Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Abstract: Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121-each Convolutional Neural Network architecture designed for classification tasks-this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.


The Research on the Application of Artificial Intelligence in Visual Art- based on Souvenir Design

Abstract: This paper will introduce the application of artificial intelligence (AI), machine learning, and deep learning in art design and visual arts, and how these technologies can be used to create unique souvenirs. In the field of art design, AI and machine learning can be used to automatically generate artwork and patterns, providing more inspiration and creativity, and can also help artists better understand their audience and market. The application of deep learning in the field of visual arts includes image recognition, image classification, image generation, and so on. In the field of souvenir design, the use of AI and machine learning can help designers better understand market needs and consumer trends to create unique souvenirs. Taken together, the application of AI, machine learning, and deep learning technologies has great potential and creativity in the fields of art design and souvenirs.


Variable Selection in Data Analysis: A Synthetic Data Toolkit

Abstract: Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs’ resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study.


Video-Based Gaze Detection for Oculomotor Abnormality Measurements

Abstract: Measuring oculomotor abnormalities in human subjects is challenging due to the delicate spatio-temporal nature of the oculometric measures (OMs) used to assess eye movement abilities. Some OMs require a gaze estimation accuracy of less than 2 degrees and a sample rate that enables the detection of movements lasting less than 100 ms. While past studies and applications have used dedicated and limiting eye tracking devices to extract OMs, recent advances in imaging sensors and computer vision have enabled video-based gaze detection. Here, we present a self-calibrating neural network model for gaze detection that is suitable for oculomotor abnormality measurement applications. The model considers stimuli target locations while the examined subjects perform visual tasks and calibrate its gaze estimation output in real time. The model was validated in a clinical trial and achieved an axial accuracy of 0.93 degrees and 1.31 degrees for horizontal and vertical gaze estimation locations, respectively, as well as an absolute accuracy of 1.80 degrees. The performance of the proposed model enables the extraction of OMs using affordable and accessible setups—such as desktop computers and laptops—without the need to restrain the patient’s head or to use dedicated equipment. This newly introduced approach may significantly ease patient burden and improve clinical results in any medical field that requires eye movement measurements.


Detecting Fake Social Media Profiles Using the Majority Voting Approach

Abstract: INTRODUCTION: The rise of social media platforms has brought about a concerning surge in the creation of fraudulent user profiles, with intentions ranging from spreading false information and perpetrating fraud to engaging in cyberbullying. The detection of these deceptive profiles has emerged as a critical imperative to safeguard the trustworthiness and security of online communities.OBJECTIVES: This paper focused on the detection and identification of fake social media profiles.METHODS: This paper introduces an innovative approach for discerning and categorizing counterfeit social media profiles by leveraging the majority voting approach. The proposed methodology integrates a range of machine learning algorithms, including Decision Trees, XGBoost, Random Forest, Extra Trees, Logistic Regression, AdaBoost and K-Nearest Neighbors each tailored to capture distinct facets of user behavior and profile attributes. This amalgamation of diverse methods results in an ensemble of classifiers, which are subsequently subjected to a majority voting mechanism to render a conclusive judgment regarding the legitimacy of a given social media profile.RESULTS: We conducted thorough experiments using a dataset containing both legitimate and fake social media profiles to determine the efficiency of our methodology. Our findings substantiate that the Majority Voting Technique surpasses individual classifiers, attaining an accuracy rate of 99.12%, a precision rate of 99.12%, a recall rate of 99.12%, and an F1-score of 99.12%.CONCLUSION: The results show that the majority vote method is reliable for detecting and recognising fake social media profiles.


COVID-19 discourse in linguistic landscape: Linguistic and semiotic analysis of directive signs

Abstract: The COVID-19 pandemic has significantly impacted the way people communicate and convey information, particularly in public spaces. This paper investigates the use of language and signs in COVID-19-related directives in Japan from a pragmatic perspective. Using data collected through a linguistic landscape survey, the study qualitatively examines the use of politeness in linguistic formulation and the semiotic use of space through the combination of signs and objects. The analyses reveal the differences in politeness strategies used in top-down signs compared to bottom-up signs. Top-down signs, such as those produced by the government or large organizations, tend to use an invitation form and incomplete sentences, whereas bottom-up signs, created by individuals or small shops, often use more formal language and formulaic request expressions. The study highlights the role of addressers of bottom-up signs as “handlers” who play an active role in pragmatically utilizing space through the arrangement of semiotic signs. The study argues that a detailed linguistic and semiotic analysis is crucial in comprehending how meaning is co-constructed in the linguistic and semiotic landscape. This is particularly relevant in the context of the COVID-19 pandemic, where clear and effective communication is of utmost importance in order to maintain public health and safety. The findings of this study provide valuable insights into the use of language and signs in public spaces, and have implications for the development of effective communicative strategies in the context of the pandemic and beyond.


Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning

Abstract: The intelligent identification of coal gangue on industrial conveyor belts is a crucial technology for the precise sorting of coal gangue. To address the issues in coal gangue detection algorithms, such as high false negative rates, complex network structures, and substantial model weights, an optimized coal gangue detection algorithm based on YOLOv5s is proposed. In the backbone network, a feature refinement module is employed for feature extraction, enhancing the capability to extract features for coal and gangue. The improved BIFPN structure is employed as the feature pyramid, augmenting the model’s capability for cross-scale feature fusion. In the prediction layer, the ESIOU is utilized as the bounding box regression loss function to rectify the misalignment issue between predicted and actual box angles. This approach expedites the convergence speed of the network while concurrently enhancing the accuracy of coal gangue detection. Channel pruning is implemented on the network to diminish model computational complexity and weight, consequently augmenting detection speed. The experimental results demonstrate that the refined YOLOv5s coal gangue detection algorithm outperforms the original YOLOv5s algorithm, achieving a notable accuracy enhancement of 2.2% to reach 93.8%. Concurrently, a substantial reduction in model weight by 38.8% is observed, resulting in a notable 56.2% increase in inference speed. These advancements meet the detection requirements for scenarios involving mixed coal gangue.


Discussing the Formal Components of Material Objects: A New Reply to Bennett

Abstract: Abstract Recently mereological hylomorphism, the theory in which form and matter are considered to be proper parts of objects, has become very important among contemporary metaphysicians. The present work aims to analyse and dismantle Bennett’s criticism regarding the existence of formal proper parts. To do this, I will start by presenting Koslicki’s mereological hylomorphism. Next, I will focus on Bennett’s critique which seeks to deny the existence of formal proper parts. Finally, I will analyse critically the Bennett’s criticism focusing on the scenario of lump of clay and statue. I will show Bennett’s proposal is not explanatorily better than Koslicki’s hylomorphism because she needs to accept a counter-intuitive thesis. Therefore, we should prefer Koslicki’s mereological hylomorphism.


Dialogue System for Casual and Continuous Word Learning

Abstract: Facilitating causal and continuous learning in individuals with low motivation presents a formidable challenge owing to the diverse methods and systems available for vocabulary acquisition. Therefore, this study developed a non‐task‐oriented dialogue system to facilitate casual and continuous word learning. Specifically, dialogue systems capable of processing text‐based inputs, encompassing words registered by learners, were formulated. Learners could engage in the acquisition of these words through informal conversation. The evaluation results demonstrated the effectiveness of the proposed method for fostering casual and continuous learning. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.


Beyond the pages: unleashing holograms and holographic technology in Caribbean academic libraries

Abstract: Purpose This paper is intended to spark discourse on the impact of holograms and holographic technology on libraries and the possibility of their usage in Caribbean academic libraries for assisting users, especially those from the physically challenged community, as well as distance library users. Design/methodology/approach A thorough assessment of literature was done of all academic and higher institution libraries across the English-speaking Caribbean to determine the effective use of holograms and holographic technology in Caribbean libraries. Literature from several developed countries was studied, paying keen attention to the emerging holograms and holographic technology and the plausibility for inclusion and exploitation by academic libraries. Findings Revealing the possibilities for fostering great levels of efficacy, as well as saving time and reducing the possibility for inefficiencies in the system through smoother check-in and check-out while offering more accountability. Research limitations/implications This research focused primarily on holograms and holographic technology that can assist libraries and their users in the near future. It further attested to the need for there to be greater consideration for physically challenged individuals and their access to material from the library. Originality/value This paper is the genesis for discussion surrounding holograms and holographic technology in the English-speaking Caribbean. It sought to bring awareness to the possible uses of holograms while exploring the practical implications of its use in libraries across the English-speaking Caribbean.


Siri as an interactive pronunciation coach: its impact on EFL learners

Abstract: Language is a powerful tool that facilitates communication, knowledge acquisition, and negotiation of meaning, but it is a complex skill for English as foreign language (EFL) learners who often encounter various challenges and complexities. Among the critical language skills, pronunciation holds a significant place. As much as it is challenging, many EFL teachers find it necessary to teach it explicitly, so exploring innovative approaches to address this challenge is essential. This preliminary study investigated how Siri, a voice assistant tool, can be integrated with in-class instruction to improve the pronunciation of EFL learners. The study demonstrated that Siri could lead to better learning gains for students than using only in-class instructions and feedback when combined with teacher instructions. Forty EFL learners participated; twenty-five who did not have iPhones compatible with Siri were treated as the control group, whereas fifteen with iPhone devices compatible with Siri were treated as the experimental group. Each group was assigned an experienced English teacher to help give instructions and feedback. The primary data collection method involved students’ reading the assigned tasks, recording, and sending them to their respective teachers and peers for feedback and peer review, respectively. Cohen’s kappa coefficient and independent t-test were used to analyze the data. The results showed that Siri improved the students’ pronunciation skills more than in-class instruction.


Optimal trajectory planning of robot energy consumption based on improved sparrow search algorithm

Abstract: In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption.


WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation Model

Abstract: This paper proposes a novel weight-enriched ranking-based baseline model, WeightedSLIM, aiming to provide more accurate top-N recommendations from implicit user feedback. The model utilizes ItemRank to calculate the ranking score of each item, which is then used as an item weight within the Sparse Linear Model (SLIM), while using Bayesian Personalized Ranking (BPR) to optimize the item similarity matrix. Experiments, conducted for performance comparison of the proposed model with existing recommendation models, demonstrate that it can indeed provide better recommendations and can be used as a strong baseline for top-N recommendations.


<i>Clingraph</i>: A System for ASP-based Visualization

Abstract: Abstract We present the Answer Set Programming (ASP)-based visualization tool clingraph , which aims at visualizing various concepts of ASP by means of ASP itself. This idea traces back to the aspviz tool and clingraph redevelops and extends it in the context of modern ASP systems. More precisely, clingraph takes graph specifications in terms of ASP facts and hands them over to the graph visualization system graphviz . The use of ASP provides a great interface between logic programs and/or answer sets and their visualization. Also, clingraph offers a Python application programming interface (API) that extends this ease of interfacing to clingo ’s API and in turn to connect and monitor various aspects of the solving process.


TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

Abstract: Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences using a CNN and RNN presents challenges in capturing the correlations between two signals. To tackle this issue, the VIT (vision in transformer) approach was introduced into the discriminator network. The IQ signal is treated as a single-channel, two-dimensional image, divided into blocks of 2 × 2 pixels, with absolute position embedding added. The generator network maps the input noise to the same dimension as the IQ signal dimension × embedding vector dimension and adds two identical position embedding data points to the network learning. In the transformer network, prob sparse attention is employed as a replacement for multi-head attention to tackle the issue of high computational complexity. Finally, combined with the MLP structure, the transformer-based generator and discriminator are designed. The signal similarity evaluation index was constructed, and experiments showed that the reconstructed signal under QPSK and BPSK modulation had good reconstruction quality in the time-domain waveform, constellation diagram, and spectrogram at a high SNR. Compared with other reconstruction algorithms, the proposed algorithm improved the quality of the reconstructed signal while reducing the complexity of the algorithm.


Enhancing Human Action Recognition with 3D Skeleton Data: A Comprehensive Study of Deep Learning and Data Augmentation

Abstract: Human Action Recognition (HAR) is an important field that identifies human behavior through sensor data. Three-dimensional human skeleton data extracted from the Kinect depth sensor have emerged as a powerful alternative to mitigate the effects of lighting and occlusion of traditional 2D RGB or grayscale image-based HAR. Data augmentation is a key technique to enhance model generalization and robustness in deep learning while suppressing overfitting to training data. In this paper, we conduct a comprehensive study of various data augmentation techniques specific to skeletal data, which aim to improve the accuracy of deep learning models. These augmentation methods include spatial augmentation, which generates augmented samples from the original 3D skeleton sequence, and temporal augmentation, which is designed to capture subtle temporal changes in motion. The evaluation covers two publicly available datasets and a proprietary dataset and employs three neural network models. The results highlight the impact of temporal augmentation on model performance on the skeleton datasets, while exhibiting the nuanced impact of spatial augmentation. The findings underscore the importance of tailoring augmentation strategies to specific dataset characteristics and actions, providing novel perspectives for model selection in skeleton-based human action recognition tasks.


COVID-19 vaccine confidence index and economic uncertainty indices: empirical evidence from the payment-based system cryptocurrency market

Abstract: Purpose We investigated the association of COVID-19 indicators and economic uncertainty indices on payment-based system cryptocurrency (i.e. Bitcoin, Ripple and Dogecoin) returns. Design/methodology/approach We used an autoregressive distributed lag (ARDL) model for panel data and performed robustness checks by utilizing a random effect model (REM) and generalized method of moments (GMM). There are 25 most adopted cryptocurrency’s countries and the data spans from 22 March 2021 to 6 May 2022. Findings This research discovered four findings: (1) the index of COVID-19 vaccine confidence (VCI) recovers the economic and Bitcoin has become more attractive, causing investors to shift their investment from Dogecoin to Bitcoin. However, the VCI was revealed to be insignificant to Ripple; (2) during uncertain times, Bitcoin could perform as a diversifier, while Ripple could behave as a diversifier, safe haven or hedge. Meanwhile, the movement of Dogecoin prices tended to be influenced by public figures’ actions; (3) public opinion on Twitter and government policy changes regarding COVID-19 and economy had a crucial role in investment decision making; and (4) the COVID-19 variants revealed insignificant results to payment-based system cryptocurrency returns. Originality/value This study contributed to verifying the vaccine confidence index effect on payment-based system cryptocurrency returns. Also, we further investigated the uncertainty indicators impacting on cryptocurrency returns during the COVID-19 pandemic. Lastly, we utilized the COVID-19 variants as a cryptocurrency returns’ new determinant.


A Multibranch Deep Neural Network for the Superresolution of Solar Magnetograms

Abstract: Abstract The existing superresolution (SR) models for solar magnetograms are mostly borrowed from the SR models for natural images. They are less effective for processing solar magnetograms with a very large dynamic range and very rich image features. In this paper, a multibranch superresolution (MBSR) model is specially designed for solar magnetograms. First, we split a low-resolution magnetogram into a group of overlapping image patches, and classify them into three categories according to magnetic flux intensity, namely simple, medium, and complex. Then, image patches of each category are fed into the corresponding branch of the MBSR network, the lightweight branch for simple image patches and the heavyweight one for complex image patches. The advantage of such a strategy is twofold. On the one hand, active regions are allocated more computational resources to train a heavyweight branch more fully, while quiet regions are allocated fewer computational resources to train a lightweight branch for saving computational resources. On the other hand, a lightweight network with a simple nonlinear function is preferable to simple regions, while a heavyweight one may be underfitting. Additionally, to verify the effectiveness of the proposed model, a magnetic field structure similarity metric is proposed to measure the artifacts of the generated high-resolution (HR) magnetograms. Experimental results show that the proposed MBSR model generates HR magnetograms highly consistent with the HMI ones, and achieves the best performance over five objective metrics, including peak signal-to-noise ratio and structure similarity, etc.


Deep learning for cancer cell detection: do we need dedicated models?

Abstract: Abstract This article proposes a novel concept for a two-step Ki-67/lymphocytes classification cell detection pipeline on Ki-67 stained histopathological slides utilizing commonly available and undedicated, in terms of the medical problem considered, deep learning models. Models used vary in implementation, complexity, and applications, allowing for the use of a dedicated architecture depending on the physician’s needs. Moreover, generic models’ performance was compared with the problem-dedicated one. Experiments highlight that with relatively small training datasets, commonly used architectures for instance segmentation and object detection are competitive with a dedicated model. To ensure generalization and minimize biased sampling, experiments were performed on data derived from two unrelated histopathology laboratories.


Circumventing Stragglers and Staleness in Distributed CNN using LSTM

Abstract:
INTRODUCTION: Using neural networks for these inherently distributed applications is challenging and time-consuming. There is a crucial need for a framework that supports a distributed deep neural network to yield accurate results at an accelerated time.
 METHODS: In the proposed framework, any experienced novice user can utilize and execute the neural network models in a distributed manner with the automated hyperparameter tuning feature. In addition, the proposed framework is provided in AWS Sage maker for scaling the distribution and achieving exascale FLOPS. We benchmarked the framework performance by applying it to a medical dataset.
 RESULTS: The maximum performance is achieved with a speedup of 6.59 in 5 nodes. The model encourages expert/ novice neural network users to apply neural network models in the distributed platform and get enhanced results with accelerated training time. There has been a lot of research on how to improve the training time of Convolutional Neural Networks (CNNs) using distributed models, with a particular emphasis on automating the hyperparameter tweaking process. The study shows that training times may be decreased across the board by not just manually tweaking hyperparameters, but also by using L2 regularization, a dropout layer, and ConvLSTM for automatic hyperparameter modification.
 CONCLUSION: The proposed method improved the training speed for model-parallel setups by 1.4% and increased the speed for parallel data by 2.206%. Data-parallel execution achieved a high accuracy of 93.3825%, whereas model-parallel execution achieved a top accuracy of 89.59%.


Sequence-Aware Graph Neural Network Incorporating Neighborhood Information for Session-Based Recommendation

Abstract: Abstract Session-based recommendation is an important part of many e-commerce websites. Its purpose is to make recommendations based on the interaction behavior of anonymous users in a short period of time. Graph neural network can capture complex interactions in sessions, and they are a class of methods with better performance among existing session-based recommendation models. However, most existing models only learn item features for a single session. At the same time, GNNs are not good at capturing long-distance dependencies in a session, which leads to limited performance improvements for them. To address this deficiency, we propose sequence-aware graph neural network incorporating neighborhood information, named SAN-GNN. We construct a session graph and a neighborhood graph to learn item representations. For neighborhood graph, we propose a neighborhood Information extractor for the neighborhood graph to learn the neighbor information of nodes on the neighborhood graph. For the graph model of the session layer, we propose a session graph attention(SGA) module to learn the item representation of the target session. SGA uses Ta-LSTM to learn sequential dependencies in the target session and uses GCN with an integrated attention mechanism to learn node feature relevant to the target item. Exhaustive experiments on three public real-world datasets show that SAN-GNN outperforms the most advanced existing session-based method.


Hybrid time-spatial video saliency detection method to enhance human action recognition systems

Abstract: Abstract Since digital media has become increasingly popular, video processing has expanded in recent years. Video processing systems require high levels of processing, which is one of the challenges in this field. Various approaches, such as hardware upgrades, algorithmic optimizations, and removing unnecessary information, have been suggested to solve this problem. This study proposes a video saliency map based method that identifies the critical parts of the video and improves the system’s overall performance. Using an image registration algorithm, the proposed method first removes the camera’s motion. Subsequently, each video frame’s color, edge, and gradient information are used to obtain a spatial saliency map. Combining spatial saliency with motion information derived from optical flow and color-based segmentation can produce a saliency map containing both motion and spatial data. A nonlinear function is suggested to properly combine the temporal and spatial saliency maps, which was optimized using a multi-objective genetic algorithm. The proposed saliency map method was added as a preprocessing step in several Human Action Recognition (HAR) systems based on deep learning, and its performance was evaluated. Furthermore, the proposed method was compared with similar methods based on saliency maps, and the superiority of the proposed method was confirmed. The results show that the proposed method can improve HAR efficiency by up to 6.5% relative to HAR methods with no preprocessing step and 3.9% compared to the HAR method containing a temporal saliency map.


Bayesian active learning with model selection for spectral experiments

Abstract: Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments. Therefore, we proposed an active learning with model selection method using multiple parametric models as learning models. Important points for model selection and its parameter estimation were actively measured using Bayesian posterior distribution. The present study demonstrated the effectiveness of our proposed method for spectral deconvolution and Hamiltonian selection in X-ray photoelectron spectroscopy.


Underactuated unmanned surface vehicles formation of obstacle avoidance and assembly: A disturbed fluid-based solution

Abstract: Unmanned surface vehicles (USVs) are highly manoeuvrable and autonomous, and hold significant potential for both military and civilian applications, particularly in formation operations. However, because of their underactuated nature, USVs struggle to navigate in complex maritime conditions during formation. At present, most of the technology is devoted to Unmanned Areial Vehicles and ground robots; these methods cannot be well applied to underactuated USVs. Moreover, the rationality of local path planning decision-making for underactuated USVs formation is still lacking. This study proposes an interfered fluid dynamic system (IFDS)-based local path planning method, called USV-IFDS, specifically designed for the formation of underactuated USVs. This method incorporates the IFDS obstacle avoidance approach, while adapting it through modifications and the inclusion of the kinematic constraints of USVs, thereby enhancing its applicability to the maritime environment. By decomposing the flow field velocity vector and implementing a formation control strategy, we effectively address the challenges in forming underactuated USVs and enhance the efficiency of USV formation local path planning. The proposed formation technique is predicated on the highly robust virtual structure method. Simulations of formation local path planning indicate that our method produces smooth paths, therefore validating its practical applicability to underactuated USV formations.


A novel method for video enhancement under low light using BFR-SEQT technique

Abstract: As typical frame rates allow limited exposure time, camera-captured videos under low-light conditions often suffer from poor contrast and noise. Existing models failed to consider dark and light areas' boundary pixels and varying low-illuminated night videos' weather conditions for removing noise and enhancing contrast. Hence, the video's visual appearance under low-light is improved using BFR-SEQT. Primarily, a video is inputted and converted into frames. Also, colour space is converted from which static and dynamic pixels are detected regarding frame differences. Using LoG-MF and KF algorithms, noise is removed from which foreground and background are separated using SD-FCM. Motion is estimated and features are extracted to enhance contrast. Then, pixel grouping using LC-GOA is done. Lastly, enhanced outputs from both phases are reconstructed and enhanced video is obtained. The proposed model improves video quality by enhancing contrast and removing noise with high PSNR values (27.6589db and 24.5478db), thus outperforming conventional methods.


Optimizing the Educational Potential of Virtual Reality: The Effects of Virtual Reality and Pop Quizzes on Spatial Presence and Efficacy Perceptions

Abstract: Reflecting the trend of virtual reality (VR) studies in crisis and disaster communication, this study designed VR content on fire evacuation and aimed to investigate the effects of VR for educational purposes on perceptual and cognitive factors. Using a 2 (viewing condition: video vs. VR) × 2 (quiz: presence vs. absence) between-subjects design, an experiment was conducted with 176 university students in South Korea. Findings revealed that participants who were exposed to VR (compared with video) experienced greater spatial presence and, in turn, had higher response efficacy and self-efficacy. The inclusion of pop quizzes in the material hindered the evaluation of the direct and indirect effects of VR. The findings of this study can contribute to expanding the theoretical framework of VR research and identifying learning-related factors to develop more effective educational materials related to fire evacuation.


The effects of reading strategy training on students’ reading strategy use and critical reading ability in EFL reading classes

Abstract: This research examined the impact of explicit reading strategy training on Ethiopian university students’ reading strategy use and critical reading ability in the English as a Foreign Language (EFL) context. Two intact classes, a control group (n = 35) and an experimental group (n = 45), participated in the study. The experimental group received 12 weeks of reading strategy training integrated into their English reading skills I classes. A mixed-method quasi-experimental design was employed, using a reading strategy questionnaire, a critical reading ability test, and a think-aloud technique for data collection. Students who received reading strategy instruction demonstrated notable improvements in reading strategy use and critical reading ability. The independent sample t-test and a paired sample t-test showed significant differences in both areas. Qualitative data highlighted compensation and memory strategies, which were not captured quantitatively. The think-aloud data revealed the predominant use of cognitive and metacognitive strategies, along with affective, social, memory, and compensation strategies. Cognitive strategies deepen understanding, and metacognitive techniques enhance interpretation in reading. These findings shed light on the responses of Ethiopian university students to explicit reading strategy training, emphasizing its significance in improving reading strategy use and critical reading ability in an EFL context.


An effective electricity worker identification approach based on Yolov3-Arcface

Abstract: To address the issues of low efficiency and high complexity of detection models for electric power workers in distribution rooms, the electric power worker identification approach is proposed. The ArcFace loss function is used as the coordinate regression loss of the target box. According to the score, the template box with the highest score is selected for prediction, which speeds up the rate of convergence. Dimensional clustering is used to set template boxes for bounding box prediction. The experimental results show that the improved YOLOv3 is a high-performance and lightweight model. The electric power worker identification approach proposed in this paper has a high-speed recognition process, accurate recognition results. The effectiveness of the approach is verified with better detection performance and robustness.


NERSkill.Id: Annotated Dataset of Indonesian's Skill Entity Recognition

Abstract: NERSkill.Id is a manually annotated named entity recognition (NER) dataset focused on skill entities in the Indonesian language. The dataset comprises 418.868 tokens, each accompanied by corresponding tags following the BIO scheme. Notably, 15,51% of these tokens represent named entities, falling into three distinct categories: hard skill, soft skill, and technology. To construct this dataset, data were gathered from a job portal and subsequently processed using open-source libraries. Given the scarcity of annotated corpora for Indonesian, NERSkill.Id fills a significant void and offers immense value to multiple stakeholders. NLP researchers can harness the dataset's richness to advance skill entity recognition technology in the Indonesian language. Companies and recruiters can benefit by employing NERSkill.Id to enhance talent acquisition and job matching processes through accurate skill identification. Furthermore, educational institutions can leverage the dataset to adapt their courses and training programs to meet the evolving needs of the job market. This dataset can be effectively utilized for training and evaluating named entity recognition systems, empowering advancements in skill entity recognition for the Indonesian language.


Effect of leadership styles on turnover intention among staff nurses in private hospitals: the moderating effect of perceived organizational support

Abstract: Leadership styles have often been proven to support employees in performing their duties better and with more efficiency while enabling them to have extended organizational tenures. Staff nurses are an essential resource of hospitals to ensure proper administration and quality patient health care. The study aims to determine how transformational and authentic leadership styles affect the staff nurses' turnover intention in private hospitals. In addition, it also finds the moderating effect of perceived organizational support. An explanatory quantitative research design with a cross-sectional investigation and a stratified sampling strategy was used for the study. Data from 296 nurses from the eight chosen private hospitals in the Kingdom of Bahrain were gathered using a questionnaire with 24 items. Smart-PLS was employed to conduct PLS-SEM (partial least squares structural equation modeling) to measure direct and indirect effects. The result indicates that transformational, authentic leadership styles and perceived organizational support significantly negatively affect nurses' turnover intention. The study confirms the negative moderating effect of perceived organizational support between transformational leadership and turnover intention and the positive moderating effect of perceived organizational support between authentic leadership and turnover intention. Managers should concentrate on the leadership style to avoid its impact on turnover intention. By considering human resource practices such as communication and training strategies to cope with the negative effect of turnover intention, organizations can enhance employee engagement, improve job satisfaction, and foster a more stable and productive work environment. The present research revealed the adverse impact of turnover intention within hospitals by examining its association with leadership styles. The research made a significant contribution to the existing literature by delving into the impact of leadership styles on turnover intention, focusing on the moderating effect of perceived organizational support. The study's findings shed light on the intricate relationship between leadership practices and employee turnover, providing valuable insights for both scholars and practitioners in the field. The study used a cross-sectional design to collect data and ensured the absence of standard method variance. The research enhanced the social dominance theory (SDT) by examining how perceived organizational support moderates the relationship between leadership styles and turnover intention.


Personalized bundle recommendation using preference elicitation and the Choquet integral

Abstract: Bundle recommendation aims to generate bundles of associated products that users tend to consume as a whole under certain circumstances. Modeling the bundle utility for users is a non-trivial task, as it requires to account for the potential interdependencies between bundle attributes. To address this challenge, we introduce a new preference-based approach for bundle recommendation exploiting the Choquet integral. This allows us to formalize preferences for coalitions of environmental-related attributes, thus recommending product bundles accounting for synergies among product attributes. An experimental evaluation of a dataset of local food products in Northern Italy shows how the Choquet integral allows the natural formalization of a sensible notion of environmental friendliness and that standard approaches based on weighted sums of attributes end up recommending bundles with lower environmental friendliness even if weights are explicitly learned to maximize it. We further show how preference elicitation strategies can be leveraged to acquire weights of the Choquet integral from user feedback in terms of preferences over candidate bundles, and show how a handful of queries allow to recommend optimal bundles for a diverse set of user prototypes.


Model of adoption of virtual tools by university students in the context of an emerging economy

Abstract: Introduction The impact of COVID-19 on higher education has necessitated a reevaluation of teaching methodologies, student engagement, and the integration of information and communication technologies (ICT) as pedagogical tools. This shift creates opportunities for innovation and collaborative learning environments among students. Methods This study aims to investigate the adoption of virtual tools by university students in Medellín. A sample of 373 students was surveyed using a quantitative approach with a descriptive scope. Factor analysis techniques were employed utilizing SPSS software to assess the associative relationships among variables and hypotheses within the framework of the Technology Acceptance Model (TAM). Results The analysis revealed significant correlations, particularly between perceived ease of use and perceived usefulness. Notably, technological devices such as laptops and smartphones were identified as valuable tools for educational purposes, contributing to a positive attitude towards their utilization among students. Discussion The findings underscore the importance of students’ perceptions in shaping their attitudes towards ICT tools for teaching and learning. Understanding these perceptions is crucial for designing effective pedagogical strategies, particularly in crisis situations such as the COVID-19 pandemic, where virtual tools play a vital role in maintaining educational continuity. This study contributes to the ongoing discourse on the integration of virtual tools in higher education, highlighting avenues for further research and practical implications for educators and policymakers.


Modeling correlated pairs of mammogram images

Abstract: Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.


On-manifold projected gradient descent

Abstract: This study provides a computable, direct, and mathematically rigorous approximation to the differential geometry of class manifolds for high-dimensional data, along with non-linear projections from input space onto these class manifolds. The tools are applied to the setting of neural network image classifiers, where we generate novel, on-manifold data samples and implement a projected gradient descent algorithm for on-manifold adversarial training. The susceptibility of neural networks (NNs) to adversarial attack highlights the brittle nature of NN decision boundaries in input space. Introducing adversarial examples during training has been shown to reduce the susceptibility of NNs to adversarial attack; however, it has also been shown to reduce the accuracy of the classifier if the examples are not valid examples for that class. Realistic “on-manifold” examples have been previously generated from class manifolds in the latent space of an autoencoder. Our study explores these phenomena in a geometric and computational setting that is much closer to the raw, high-dimensional input space than what can be provided by VAE or other black box dimensionality reductions. We employ conformally invariant diffusion maps (CIDM) to approximate class manifolds in diffusion coordinates and develop the Nyström projection to project novel points onto class manifolds in this setting. On top of the manifold approximation, we leverage the spectral exterior calculus (SEC) to determine geometric quantities such as tangent vectors of the manifold. We use these tools to obtain adversarial examples that reside on a class manifold, yet fool a classifier. These misclassifications then become explainable in terms of human-understandable manipulations within the data, by expressing the on-manifold adversary in the semantic basis on the manifold.


A novel medical image data protection scheme for smart healthcare system

Abstract: Abstract The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks.


Automatic speech recognition and the transcription of indistinct forensic audio: how do the new generation of systems fare?

Abstract: This study provides an update on an earlier study in the “Capturing Talk” research topic, which aimed to demonstrate how automatic speech recognition (ASR) systems work with indistinct forensic-like audio, in comparison with good-quality audio. Since that time, there has been rapid technological advancement, with newer systems having access to extremely large language models and having their performance proclaimed as being human-like in accuracy. This study compares various ASR systems, including OpenAI’s Whisper, to continue to test how well automatic speaker recognition works with forensic-like audio. The results show that the transcription of a good-quality audio file is at ceiling for some systems, with no errors. For the poor-quality (forensic-like) audio, Whisper was the best performing system but had only 50% of the entire speech material correct. The results for the poor-quality audio were also generally variable across the systems, with differences depending on whether a .wav or .mp3 file was used and differences between earlier and later versions of the same system. Additionally, and against expectations, Whisper showed a drop in performance over a 2-month period. While more material was transcribed in the later attempt, more was also incorrect. This study concludes that forensic-like audio is not suitable for automatic analysis.


Design of Human-Machine Integration System to Meet Diverse Interactive Tasks

Abstract: A flexible production model in which humans and robots collaborate has become an urgent need for industrial production. Therefore, research on human-machine integration systems has received more and more attention in recent years. Human-computer interaction is widely used in industrial production and entertainment life, such as industrial control, VR games, etc. In this article, we designed and implemented a human-machine fusion system that meets different interactive tasks. Human movement estimation and robot interactive control are the key technologies. First, we propose a human posture solution method based on information fusion of multiple cameras and inertial measurement units. This method constructs an optimization problem, fuses the 2D joint detection information under multiple cameras and the measurement information of the worn inertial measurement unit, and optimizes the estimation of human kinematic posture, which improves the problems of incomplete posture information and sensitivity to noise under a single sensor. Therefore, this method improves the accuracy of pose estimation. Secondly, combining the kinematic characteristics of the robot and the characteristics of human-computer interaction, we designed a robot control strategy based on target point tracking and model predictive control, so that the robot can adapt to dynamic environments and different interaction needs by adjusting control parameters. At the same time, our design ensures the safety of robots and operators. Finally, we conducted human-computer interaction experiments such as action following, item delivery, and active obstacle avoidance. The experimental results show the effectiveness and reliability of the designed robot interaction system in a human-machine fusion environment.


Deep integrated fusion of local and global features for cervical cell classification

Abstract: Cervical cytology image classification is of great significance to the cervical cancer diagnosis and prognosis. Recently, convolutional neural network (CNN) and visual transformer have been adopted as two branches to learn the features for image classification by simply adding local and global features. However, such the simple addition may not be effective to integrate these features. In this study, we explore the synergy of local and global features for cytology images for classification tasks. Specifically, we design a Deep Integrated Feature Fusion (DIFF) block to synergize local and global features of cytology images from a CNN branch and a transformer branch. Our proposed method is evaluated on three cervical cell image datasets (SIPaKMeD, CRIC, Herlev) and another large blood cell dataset BCCD for several multi-class and binary classification tasks. Experimental results demonstrate the effectiveness of the proposed method in cervical cell classification, which could assist medical specialists to better diagnose cervical cancer.


Attitude and behavioral intention for using metaverse in education: learner’s perspective

Abstract: Purpose This study aims to understand and analyze the aspects influencing students’ attitudes and behavior toward the use of metaverse in education. The metaverse is currently viewed as technology with immense prospects. However, the practice of the metaverse for educational motives is rarely deliberated. Design/methodology/approach To assess the effect of the metaverse on students' knowledge and use of resources, general interests and attitudes toward the metaverse in education, a survey was conducted. The collected data were analyzed using a confirmatory factor analysis (CFA) in the first phase to address the various validity parameters. In the second phase, path analysis of the model was performed using structural equation modeling (SEM). Findings The study investigated how students intended to behave while using the metaverse for learning. The attitude toward adopting metaverse as technology is influenced by perceived utility and simplicity of use. This leads to behavioral intention as well. Studies reveal that the aspect of perceived usefulness is considered to be more significant in assessing the intention of use. Research limitations/implications This quantitative study contributes to the literature on metaverse, which is in the growing stage. In the educational sector, the existing studies are scarce; hence, the addition to the literature on metaverse is quite significant in the education domain. Practical implications The study benefits the students and the academicians because metaverse is largely considered an integral part of technology platforms, which has to be included in the learning systems eventually. There are few courses where the use of metaverse is already initiated at an introductory level, thus opening a broad spectrum of opportunities at all levels. It can provide scholars access to a massive array of resources, including multimedia presentations, interactive objects that support the delivery of lessons, videos, images and audio recordings. Originality/value This study adds to the existing literature by examining the impact of metaverse in education. The research focused on the students pursuing higher education who were mostly aware of metaverse and were open to the idea of learning and understanding through technology inclusion.


Physiological Data for User Experience and Quality of Experience: A Systematic Review (2018–2022)

Abstract: The evaluation of human responses in multimedia experiences using physiological data has a well-established presence in the academic literature. However, this field is currently undergoing transformative changes, driven by the accessibility of diverse and cost-effective devices, innovative software analysis methods, and the emergence of novel application domains such as Virtual and Augmented Reality and mulsemedia. To address the imperative of contextualizing these evolving trends in a contemporary context, this paper presents a systematic review with the objective of delineating the array of physiological data utilized in assessing Quality of Experience (QoE) and User Experience (UX) in multimedia studies. It also examines the devices employed for data collection and the analytical techniques applied to interpret the acquired data. While our review exposes both constraints and promising discoveries in these domains, it also emphasizes the escalating significance and practicality of leveraging physiological data in user assessments, especially as the boundaries between the physical and digital domains continue to blur.


Transformative Virtual Reality Games for Adaptive Sports Training: Exploring the Perceptions of Individuals with Mobility Impairments

Abstract: The rise of virtual reality (VR) technology has opened up new opportunities for individuals with mobility impairments to participate in adaptive sports training. This paper reports on a survey study of individuals with mobility impairments to explore their expectations and needs when designing and developing VR games for adaptive sports training. Results indicate that participants are highly motivated to engage in VR adaptive sports training due to its promising potential and have specific requirements for game design and development, including ease of use, realistic gameplay, and accessible controls. This study highlights the transformative potential of VR technology in providing equal opportunities for individuals with mobility impairments to participate in adaptive sports training and promotes the development of inclusive VR games for disable users. Our findings have actionable implications for game designers, developers, and researchers, as they underscore the importance of accessibility and inclusiveness in the design and development of VR games for adaptive sports training.


Artificial intelligence and predictive marketing: an ethical framework from managers’ perspective

Abstract: Purpose Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area. Design/methodology/approach The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data. Findings Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior. Originality/value The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.


Vernacular constructions of the relationship between freedom of speech and (potential) hate speech: The case of Finland

Abstract: Abstract The blurred distinction between freedom of expression and hate speech in ever more polarised public debates across Europe and beyond has prompted research on hate speech, particularly focusing on right‐wing populist politicians. Little is known, however, about how this distinction is construed by ordinary citizens. Deploying the concept of retrogressive mobilisation, this study examines how cases of (potential) political hate speech – one targeting racialised minorities, the other the LGBTQ+ community – are interpreted and negotiated by ordinary citizens through their comments on online news in Finland. Deploying a critical discursive psychological approach, we analyse the vernacular meanings that ordinary citizens attach to the notions of political hate speech, thereby highlighting the dynamic relationship between political and everyday discourse. We evidence three discursive constructions of the relationship between freedom of expression and (potential) hate speech. In these constructions, the same rhetorical resources, especially the liberal arguments of equality and freedom of expression, were deployed to service the opposite discursive functions – that is, for both ‘liberal’ and ‘illiberal’ ends – to condemn and justify discrimination against minoritised groups. Our study contributes to the social psychological understanding of contemporary hate speech and builds a bridge between social psychology and the more recent field of anti‐gender research.


A Novel Tracking Algorithm Based on Waveform Selection for Maneuvering Targets in Clutter

Abstract: Abstract The cognition radar utilizes the dynamic waveform configuration to adapt to the environment to improve tracking performance. A novel tracking algorithm based on waveform selection is proposed to address the maneuvering aircraft tracking problem in clutter. Based on the modified current statistical (MCS) model, the modified probabilistic data association filter (MPDAF) is integrated with the square-root cubature Kalman filter (SCKF) to deal with the nonlinear measurement and the clutter. Additionally, the waveform library is established by applying the fractional Fourier transform (FRFT) to a base waveform, and a direct method is proposed to select the optimal waveform to minimize the posterior state error covariance matrix. The simulation results showed that, compared with the filters with the fixed waveform as well as the state-of-the-art algorithms, the proposed algorithm achieved higher estimation precision and lower track loss percentage while maintaining a low computational burden.


Self‐attention residual network‐based spatial super‐resolution synthesis for time‐varying volumetric data

Abstract: Abstract In the field of scientific visualization, the upscaling of time‐varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large‐scale, time‐varying simulation data. This paper proposes self‐attention residual network‐based spatial super‐resolution (SARN‐SSR), a spatial super‐resolution model based on self‐attention residual networks that can generate time‐varying data with temporal coherence. SARN‐SSR consists of two components: a generator and a discriminator. The generator takes the low‐resolution volume sequences as the input and gives the corresponding high‐resolution volume sequences as the output. The discriminator takes both synthesized and real high‐resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN‐SSR, four sets of time‐varying volume datasets are applied from scientific simulation. In addition, SARN‐SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning‐based techniques and one traditional technique. The experimental results show that by using this method, the closest time‐varying data to the ground truth can be obtained.


Calculation and Analysis of Specific Losses of Active Power in Overhead Power Lines due to Corona in View of Climatic Data

Abstract: . It is a very challenging task to correctly use weather data to calculate the active power loss due to corona in overhead power lines. One of the significant reasons for this was the problem of accuracy of determining weather conditions and the use of the forecast for calculating extended overhead lines. In the USSR (Union of Soviet Socialist Republics), based on field tests and experimental data, recommendations were developed for accounting for losses of electric energy due to corona and interference in overhead lines, the average values of which were given in the relevant instruction for the regulation and justification of the norm of electricity consumption for its transmission over electric networks. According to this regulatory document, the specific cost of active power due to corona in overhead lines, averaged by design, was determined depending on weather conditions, which were divided into four groups. In this paper, the effect of active power losses on corona is estimated using various factors on the example of high-voltage overhead lines in the main electrical networks of the Republic of Belarus of various rated voltages. Dependences of active power losses due to corona have been constructed and modeled, taking into account different climatic and weather conditions observed in the country. The determination of metrological parameters was based on the analysis of weather data and patterns of types of weather conditions. In order to improve the accuracy of calculating the specific losses of active power due to corona, all possible weather conditions were considered, as well as their combinations. Afterwards, the data on weather conditions were processed and their types were reduced to one of the four existing types of weather. A method for determining electric power losses due to corona in overhead lines based on the average relative air density and geometric parameters of the predominant section of overhead lines has been proposed and presented. It is shown that the usage of exclusively rated voltage can cause errors in determining both the specific and actual values of active power losses for corona.


Modelling Exchange with Blockchain for the Collaborative Design of a Building Envelope in BIM

Abstract: Introduction The main challenge in using building information modelling (BIM) is the centralised collaborative framework in the various design software ( e.g ., AutoCAD, Revit, RHINO, and ArchiCAD) used by professionals across various disciplines, which causes difficulties during data exchange. Methods The existing framework may increase the project lead time, require more effort, and result in design conflicts and misunderstandings. Thus, this paper has proposed a dynamic information storage with blockchain system (DISBS) using a physical curtain wall project for practical implementation. In addition, this paper has further discussed the linkages and verified and analysed the effectiveness of the proposed methodology. Results Through the DISBS proposed in this research, the characteristics and achievable functions of blockchain technology are presented. Various applications for the field of construction engineering are highly developable within this research. Conclusion The DISBS can improve not only the management quality of construction projects, but also promote mutual trust in the construction industry and optimize architecture, engineering, and construction (AECO) productivity.


Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence

Abstract: Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data. Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation. Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples. Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.


Geometric dual and sum‐rank minimal codes

Abstract: Abstract The main purpose of this paper is to further study the structure, parameters and constructions of the recently introduced minimal codes in the sum‐rank metric. These objects form a bridge between the classical minimal codes in the Hamming metric, the subject of intense research over the past three decades partly because of their cryptographic properties, and the more recent rank‐metric minimal codes. We prove some bounds on their parameters, existence results, and, via a tool that we name geometric dual, we manage to construct minimal codes with few weights. A generalization of the celebrated Ashikhmin–Barg condition is proved and used to ensure the minimality of certain constructions.


Optimization of Internet of Things Remote Desktop Protocol for Low-Bandwidth Environments Using Convolutional Neural Networks

Abstract: This paper discusses optimizing desktop image quality and bandwidth consumption in remote IoT GUI desktop scenarios. Remote desktop tools, which are crucial for work efficiency, typically employ image compression techniques to manage bandwidth. Although JPEG is widely used for its efficiency in eliminating redundancy, it can introduce quality loss with increased compression. Recently, deep learning-based compression techniques have emerged, challenging traditional methods like JPEG. This study introduces an optimized RFB (Remote Frame Buffer) protocol based on a convolutional neural network (CNN) image compression algorithm, focusing on human visual perception in desktop image processing. The improved RFB protocol proposed in this paper, compared to the unoptimized RFB protocol, can save 30-80% of bandwidth consumption and enhances remote desktop image quality, as evidenced by improved PSNR and MS-SSIM values between the remote desktop image and the original image, thus providing superior desktop image transmission quality.


Diving into Clarity: Restoring Underwater Images using Deep Learning

Abstract: Abstract In this paper we propose a learning-based restoration approach to learn the optimal parameters for enhancing the quality of different types of underwater images and apply a set of intensity transformation techniques to process raw underwater images. The methodology comprises two steps. Firstly, a Convolutional Neural Network (CNN) Regression model is employed to learn enhancing parameters for each underwater image type. Trained on a diverse dataset, the CNN captures complex relationships, enabling generalization to various underwater conditions. Secondly, we apply intensity transformation techniques to raw underwater images. These transformations collectively compensate for visual information loss due to underwater degradation, enhancing overall image quality. In order to evaluate the performance of our proposed approach, we conducted qualitative and quantitative experiments using well-known underwater image datasets (U45 and UIEB), and using the proposed challenging dataset composed by 276 underwater images from the Amazon region (AUID). The results demonstrate that our approach achieves an impressive accuracy rate in different underwater image datasets. For U45 and UIEB datasets, regarding PSNR and SSIM quality metrics, we achieved 26.967, 0.847, 27.299 and 0.793, respectively. Meanwhile, the best comparison techniques achieved 26.879, 0.831, 27.157 and 0.788, respectively.


Computational investigation and validation of new MEXICO experiment

Abstract: A computational investigation of New MEXICO test cases operating under axial flow conditions is reported. Three wind speed cases (10, 15, 24 m/s) corresponding to three different tip speed ratios (10, 6.67, 4.17) when the turbine operates at 425.1 rpm were considered. ANSYS CFX 2021R1 was employed to perform simulations using Single Reference Frame (SRF) and Multiple Reference Frame (MRF) approaches. The flow field is computed by solving unsteady Reynolds Averaged Navier-Stokes (uRANS) equations coupled with SST k-ω turbulence model and Gamma-Theta transition model. Validation involved comparing CFD-predicted integral quantities, static pressure distributions, and loads with corresponding experimental values demonstrating reasonably good agreement at all three wind speeds. Overall, SRF exhibited slightly better wake predictions (hypothetical), while MRF predictions were closer to measurements for integral quantities, static pressure and loads. This study demonstrates the utility of uRANS-based 3D CFD computations in wind turbine aerodynamics studies.


A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

Abstract: In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.


Enhanced mechanism to prioritize the cloud data privacy factors using AHP and TOPSIS: a hybrid approach

Abstract: Abstract Cloud computing is a new paradigm in this new cyber era. Nowadays, most organizations are showing more reliability in this environment. The increasing reliability of the Cloud also makes it vulnerable. As vulnerability increases, there will be a greater need for privacy in terms of data, and utilizing secure services is highly recommended. So, data on the Cloud must have some privacy mechanisms to ensure personal and organizational privacy. So, for this, we must have an authentic way to increase the trust and reliability of the organization and individuals The authors have tried to create a way to rank things that uses the Analytical Hieratical Process (AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Based on the result and comparison, produce some hidden advantages named cost, benefit, risk and opportunity-based outcomes of the result. In this paper, we are developing a cloud data privacy model; for this, we have done an intensive literature review by including Privacy factors such as Access Control, Authentication, Authorization, Trustworthiness, Confidentiality, Integrity, and Availability. Based on that review, we have chosen a few parameters that affect cloud data privacy in all the phases of the data life cycle. Most of the already available methods must be revised per the industry’s current trends. Here, we will use Analytical Hieratical Process and Technique for Order Preference by Similarity to the Ideal Solution method to prove that our claim is better than other cloud data privacy models. In this paper, the author has selected the weights of the individual cloud data privacy criteria and further calculated the rank of individual data privacy criteria using the AHP method and subsequently utilized the final weights as input of the TOPSIS method to rank the cloud data privacy criteria.


Interval Type-2 Mutual Subsethood Cauchy Fuzzy Neural Inference System (IT2MSCFuNIS)

Abstract: Abstract An interval type-2 (IT2) mutual subsethood Cauchy fuzzy neural inference system has been proposed in this paper. The network architecture consists of 3-layers with all connection weights being IT2 Cauchy fuzzy membership functions (CMFs). The crisp inputs to the system are fuzzified into IT2CMFs with fixed centers and uncertain spreads. The hidden layer represents the rule-based knowledge. The firing degree of the antecedent part of each rule at the hidden layer is computed by aggregating the product of the mutual subsethood similarity measures between the inputs and the connection weights. A volume defuzzification is used to compute the numeric output. A gradient descent back-propagation algorithm is used to train the model. The novelty of the proposed model is threefold. First, is enriching the theory of the mutual subsethood fuzzy neural models by adopting the Cauchy membership function (CMF) as another powerful fuzzy basis function (FBF) rather than the classical choice of Gaussian fuzzy membership functions (GMFs). Second, is the success of computing the mutual subsethood similarity measure between the IT2CMFs and all the model parameters’ updating equations in analytic closed-form formulas, not numerically or approximately. Third, is the ability to extract the type-1 (T1) mutual subsethood Cauchy fuzzy neural inference system (T1MSCFuNIS) with all its analytic closed-form formulas directly as a special case from the general formulas of IT2MSCFuNIS model. Such a novelty makes the proposed model a concrete and effective development of the theory of mutual subsethood fuzzy neural models. Both IT2MSCFuNIS and T1MSCFuNIS models have been tested using different examples from the domains of function approximation, classification, and prediction. The results ensure the efficacy of both models compared with other models reported in the literature.


GeoViewMatch: A Multi-Scale Feature-Matching Network for Cross-View Geo-Localization Using Swin-Transformer and Contrastive Learning

Abstract: Cross-view geo-localization aims to locate street-view images by matching them with a collection of GPS-tagged remote sensing (RS) images. Due to the significant viewpoint and appearance differences between street-view images and RS images, this task is highly challenging. While deep learning-based methods have shown their dominance in the cross-view geo-localization task, existing models have difficulties in extracting comprehensive meaningful features from both domains of images. This limitation results in not establishing accurate and robust dependencies between street-view images and the corresponding RS images. To address the aforementioned issues, this paper proposes a novel and lightweight neural network for cross-view geo-localization. Firstly, in order to capture more diverse information, we propose a module for extracting multi-scale features from images. Secondly, we introduce contrastive learning and design a contrastive loss to further enhance the robustness in extracting and aligning meaningful multi-scale features. Finally, we conduct comprehensive experiments on two open benchmarks. The experimental results have demonstrated the superiority of the proposed method over the state-of-the-art methods.


Artificial Intelligence in Hospitality and Tourism: Insights From Industry Practices, Research Literature, and Expert Opinions

Abstract: Given that artificial intelligence (AI) is significantly transforming businesses, it is crucial to examine how AI will change the future of the hospitality and tourism industry. By integrating multiple data sources (i.e., practitioner literature, research literature, and expert opinions), we suggest three trends constituting opportunities and challenges (AI applications in different business sectors, primary AI functions, emerging AI topics), three possible themes of change (adoption and acceptance, operations management, AI in marketing), as well as four directions for future research (AI interaction, AI and organizational decision making, organizational implications, and managerial issues). Our findings present a detailed picture of AI development and applications along with predictions regarding its place in the industry. Finally, we outline a research agenda that addresses key issues for stakeholders in hospitality and tourism: individuals, including customers and employees; organizations and businesses; and public policymakers and governments.


Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot

Abstract: Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV's flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR's end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research.


Readiness of future teachers to work at school: Review of theoretical and empirical research

Abstract: Introduction. The current review article studies and systematises modern knowledge about the readiness of future teachers to work at school. The relevance of the study is due to the unwillingness of graduates of pedagogical universities to work in their speciality, the departure of novice teachers from the profession, and the increase in the number of open vacancies. The speed of ongoing changes, information and technological progress, the need to carry out online education and use artificial intelligence make it necessary to systematise the results of modern research. A review of various scientific fields allows us to take a step towards interdisciplinarity and describe different points of view in theoretical and empirical research. Moreover, it is important to answer the question of the representation of concepts and models of readiness, as well as to investigate the areas of further research directions. Aim. The present research aims to review and systematise international and Russian theoretical and empirical studies on the readiness of future teachers to work at school. Methodology and research methods. The methodology of the study was a descriptive (semi-systematic) approach to the review of theoretical and empirical studies. The search strategy consisted of the following stages: (1) identification of research questions; (2) search for relevant studies; (3) selection and exclusion for analysis; (4) data comparison; (5) generalisation and presentation. The methods of systematic search, comparative and content analysis were employed. The search for English-language publications was conducted in World Scientific, Scopus and Elsevier (ScienceDirect), which are in open access (open access&open archive). A total of 531 sources were identified. Then, by the Prisma method, 119 articles were selected, with a search depth from 2016 to 2023. The analysis of Russian-language articles was carried out in the scientific electronic library eLIBRARY.RU. A set of words “readiness&future teachers&work at school” was used for the search; as a result, 120 sources were obtained. The selected articles were analysed from the point of view of concepts, directions, models and factors of readiness for professional pedagogical activity and work at school. Results. The number of studies on this topic has been growing since 2008, with the highest values observed in 2016 and 2020. In 54.6 % of publications, theoretical models are considered. It is revealed that the concept of “readiness” of a graduate of a pedagogical university/college or already a teacher to work at school significantly depends on the field of scientific knowledge within which this term is considered. The thematic mismatch of Russian and foreign studies has been established. 39.7 % of Russian studies are devoted to professional competencies, and every fourth foreign article (25.7 %) focuses on educational outcomes. The most frequent methods of data collection in empirical studies are interviews, surveys and experiments. External and internal factors of readiness are revealed. External ones are the content of the educational programme at a pedagogical university or college, the duration of training and the conditions for passing pedagogical practice. Internal factors include the choice of specialisation in accordance with individual preferences, knowledge of pedagogical support resources and modern teaching technologies, options, and features of the school context. Scientific novelty. For the first time, a systematic review of international and Russian studies on this topic has been conducted. A unique contribution to the field of research is the resulting database of publications in this area, which is publicly available for researchers and other interested parties (certificate of state registration of the database № 2023622782). The results obtained make it possible to develop a comprehensive interdisciplinary model of future teachers’ readiness to work at school, considering all the changes and challenges of an information and technologically advanced society. Practical significance is due to the possibility of using the results obtained in the development of educational programmes in pedagogical training. Based on the data obtained, future researchers and teachers can conduct a meta-analysis, qualitative and quantitative assessment of readiness for professional pedagogical activity. The research outcomes make it possible to conduct further research and evaluate the author’s readiness models for completeness, adequacy, and validity.


Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety

Abstract: Abstract Explainability and Safety engender trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyze data and knowledge with statistical and symbolic AI methods relevant to the AI application––neither alone will do. Consequently, we argue and seek to demonstrate that the NeuroSymbolic AI approach is better suited for making AI a trusted AI system. We present the CREST framework that shows how C onsistency, R eliability, user‐level E xplainability, and S afety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well‐being. This article focuses on Large Language Models (LLMs) as the chosen AI system within the CREST framework. LLMs have garnered substantial attention from researchers due to their versatility in handling a broad array of natural language processing (NLP) scenarios. As examples, ChatGPT and Google's MedPaLM have emerged as highly promising platforms for providing information in general and health‐related queries, respectively. Nevertheless, these models remain black boxes despite incorporating human feedback and instruction‐guided tuning. For instance, ChatGPT can generate unsafe responses despite instituting safety guardrails. CREST presents a plausible approach harnessing procedural and graph‐based knowledge within a NeuroSymbolic framework to shed light on the challenges associated with LLMs.


Religiosity and financial development in Africa: evidence from panel quantile regression

Abstract: As secularism is increasing and religious diversity is regaining momentum worldwide, it is important to understand how systemic religious differences have influenced financial development in Africa. To this end, this study seeks to investigate the impact of religiosity on financial development in Africa by using freedom of religion to proxy religiosity and domestic credit to the private sector to proxy financial development covered from 2000 to 2020. The results from the panel quantile regression show that religiosity is negatively associated with the use of financial services across the quantile, whereas freedom of association, assembly, and civil society, and security and safety are marginally stronger within the lower, middle, and upper quantiles. The findings underscore that, fundamental human rights exert significant influence on financial development in Africa. Our findings contribute to literature by expanding knowledge on the role of personal freedom on economic activities hence, African governments and policymakers constitutionalize freedom as a fundamental human right, as freedom matters for financial development in Africa.


Human-Centered Explanations: Lessons Learned from Image Classification for Medical and Clinical Decision Making

Abstract: Abstract To date, there is no universal explanatory method for making decisions of an AI-based system transparent to human decision makers. This is because, depending on the application domain, data modality, and classification model, the requirements for the expressiveness of explanations vary. Explainees, whether experts or novices (e.g., in medical and clinical diagnosis) or developers, have different information needs. To address the explanation gap, we motivate human-centered explanations and demonstrate the need for combined and expressive approaches based on two image classification use cases: digital pathology and clinical pain detection using facial expressions. Various explanatory approaches that have emerged or been applied in the three-year research project “Transparent Medical Expert Companion” are shortly reviewed and categorized in expressiveness according to their modality and scope. Their suitability for different contexts of explanation is assessed with regard to the explainees’ need for information. The article highlights open challenges and suggests future directions for integrative explanation frameworks.


AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography

Abstract: Abstract The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) focuses on creating trustworthy AI for a variety of environmental and Earth science phenomena. AI2ES includes leading experts from AI, atmospheric and ocean science, risk communication, and education, who work synergistically to develop and test trustworthy AI methods that transform our understanding and prediction of the environment. Trust is a social phenomenon, and our integration of risk communication research across AI2ES activities provides an empirical foundation for developing user‐informed, trustworthy AI. AI2ES also features activities to broaden participation and for workforce development that are fully integrated with AI2ES research on trustworthy AI, environmental science, and risk communication.


Web Semantic-Based Robust Graph Contrastive Learning for Recommendation via Invariant Learning

Abstract: The use of contrastive learning (CL) in recommendation has advanced significantly. Recently, some works use perturbations in the embedding space to obtain enhanced views of nodes. This makes the representation distribution of nodes more even and then improve recommendation effectiveness. In this article, the authors provide an explanation on the role of added noises in the embedding space from the perspective of invariant learning and feature selection. Guided by this thinking, the authors devise a more reasonable method for generating random noises and put forward web semantic based robust graph contrastive learning for recommendation via invariant learning, a novel graph CL-based recommendation model, named RobustGCL. RobustGCL, randomly zeros the values of certain dimensions in the noise vectors at a fixed ratio. In this way, RobustGCL can identify invariant and variant features and then learn invariant and variant representations. Tests on publicly available datasets show that our proposed approach can learn invariant representations and achieve better performance.


Convergence analysis of particle swarm optimization algorithms for different constriction factors

Abstract: Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research. This article proposes a mechanism for controlling the velocity by applying a method involving constriction factor in standard swarm optimization algorithm, that is called CSPSO. In addition, the mathematical CSPSO model with the time step attractor is presented to study the convergence condition and the corresponding stability. As a result, constriction standard particle swarm optimization that we consider has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, CSPSO modifies all terms of the PSO velocity equation. We test the effectiveness of the CSPSO algorithm based on constriction coefficient with some benchmark functions and compare it with other basic PSO variant algorithms. The theoretical convergence and experimental analyses results are also demonstrated in tables and graphically.


Use of Autoencoders for Improving the Performance of Classification and Interpretation of Convolutional Neural Networks in Histopathological Images

Abstract: Breast cancer is one of the most common types of cancer and it presents itself as being the leading cause of death among women. If its diagnosis occur soon enough, the probability to achieve the cure of the patient can be increased. Recently, it has been more common the use of deep neural network techniques to aid pathologists in their prognosis, but they still do not fully trust them because they lack interpretability. In light of that, this work investigates if previous training of the models as encoders could enhance their accuracy in both classification and interpretability. There were implemented three models to the BreakHis and BreCaHAD dataset: NASNet Mobile, DenseNET201, and MobileNetV2. The experiments have shown that the three models increased the classification performance and two models improved their interpretability using the proposed strategy. DenseNet201 encoder has performed almost 23% better than its vanilla version in classifying a tumor and the NASNet Mobile encoder has improved 28.5% in its tumor interpretation.


Influence of Quality, Procedures, and Service Facilities on Community Satisfaction with Hydrological Data Services at the Sulawesi II Gorontalo River Basin Center

Abstract: This research aims to determine and analyze the influence of service quality, service procedures and service facilities on community satisfaction with hydrological data services at the Sulawesi II Gorontalo River Regional Office. The multiple linear regression quantitative analysis model is used to analyze the influence of the independent variable on the dependent variable. The research results show that overall service quality, service procedures and service facilities influence public satisfaction, but for service quality the influence is not that significant. This shows that increasing community satisfaction is jointly influenced by service quality, service procedures and service facilities.