Reviewerly/exHarmony huggingface dataset:
Concept of a Modular Multirotor Heavy Lift Unmanned Aerial Vehicle Platform

This paper presents a novel concept of a modular multirotor aerial robotic platform that can be used for specific profiles of heavy payload missions. A comprehensive mathematical model of the multirotor unmanned aerial vehicle (UAV) is presented, which is divided into the dynamic model and the control allocation scheme that describes the configuration of the aircraft. The components of the propulsion and energy module are selected, and the characterization of the propulsion units is carried out, as well as a preliminary analysis of the module parameters with regard to the considered payloads from 5 to 50 kg. The main goal is to design a modular aircraft that consists of easy-to-assemble modules and that enables the assembly of different propulsion and energy module configurations. Based on the analysis of system parameters, a modular aerial system is designed and the process of manufacturing using rapid prototyping technologies is presented. Based on the parameters obtained from the aircraft assembly CAD model and the implemented mathematical model, simulations are conducted for the two considered missions in the field of smart agriculture. For the purpose of conducting preliminary experiments, a quadrotor aircraft is built and tested in the case of attitude and remote control.
Hyperspectral Image Denoising via Robust Subspace Estimation and Group Sparsity Constraint

Hyperspectral cameras capture electromagnetic information within hundreds of narrow spectral bands, producing hyperspectral images (HSIs) with the capability to accurately characterize the attribute information of objects. However, mixed noise induced by instrument and atmospheric effects hinders the interpretations and applications of the HSIs. In this paper, we propose a novel subspace representation based mixed noise removal method for hyperspectral images via Robust Subspace Estimation and weighted Group Sparsity constraint (RoSEGS). An outlier detection method is proposed to effectively detect sparse noise and replace the sparse noise with new estimates. A subspace estimation strategy, which is robust to mixed noise, is proposed. The subspace is first estimated after sparse noise detection and then optimized iteratively. In addition to the introduction of a state-of-the-art denoiser based on the plug-and-play technique to exploit self-similarity characteristics of the eigen-images, we impose a weighted group sparse regularization on the eigen-images to better promote the group sparsity of the spatial differences between the eigen-images, which further improves the denoising performance. We performed extensive experiments on two simulated and two real HSIs to fully demonstrate the effectiveness of the proposed method in comparison with seven state-of-the-art competitors.
Toward secret data location via fuzzy logic and convolutional neural network

Locating hidden data in digital images, otherwise called steganalysis, is a process of identifying the existence of secret messages within digital images. Steganalysis is used to manage digital data transmission by detecting the possible hidden information that can be used to violate the network policy; hence, it helps the development of policies and regulations aimed at strong protection from cyber threats to individuals’ and organizations’ data. The research works in the field of information security commonly focus on developing the locating approaches for non-adaptive steganography, which present a problem of less investigation of the complex challenge of locating the payload embedded with an adaptive steganographic algorithm. In this article, we propose a method to locate hidden data in a digital image in three stages: a) Identification of the modification maps between the carrier and final images. b) Using the modification maps as input to Mamdani fuzzy inference with four input membership functions: covariance map matrix, compass mean matrix, distance vector matrix, and pixel intensity matrix, and one output membership function, notably the fuzzy correlation maps. c) Feeding the fuzzy correlation maps to a convolutional neural network to identify the pixels with confidential data from the innocent pixels. By experimenting with our method against four steganographic algorithms, namely, HILL, HUGO-BD, WOW, and S-UNIWARD, the recall rates for the four algorithms initially increase in a similar range and improve with increasing payload capacity, which justifies the outperformance of the proposed strategy over the existing methods.
Digital humanism and artificial intelligence: the role of emotions beyond the human–machine interaction in Society 5.0

Purpose By considering the challenges of Industry 5.0, the purpose of this study is to analyze the role of heuristic factors in the technical qualities and emotions of Millennials and Generation Z (Gen Z) to assess their acceptance of the use of artificial intelligence (AI) devices such as robots. For this purpose, this paper uses the innovative AI device use acceptance (AIDUA) framework. This research evaluates the implications of human–machine interactions for the usage of robots and AI in daily life. Design/methodology/approach The proposed AIDUA model is tested using data collected from Millennials and Gen Z. First, a principal components analysis technique is used to validate each measure. Second, a multiple regression analysis using IBM SPSS 26.0 is conducted. Findings The results of this study suggest that human–machine interaction is a part of a complex process in which there are different elements determining individuals’ acceptance of the use of AI devices during daily life. This paper outlines both the theoretical and practical implications. This study enriches the AIDUA model by connoting it with features and emotions belonging to the younger generation. Additionally, this research offers technology companies suggestions for addressing future efforts on technical performance and on the alignments of the expectations of young people in Society 5.0. Originality/value First, the originality of this paper lies in highlighting the binary role of emotions in triggering the use of AI devices and robots. Second, the focus on Millennials and Gen Z offers a new lens for the interpretation of longitudinal phenomena in the adoption of AI. Finally, the findings of this paper contribute to the development of a new perspective regarding a “heartly collaborative” approach in Society 5.0.
An aspect sentiment classification model for graph attention networks incorporating syntactic, semantic, and knowledge

Aspect sentiment classification tasks aim to identify the aspect in a sentence and express the sentiment polarities of the aspect. In existing studies, combining different deep learning models has achieved good results in aspectual sentiment classification tasks; however, problems such as complex syntactic parsing relationships, insensitivity to syntactic structure information and lack of exploitation of external sentiment knowledge. We propose a graph attention network (SSK-GAT) model incorporating syntactic, semantic and knowledge that considers reshaped syntactic dependencies, multi-head self-attention to capture semantic information of context and introduction of an emotion knowledge base to enhance aspect-related emotional information. In previous studies, aspect and context were modeled separately. Hence, to enhance the representation of sentiment between the aspect and contexts, we use the interactive modeling of aspect and context at the output layer to obtain deeper information through mutual learning. Our SSK-GAT model improved on three publicly available datasets compared to the baseline approaches.
Underwater active polarization descattering based on a single polarized image

Active polarization imaging techniques have tremendous potential for a variety of underwater applications. However, multiple polarization images as input are necessary for almost all methods, thereby limiting the range of applicable scenarios. In this paper, via taking full advantage of the polarization feature of target reflective light, the cross-polarized backscatter image is reconstructed via introducing an exponential function for the first time, only based on mapping relations of co-polarized image. Compared with rotating the polarizer, the result performs a more uniform and continuous distribution of grayscale. Furthermore, the relationship of degree of polarization (DOP) between the whole scene and backscattered light is established. This leads to an accurate estimation of backscattered noise and high-contrast restored images. Besides, single-input greatly simplifies the experimental process and upgrades efficiency. Experimental results demonstrate the advancement of the proposed method for objects with high polarization under various turbidities.
An efficient multilevel image thresholding method based on improved heap-based optimizer

Image segmentation is the process of separating pixels of an image into multiple classes, enabling the analysis of objects in the image. Multilevel thresholding (MTH) is a method used to perform this task, and the problem is to obtain an optimal threshold that properly segments each image. Methods such as the Kapur entropy or the Otsu method, which can be used as objective functions to determine the optimal threshold, are efficient in determining the best threshold for bi-level thresholding; however, they are not effective for MTH due to their high computational cost. This paper integrates an efficient method for MTH image segmentation called the heap-based optimizer (HBO) with opposition-based learning termed improved heap-based optimizer (IHBO) to solve the problem of high computational cost for MTH and overcome the weaknesses of the original HBO. The IHBO was proposed to improve the convergence rate and local search efficiency of search agents of the basic HBO, the IHBO is applied to solve the problem of MTH using the Otsu and Kapur methods as objective functions. The performance of the IHBO-based method was evaluated on the CEC'2020 test suite and compared against seven well-known metaheuristic algorithms including the basic HBO, salp swarm algorithm, moth flame optimization, gray wolf optimization, sine cosine algorithm, harmony search optimization, and electromagnetism optimization. The experimental results revealed that the proposed IHBO algorithm outperformed the counterparts in terms of the fitness values as well as other performance indicators, such as the structural similarity index (SSIM), feature similarity index (FSIM), peak signal-to-noise ratio. Therefore, the IHBO algorithm was found to be superior to other segmentation methods for MTH image segmentation.
A Multiple-Medical-Image Encryption Method Based on SHA-256 and DNA Encoding

Ensuring the privacy and secrecy of digital medical images has become a pressing issue as a result of the quick development of smart medical technology and the exponential growth in the quantity of medical images transmitted and stored in networks. The lightweight multiple-image encryption approach for medical images that is suggested in this research can encrypt/decrypt any number of medical photos of varied sizes with just one encryption operation and has a computational cost that is similar to encrypting a single image. The plaintext images with different sizes are filled at the right and bottom of the image to ensure that the size of all plaintext images is uniform; then, all the filled images are stacked to obtain a superimposed image. The initial key, which is generated using the SHA-256 technique, is then used as the starting value of the linear congruence algorithm to create the encryption key sequence. The cipher picture is then created by encrypting the superimposed image with the encryption key and DNA encoding. The algorithm can be made even more secure by implementing a decryption mechanism that decrypts the image independently in order to reduce the possibility of information leaking during the decryption process. The outcomes of the simulation experiment demonstrate the algorithm's strong security and resistance to interference such as noise pollution and lost image content.
Semantic attention and relative scene depth-guided network for underwater image enhancement

In this paper, to solve unique underwater degradation challenges covering low contrast, color deviation and blurring, etc., a novel semantic attention and relative scene depth-guided network (SARSDN) for underwater image enhancement is proposed. Main contributions are as follows: (1) By combining with diverse characteristics of red–green–blue, hue-saturation-value and Lab spaces, the multi-color space feature representation network (MSFRN) is elaborately developed, such that domain shifting can be effectively alleviated; (2) By utilizing position attention and devising multi-dilated-convolution depth perception unit, the underwater relative scene depth estimation network (URSDEN) is proposed to adapt attention weights to regions with different degrees of degradation, thereby exclusively accommodating scene depth-dependent attenuation and scattering; (3) The underwater scene semantic segmentation network (USSSN) is devised to estimate semantic attention map for reducing artifacts and increasing integrity of foreground objects during underwater image enhancement by virtue of encoder–decoder framework with deformable convolution network; and (4) The entire SARSDN scheme is ultimately created in a modular manner by integrating MSFRN, URSDEN and USSSN modules. Comprehensive experiments and comparisons thoroughly illustrate that the developed SARSDN framework outperforms typical underwater image enhancement approaches from both subjective and objective aspects, where UIQM scores are 0.8263, 0.9393, 1.1817, 0.5289, 0.6517, 0.5393, 0.7917 and 0.4651 higher than those of IBLA, ULAP, HLRP, UCM, RGHS, MLLE, UGAN and FUnIE-GAN schemes, respectively.
Risk measurement and application of the international carbon market in the era of global conflict: A data-driven study using FCM

The accurate measurement and effective estimation of carbon market risk are crucial for practitioners and policymakers to mobilize resources toward the transition to a climate-resilient economy, particularly in a new era of global conflict. However, existing studies that have explored factors contributing to carbon market risk primarily relied on experience or subjective judgment in selecting risk-related factors. Such approaches undermine the estimation accuracy while making it difficult to ascertain causal inferences related to the risk spillover. To fill the gap, we adopted a data-driven factor analysis strategy by introducing the Fuzzy Cognitive Maps (FCM) model to establish a carbon market network and identify risk-related factors. We then evaluate the carbon market's risk level and spillover effects using combined econometric methods and explore their application in portfolio management. We report three main findings. First, based on our sample of 3217 observations between 2008 and 2022, five factors influencing carbon market risk emerged from the FCM, including OIL, COAL, SP500ENERGY, SPCLEANENERGY, and GPR. Second, we find a notable rise in risk spillover from GPR to EUA during the Russia-Ukraine conflict and an escalation of total cross-market spillover during extreme events. Third, our study presents new evidence on the hedging effect for EUA of the SP500ENERGY before the Russia-Ukraine conflict and of the SPCLEANENERGY during the conflict. Finally, implications are discussed for policymakers and investors.
Diversity-Promoting Ensemble for Medical Image Segmentation

Medical image segmentation is an actively studied task in medical imaging, where the precision of the annotations is of utter importance towards accurate diagnosis and treatment. In recent years, the task has been approached with various deep learning systems, among the most popular models being U-Net. In this work, we propose a novel strategy to generate ensembles of different architectures for medical image segmentation, by leveraging the diversity (decorrelation) of the models forming the ensemble. More specifically, we utilize the Dice score among model pairs to estimate the correlation between the outputs of the two models forming each pair. To promote diversity, we select models with low Dice scores among each other. We carry out gastro-intestinal tract image segmentation experiments to compare our diversity-promoting ensemble (DiPE) with another strategy to create ensembles based on selecting the top scoring U-Net models. Our empirical results show that DiPE surpasses both individual models as well as the ensemble creation strategy based on selecting the top scoring models.
Site selection for floating photovoltaic system on dam reservoirs using sine trigonometric decision making model

Floating photovoltaic is a great alternative to traditional solar energy because it can increase current electricity production while working compatible with hydropower plants and the power infrastructure. The aim of this study is to present a site selection suitability analysis using fuzzy sine trigonometric based Ordinal Priority Approach and Distance from Average Solution method. Ten site selection criteria related to the technical, economic and social aspects were identified and a questionnaire was conducted among the field experts of solar energy. Then, a consensus analysis was carried out using consistent questionnaire results to reach a definitive decision of experts. This analysis gives the relative weights of each site selection criterion. After that, 8 dam reservoirs, which are utilized for energy production were selected in different locations in Turkey to deploy floating photovoltaics. The solar energy experts filled out another questionnaire to evaluate each selected site for each site selection criterion. Then a new consensus analysis is performed for selected sites. According to the result of the study, although the weights of selected sites seem close to each other, BayramhacılıDam, located in Aydın, Western Turkey, is found as the most feasible site among other alternatives to establish a floating photovoltaic.
An Optimized Privacy Information Exchange Schema for Explainable AI Empowered WiMAX-based IoT networks

The Internet of Things (IoT) has recently surpassed wired communication. WiMAX is a wireless transmission technology that allows for faster internet access. Wireless network innovations, like some other communication networks, are not safe and secure. Security and authorization models are intended to prevent unauthorized use of network services. Numerous authorization and encrypted communication mechanisms have been introduced for WiMAX privacy, but the communication systems are still insecure and vulnerable to attacks such as zero-day attacks, rouge base station attacks, Man in the Middle (MITM) attacks, and Denial of Service (DoS) attacks. Wireless technologies have come a long way in the last few decades. Because most wireless transmission systems rely on radio signals, the system channel is essentially vulnerable to interception. As a result, data security is always critical in the presentation of a system. Because WiMAX is a wireless communication technology, it is particularly vulnerable to interception, so security is a top priority. Individuals must be protected from security breaches that occur across network interfaces, networking devices, and everything in between. Robust security management is required to protect WiMAX from attacks and vulnerabilities, despite the fact that emerging Artificial Intelligence (AI) technologies necessitate different security governance than existing technologies. We proposed an Optimized Privacy Information Exchange Schema for Explainable AI Empowered WiMAX-based IoT that addresses vulnerabilities and threats during the identification and authorization phases to improve the functionality and performance characteristics of the traditional system. The Scyther tool was used to validate the proposed privacy scheme, which is safer and more secure than existing systems.
Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques

Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology images. Initially, the images are preprocessed and then segmented using the multilevel image thresholding technique. Multilevel thresholding is considered an optimization problem. Particle swarm optimization (PSO) and its variants, darwinian particle swarm optimization (DPSO), and fractional order darwinian particle swarm optimization (FODPSO) are used to solve the optimization problem and they generate the threshold values. The threshold values obtained are used to segment the lesion regions from the images of the colonoscopy tissue data set. Segmented images containing the lesion regions are then postprocessed to remove unnecessary regions. Experimental results reveal that the FODPSO algorithm with Otsu's discriminant criterion as the objective function achieves the best accuracy, Dice and Jaccard values of 0.89, 0.68 and 0.52, respectively, for the colonoscopy data set. The FODPSO algorithm also outperforms other optimization methods such as artificial bee colony and the firefly algorithms in terms of the accuracy, Dice and Jaccard values.
Chatbot-based learning of logical fallacies in EFL writing: perceived effectiveness in improving target knowledge and learner motivation

ABSTRACTChatbots have been increasingly applied for EFL education and demonstrated overall usefulness in improving knowledge and motivation, while this technology has yet to be used for learning logical fallacies (i.e. errors in reasoning) in EFL writing. However, knowledge of logical fallacies is essential, with which learners can avoid fallacies in EFL writing and have enhanced writing quality. To fill in the gap, this study investigated the perceived effectiveness of chatbots in developing knowledge of logical fallacy in EFL writing and enhancing learner motivation. Features of this learning method were also explored based on the comparison against website-based learning. Two groups of 15 Chinese EFL learners engaged in five-week autonomous, out-of-class, out-of-class learning of logical fallacies in EFL writing using a chatbot or a website. Semi-structured interviews, pre-post tests of fallacy knowledge and pre-post motivation questionnaires were conducted. The results showed that the chatbot was perceived as slightly less effective than the website in developing target knowledge but more effective in improving motivation. Compared to the website, chatbots were advantageous in high-quality human-computer interactions, study plan making, and high accessibility. Based on the research results, we discussed how this technology might influence fallacy learning based on the self-regulated learning framework.KEYWORDS: ChatbotEnglish writinglogical fallacysecond language writingtechnology-enhanced language learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRuofei ZhangRuofei Zhang is a is a PhD candidate at the Education University of Hong Kong. Her research interests include technology-enhanced language learning, game-based language learning, and self-regulated language learningDi ZouDi Zou is the corresponding author of this paper. She is an Assistant Professor at the Education University of Hong Kong. Her research interests include technology-enhanced language learning, game-based language learning, and AI in English language education.Gary ChengGary Cheng is an Associate Professor at the Department of Mathematics and Information Technology at The Education University of Hong Kong. His research interests include but are not limited to: Information Technology Supported L2 Learning, ePortfolio-mediated Learning, Computer Programming Education, Online Learning Management System, e-Assessment, and Learning Analytics.