.


Quantum Computing and Machine Learning on an Integrated Photonics Platform

Abstract: Integrated photonic chips leverage the recent developments in integrated circuit technology, along with the control and manipulation of light signals, to realize the integration of multiple optical components onto a single chip. By exploiting the power of light, integrated photonic chips offer numerous advantages over traditional optical and electronic systems, including miniaturization, high-speed data processing and improved energy efficiency. In this review, we survey the current status of quantum computation, optical neural networks and the realization of some algorithms on integrated optical chips.


On the reasons for the removal of A.T. Tvardovsky from the position of head of the editorial office of the magazine “New World”

Abstract: The subject of the article is the activities of the “New World” journal in the 1950-1960s. The author examines the role of the magazine at the beginning of the reform of Russian literature; the position of the editor-in-chief of “New World” regarding the function of literature designed to reflect reality; the reasons why A. Tvardovsky was removed from the post of editorial director. The work indicates that A. Tvardovsky served as editor-in-chief of the “New World” for 16 years, but during his career he faced dismissal twice. The article reflects the direction and main ideas of liberal thought of the New World magazine, which in the 1950s and 1960s was under the leadership of the editor-in-chief A. Tvardovsky. When studying the problems associated with the magazine’s position on the need to provide literary creativity with a certain freedom. When considering the activities of the magazine and its editor in certain historical and sociocultural conditions, a comparative historical method was used. The main conclusion of the study is that A. Tvardovsky, through his activities as editor-in-chief of the publication “New World” and his principled position on the freedom of literary creativity, made it possible to outline the beginning of the Thaw period in the Soviet literary process. The novelty of the research lies in the fact that the article systematizes information about the reasons for the removal of A. Tvardovsky from the post of editorial director and proves the role of the periodical in question in the process of making a turn in Russian literature towards the realistic tradition. A. Tvardovsky even published those works that were rejected by other leading journals due to the discrepancy between the author’s ideas and the ideas of the ruling party.


GraphSAGE++: Weighted Multi-scale GNN for Graph Representation Learning

Abstract: Abstract Graph neural networks (GNNs) have emerged as a powerful tool in graph representation learning. However, they are increasingly challenged by over-smoothing as network depth grows, compromising their ability to capture and represent complex graph structures. Additionally, some popular GNN variants only consider local neighbor information during node updating, ignoring the global structural information and leading to inadequate learning and differentiation of graph structures. To address these challenges, we introduce a novel graph neural network framework, GraphSAGE++. Our model extracts the representation of the target node at each layer and then concatenates all layer weighted representations to obtain the final result. In addition, the strategies combining double aggregations with weighted concatenation are proposed, which significantly enhance the model’s discernment and preservation of structural information. Empirical results on various datasets demonstrate that GraphSAGE++ excels in vertex classification, link prediction, and visualization tasks, surpassing existing methods in effectiveness.


Countermeasure Strategies to Address Cybersecurity Challenges Amidst Major Crises in the Higher Education and Research Sector: An Organisational Learning Perspective

Abstract: Purpose: The purpose of this research paper was to analyse the counterstrategies to mitigate cybersecurity challenges using organisational learning loops amidst major crises in the Higher Education and Research Sector (HERS). The authors proposed the learning loop framework revealing several counterstrategies to mitigate cybersecurity issues in HERS. The counterstrategies are explored, and their implications for research and practice are discussed. Methodology: The qualitative methodology was adopted, and semi-structured interviews with cybersecurity experts and top managers were conducted. Results: This exploratory paper proposed the learning loop framework revealing introducing new policies and procedures, changing existing systems, partnership with other companies, integrating new software, improving employee learning, enhancing security, and monitoring and evaluating security measures as significant counterstrategies to ensure the cyber-safe working environment in HERS. These counterstrategies will help to tackle cybersecurity in HERS, not only during the current major crisis but also in the future. Implications: The outcomes provide insightful implications for both theory and practice. This study proposes a learning framework that prioritises counterstrategies to mitigate cybersecurity challenges in HERS amidst a major crisis. The proposed model can help HERS be more efficient in mitigating cybersecurity issues in future crises. The counterstrategies can also be tested, adopted, and implemented by practitioners working in other sectors to mitigate cybersecurity issues during and after major crises. Future research can focus on addressing the shortcomings and limitations of the proposed learning framework adopted by HERS.


AI on the Road: NVIDIA Jetson Nano-Powered Computer Vision-Based System for Real-Time Pedestrian and Priority Sign Detection

Abstract: Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies.


Distinguishing Geogenic Load and Anthropogenic Contribution to Soil Contamination in Mineralised Mountain Landscape of Ore Mountains (Czech Republic) Using Cumulative Distribution Functions

Abstract: In ore regions impacted by mining and metal smelting emissions, distinguishing between geogenic anomalies and anthropogenic contamination poses a significant challenge. In a study from two areas with different mining histories in the Ore Mountains, Czech Republic, we demonstrate that sampling of topsoils and subsoils respecting local geology and correct soil data treatment respecting soil textural variability effects are indispensable to the correct construction and interpretation of geochemical maps and identification of anthropogenic contamination by As, Cu, Pb, and Zn. The list of analysed elements must include both potentially toxic and lithogenic elements, otherwise natural controls of soil composition cannot be deciphered. By using empirical cumulative distribution functions, we found that local backgrounds for As/Fe and Pb/Ti are naturally elevated (5.7 to 9.8 times and 2.1 to 2.7 times higher, respectively) compared to the global averages. We constructed geochemical maps with topsoil minus subsoil concentrations to show the main directions of spreading anthropogenic contamination. The anthropogenic diffuse contamination contribution was calculated and expressed as an enrichment relative to the local background (16% and 12% for As/Fe and 17% and 14% for Pb/Ti, respectively). This corresponds to topsoil enrichment by ca. 15 and 14 mg kg−1 for As and ca. 35 and 42 mg kg−1 for Pb in the two study areas. The obtained estimates were comparable to the results from the local peat archives. The approach we used is efficient in deciphering natural and anthropogenic controls of PTEs in geochemically complicated areas.


Far-field far-subwavelength spatial resolution using relative motion with structured illumination

Abstract: Relative motion of structured optical illumination with respect to an object and far-field measurement of intensity are presented as a means to obtain far-subwavelength spatial resolution with a direct imaging arrangement. The principle behind this approach is that the variable interaction of an object with a background field generates information about nanometer-scale features that is encoded in the propagating plane-wave spectrum, allowing far-field data that are modulated with motion according to the nanostructure. Information theory supports this superresolution mechanism and illustrates sensitivity with respect to the illumination and detection arrangements. Simulations indicate that available lasers and detectors would enable a resolution of $\ensuremath{\lambda}/1000$ with modest signal-to-noise requirements and single-pixel detection. Relative motion in structured fields is shown to enhance spatial resolution achievable using data inversion with constraints. Importantly, far-subwavelength sensitivity is shown to be achievable even when the illuminating field is unknown. These results suggest applications that include material defect detection and unlabeled protein sensing, and direct extensions to estimating geometrical features at unprecedented spatial resolution become possible.


A genre-oriented analysis of <i>TikTok</i> instructional discourse

Abstract: Abstract This study intends to contribute to the analysis of digital instructional discourse in order to gain an insight into how the EFL classroom has in some sense shifted online in the hands of amateur experts ( Tolson 2010 ; Bhatia 2018 ), who create an informal learning environment by drawing on their discursive competence, disciplinary knowledge and professional practice. By incorporating quantitative, qualitative, and case study data, this article considers the value of genre analysis in educational social media research. It focuses, particularly, on the rhetorical and lexico-grammatical features of a selection of TikTok video mini-lessons targeting Italian speakers of English as a foreign language. Analysis of the data reveals that digital language teaching discourse on TikTok is a structured event with recurrent rhetorical patterns and linguistic features for achieving both pedagogical and promotional communicative purposes. The research bears considerable relevance given the need for the analysis of transformation processes in instructional discourse amid the widespread use of information and communication technologies and the advance of online learning.


Union-find quantum decoding without union-find

Abstract: The union-find decoder is a leading algorithmic approach to the correction of quantum errors on the surface code, achieving code thresholds comparable to minimum-weight perfect matching (MWPM) with amortized computational time scaling near-linearly in the number of physical qubits. This complexity is achieved via optimizations provided by the disjoint-set data structure. We demonstrate, however, that the behavior of the decoder at scale underutilizes this data structure for twofold analytic and algorithmic reasons, and that improvements and simplifications can be made to architectural designs to reduce resource overhead in practice. To reinforce this, we model the behavior of erasure clusters formed by the decoder and show that there does not exist a percolation threshold within the data structure for any mode of operation. This yields a linear-time worst-case complexity for the decoder at scale, even with a naive implementation omitting popular optimizations.


Unraveling the Potential of ChatGPT: Investigating the Efficacy of Reading Text Adaptation

Abstract: The purpose of this study is to investigate the effectiveness of ChatGPT in reading text adaptation. Its primary focus is on examining ChatGPT's ability to adapt texts with particular attention to its contextual understanding and limitations. Employing a mixed-methods approach, the research combines quantitative analysis and qualitative evaluation. Quantitative measures assess the consistency of ChatGPT's responses across various text adaptation scenarios, whereas qualitative evaluations involve the assessment of language teachers' attitudes and perceptions towards the use of ChatGPT for text adaptation in their teaching practices. Ethical considerations pertaining to potential biases and misinformation in the model's output are also discussed. The outcomes of this investigation contribute to understanding ChatGPT's strengths and limitations in reading text adaptation. The research has practical implications for domains such as language education, content creation, and information retrieval, where accurate and adaptable text comprehension is crucial.


Research on Spatiotemporal Heterogeneity of the Impact of Earthquakes on Global Copper Ore Supply Based on Geographically Weighted Regression

Abstract: The large and super large copper deposits worldwide are mainly distributed in the Pacific Rim and Gondwana metallogenic domains, and they are highly coupled with the spatial location of seismically active zones. Major copper-producing countries such as Chile are located in areas with high seismic activity. Earthquakes often cause copper mines to shut down, copper prices to soar, and even mining disasters, affecting the stable supply of global copper. In order to study the impact of earthquakes on the global copper ore supply, information on resource endowments, production, and earthquake data from the past 30 years are collected. This article mainly analyzes (1) the spatial correlation between earthquakes and copper mine projects, and the spatial characteristics of earthquakes’ impact on copper mine production, using correlation analysis and geographically weighted regression (GWR); (2) the impact of sudden earthquake events on the export volume and price of copper mines from the perspective of time, using the earthquake index constructed based on the magnitude of the earthquake, the depth of the epicenter, and the distance from the copper mine. The results indicate that the regions with high spatial correlation between copper mine projects and earthquakes are mainly located along the Pacific coast of South and North Americas. Earthquakes can lead to an increase in copper prices, but they will fall significantly in the short term. The impact of earthquakes on export volume generally has a delay period of 1–2 months, resulting in a short-term decrease in export volume. This article quantitatively evaluates the impact of earthquake risk on the supply chain from both spatial and temporal dimensions, providing reference for supply chain risk monitoring, risk impact assessment, and prediction. On the basis of the research results of this article, mineral resource management departments can quantitatively evaluate the spatiotemporal impact of natural risks such as earthquakes on the supply chain, adjust management policies in a timely manner, and improve the level of refined management of supply chain risks.


Two methods for breaking down a quantum algorithm

Abstract: Despite the promise that fault-tolerant quantum computers can efficiently solve classically intractable problems, it remains a major challenge to find quantum algorithms that may reach computational advantage in the present era of noisy, small-scale quantum hardware. Thus, there is a substantial ongoing effort to create new quantum algorithms (or adapt existing ones) to accommodate depth and space restrictions. By adopting a hybrid query perspective, we identify and characterize two methods of breaking down quantum algorithms into rounds of lower (query) depth, designating these approaches as ``parallelization'' and ``interpolation.'' To the best of our knowledge, these had not been explicitly identified and compared side by side, although one can find instances of them in the literature. We apply them to two problems with known quantum speedup: calculating the $k$-threshold function and computing a NAND tree. We show that for the first problem parallelization offers the best performance, while for the second interpolation is the better choice. This illustrates that no approach is strictly better than the other, and that there is more than one good way to break down a quantum algorithm into a hybrid quantum-classical algorithm.


EAO: Enhanced aquila optimizer for solving optimization problem

Abstract: The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm’s convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO’s viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO’s robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design.


Sparse Random Hamiltonians Are Quantumly Easy

Abstract: Identification of a large class of Hamiltonians that are easy to solve on quantum computers but difficult on classical ones provides a possible path to practical quantum advantage in the simulation of quantum systems.


Shapley value: from cooperative game to explainable artificial intelligence

Abstract: Abstract With the tremendous success of machine learning (ML), concerns about their black-box nature have grown. The issue of interpretability affects trust in ML systems and raises ethical concerns such as algorithmic bias. In recent years, the feature attribution explanation method based on Shapley value has become the mainstream explainable artificial intelligence approach for explaining ML models. This paper provides a comprehensive overview of Shapley value-based attribution methods. We begin by outlining the foundational theory of Shapley value rooted in cooperative game theory and discussing its desirable properties. To enhance comprehension and aid in identifying relevant algorithms, we propose a comprehensive classification framework for existing Shapley value-based feature attribution methods from three dimensions: Shapley value type, feature replacement method, and approximation method. Furthermore, we emphasize the practical application of the Shapley value at different stages of ML model development, encompassing pre-modeling, modeling, and post-modeling phases. Finally, this work summarizes the limitations associated with the Shapley value and discusses potential directions for future research.


Classification Of Adaptive Back Propagation Neural Network Along with Fuzzy Logic in Chronic Kidney Disease

Abstract: A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98% accurate in terms of efficiency.


The use of XR technology in criminological research: a scoping review

Abstract: Abstract Objectives Criminology has begun to use virtual reality (VR) as a tool to understand criminal behaviour. There have been many advances in VR that open up the possibility of novel research designs, in addition to the inclusion of augmented reality (AR) as a potential research tool. A review of the efficacy of the use of AR and VR (together known as XR) within criminology is now required to guide the further development of this field. Methods We employed a scoping review, which after screening, incorporated 41 papers for review. Results We uncovered three important characteristics for the papers: research topics for which XR is employed, the prominence of VR research over AR and XR and methodological rigour. VR is mostly employed for creating scenarios, in particular in research on memory and aggression. Studies vary greatly in their use of interaction within VR scenes, description of technical specifications and sample sizes. Conclusions Despite the identification of some methodological limitations in the existing literature, VR has found its place in criminological research. With technological advancement, AR may also find its place in the future, allowing for the ongoing development of research applications. Creating comprehensive guidelines on how to use XR technology in criminology remains necessary.


An efficient deepfake video detection using robust deep learning

Abstract:
Abstract
 The creation and manipulation of synthetic images have evolved rapidly, creating serious concerns about their effects on society. Although there have been various attempts to identify deep fake videos, these approaches are not universal. Identifying these misleading deepfakes is the first step in preventing them from following on social media sites. We introduce a unique deep-learning technique to identify fraudulent clips. Most deepfake identifiers currently focus on identifying face exchange, lip synchronous, expression modification, puppeteers, and other factors. However, exploring a consistent basis for all forms of fake video and images in real-time forensics is challenging. We propose a hybrid technique that takes input from videos of successive targeted frames, then feeds these to the ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network for training and classification. This proposed method helps identify artifacts in deepfake images that do not seem real. To assess the robustness of our proposed model, we used the open deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++.) We achieved 96.23% accuracy when using the FF++ digital record. In contrast, we attained 78.33% accuracy using the aggregated records from FF++ and DFDC. We performed extensive experiments and believe that our proposed method provides more significant results than existing techniques.


Online teaching intention among distance education course tutors: Modelling the effects of human resource factors and moderating role of gender

Abstract: This study examined the effect of human resource factors and the moderating role of gender on online teaching intention among course tutors in distance education. A quantitative approach and survey design were adopted for the study. Data were gathered with a self-administered questionnaire from 539-course tutors (teachers on the distance mode) in a university of distance education programmes. Data were analysed with the Partial Least Squares-Structural Equation Modelling (PLS-SEM) technique. The study found that remuneration and work relations were the two most important human resource factors that have significant predictive relationships with online use intention. Regarding the moderating role of gender, the results revealed that remuneration was relevant to both male and female facilitators. In addition, while work relations were found to be very important to male facilitators, retrenchment was considered to be important among female facilitators. It was therefore recommended that managers of distance education institutions pay attention to gender-biased human resource factors such as remuneration, work relations, and retrenchment as they embark on the use of online learning technologies for distance education delivery.


Exceptional Set Estimate Through Brascamp–Lieb Inequality

Abstract: Abstract Fix integers $1\le k&lt;n$, and numbers $a,s$ satisfying $0&lt;s&lt;\min \{k,a\}$. The problem of exceptional set estimate is to determine $$\begin{align*} & T(a,s):=\sup_{A\subset \mathbb{R}^{n}\ \textrm{dim}A=a}\textrm{dim}(\{ V\in G(k,n): \textrm{dim}(\pi_{V}(A))&lt;s \}). \end{align*}$$In this paper, we prove a new upper bound for $T(a,s)$ by using Brascamp–Lieb inequality. As one of the corollaries, we obtain the estimate $$\begin{align*} & T(a,\frac{k}{n}a)\le k(n-k)-\min\{k,n-k\}, \end{align*}$$which improves a previous result $T(a,\frac{k}{n}a)\le k(n-k)-1$ of He. By constructing examples, we can determine the explicit value of $T(a,s)$ for certain $(a,s)$: When $k\le \frac{n}{2}$, $\beta \in (0,1]$ and $\gamma \in (\beta ,\frac{k}{n}(1+\beta )]$, we have $$\begin{align*} &T(1+\beta,\gamma)=k(n-k)-k.\end{align*}$$ When $k\ge \frac{n}{2}$, $\beta \in (0,1]$ and $\gamma \in (\beta , (1-\frac{k}{n})+\frac{k}{n}\beta ]$, we have $$\begin{align*} &T(n-1+\beta,k-1+\gamma)=k(n-k)-(n-k).\end{align*}$$


Emphasizing Writing Acts: The Exclamation Point in German as a Lexical Operator for Verum

Abstract: Abstract This paper is concerned with the interpretation of the exclamation point in German; for example, Es hat geschneit! ‘It has been snowing!’. I argue that the exclamation point contributes a lexical operator for verum at the layer of writing acts. Specifically, it introduces the writer’s wish to ensure the recognition of the writing act in its scope. This lexicon-based proposal builds on a lexicon-based analysis of verum focus. However, while verum focus is related to the propositional layer, the exclamation point is argued to relate to the act layer. The following advantages are defended: (i) The proposal accounts for both the functional kinship between exclamation points and verum focus and their distributional differences. On the one hand, both means introduce affirmative emphasis by a bouletic attitude; on the other hand, the exclamation point has a much wider distribution than verum focus, as it is not bound to contexts that provide a controversy between a proposition and its negation. (ii) The difference in semantic scope can be traced back to a structural difference. While verum focus is integrated into its host clause, exclamation points occupy a peripheral structural position, which is typical of act modifiers. (iii) The lexicon-based approach to the interpretation of exclamation points has noteworthy broader implications. It provides an independent cross-modal piece of evidence in favor of a lexicon-based analysis of verum focus instead of a focus-based one. Furthermore, it is amenable to a formal compositional implementation. This implementation comprises first steps toward a formal model of writing acts in terms of Commitment Space Semantics and advances the general hypothesis that the graphematic form licenses a systematic mapping from form to meaning in its own right.


Testing and refinement of elemental proxies in tropical lakes from the Indian subcontinent

Abstract: Abstract The conventional approach of using modern values of lake hydrochemistry and sediment composition as key parameters for identifying proxies for environmental reconstruction is complicated by regional climate, catchment geology, lake morphometry and anthropogenic input. In contrast to modern studies from lakes in the north‐temperate regions, reliable proxy indicators for past environmental change from tropical lake systems are still lacking. In this study, we compare new surface geochemical and mineralogical data from the Manasbal Lake, Kashmir (India), with other lakes situated in climatically and geomorphologically distinct regimes with varying degrees of pollution (Tso Moriri in north‐western Himalaya and Lonar in central India). We characterise external (e.g., geology and weathering), internal (e.g., lake water mixing and evaporation) factors and human activities influencing sediment composition and spatial distribution of proxies in each of these lakes to evaluate the usefulness of some of the commonly used proxies, as well as ascertain conditions where they might be applicable. Our results show that (i) morphometric parameters (such as lake size, water depth and catchment vs. lake area) variably influence the spatial distribution of sediments and proxies on the lake bottom highlighting the limitations of using single cores for long‐term palaeoclimate reconstruction; (ii) hydrochemical proxies (such as Na + /Na + +Ca 2+ and Mg/Ca) can delineate endogenic (Lonar and Tso Moriri lakes) versus allogenic (Manasbal Lake) influence on modern sediment characteristics; (iii) the compilation of heavy metal data (e.g., Fe, Zn, Mn, Cr and Ni) from all three lake basins with varying degrees of pollution highlights the limitations of using them to characterise weathering conditions in these basins; and (iv) human impact must be precluded before using the most promising endogenic minerals (calcite, aragonite, gypsum and pyrite) as palaeoenvironmental/palaeoclimate indicators.


Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

Abstract: In medical imaging, precise recognition of Breast Cancer (BC) is a challenge due to the complications of breast tissues. Histopathological detection is still considered the standard in BC detection. Still, the dramatic increase in workload and the complexity of histopathological image (HPI) make this task labor-intensive and dependent on the pathologist, making the advance of automated and precise HPI analysis techniques needed. Due to the automated feature extraction capability, Deep Learning (DL) methods have been effectively used in different sectors, particularly in the medical imaging sector. This study develops the future search algorithm with a DL-based breast cancer detection and classification (FSADL-BCDC) method. The FSADL-BCDC technique examines HPIs to detect and classify BC. To achieve this, the FSADL-BCDC technique implements Wiener Filtering (WF)-based preprocessing to eliminate the noise in the images. Additionally, the FSADL-BCDC uses the ResNeXt method for feature extraction with a Future Search Algorithm (FSA)-based tuning procedure. For BCDC, the FSADL-BCDC technique employs a Hybrid Convolutional Neural Network along with the Long Short-Term Memory (HCNN-LSTM) approach. Finally, the Sunflower Optimization (SFO) approach adjusts the hyperparameter values of the HCNN-LSTM. The outcomes of the FSADL-BCDC are inspected on a standard medical image dataset. Extensive relational studies highlighted the improved performance of the FSADL-BCDC approach in comparison with known methods by exhibiting an output of 96.94% and 98.69% under diverse datasets.


Governance, debt service, information technology and access to electricity in Africa

Abstract: Abstract The study investigates the role of governance (i.e., ‘voice and accountability,’ political stability/no violence, regulatory quality, government effectiveness, corruption‐control and the rule of law) in the incidence of short‐term debt services on infrastructure development in the perspective of telecommunication infrastructure and access to electricity. The focus of the study is on 52 African countries for the period 2002–2021. The generalised method of moments is employed as estimation strategy and the following findings are established. Debt service has a negative unconditional effect on access to electricity and telecommunication infrastructure. Governance dynamics moderate the negative effect of debt service on infrastructure dynamics. Effective moderation is from regulatory quality and corruption‐control for access to electricity and from government effectiveness, regulatory quality, corruption‐control and rule of law, for telecommunication infrastructure. Policy implications are discussed.


ADAM: a robotic companion for enhanced quality of life in aging populations

Abstract: One of the major problems of today's society is the rapid aging of its population. Life expectancy is increasing, but the quality of life is not. Faced with the growing number of people who require cognitive or physical assistance, new technological tools are emerging to help them. In this article, we present the ADAM robot, a new robot designed for domestic physical assistance. It mainly consists of a mobile base, two arms with grippers and vision systems. All this allows the performance of physical tasks that require navigation and manipulation of the environment. Among ADAM's features are its modularity, its adaptability to indoor environments and its versatility to function as an experimental platform and for service applications. In addition, it is designed to work respecting the user's personal space and is collaborative, so it can learn from experiences taught by them. We present the design of the robot as well as examples of use in domestic environments both alone and in collaboration with other domestic platforms, demonstrating its potential.


A stochastic variance reduced gradient method with adaptive step for stochastic optimization

Abstract: Abstract In this paper, we propose a stochastic variance reduction gradient method with adaptive step size, referred to as the SVRG‐New BB method, to solve the convex stochastic optimization problem. The method could be roughly viewed as a hybrid of the SVRG algorithm and a new BB step mechanism. Under the condition that the objective function is strongly convex, we provide the linear convergence proof of this algorithm. Numerical experiment results show that the performance of the SVRG‐New BB algorithm can surpass other existing algorithms if parameters in the algorithm are properly chosen.


Challenges and solutions in the development of blockchain applications: Extraction from SLR and empirical study

Abstract: Abstract Data protection is the most prior and valuable concern as stealing personal/organizational data by cyber criminals may arise issues ranging from privacy disclosure to system hacking. Blockchain is considered a top leading technology with the influence to reshape future industries, but like other revolutionary technologies, blockchain has its merits and demerits as well. However, due to its nascent stage, blockchain technology poses numerous challenges that hinder the effective development of blockchain. To uncover this potential hindrance and group the relevant barriers, a total of 12 challenges that may hinder the progress of blockchain application development have been identified from a sample of 52 primary studies. Based on the results of our research, the six challenges are considered critical an account for more than 30% occurrence. The critical challenges are “Lack of Proper Development Tools and Technology,” “Security Precaution Measures,” “Lack of Governance and Standards,” “Lack of Professional Expertise with Essential Skills and Knowledge,” “Lack of Organizational Support,” and “Interoperability Integration.” Using a comprehensive systematic literature review (SLR) and a questionnaire survey, a list of 65 solutions/practices has been identified to address the challenges that were identified. These solutions/practices will help blockchain developers to address the identified challenges and develop benign blockchain application in future. The results of our questionnaire survey largely align with the findings of the SLR. However, there are variations in the ranking of the challenges between the two datasets. The finding of this paper is to provide insights that can assist in streamlining and optimizing the development process of blockchain applications with greater ease and efficiency.


Two‐Server Oblivious Transfer for Quantum Messages

Abstract: Abstract Oblivious transfer is considered as a cryptographic primitive task for quantum information processing over a quantum network. It is an essential building block for secure multiparty computation. It is known that one‐server oblivious transfer is impossible. When the task is the transmission of classical messages, protocols for two‐server oblivious transfer exist, i.e., existing protocols work under the assumption that two servers do not communicate with each other. However, when the task is the transmission of a quantum state, no existing method works even under the above two‐server assumption. Two‐server oblivious transfer protocols for quantum messages are proposed for the first time.


The effectiveness of VR environment on primary and secondary school students’ learning performance in science courses

Abstract: VR technology is being widely used in education due to it's a sense of immersion, real-time interaction, and ability to stimulate imagination. However, there is a lack of research comparing students at different stages. This study used a quasi-experimental design involving 73 fourth-grade and 86 eighth-grade students in two schools in northwest China minority gathering area. One class in each grade level used Virtual Reality (VR) headsets device to teach science content, while the other used interactive whiteboards. The study aimed to examine the differences in students' learning performance, problem-solving ability, self-efficacy, and technology acceptance. The findings showed that VR environment: (a) enhanced the learning performance of primary school students but did not have a significant impact on secondary students; (b) significantly improved problem-solving ability for both primary and secondary school students; (c) significantly promoted self-efficacy for both primary and secondary students; and (d) had a significant influence on technology acceptance for both primary and secondary students. Therefore, we recommended that teachers could integrate VR technology into science labs to enhance secondary students' problem-solving ability and self-efficacy, and improve primary students' learning performance, problem-solving ability, and self-efficacy.


Pelatihan MIT App Inventor sebagai Upaya Meningkatkan Kemampuan Berpikir Logis Siswa SMAN 4 Parepare

Abstract: This community service activity aims to improve the logical thinking abilities of SMAN 4 Parepare students by teaching the application of mathematical logic in compiling computer instructions based on MIT App Inventor. The method for implementing this service was problem-based learning training by taking the example of the traditional game of rock-paper-scissors as a learning case. Next, create a rock-paper-scissors game program using MIT App Inventor as a student learning experience in applying mathematical logic concepts. The evaluation instrument employed was a questionnaire to collect pre-test and post-test results, which were then analyzed using descriptive statistics and paired sample t-tests. The results of this service showed an increase in the average participant score on the post-test results, where the average pre-test score was 7.24 out of 15, while the average post-test score was 9.17 out of 15.


Velocity-aware spatial-temporal attention LSTM model for inverse dynamic model learning of manipulators

Abstract: Introduction An accurate inverse dynamics model of manipulators can be effectively learned using neural networks. However, further research is required to investigate the impact of spatiotemporal variations in manipulator motion sequences on network learning. In this work, the Velocity Aware Spatial-Temporal Attention Residual LSTM neural network (VA-STA-ResLSTM) is proposed to learn a more accurate inverse dynamics model, which uses a velocity-aware spatial-temporal attention mechanism to extract dynamic spatiotemporal features selectively from the motion sequence of the serial manipulator. Methods The multi-layer perception (MLP) attention mechanism is adopted to capture the correlation between joint position and velocity in the motion sequence, and the state correlation between hidden units in the LSTM network to reduce the weight of invalid features. A velocity-aware state fusion approach of LSTM network hidden units' states is proposed, which utilizes variation in joint velocity to adapt to the temporal characteristics of the manipulator dynamic motion, improving the generalization and accuracy of the neural network. Results Comparative experiments have been conducted on two open datasets and a self-built dataset. Specifically, the proposed method achieved an average accuracy improvement of 61.88% and 43.93% on the two different open datasets and 71.13% on the self-built dataset compared to the LSTM network. These results demonstrate a significant advancement in accuracy for the proposed method. Discussion Compared with the state-of-the-art inverse dynamics model learning methods of manipulators, the modeling accuracy of the proposed method in this paper is higher by an average of 10%. Finally, by visualizing attention weights to explain the training procedure, it was found that dynamic modeling only relies on partial features, which is meaningful for future optimization of inverse dynamic model learning methods.


A terminological frame-network model of the conceptual and semantic organization of notions of computational linguistics

Abstract: The research object is the conceptual and semantic organization of the terminological apparatus, the research subject is the logical and conceptual structure of notions denoted by terms of computational linguistics. The study aims to identify the features of the systemic organization of notions of computational linguistics based on the use of a terminological frame-network model. The paper briefly analyzes the use of the frame approach to modeling the structure of terminological systems by linguists. A terminological frame-network model of the subject area “Computational linguistics” is proposed. The study is illustrated with fragments of the terminological network of frames of the subject area “Synonymy of terms of computational linguistics” presented in the form of diagrams. The work is novel in that it provides an original definition of a terminological network of frames and is the first to formulate the rules for its construction. The research findings showed the possibility of using a terminological frame-network model to analyze the logical and conceptual structure of the subject area “Computational linguistics” and the organization of semantic term-formation processes and lexicosemantic relations in the terminology under study.


Enhancing teachers’ instruction on how to study: an exploration of the effectiveness of learning strategies for particular secondary school subjects

Abstract: Teachers’ instructions on learning strategies play a key role on how students study. However, how the effectiveness of learning strategies can be matched to different types of knowledge in a diversity of subject content has remained unexplored. In the present study, we related the effectiveness of learning strategies to different types of knowledge through an umbrella review. Furthermore, using focus-groups interviews, we explored the relation between school subjects and these knowledge types. We concluded that the effects of particular learning strategies vary across different learning objectives of the subjects and we offered suggestions for the instruction of effective learning strategies.


Collective Displacement of Regional Cryptomarket Vendors—A Study on the Aftermath of the Flugsvamp 3.0 Closure

Abstract: The cryptomarket ecosystem has become increasingly volatile and fragmented with sites shutting down on short notice. Displacement to new marketplaces is tricky when the original location was domestically oriented. We examine the spatial and temporal displacement of 83 Swedish vendors in the aftermath of the Flugsvamp 3.0 shutdown. Vendors rejected the successor Flugsvamp 4.0 and moved to German-run Archetyp Market. Using quantitative cross-sectional data from Archetyp Market we measure bivariate correlations between the temporal displacement and status-related variables. We found moderately strong correlation between vendors’ number of sales per day and the order of their relocation to Archetyp. We also examined cryptomarket discussion forums and blogs during the time of the Flugsvamp 3.0 shutdown. This qualitative data supported the finding that migration choices of high-status vendors inspired others to follow.


Affine combination‐based over‐sampling for imbalanced regression

Abstract: Abstract Imbalanced domain prediction analysis is currently one of the hot research topics. Many real‐world data mining analyses involve using imbalanced data to obtain predictive models. In the context of imbalance, research on classification problems has been extensive, but research on regression problems is negligible. Rare values rarely occur in imbalanced regression problems, but the focus is on accurately predicting the continuous target variables of rare instances. One of the challenges in imbalanced regression is finding a suitable strategy to rebalance the original dataset in order to improve the predictive performance of the model in rare instances. In this study, two algorithms are proposed: sigma nearest over‐sampling based on convex combination for regression (SNOCCR) and affine combination‐based over‐sampling (ACOS). ACOS rebalances the original dataset by generating new instances through the affine combinations of the original examples. The region where the new instances are generated can be adjusted based on the distribution of the data, ensuring that the generated cases better mimic the distribution of the original examples. The comparison among ACOS, SNOCCR, and other preprocessing methods was conducted on 15 datasets to validate the predictive performance of models trained on rebalanced datasets for rare instances. The experimental results indicate that ACOS outperforms other existing methods.


Continuous Variable Quantum Teleportation in a Dissipative Environment: Comparison of Non‐Gaussian Operations Before and After Noisy Channel

Abstract: Abstract This article explores the relative advantages in continuous‐variable quantum teleportation when non‐Gaussian operations, namely, photon subtraction, addition, and catalysis, are performed before and after interaction with a noisy channel. The resource state for teleporting unknown coherent and squeezed vacuum states is generated using two distinct strategies: i) Implementation of non‐Gaussian operations on two mode squeezed vacuum (TMSV) state before interaction with a noisy channel, ii) Implementation of non‐Gaussian operations after interaction of TMSV state with a noisy channel. The results show that non‐Gaussian operations can enhance teleportation fidelity in a dissipative environment. However, the most advantageous non‐Gaussian operation and appropriate strategy depend on the state and channel parameters. The results suggest that employing such strategies can be advantageous in other non‐Gaussian continuous variable quantum information (QIP) processing tasks in a dissipative environment.


A speed‐area‐efficient hardware ECPM‐engine in GF(<i>p</i>) over generic Weierstrass curves

Abstract: Abstract This paper proposes a 256‐bit speed‐area‐efficient hardware elliptic curve point‐multiplication engine (ECPM‐engine) in GF( p ) over generic Weierstrass curves, which is optimized by a new speed‐area‐efficient radix‐64 Montgomery modular multiplication (R64MMM) and a novel Montgomery ladder scheduling. The R64MMM calls one 129‐bit adder and one (64x64+129)‐bit multiply‐accumulator (64‐129‐MAC) in parallel to make a trade‐off between speed and area. The novel Montgomery ladder scheduling is used to improve the utilization of MAC in ECPM operation. In this ECPM‐engine, both MAC utilization in R64MMM operations and R64MMM utilization in ECPM operations are close to 100%. The result shows that the proposed ECPM‐engine consumes 72k gates when the clock frequency is 714 MHz with a 90 nm standard cell library, and it computes one 256‐bit ECPM in 0.14 ms.


Exploring substitution random functions composed of stationary multi-Gaussian processes

Abstract: Abstract Simulation of random fields is widely used in Earth sciences for modeling and uncertainty quantification. The spatial features of these fields may have a strong impact on the forecasts made using these fields. For instance, in flow and transport problems the connectivity of the permeability fields is a crucial aspect. Multi-Gaussian random fields are the most common tools to analyze and model continuous fields. Their spatial correlation structure is described by a covariance or variogram model. However, these types of spatial models are unable to represent highly or poorly connected structures even if a broad range of covariance models can be employed. With this type of model, the regions with values close to the mean are always well connected whereas the regions of low or high values are isolated. Substitution random functions (SRFs) belong to another broad class of random functions that are more flexible. SRFs are constructed by composing ( $$Z=Y\circ T$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Z</mml:mi> <mml:mo>=</mml:mo> <mml:mi>Y</mml:mi> <mml:mo>∘</mml:mo> <mml:mi>T</mml:mi> </mml:mrow> </mml:math> ) two stochastic processes: the directing function T (latent field) and the coding process Y (modifying the latent field in a stochastic manner). In this paper, we study the properties of SRFs obtained by combining stationary multi-Gaussian random fields for both T and Y with bounded variograms. The resulting SRFs Z are stationary, but as T has a finite variance Z is not ergodic for the mean and the covariance. This means that single realizations behave differently from each other. We propose a simple technique to control which values (low, intermediate, or high) are connected. It consists of adding a control point on the process Y to guide every single realization. The conditioning to local values is obtained using a Gibbs sampler.


Anomaly sound detection of industrial devices by using teacher-student incremental continual learning

Abstract: Abstract Normal production processes will be substantially impacted by industrial devices in abnormal working conditions. Anomaly sound detection (ASD) model can monitor the working condition of devices by the non-contact and non-invasive way. When new device data is introduced, traditional ASD models are trained using data from all devices, to accommodate every device. However, in real-world settings, the kinds and amounts of devices are constantly changing, which raises difficulties for the current ASD models. This paper proposes a teacher-student incremental learning method for ASD models, aiming to solve ASD model scalability problem. In this paradigm, teacher model has knowledge of all the old devices. The objective of student model is to learn new device knowledge, while avoiding the forgetting of old device knowledge. When student model learns new device data, teacher model transfers the acoustic feature knowledge of old devices to student model via knowledge distillation. Furthermore, the imbalance between old and new knowledge causes challenges, such as knowledge forgetting or lower learning efficiency for student model. This paper presents a dual-teacher-student (DTS) model to solve the problem of knowledge imbalance. Different teacher models for new and old devices in DTS, directing student model to accomplish continuous and deep integration of knowledge. Evaluation for proposed method on the DCASE 2020 Task2 dataset. The results show, the proposed method outperforms other methods in terms of learning capability and robustness during the incremental learning process. Analysis of significance test on the experimental results demonstrates that the method outperforms other methods statistically.


Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator

Abstract: Summary Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.


Three-dimentional reconstruction of underwater side-scan sonar images based on shape-from-shading and monocular depth fusion

Abstract: Abstract Modern marine research requires high-precision three-dimensional (3D) underwater data. Underwater environments experience severe visible light attenuation, which leads to inferior imaging compared with air. In contrast, sound waves are less affected underwater; hence side-scan sonar is used for underwater 3D reconstruction. Typically, the shape-from-shading algorithm (SfS) is widely used to reconstruct surface normal or heights from side-scan sonar images. However, this approach has challenges because of global information loss and noise. To address these issues, this study introduces a surface-normal fusion method. Specifically, we propose a frequency separation SfS algorithm using a discrete cosine transform, which provides a surface-normal map with less noise. We then fuse the surface-normal map with a novel depth estimation network to achieve high-precision 3D reconstruction of underwater side-scan sonar images. We conducted experiments on synthetic, NYU-depth-v2, and real side-scan sonar datasets to demonstrate the effectiveness of the proposed method.


RSRNeT: a novel multi-modal network framework for named entity recognition and relation extraction

Abstract: Named entity recognition (NER) and relation extraction (RE) are two important technologies employed in knowledge extraction for constructing knowledge graphs. Uni-modal NER and RE approaches solely rely on text information for knowledge extraction, leading to various limitations, such as suboptimal performance and low efficiency in recognizing polysemous words. With the development of multi-modal learning, multi-modal named entity recognition (MNER) and multi-modal relation extraction (MRE) have been introduced to improve recognition performance. However, existing MNER and MRE methods often encounter reduced efficiency when the text includes unrelated images. To address this problem, we propose a novel multi-modal network framework for named entity recognition and relation extraction called RSRNeT. In RSRNeT, we focus on extracting visual features more fully and designing a multi-scale visual feature extraction module based on ResNeSt network. On the other hand, we also emphasize fusing multi-modal features more comprehensively while minimizing interference from irrelevant images. To address this issue, we propose a multi-modal feature fusing module based on RoBERTa network. These two modules enable us to learn superior visual and textual representations, reducing errors caused by irrelevant images. Our approach has undergone extensive evaluation and comparison with various baseline models on MNER and MRE tasks. Experimental results show that our method achieves state-of-the-art performance in recall and F1 score on three public datasets: Twitter2015, Twitter2017 and MNRE.


CSINet: A Cross-Scale Interaction Network for Lightweight Image Super-Resolution

Abstract: In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it often comes at the expense of heightened computational demands and greater memory usage, which can restrict practical deployment. To mitigate this challenge, we have incorporated a technique called factorized convolution and introduced the efficient Cross-Scale Interaction Block (CSIB). CSIB employs a dual-branch structure, with one branch extracting local features and the other capturing global features. Interaction operations take place in the middle of this dual-branch structure, facilitating the integration of cross-scale contextual information. To further refine the aggregated contextual information, we designed an Efficient Large Kernel Attention (ELKA) using large convolutional kernels and a gating mechanism. By stacking CSIBs, we have created a lightweight cross-scale interaction network for image super-resolution named "CSINet". This innovative approach significantly reduces computational costs while maintaining performance, providing an efficient solution for practical applications. The experimental results convincingly demonstrate that our CSINet surpasses the majority of the state-of-the-art lightweight super-resolution techniques used on widely recognized benchmark datasets. Moreover, our smaller model, CSINet-S, shows an excellent performance record on lightweight super-resolution benchmarks with extremely low parameters and Multi-Adds (e.g., 33.82 dB@Set14 × 2 with only 248 K parameters).


Improving 3D Object Detection with Context-Aware and Dimensional Interaction Attention

Abstract: Abstract Recently, 3D object detection technology based on point clouds has developed rapidly. However, too few points of distant and occluded objects are scanned by the sensor, and thus these objects suffer from too insufficient features to be detected. This case damages the detection accuracy. Therefore, we constitute a novel 3D object detection with Context-aware and dimensional Interaction Attention Network (CIANet) to explore vital geometric cues for enriching the feature representation of the object, thus boosting the overall detection performance. Specifically, in the first stage, we employ the 3D sparse convolution to extract voxel features, and then construct a Channel-Spatial Hybrid Attention (CSHA) module and a Contextual Self-Attention (CSA) module to enhance voxel features for generating proposals. The CSHA module aims to enhance the key information of the channel and spatial domains of 2D Bird’s Eye View (BEV) features, and the CSA module is applied to supplement contextual information to the enhanced BEV features, thus generating accurate proposals. In the second stage, we construct a Dimensional Interaction Attention (DIA) module to refine Region of Interest (RoI) features within the proposals. It enhances the interactions among the channel and spatial dimensions of RoI features to learn accurate boundaries of objects for proposal refinement. Extensive experiments on the KITTI and Waymo benchmarks show the superior detection performance of CIANet compared to recent methods, especially for objects such as pedestrians and cyclists.


A Smart Ad Display System

Abstract: This paper proposes a smart ad display system to provide personalized delivery of video ads. The proposed system records consumers’ facial expression and eye gaze stream data as they watch an ad and analyzes data at the frame level. The recognized facial expression and detected eye gaze are matched to the corresponding frame of the video ad, thereby linking facial expressions to specific visual objects appearing in the ad. By tracking a consumer’s facial expressions in response to various visual objects in real time, the system learns the consumer’s individual preferences toward different ads, searches the ad pool, and selects and subsequently displays a new ad that is most likely to elicit positive attitudinal and behavioral responses. We demonstrate the feasibility and effectiveness of the proposed system with two empirical studies. The results show that by tracking a consumer’s facial responses to only one ad or even part of an ad, our proposed system is able to make reasonably accurate inferences about a consumer’s ad preferences, with or without using information about other consumers. These inferences are used to make personalized recommendations that help enhance consumers’ ad viewing experiences and elicit favorable responses.


A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times

Abstract: In the hospitality industry, lead time data are a form of compositional data that are crucial for business planning, resource allocation, and staffing. Hospitality businesses accrue fees daily, but recognition of these fees is often deferred. This paper presents a novel class of Bayesian time series models, the Bayesian Dirichlet auto-regressive moving average (B-DARMA) model, designed specifically for compositional time series. The model is motivated by the analysis of five years of daily fees data from Airbnb, with the aim of forecasting the proportion of future fees that will be recognized in 12 consecutive monthly intervals. Each day’s compositional data are modeled as Dirichlet distributed, given the mean and a scale parameter. The mean is modeled using a vector auto-regressive moving average process, which depends on previous compositional data, previous compositional parameters, and daily covariates. The B-DARMA model provides a robust solution for analyzing large compositional vectors and time series of varying lengths. It offers efficiency gains through the choice of priors, yields interpretable parameters for inference, and produces reasonable forecasts. The paper also explores the use of normal and horseshoe priors for the vector auto-regressive and vector moving average coefficients, and for regression coefficients. The efficacy of the B-DARMA model is demonstrated through simulation studies and an analysis of Airbnb data.


Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak

Abstract: The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.


Conflicting Marks Archive Dataset: A Dataset of Conflicting Marks from the Brazilian Intellectual Property Office

Abstract: A registered trademark represents one of a company’s most valuable intellectual assets, acting as a safeguard against possible reputational damage and financial losses resulting from infringements of this intellectual property. To be registered, a mark must be unique and distinctive in relation to other trademarks which are already registered. In this paper, we describe the CMAD, an acronym for Conflicting Marks Archive Dataset. This dataset has been meticulously organized into pairs of marks (Number of pairs = 18,355) involved in copyright infringement across word, figurative and mixed marks. Organizations sought to register these marks with the National Institute of Industrial Property (INPI) in Brazil, and had their applications denied after analysis by intellectual property specialists. The robustness of this dataset is ensured by the intrinsic similarity of the conflicting marks, since the decisions were made by INPI specialists. This characteristic provides a reliable basis for the development and testing of tools designed to analyze similarity between marks, thus contributing to the evolution of practices and computer-based solutions in the field of intellectual property.


A Lightweight Context-Aware Feature Transformer Network for Human Pose Estimation

Abstract: We propose a Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design the Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes 3×3 convolution in the bottleneck of HRNet with a Contextual Transformer (CoT) block while reducing the complexity of the network. Specifically, the CoT first produces keys with static contextual information through 3×3 convolution. Then, relying on query and contextualization keys, dynamic contexts are generated through two concatenated 1×1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module (AFAM). Technically, given an intermediate input, the AFAM successively deduces attention maps along the channel and spatial dimensions. Then, an adaptive refinement module (ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above procedures, our lightweight network provides powerful clues for the detection of keypoints. Experiments are performed on the COCO and MPII datasets. The model achieves a 76.2 AP on the COCO val2017 dataset. Compared to other methods with a CNN as the backbone, CaFTNet has a 72.9% reduced number of parameters. On the MPII dataset, our method uses only 60.7% of the number of parameters, acquiring similar results to other methods with a CNN as the backbone.


Surface trap with adjustable ion couplings for scalable and parallel gates

Abstract: We describe the design and operation of a surface-electrode Paul trap for parallel entangling gate implementation. In particular, we demonstrate the possibility of separating or coupling ion motion by adjusting the DC-voltages on a set of electrodes and show the possibility of parallel MS-gate operations for specific voltage configurations. We verify the scalability of this approach and characterize the performance of these gates in the presence of the finite phonon mode occupation and of the finite drift of the phonon frequencies. Additionally, we investigate how the number of ions per individual trapping site and anharmonic potential terms affect the coupling between the wells.


Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning

Abstract: Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness.


Channel estimation and pilot reduction for mmWave massive MIMO systems using deep neural networks

Abstract: In this paper, we propose deep learning-based channel estimation and pilot reduction for mmWave point-to-point multi-input multi-output systems. The proposed scheme consists of a two-step approach where the first step is applying a denoising autoencoder for channel estimation. With the denoising characteristic of autoencoder, sparse channel estimation can be conducted although the orthogonality of pilot sequences is not guaranteed due to shorter pilots. The second step is exploiting the temporal correlation of the channel, using the previous estimate to extract information for the current estimate. Through simulation, the proposed scheme shows superior performance with reduced pilots.


Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Abstract: Random-walk-based graph representation methods have been widely applied in User Identity Linkage (UIL) tasks, which links overlapping users between two different social networks. It can help us to obtain more comprehensive portraits of criminals, which is helpful for improving cyberspace governance. Yet, random walk generates a large number of repeating sequences, causing unnecessary computation and storage overhead. This paper proposes a novel method called Edge-Removing Walk (ERW) that can replace random walk in random-walk-based models. It removes edges once they are walked in a walk round to capture the l−hop features without repetition, and it walks the whole graph for several rounds to capture the different kinds of paths starting from a specific node. Experiments proved that ERW can exponentially improve the efficiency for random-walk-based UIL models, even maintaining better performance. We finally generalize ERW into a general User Identity Linkage framework called ERW-UIL and verify its performance.


A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems

Abstract: Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.


Development of an Automated Wooden Handle Packaging System with Integrated Counting Technology

Abstract: Manual counting and packaging processes often involve repetitive, error-prone tasks. Specifically, packaging wooden handles, utilized in gardening tools and cutlery, typically relies on labor-intensive methods with dimensions varying in diameter, length and mass. These variations complicate packaging, requiring precise counting and diverse handling solutions. This article introduces an automated counting structure tailored for a wide array of wooden handles manufactured by a company in northern Portugal. Employing standardized mechanical design methodologies, we delineate crucial stages encompassing the design, development, implementation and testing of this specialized counting equipment. The machine has been partially integrated into the management system of the company, taking into account future global integration according to the Industry 4.0 concept.


Succeeding India's Retail Digital Currency

Abstract: The study attempts to critically assess the possibilities of success of the Indian retail digital currency (DC). The Central Bank Digital Currency, popularly known as CBDC, issues in any of the two forms—token and account types—and the Reserve Bank of India (RBI) prefers the former route. A token is a currency or any digital and/or physical asset. Theoretically, tokenization can buy, sell, and transfer digital or real-life assets fully or fractionally. Amending the RBI Act and expanding the scope of the definition of 'bank notes', DC incorporates and enjoys equivalent legal tender like physical currency notes and coins. Albeit the Indians, along with cash, the latter is still preferred and enjoying the status of a 'king' even post-demonetization years. The DC in retail likely could be a game changer in the Indian financial market if the RBI could comprehensively address the teething problems associated with DC.


Investigating chatbot users’ e-satisfaction and patronage intention through social presence and flow: Indian online travel agencies (OTAs)

Abstract: Purpose The concept of ubiquitous assimilation in e-commerce, denoting the seamless integration of technologies into customer shopping experiences, has played a pivotal role in aiding e-satisfaction and, consequently, fostering patronage intention. Among these, text-based chatbots are significant innovations. In light of this, the paper aims to develop a conceptual framework and comprehend the patronage behaviour of artificial intelligence-enabled chatbot users by using chatbot usability cues and to determine whether the social presence and flow theories impact e-satisfaction, which leads to users’ patronage intention. The current research provides insights into online travel agencies (OTAs), a crucial segment within the travel and tourism sector. Given the significance of building a loyal clientele and cultivating patronage in this industry, these insights are of paramount importance for achieving sustained profitability and growth. Design/methodology/approach The research framework primarily focused on the factors that precede e-satisfaction and patronage intention among chatbot users, which include social presence, flow, perceived anthropomorphism and need for human interaction. The researchers collected the data by surveying 397 OTA chatbot users by using an online questionnaire. The data of this cross-sectional study were analysed using covariance-based structural equation modelling. Findings Findings reveal that e-satisfaction is positively linked with patronage intention and the variables of social presence and flow impact e-satisfaction along with chatbot usability cues. There were direct and indirect relations between chatbot usability and e-satisfaction. Moreover, the personal attributes, “need for human interaction” and, “perceived anthropomorphism” were found to moderate relations between chatbot usability cues, social presence and flow. Originality/value The impact of chatbot’s usability cues/attributes on e-satisfaction, along with perceived attributes – social presence and flow in the realm of OTAs contributes to the human–chatbot interaction literature. Moreover, the interacting effects of perceived anthropomorphism and the need for human interaction are unique in the current contextual relations.


Understanding users' voice assistant exploration intention: unraveling the differential mechanisms of the multiple dimensions of perceived intelligence

Abstract: Purpose The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs on users’ exploration intention (UEI) and how these antecedents can collectively result in the highest level of UEI. Design/methodology/approach An online survey on Amazon Mechanical Turk is employed. The model is tested utilizing the structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach from the collected data of VA users ( N = 244). Findings According to the SEM outcomes, perceptual, cognitive, emotional and social intelligence have different mechanisms on UEI. Findings from the fsQCA reinforce the SEM results and provide the configurations that enhanced UEI. Originality/value This study extends the conceptual framework of perceived intelligence and enriches the literature on anthropomorphism and users’ exploration. These findings also provide insightful suggestions for practitioners regarding the design of VA products.