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Over the last twenty years, patient opinions of the received healthcare services have emerged as a crucial quality indicator and a valuable instrument for evaluating the service quality of health organizations. However, many surveyors still utilized uncertain information for conducting this evaluation process. Whereas, in the past few years, the issues of inconsistency, indeterminacy, and uncertainty in data have been growing and have become a matter of concern in dealing with decision-making issues. This paper proposes an improved RAFSI method based on a single-valued trapezoidal neutrosophic number (SVTraNN) as a multi-attribute framework for dealing with a healthcare service quality evaluation. The proposed framework consists of two features: a feature for determining numerical criteria weights involving the procedures of the Ordinal Priority Approach (OPA) and a feature for selecting the best polyclinic through contributing our new SVTraNN Harmonic and arithmetic mean to yield criterion functions in the RAFSI method. This paper then compares the results obtained by the proposed method with other SVTraNN decision-making methods. A comparative analysis and rank reversal testing of the improved method demonstrated that the multi-attribute framework proffers the option of capability and stability in overcoming inconsistency, indeterminacy, and uncertainty information efficiently during the healthcare service quality evaluation. [ABSTRACT FROM AUTHOR]

As one of fundamental problems in robotics, coverage path planning (CPP) requires the robot path to cover the entire workspace which has been employed in several essential applications such as cleaning robots, land mine detector, lawnmowers and automated harvesters. Unlike most of existing studies considering the CPP problem under a unrealistic assumption of infinity energy, this paper takes the battery limitation of robots into account. This poses a significant challenge for enabling an efficient coverage path while satisfying the limited energy constraint, even in a priori known environment. Handling this challenge, we propose a BWave Framework that guides the robot to move following an improved Boustrophedon-like motion and a special area prioritization and especially, to return a charging station effectively before an exhausted energy. To that end, a weighted map is applied for recognizing the special areas, namely trap regions, and governing the robot to enter these fields in priority. Moreover, a return matrix, which forms the shortest-path tree from the charging station, is pre-computed to not only validate the energy requirement, but also speed up the calculation process of return and advance paths during the robot's operation. We then evaluate BWave Framework extensively in various scenarios in both generated and real-life indoor maps datasets. The results show that compared to typical baseline methods, BWave Framework achieves the CPP solution at a significantly accelerated running time, namely 51.5 to 72.8 times lower for generated maps, and 44.8 to 255 times for real maps, while reducing the total path length by 2.4%–17.6% and by 2.9%–18.5%, respectively. Moreover, the proposed method also outperforms the baselines in terms of overlap rate, number of returns and accounts for a lower number of deadlocks. • This paper tackles coverage path planning with energy constraints. • It introduces BWave, a novel framework for complete coverage. • BWave has two phases: environment knowledge extracting and complete coverage. • BWave is evaluated in various scenarios and compared with state-of-the-art methods. • BWave performs well in environments with many trap areas. [ABSTRACT FROM AUTHOR]

This paper constructs a supply chain model consisting of two manufacturers and one platform, where the platform provides sales service to enhance the demand. The platform has demand information and decides whether and with which manufacturer to share the information. In practice, if the platform shares the information, the manufacturer can use the shared information to guide pricing decisions and further use Consumer-to-Manufacturer (C2M) strategy to drive product production. C2M means that the manufacturer produces products based on the shared information, making products satisfy consumers' needs, thereby increasing the market demand. Therefore, this paper uses a theoretical model to answer a key question: considering the role of C2M, whether the platform prefers to share demand information with others, and if the platform shares, which strategy is most beneficial to each player? First, this paper obtains the optimal equilibrium solutions under three strategies, that is, no information sharing (strategy NN), information sharing with one manufacturer (strategy SN or strategy NS), and information sharing with two manufacturers (strategy SS). Second, this paper derives that the information effect is the highest for all players under strategy SS. Whether the platform shares information with one or two manufacturers, the market effect has positive influences on both players. The cost effect does not affect the platform but hurts the informed manufacturer. Third, the conditions of the platform sharing information are obtained. There is a sales service efficiency threshold, if the sales service is more efficient, the platform shares the information with one manufacturer; otherwise, strategy SS is optimal. Fourth, through comparative analysis, this paper points out that when the C2M costs are lower, both manufacturers prefer the platform to choose strategy SS. Interestingly, when the C2M costs are higher, both manufacturers want the competitor to have information, which is different from the conclusions of previous studies. In addition, strategy SS makes the platform, and two manufacturers achieve Pareto optimality under certain conditions. Finally, the results are verified by numerical experiments. [ABSTRACT FROM AUTHOR]

• An efficient slime mould algorithm PPMSMA with 3 improved strategies is proposed. • A set of theoretical and experimental analysis methods are proposed and used. • The time complexity, spatial complexity and convergence of PPMSMA are proved. • Multiple experimental methods are used to test the performance of PPMSMA. • The application ability of PPMSMA is tested by solving engineering problems. Metaheuristic intelligent optimization algorithms are effective methods for solving high-dimensional nonlinear complex optimization problems. The slime mould algorithm is a novel intelligent optimization algorithm proposed in 2020. However, the basic slime mould algorithm still has some shortcomings, such as slow convergence rate, easy to fall into local extremum, and unbalanced exploration and development capability. To further improve and expand the optimization ability and application scope of the slime mould algorithm, and enhance its performance in solving large-scale complex optimization problems, this paper proposes a slime mould algorithm (PPMSMA) based on Gaussian perturbation and phased position update, positive variation, and multi-strategy greedy selection. Firstly, Gaussian perturbation and phased position update mechanism are introduced to avoid the difficulty of the algorithm to jump out of the local extrema and also to speed up the convergence of the algorithm. Then, a positive variation strategy based on the sine cosine mechanism is introduced to move the variation of the population towards a better direction. Finally, a multi-strategy greedy selection mechanism is introduced, which effectively improves the search ability of the algorithm. The analysis and research on the optimization ability and performance of metaheuristic algorithms mainly include two aspects: theoretical analysis and experimental testing. Theoretical analysis has always been a relatively weak link in the research of metaheuristic algorithms, and there is currently no clear and effective method formed. For experimental testing, although there are more methods, they often lack systematization and adequacy. In this paper, a more complete, fine-grained and systematic approach to theoretical and experimental analysis is proposed. In the theoretical analysis part, the time complexity and spatial complexity of the PPMSMA algorithm are analytically proved to be the same as the basic slime mould algorithm, and the probability measure method is used to prove that PPMSMA algorithm can converge to the global optimal solution. In the simulation experiment section, the PPMSMA algorithm is compared with multiple sets of 10 representative comparison algorithms on the CEC2017 complex test function set suite for optimization accuracy analysis, Friedman comprehensive ranking analysis, average optimization rate analysis of PPMSMA relative to other algorithms, convergence curve analysis, and Wilcoxon rank-sum test analysis. To further examine the scalability of the improved algorithm in solving large-scale optimization problems, PPMSMA is compared with the above 10 comparative algorithms under 1000 dimensional conditions in the large-scale global optimization test set CEC2010, and the solution stability of each algorithm is analyzed through violin plots. The results show that the PPMSMA algorithm has significantly improved convergence performance, optimization accuracy, and solution stability in both high-dimensional and large-scale complex problems, and has significant advantages compared to multiple sets of 10 representative comparative algorithms. Finally, PPMSMA and 10 other comparative algorithms are used to solve engineering design optimization problems with different complexities. The experimental results validate the universality, reliability, and superiority of PPMSMA in handling engineering design constraint optimization problems. [ABSTRACT FROM AUTHOR]

Rice classification and quality detection are therefore crucial for ensuring the safety and quality of rice for human consumption and reducing the financial losses associated with rice spoilage. Accurate and efficient rice classification and quality detection techniques can help farmers, traders, and regulators identify the most valuable and high-quality rice cultivars, enabling them to make better crop management, storage, and transportation decisions. A system to automatically classify different types of rice grains is a valuable and crucial area of research in modern agriculture. Various methodologies have been used in recent years to identify agricultural products, including color-based, texture-based, and statistical-based features. This paper introduces a deep learning-based classification algorithm using ResNet deep models to represent the structural content of different varieties of rice grains. ResNet is a proven deep-learning model with impressive performance in various computer vision tasks, including signal classification. ResNet is adept at learning rich representations of images and generalizing them to new data making it a reliable choice for rice classification by combining residual learning and a well-structured architecture. In this paper, different architectures of ResNet, such as ResNet34, ResNet50, and a transferred version of ResNet50 using a transfer learning technique, are designed for performance evaluation in rice classification and quality detection problems. The performance of the proposed algorithm is compared with the other deep learning models and dictionary learning-based algorithms. The results demonstrate that the proposed algorithm using ResNet50 deep models and transfer learning accurately identified six rice varieties with an accuracy rate of over 99.85 %. The algorithm also accurately detects rice quality for different percentages of combination with other rice varieties with an average accuracy of 98.13 %. [ABSTRACT FROM AUTHOR]

• Treat a problem of the imbalanced customer churn classification. • A new multi-strategy collaborative processing method IADASYN-FLCatBoost is proposed. • The traditional ADASYN algorithm is improved and an IADASYN algorithm is proposed. • The Focal Loss function is embedded into the CatBoost to form a new FLCatBoost. • Empirical analysis shows the proposed method performs better than similar methods. The rapid advancement of big data and artificial intelligence heralds a dual-edged era of opportunities and challenges for the banking sector. Indeed, enhancing a model's capability to accurately classify imbalanced datasets represents a critical challenge within the field of customer churn prediction (CCP). In this paper, to address the challenges presented by the problem of imbalanced customer classification, a new multi-strategy collaborative processing method named IADASYN-FLCatBoost is proposed from dual perspectives: data and algorithm. At the data level, the traditional Adaptive Synthetic (ADASYN) sampling is improved, that is, the LOF (Local Outlier Factor) algorithm is introduced to eliminate outliers, and the classification features are specially processed to synthesize new minority class samples, thus an improved ADASYN (IADASYN) algorithm is obtained. At the algorithm level, the Focal Loss is embedded into the CatBoost ensemble learning framework to form a new Focal Loss-CatBoost (FLCatBoost) to make a focal-aware, cost-sensitive version of imbalanced customer churn prediction. Moreover, the empirical analysis is conducted in conjunction with the credit card customer dataset obtained from the Kaggle platform. The results of the staged comparison experiments show that the proposed method IADASYN-FLCatBoost in this paper shows the best prediction performance. Comparing the proposed method with 5 other imbalanced classification algorithms and 20 classifiers composed of classical sampling methods and ensemble learning algorithms, it is verified that the classification effect of the proposed method performs best, and the values of Recall , F 1 score, G-mean and Area under Precision-Recall curve (AUPRC) have been significantly improved. In addition, further verification of the model also proves that the proposed method has certain generalizability and is still valid for other banks and customer churn datasets of other industries. [ABSTRACT FROM AUTHOR]

Authors are often influenced by historical experience and personal habits when selecting keywords in composing papers, and tend to choose specific keywords, resulting in a phenomenon of conformity bias. In order to improve the accuracy of literature recommendation services, it is crucial to effectively identify and eliminate the adverse effects of conformity bias. Based on the definition of a measurement for the degree of impact of conformity bias using conformity width, this paper designs an effective method for eliminating conformity bias based on one-class implicit access information. At the same time, this method further utilizes the author's own keyword preference information based on explicit weight of keywords and achieves the comprehensive fusion of various effective user access information through the design of a model. The experimental results validated the phenomenon of conformity bias in the composing process of scholars, and comprehensively analyzed and compared the characteristics and advantages of the algorithm proposed in this paper. The code is available at https://github.com/EdwardSig/CBEWC_ESWA. [ABSTRACT FROM AUTHOR]

Hate speech pertains to the expression of negative emotions by people. The spread of hate speech contributes to societal inequity by imposing adverse consequences on individuals' emotional and physical well-being, financial circumstances, and overall quality of life, thereby hindering their daily existence. Hence, the development of a hate detection and diffusion prediction system is of utmost importance. The literature study reveals two primary findings regarding the development of a hate speech model. Firstly, it is crucial to understand the potential impact of a hate speech message, specifically the extent to which it influences the users exposed to it. Secondly, numerous researchers have focused on studying information propagation, with a particular emphasis on analysing network topology and structure. This paper introduces psycho-sociological analysis as another approach to understanding hate speech spread on social networks. We develop a hate speech diffusion model considering many psycho-sociological dimensions of social media users. This paper aims to answer a few basic questions: (a) Who starts hateful tweets on social media? (b) Who are the users who share, respond to, or like such posts? (c) Can we build a better hate-speech diffusion model by understanding each user's psycho-sociological traits towards hateful information? To answer the above questions, we analyse user psycho-sociological traits by studying three different dimensions of user behaviour, i.e., inner personality, societal personality, and dark emotion using the Big5 personality model, Schwartz value model, and the Dark triad behaviour model. We develop a collection of classifiers to detect users' personality traits. In addition, we proposed a system to predict the spread of hate content on social networks. Several empirical analyses are presented in this paper in order to understand the correlation between human personality and online hateful behaviour. From our empirical analysis of hostile behaviour, several key observations are reported in this paper, a few of which include the following: (i) People with dark triad orientations are mostly the originators of online hate speech, (ii) Those who post sexist tweets are often Machiavellian, (iii) Narcissistic users are more likely to make racist tweets, and (iv) Those who post sexist and racist tweets on social networks are often psychopathic. Using these insights, we have mapped the user behaviour dimension into a vector space called behaviour embedding. This space is used for developing the hate-speech diffusion model. We show that the proposed models also perform better than the state-of-the-art with a significant F 1 -score margin. • Hate spreaders can be identified by DL model using physiological features. • Dark triad traits are linked to the hate user and hate post spreader. • Deep CNN network to detect hate users on social networks. • Diffusion system predicts who will spread the hate post on social media. [ABSTRACT FROM AUTHOR]

The Fetal Electrocardiogram (FECG) signal plays a crucial role in monitoring the health of the fetus, but there are numerous challenges in eliminating the maternal thorax signal and reducing noise interference. This paper proposes a novel objective function that combines a Least Mean Squares (LMS) adaptive filter with a heuristic algorithms to enhance the quality of the extracted FECG signal. To achieve better results, we introduce the Discrete Artificial Bee Colony (DABC) algorithm with a new initialization strategy, a random wavelet basic function strategy, and Gaussian distribution. These improvements enhance global search capabilities and ensure a faster convergence rate. The application of heuristic algorithms can reduce noise signals and provides clearer and more accurate results compared to the traditional LMS filter. Furthermore, the effectiveness of this innovative algorithm is compared with other widely used heuristic algorithms. The experiment results demonstrate that the novel algorithm significantly enhances performance by up to 8% compared to other conventional extraction methods in some indicators. • A new objective function is designed to combine heuristic algorithms and LMS filter to extract the FECG signal from AECG signal. • This paper propose a novel Discrete Artificial Bee Colony, which introduce a tailored initialization strategy to improve the algorithm's convergence speed, based on the specific characteristics of the FECG signal extraction problem. The novel algorithm can obtain clearer extracted signal than other existed heuristic algorithms. • We incorporate the strategies of random wavelet basic functions and Gaussian distribution in the DABC algorithm to enhance the global search ability and prevent the population from prematurely becoming trapped in a local optimum. [ABSTRACT FROM AUTHOR]

Estimating the full state of physical systems, including thermal and flow status, from sparse measurements of limited sensors is a critical technology for perception and control. Neural networks have been used in recent studies to reconstruct the global field in a supervised learning paradigm. However, these studies encounter two major challenges: the lack of interpretability of black-box models and performance bottleneck caused by network structure and parameter optimization. This paper proposes a hybrid method based on proper orthogonal decomposition (POD) and deep neural networks (DNNs) to further enhance the interpretability and accuracy of flow and heat field reconstruction. The key idea is to leverage the inherent data modes extracted by POD that capture essential features in physical fields, and formulate the reconstruction problem as finding an optimal linear combination of dominant POD modes. To reduce the error introduced by underfitting and model structure, this paper estimates the coefficients of POD modes by establishing and solving a linear optimization problem that minimizes the gap between the recovered field and the exact measurements, rather than employing regression models. However, the underdetermined issue cased by the sparse measurements restricts the optimization problem to obtain a proper solution. To alleviate this problem, this paper presents to utilize the powerful non-linear approximation ability of DNNs to produce a reference field as auxiliary observations, which combines exact measurements to jointly constrain the optimization problem solving. Finally, the global physical field is reconstructed by superposing dominant POD modes weighted with the solved coefficients. By combining with POD technology, the proposed method can also improve the performance of neural networks on reconstruction problems with large-scale and irregular domains. The experiments conducted on the fluid and thermal benchmarks demonstrate that the proposed method can significantly boost neural network reconstruction performance and outperform existing POD-based methods. • A hybrid method based on POD and deep neural networks is developed for flow and heat field reconstruction from sparse measurements. • The data modes extracted by POD are explicitly used to alleviate black-box problems of neural networks. • The field reconstruction problem is converted to a linear optimization jointly constrained by exact measurements and DNN predictions. • The underdetermined issues with sparse measurements can be addressed. [ABSTRACT FROM AUTHOR]

State-of-the-art dense stereo matching algorithms have achieved excellent performance, demonstrating a capability to attain precise matching in most areas. However, current such methods rarely achieve this when images are captured under poor conditions. To improve the accuracy of the algorithm in such cases, this paper introduces a post-optimization algorithm to rectify matching errors and enhance outcomes. The main research areas of this paper include three aspects. (1) Disparities are classified into reliable and unreliable results based on the analysis of geometric matching relationships, local features in the images, and components within the matching cost function; (2) Subsequent analysis of horizontal image features identifies local characteristic indices calculated through integration along the horizontal axis, which establish specific matching criteria, forming the foundation for a cost volume that encompasses these distinct matches; (3) A redefined matching cost function is applied to previously classified unreliable results to rectify matching errors. This energy function is based on the cost volume above. Experimental results validate the efficacy of the proposed post-optimization algorithm, reducing the average matching errors from 8.66% to 5.85%. [ABSTRACT FROM AUTHOR]

Today's Internet of Vehicles (IoV) faces many security risks in the data transmission process, and image data is more vulnerable to security threats in the transmission process due to its special characteristics such as large amounts of information and high visibility. Therefore, to guarantee the dependability of data transmission in the IoV environment, this paper designs a blind signature scheme for IoV based on two-dimensional sine cosine cross-chaotic mapping (2D-SCML) image encryption and lattice cipher (BSS-IoV). The innovation of this scheme is that it aims at blind signature of image information, blinds it before sending the information, and combines the lattice public key encryption algorithm to better ensure the safe and reliable transmission of information and reduce the risk of information disclosure. To further ensure the security of the scheme, an image encryption algorithm based on 2D-SCML and pixel splitting (2PS-IEA) is proposed, which is used to blind the information and thus reduce the risk of information leakage on the one hand, on the other hand, it is used in the signature process to ensure the security of the signed information. The 2D-SCML is derived from the cross-model structure proposed in this paper. Through simulation results and experimental analysis, the values of NPCR and UACI, respectively, 99.6094% and 33.4635%, are close to ideal values. And 50% of the cut image can also recover the rough information, which indicates that the signature scheme has the security against differential attacks, cut attacks and noise attacks. Moreover, the security analysis shows that the scheme has the anti-tamper, anti-repudiation and traceability. • Designed blind signature scheme for IoV using image encryption and lattice cipher. • Proposed a crossover model structure is proposed. • Designed a 2D-SCML mapping with superior performance based on this model. • Devised an image encryption algorithm based on 2D-SCML and pixel splitting. • The signature scheme and encryption scheme are analyzed experimentally. [ABSTRACT FROM AUTHOR]

This paper discusses the use of portfolio approaches based on bandit algorithms to optimize multicriteria decision-making in recommender systems (accuracy and diversity). While previous research has primarily focused on single-item recommendations, this study extends the research to consider the recommendation of several items per iteration. Two methods, Multiple-play Gorthaur and Budgeted-Gorthaur, are proposed to solve the algorithm selection problem and their performances on real-world datasets are compared. Both methods provide a generalization of the Gorthaur method, which enables it to operate with any Multi-Armed Bandit (MAB) and Contextual Multi-Armed Bandit (CMAB) algorithm as meta-algorithm in a multi-item recommendation scenario. For Multiple-play Gorthaur, an empirical evaluation shows that the use of Thompson Sampling for algorithm selection (Gorthaur-TS) yields better results than the original EXP3 method (Gorthaur-EXP3) and the exclusive use of the optimal algorithm in the portfolio in contextual recommendation problems. Additionally, the paper includes a theoretical regret analysis based on the TS sketch proof applied for this variant of the method. Concerning Budgeted-Gorthaur, experiments show that it allows more flexibility to achieve a suitable trade-off between criteria and a broader coverage of the Pareto set of solutions, overcoming a natural limit of "a-priori" methods. Finally, this paper provides a detailed review, including pseudocodes and theoretical bounds, for all the fundamental MAB and CMAB algorithms used in this study. • Bandit literature lacks formal algorithm review, hindering clarity and comparability. • There is no silver bullet: no algorithm can be the best performer in every instance. • Recommender systems need to balance accuracy, diversity, multi-item recommendations. • Optimal algorithm balances criteria, matching decision maker's preferred trade-off. • Dynamic selection ensures safe performance when optimal algorithm is unknown. [ABSTRACT FROM AUTHOR]

• Newbie study using standard ML techniques with HAR Application and Discussions. • Activities found in Literature with the corresponding reference. • Co-occurrences between activities and sensors with the corresponding reference. • Summary and comparison among the different datasets found in Literature. • Summary of the experimentation settings with performance scores found in Literature. Human Activity Recognition (HAR) is an essential area of research related to the ability of smartphones to retrieve information through embedded sensors and recognize the activity that humans are performing. Researchers have recognized people's activities by processing the data received from the sensors with Machine Learning Models. This work is intended to be a hands-on survey with practical's tables capable of guiding the reader through the sensors used in modern smartphones and highly cited developed machine learning models that perform human activity recognition. Several papers in the literature have been studied, paying attention to the preprocessing, feature extraction, feature selection, and classification techniques of the HAR system. In addition, several summary tables illustrating HAR approaches have been provided: most popular human activities in the literature with paper references, the most popular datasets available for download (Analyzing their characteristics, such as the number of subjects involved, the activities recorded, and the sensors with online-availability), co-occurrences between activities and sensors, and a summary table showing the performance obtained by researchers. =The paper's goal is to recommend, through the discussion phase and thanks to the tables, the current state of the art on this topic. [ABSTRACT FROM AUTHOR]

Roundwood sorting is still a manual process in many Swiss sawmills, requiring employees to visually inspect and categorize thousands of logs per day. The heavy workload can be both physically and mentally taxing and can lead to increased rates of human error. State-of-the-art automation systems like X-ray log scanners are expensive and difficult to integrate into existing process lines. This paper proposes a novel recommendation system that leverages recent advances in image classification to automate roundwood classification by quality and species. The system integrates a camera to capture cross-sectional images of logs and record numerical data, such as length, taper, and diameter. The analysis of the resulting dataset highlights the challenges of data imbalance and noise, which makes classification difficult and, in some cases, impossible. However, by using selected datasets with reduced noise, state-of-the-art Convolutional Neural Networks (CNNs) can extract quality and species features. Quality models learn from a manually selected and simplified dataset, featuring samples that experts can clearly classify based on the image's information. Species models are trained on a label-noise-reduced dataset, reflecting real-world complexity. The accuracy on the selected dataset for three quality classes is 80%. The species determination is less challenging and reaches 91% accuracy on a synchronized dataset for the main species spruce and fir. Overall, this paper highlights the potential of Machine Learning in augmenting the roundwood sorting processes and presents a novel system that can improve the efficiency and accuracy of the process. [Display omitted] • Automation of roundwood sorting: Replaces manual sorting with image-based AI. • Integrated camera system in roundwood sorting to collect labeled dataset. • Species prediction: 91% accuracy in spruce–fir distinction. • Quality prediction on complexity reduced dataset: 80% accuracy between three main quality levels. • Efficient, adaptable & scalable system which is easy to integrate into existing process lines. [ABSTRACT FROM AUTHOR]

The proliferation of malware in recent years has posed a significant threat to the security of computers and mobile devices. Detecting malware, especially on the Android platform, has become a growing concern for researchers and the software industry. This paper proposes a new method for detecting Android malware based on unbalanced heterogeneous graph embedding. First of all, most malware datasets contain an imbalance of malicious and benign samples, since some types of malware are scarce and difficult to collect. Thus, as a result of this problem, the classification algorithm is unable to analyze the minority samples through sufficient data, resulting in poor downstream classifier performance, in light of the fact that adversarial generation networks possess the characteristic of completing data, an algorithm for generating graph structure data is presented, in which nodes are generated to simulate the distribution of minority nodes within a network topology. Then, considering that heterogeneous information networks have the characteristics of retaining rich node semantic features and mining implicit relationships, heterogeneous graphs are used to construct models for different types of entities (i.e. Apps, APIs, permissions, intents, etc.) and different meta-paths. Finally, a new method is introduced to alleviate the over-smoothing phenomenon of node information in the propagation of deep network. In the deep GCN, we first sample the leader nodes of each layer node, and then add a residual connection and an identity map in order to determine the characteristics of the high-order leader. In this paper, a self-attention-based semantic fusion method is also applied to adaptively fuse embedded representations of software nodes under different meta-paths. The test results demonstrate that the proposed IHODroid model effectively detects malicious software. In the DREBIN dataset, which consists of 123,453 Android applications and 5,560 malicious samples, the IHODroid model achieves an accuracy of 0.9360 and an F1 score of 0.9360, outperforming other state-of-the-art baseline methods. • A new generative adversarial network model has been proposed for balancing data. • Heterogeneous graphs are used for modeling malware detection. • A new method is introduced to alleviate the over-smoothing phenomenon. [ABSTRACT FROM AUTHOR]

Complex networks have a large number of nodes and edges, which prevents the understanding of network structure and the discovery of valid information. This paper proposes a new community detection method for simplified networks. First, a similarity measure is defined, the path and attribute information can reflect the potential relationship between nodes that are not directly connected. Based on the defined similarity, an Importance Score(IS) is constructed to show the importance of each node, it reflects the density around each node. Then, the simplification processes can be realized on complex networks. On the simplified network, this paper proposes a novel community detection method, in which the community structure of the simplified network is detected. The experiments were conducted on real networks and compared with several widely used methods. The experimental results illustrate that the proposed method is more advantageous and can visually and effectively uncover the community structure. [ABSTRACT FROM AUTHOR]

Intelligent applications are highly susceptible to uncertainty and inconsistency due to the intense and intricate interactions among their autonomous components (or agents), making their verification theoretically and practically challenging. This paper presents the design and implementation of a new open-source and scalable software tool for modeling and verifying intelligent applications with commitment and trust protocols under both uncertainty and inconsistency settings, using reduction-based multi-valued model checking techniques. The proposed tool is equipped with original and novel algorithms that transform our logics of multi-valued commitment (mv-CTLC) and multi-value trust (mv-TCTL) that we recently introduced to their classical two-valued commitment (CTLC) and trust (TCTL) logic versions as well as to Computational Tree Logic (CTL). Moreover, the tool transforms the mv-CTL to CTL, and it is applicable for the classical model checking by transforming the classical logics of trust and commitment to CTL. To demonstrate the practicality and applicability of the proposed tool in real settings, we present and report experimental results over two blockchain-based applications in the healthcare domain. Finally, we provide discussions and comparisons between the proposed approaches regarding scalability and efficiency. Moreover, we provide packages of more than 11 experiments, including the ones we conduct in this paper and enhanced experiments from previous works. Our findings ensure that the proposed approaches and the software tool that implements them are highly efficient and scalable, giving accurate results under varying conditions. • Design and implementation of an open-source scalable tool for systems model checking. • System modeling with multi-valued logic capturing uncertainty and inconsistency. • Practical demonstration using blockchain-based applications in healthcare domain. • Extensive experiments showing scalability, efficiency and reliability of the tool. [ABSTRACT FROM AUTHOR]

Modern power systems integrated with renewable energies (REs) contain many uncertainties. The proposed method introduces a novel approach to address the challenges associated with wind power generation uncertainty in probabilistic load flow (PLF) studies. Unlike conventional methods that use wind speed as an input, the paper advocates for utilizing wind generator output power (WGOP) as an input to the point estimate method (PEM) in solving PLF. The uniqueness lies in recognizing the distinct behavior of wind power uncertainty, where not all random samples of wind speed contribute to actual wind power production. The paper suggests a Nataf-KernelDensity-Spline-based PEM, combining the Nataf transformation, Kernel density estimation (KDE), and cubic spline interpolation. This innovative integration effectively manages wind power correlation within the analytical framework. By incorporating spline interpolation and kernel density estimation into the traditional PEM, the proposed method significantly enhances accuracy. To validate the effectiveness of the proposed approach, the method is applied to IEEE-9 and IEEE-57 bus test systems, considering uncertainties related to load, wind power generation (WPG), solar power generation (SPG), and conventional generator (CoG) outages. Comparative analysis with Monte Carlo simulation (MCS) results demonstrates that the proposed method outperforms the conventional PEM in terms of accuracy. Overall, the paper contributes a pioneering solution that not only highlights the importance of using WGOP as an input in PLF but also introduces a sophisticated method that surpasses traditional approaches, improving accuracy in power system studies involving renewable energy integration. The accuracy of the proposed method is validated by comparing its results with those obtained through Monte Carlo simulation (MCS), where the proposed method yields more accurate results than the conventional PEM. [ABSTRACT FROM AUTHOR]

Deep learning has recently demonstrated considerable promise for a variety of computer vision tasks. However, in many practical applications, large-scale labeled datasets are not available, which limits the deployment of deep learning. To address this problem, semi-supervised learning has attracted a lot of attention in the computer vision community, especially in the field of medical image analysis. This paper analyzes existing deep semi-supervised medical image segmentation studies and categories them into five main categories (i.e., pseudo-labeling, consistency regularization, GAN-based methods, contrastive learning-based methods, and hybrid methods). Afterward, we empirically analyze several representative methods by conducting experiments on two common datasets. Besides, we also point out several promising directions for future research. In summary, this paper provides a comprehensive introduction to deep semi-supervised medical image segmentation, aiming to provide a reference and comparison of methods for researchers in this field. [ABSTRACT FROM AUTHOR]

As one of the most critical variables in the hot rolling process, the accuracy of rolling force prediction is directly associated with production stability and product quality. Purely data-driven approaches, however, are severely constrained by the quantity and quality of data, posing challenges for further enhancing the accuracy of rolling force prediction. In this paper, a theory fusion deep neural network (DNN) modelling approach was proposed and applied to the prediction of rolling force during hot plate rolling. In terms of model establishment, the novel NN structure was designed in consideration of the rolling mechanism, and senior variable inputs were added at shallow locations in the network to reduce the loss of critical information. In terms of model training, the method of using rolling theory to guide the initialization of the model was proposed to enable the model to learn the theoretical features more completely in the pre-training phase. Finally, a method to optimize the overall structure of the model using the sparrow search algorithm (SSA) was proposed to ensure the best prediction performance. The model was tested with the data in the developed platform, and the results indicated that the proposed method achieves the best accuracy and stability in this paper, and the response relationship between model inputs and output was consistent with existing theoretical knowledge. Thus, the model can be trusted and flexibly applied to the actual manufacturing processes. [ABSTRACT FROM AUTHOR]

The trend in underground mine development is trackless transportation, and the scheduling optimization of underground mine trackless transportation is a current research hotspot. This paper proposes a truck scheduling optimization method for underground mine trackless transportation based on an improved estimation of distribution algorithm to address the truck scheduling problem in the underground mine trackless transportation process. The transportation process of transport trucks in underground mines is analyzed. The dispatching model of transport trucks in underground mines is constructed based on the requirements of reducing transportation costs and increasing transportation efficiencies, taking into account the truck meeting situation in the ramp section and minimizing the total shift transportation distance and the total waiting time of transport trucks as the objective functions. The improved estimation of distribution algorithm is used to solve the truck scheduling model, resulting in the optimal ore blending and scheduling schemes. The comparative analysis employs a genetic algorithm, particle swarm optimization algorithm, and immune algorithm. The results demonstrate that, compared to other algorithms, the improved estimation of distribution algorithm proposed in this paper has superior performance in terms of convergence speed and the search for the optimal solution. The total number of transportation tasks associated with the optimal ore allocation scheme is at least 82, and the waiting time associated with the optimal scheduling scheme is reduced to 7.5 min. The operation time chart of transport trucks calculated by the optimal dispatching scheme can clearly depict the location of each transport truck at any time during a shift's working time, which has significant guiding significance for the actual truck transportation in the mine. [ABSTRACT FROM AUTHOR]

Conceptual clustering is one of the key approaches for automatic concept generation from input contexts. In this paper, we propose an extension of the dominating k-means method. The proposed method introduces a flexible distance metric that enables the approximation of both Euclidean and meet(or join)-based similarity calculations. To increase the approximation accuracy, the method combines the k-means method with a Quality Threshold component. The paper shows that the method can also be used for approximation of formal concept lattices. Based on the performed tests, the method provides an efficient alternative conceptual clustering approach. • Novel extension of the k-means-based conceptual clustering algorithm. • Novel parameterized distance metric to cover different aggregation approaches. • Tool for flexible concept set reduction in formal concept analysis. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The current state of imaging spectroscopy spans diverse applications including but not limited to air-borne and ground-based computer vision systems. This paper presents the current state of research and industrial applications including precision agriculture, material classification, medical science, forensic science, face recognition and document image analysis, environment monitoring, and remote sensing, which can be aided through imaging spectroscopy. In this regard, we further discuss a comprehensive list of applications of imaging spectroscopy, pre-processing techniques, and spectral image acquisition systems. Likewise, publicly available databases and current software tools for spectral data analysis are also documented in this review. This review paper, therefore, could potentially serve as a reference and roadmap for people looking for literature, databases, applications, and tools to undertake additional research in imaging spectroscopy. • A survey on the current state of imaging spectroscopy. • Pre-processing techniques and data acquisition methods are highlighted. • We provide applications of imaging spectroscopy in various domains. • Publicly available databases and software tools for spectral data are presented. [ABSTRACT FROM AUTHOR]

Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing many conflicting objectives simultaneously. Decomposition-based many-objective evolutionary algorithms have effectively maintained a balance between convergence and diversity in recent years. However, these algorithms face challenges in accurately approximating the complex geometric structure of irregular Pareto fronts (PFs). In this paper, an information entropy-driven evolutionary algorithm based on reinforcement learning (RL-RVEA) for many-objective optimization with irregular Pareto fronts is proposed. The proposed algorithm leverages reinforcement learning to guide the evolution process by interacting with the environment to learn the shape and features of PF, which adaptively adjusts the distribution of reference vectors to cover the PFs structure effectively. Moreover, an information entropy-driven adaptive scalarization approach is designed in this paper to reflect the diversity of nondominated solutions, which facilitates the algorithm to balance multiple competing objectives adaptively and select solutions efficiently while maintaining individual diversity. To verify the effectiveness of the proposed algorithm, the RL-RVEA compared with seven state-of-the-art algorithms on the DTLZ, MaF, and WFG test suites and four real-world MaOPs. The results of the experiments demonstrate that the suggested algorithm provides a novel and practical method for addressing MaOPs with irregular PFs. • A novel RL-RVEA addresses many-objective optimization with irregular pareto fronts. • A reinforcement learning-based adaptive reference vector to guide the direction of convergence. • A scalarization approach preserves the diversity of solutions for next generation. • The RL-RVEA outperforms seven advanced many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

The last-mile delivery by unmanned aerial vehicles (UAVs), emerging from the online grocery retailing industry, has attracted much attention and interest from the scientific and industrial communities. In reality, the online retailing platform receives grocery orders and promises ultrafast delivery service to the customers via a two-echelon logistics and distribution network. The uncertain arrivals of the orders have a nontrivial negative effect on the performance of the whole delivery network, which has not been well studied in the literature. This paper investigates the parcel consolidation policy and the UAVs routes for the last-mile delivery from a transshipment site in a distribution network to the final customers. First, this study proposes a nonmyopia consolidation policy considering the upcoming grocery parcels with random arrival times to minimize the total delivery cost including the possible penalty cost due to the delivery delay to the customer. To make the problem tractable in the real-time computational setting, an approximation method based on Bayesian estimation is proposed to reduce a large number of random arrival scenarios of upcoming parcels to an expected scenario. The problem is then approached with a deterministic model under the expected scenario. Subsequently, a discrete particle swarm optimization (DPSO) algorithm is developed to solve the model. In the algorithm, a novel decoding method is designed to evaluate the particles with respect to the constraints of the load and battery capacities of a UAV and a neighborhood search based on reinforcement learning is developed to improve the quality of the particles in the searching process. The experimental results based on a case study validate the performance of the proposed parcel consolidation approach and the UAV routing algorithm. Some findings are given based on the variations of benchmarks with different distributions of customer locations. The approaches and insights in this paper could be used as a reference for last-mile delivery by UAVs. • We propose a consolidation approach for the last-mile parcel delivery by UAVs. • We formulate the problem as an approximation deterministic model. • We design a novel neighborhood search to enhance the performance of the heuristics. [ABSTRACT FROM AUTHOR]

The discriminative correlation filter (DCF) is commonly used in aerial object tracking due to its high tracking accuracy and computing speed. However, when similar object disturbances emerge in the background, the response map will generate sub-peaks, which may eventually lead to tracking failure. Meanwhile, the lack of attention to the tracked object can also cause tracking performance degradation. To these concerns, this paper proposes a novel correlation filter algorithm for real-time aerial tracking based on spatial disturbance suppression and object saliency-aware, i.e., SOCF. Firstly, this paper designs a novel spatial disturbance suppression strategy. Using the temporal information in the historical response maps, we construct a context response map, deviating it from the current response map to detect disturbance information in the background. Then, construct a spatial interference map, divide it into n × n non-overlapping regions, and suppress the negative samples in the disturbance region within the main regression. Furthermore, an object saliency-aware strategy is proposed, using a saliency detection algorithm to calculate the object-aware mask and multiplying it with the detection filter to obtain the object-aware filter. By constructing the object-aware regularization in the training phase, the trained detection filter focuses more on the object itself and can effectively separate the object from the background. Extensive experiments on four widely used unmanned aerial vehicle (UAV) datasets demonstrate that the proposed SOCF tracker achieves high tracking performance. Meanwhile, our tracker can maintain real-time aerial tracking at 48 FPS on a single CPU. • Proposing a novel correlation filter algorithm for real-time UAV tracking. • Suggesting a novel strategy to accurately suppress the spatial disturbance. • Presenting an object saliency-aware strategy to focus more on the tracked object. • Using alternating direction method of multipliers (ADMM) to optimize filter learning. • Conducting extensive experiments on four mainstream UAV datasets. [ABSTRACT FROM AUTHOR]

The detection of underwater fish targets is critical for ecological monitoring and marine biodiversity research. However, underwater fish detection is typically constrained by problems including low image quality and variable underwater surroundings. On behalf of further improving the underwater fish detection accuracy in complex underwater environments, this paper proposes a dual-path (DP) Pyramid Vision Transformer (PVT) feature extraction network named DP-FishNet. The backbone network DP-PVT composed from the PVT Network is made up of two feature extraction paths. The first represents the Vision Transformer path, which extracts global features to enhance the distinction between the foreground and background of underwater images. The second is the convolutional neural network path, which enhances the accuracy of detecting small targets by extracting local features. Additionally, to more effectively utilize the feature information extracted by the network, this paper provides a promising solution to employ the content-aware reassembly of features (Carafe) in the feature pyramid network (FPN). The seesaw loss is utilized as a classification loss to address the problem of unbalanced samples caused by the gap in the number of fish populations. According to the experimental findings, the AP and AP50 of the DP-FishNet are 76.0% and 95.2%, respectively. In comparison to currently available advanced two-stage detection algorithms, the quantity of computation and parameters is reduced by approximately 40%. DP-FishNet strengthens the ability to extract global and local features from underwater images and enhances feature reuse. DP-FishNet can be utilized to detect fish targets in actual and complicated underwater habitats. [ABSTRACT FROM AUTHOR]

In recent years, natural language processing (NLP) has gained new territory beyond its traditional use in text mining applications. This paper shows the effectiveness of NLP techniques in assessing the apparent human personality from his/her video transcript, building a bridge between NLP and computer vision-based reasoning. In this paper, a new deep learning model using attention mechanism and bidirectional LSTM layers for estimating the Big-five personality traits is provided and then tested on ChatLearn video dataset. The robustness of the approach is then tested by generalizing the method to two other datasets (B5 corpus and Mypersonality datasets). Several empirical evaluations taking into account the various inputs of the data processing pipeline have been performed to yield optimal model parameters. The developed model is tested on the APA'2016 competition dataset from Chalearn V2 Challenge Workshop for both Big-five personality trait estimation and interview score estimation. We achieved an average of 89.27% in personality trait recognition rate and 89.16% score in the Job Interview challenge. Similar trends of high accuracy score estimation is held for B5-corpus and MyPersonality datasets as well. The results outperformed several state-of-art approaches, demonstrating our approach's feasibility in extending the computer vision approach of first impression personality to natural language processing. This opens up a new direction in multimedia analysis. • New big five personality trait estimation using deep learning and NLP was developed. • The approach was tested successfully on ChatLearn video dataset. • Generalization is demonstrated using B5 corpus and MyPersonality dataset. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) based on multimodal wearable motion sensors is a valuable technology, and gymnastics action recognition is one of its important application scenarios. However, this field faces challenges such as excessive sensor node deployment, hindering effective utilization of time-series sensor data, and low accuracy in action recognition. To address these issues, this paper introduces a gymnastics action recognition system comprising a hardware setup and an action recognition framework that combines a time series data imaging method with a novel three-channel convolutional model ViTGS. Firstly, a cost-effective single-node sensor system equipped with a 6-axis IMU is devised to efficiently capture gymnastics action data. Secondly, three one-dimensional time series data image encoding methods, Gramian Angular Field (GAF), Recurrence Plot (RP), and Markov Transition Field (MTF), are employed to capture the temporal features, phase space features, and state transition features of the data respectively. Finally, the ViTGS model is proposed, which uses the ViT_B/16 network to extract the features of each channel image, and uses Gated Fusion Network (GFN) to realize the effective fusion of different channel features and uses SVM as the recognition layer to further improve the classification accuracy. The experimental results show that using single-node (left wrist) sensor data, an accuracy of 98.56% is obtained on the acceleration dataset, and an accuracy of 99.04% is obtained on the angular velocity dataset. The method of this paper is not only applicable to gymnastics action recognition but also can be generalized to other human action recognition fields. [Display omitted] [ABSTRACT FROM AUTHOR]

In this constantly evolving landscape of urbanization, the relationship between technology and automation, in regards to sustainability, holds immense significance. The intricate strands of human intelligence are seamlessly interwoven with the fabric of technological progress, giving rise to exquisite patterns of synergy and collaborative innovation. Automation is just another step in this process which started with the industrial revolution and now has paved way towards urbanization. Smart homes or home automation is a subset of Internet of Things (IoT) based automation that has added into the comfort, ease, and quality of our living standards and is now being integrated to form the concept of Smart Cities. In the past decade, various techniques and processes of smart home automation have been proposed and implemented. To extend and translate the existing methods into new one, the understanding of the former is imperative to the research procedure. This review stands as a comprehensive exploration, diving into the pivotal role of intelligent systems and expert knowledge in driving the transformation of smart homes into sustainable smart cities. By meticulously analyzing and aggregating an array of contemporary techniques used in smart homes, this paper offers profound contributions to the intersection of urban evolution and technological innovation. The review's holistic approach not only facilitates a deep understanding of smart homes' contributions but also charts a course for innovative strategies in city planning, infrastructure, and technological integration. In bridging the gap between technology and sustainable urban development, this exploration underscores the transformative power of leveraging smart home techniques to lay the foundation for harmonious and forward-thinking smart cities. The technologies cover a wide range of methodologies and intelligent systems used for communication, security and management in an urban infrastructure. The paper focuses on analysis of the technology to provide an outlook into achieving the goal of sustainable smart cities and deal with challenges like scalability and big data computation. Our comprehensive analysis yields a holistic set of technology comparisons and illuminates the promising future prospects within this domain. The information is highly insightful in creating a bigger picture for adopting state of the art technologies like Federated Learning (FL), Digital Twin and Embedded Edge computing in better planning and infrastructure management in smart cities. These findings offer reliable and potent methods to chart not only the course of research but also to enhance these technologies for the betterment of mankind's convenience and advancement. • Technical advancement needed for transformation of home automation to a next level. • Smart cities that build from the foundation of homes automations have been discussed. • Comparison with the existing papers to unlock new research directions in this area. [ABSTRACT FROM AUTHOR]

The paper presents a machine learning based Smart Intrusion and Fault Identification (SIFI) method to identify the cyber-physical abnormalities in an inverter-based cyber-physical microgrid (CPM). The SIFI method utilizes an ensemble classifier (EC) to assimilate the decisions from three distinct classifiers (C4.5 decision tree, random forest (RF), and forest by penalizing attributes (FPA)). The proposed method employs a voting mechanism to classify and localize abnormal events for improved accuracy. The paper investigates the effects of cyberattacks (Denial-of-Service (DoS) and Malicious Data Injection (MDI) attacks) and physical abnormalities caused by line faults in microgrids. To train the classifiers, SIFI employs dataset with statistical attributes extracted from measurements. The renewable alternative power systems simulation (RAPSim) software tool is utilized to model the proposed system. The effectiveness of the presented model is assessed with respect to mean value error (MVE). The efficiency of the classifier is demonstrated by comparing its performance with other classifiers in terms of the MVE. For physical abnormalities identification and localization, the approach provides enhanced outputs with the MVE of 0.157 % and 0.162 % correspondingly. For identifying MDI anomalies, the model provides better results with a lower error rate. For DoS anomaly detection, the model provides better classification performance with 0.136 % error. The extensive empirical analysis proves that the anticipated ensemble classifier-based SIFI model yields the minimum MVE for identifying physical faults and cyberattacks. Furthermore, the effectiveness of the proposed method is identified by considering a case study in which the SIFI method outperforms well for the MDI and DoS cyber-attacks in the CPM. [ABSTRACT FROM AUTHOR]

In today's economy, the necessity for responsiveness to variations in customers' permanent requirements is increasing significantly. Achieving immediate response or success in the competitive market requires boosting flexibility, agility, and efficiency in utilizing available production equipment and workforce. Implementing a Virtual Cellular Manufacturing System (VCSM) prevents the reconfiguration costs and is suitable for many systems where the machines are not removable. Owing to the significant role of human resources in VCMS, this paper assigns the workers based on their skills, interest in cooperation, and workload balancing among cells. The considerable novelty is balancing physical and time workloads simultaneously. Another strength of this research is the consideration of various routes to process each part. A comprehensive linear multi-objective mathematical model is developed for a virtual cellular manufacturing system. This paper develops the model by stating social and technical dimensions, which minimize exceptional elements and voids, maximize the workers' interest in cooperation, and minimize workload deviation. Stemming from uncertain parameters, a robust possibilistic approach is addressed. Due to the multi-objective model, the multi-choice meta-goal programming with the utility function is applied. Afterward, a real-case study is presented to validate the model. Eventually, the outcomes and sensitivity analysis are brought up. [ABSTRACT FROM AUTHOR]

Phishing is a form of deception technique that attackers often use to acquire sensitive information related to individuals and organizations fraudulently. Although Phishing attacks have been known for more than two decades, and there is ongoing research for developing effective techniques against these attacks, the increasing trend of attacks confirms the lack of robust solutions and techniques against these attacks. According to Trend Micro, over 90 percent of all Cybersecurity attacks begin with spear Phishing emails and hence there is a need for comprehensive research in the area of anti-Phishing to improve the overall Cybersecurity landscape. This paper, therefore, performs a comprehensive study and analysis of past research work in anti-Phishing. The survey also tries to study various relationships such as those between the Phishers and the motives behind Phishing and explores/assesses various tactics that are employed for launching Phishing attacks. Highlighting the role of social and cognitive factors in the success of a Phishing attack which was not focused on in earlier reviews, is one of the major contributions of this work. The paper also provides a detailed understanding of the types of Phishers and the type of Phishing performed by them with a comprehensive classification of anti-Phishing detection/prevention/awareness solutions through a systematic literature review. The contributions of leading organizations and their active role through various anti-Phishing products are also discussed in this paper to bring light to the research and development happening in the industry with respect to anti-Phishing. Finally, the cyber laws to handle Phishing attacks in various countries have been presented for readers' interest. We believe this survey brings new knowledge and a comprehensive perspective to its readers from academia and industry to explore new horizons for research activities in anti-Phishing. • Classification and discussion of various phishing attacks, motives, and their types. • The role of social and cognitive factors in the success of a phishing attack. • A comprehensive survey of various phishing detection and prevention schemes. • The role of anti-phishing organizations in handling phishing attacks. • A survey of cyber laws across various countries in handling phishing crimes. [ABSTRACT FROM AUTHOR]

In recent years, the application of the internet of things (IoT) in areas such as intelligent transportation, smart cities, and the industrial internet has become increasingly widespread. As a crucial supporting infrastructure, IoT devices are utilized in various fields to construct IoT networks. However, due to the inherent limitations of IoT devices, such as limited computing resources and low memory capacity, security concerns have become increasingly prominent. Among these concerns are Denial-of-Service (DoS) and botnet attacks, which are difficult to prevent due to their large-scale and covert nature. To address these challenges, this paper proposes a Hybrid DoS Attack Intrusion Detection System (HDA-IDS) that combines signature-based detection with anomaly-based detection to effectively identify both known and unknown DoS/botnet attacks. Additionally, this paper introduces a novel anomaly-based detection model called CL-GAN. It integrates CNN-LSTM with GAN to establish a baseline for normal behavior and detect malicious traffic. In contrast to other semi-supervised models, the CL-GAN exhibits superior accuracy, as well as shorter training and testing times, in detecting DoS and botnet attacks. In addition, experimental results demonstrate that the HDA-IDS outperforms other IDSs in detecting DoS and botnet attacks. When tested on datasets such as NSL-KDD, CICIDS2018, and Bot-IoT, the HDA-IDS achieved an average of 5% overall improvement superior performance in terms of accuracy, precision, recall, and F1-Score compared to other works. These results highlight the effectiveness of the proposed system in addressing security issues in IoT networks, and presents a general framework that addresses the challenge of large-scale attacks constructed through the dissemination of false information. [ABSTRACT FROM AUTHOR]

Online group buying platforms (OGBPs) as a new form of e-commerce, offer a new channel for vendors to provide discounts on assorted services through promotion, and attract new customers with the opportunity to experience their services. This paper is conducted based on genuine issues observed in OGBPs where a vendor decides to advertise its service by offering a discount coupon on an OGBP. Accordingly, we propose a fuzzy, dual-channel supply chain, with one OGBP and one vendor who sells her/his service through both offline and online channels. Considering the cognitive uncertainty of parameters in a supply chain, all parameters of the problem are specified as fuzzy variables based on experts' opinions. This paper develops a demand function of online customers depending on online and offline prices, sales period on the OGBP and vendor's credibility. The vendor's credibility is also affected by service level and advertisements. Furthermore, in the competitive OGB market, two game structures, i.e., a centralized model and a decentralized model called as OGBP-leader Stackelberg, are considered under different refunding and revenue sharing scenarios of unredeemed coupons. By comparing these scenarios, the purpose of this paper is to examine the optimal approach for OGBP's pricing strategy and vendor's decisions. Expected value models are developed to evaluate how members decide commission price, online price, service quality, and sales period of a service in each scenario. According to the results, the vendor or OGBP that provide a full refund to the customers maximizes the expected profits compared to No-refund policy; sharing the entire revenue from the coupons sales with the vendor increases the commission price. This increases the online price and reduces the quality, sales period and expected profits; the vendor's generosity, meaning providing a refund to customers and earning revenue from redeemed coupons, is in favor of both the vendor and OGBP; and we investigate the impact of quality and credibility factors under a fuzzy environment on optimal profits. [ABSTRACT FROM AUTHOR]

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time. • Proposed FLCR algorithm with sentence-transformer & k-d tree. • Introduced multi-topic citation recommendation for diverse contexts. • Developed large-scale dataset with 1.7 million citation contexts for evaluation. • Demonstrated significant performance improvement over previous methods. • Deployed demo for real-time citation recommendations. [ABSTRACT FROM AUTHOR]

Constructing a deformation prediction model for dams that can accurately capture deformation trends is crucial to ensure their operational safety. The accurate monitoring data are critical to the presentation of the variation patterns of the monitored quantities. However, due to various disturbances, there can be a lot of noise in the raw deformation monitoring data acquired by the sensors. Therefore, the reduction of the noise level in the data sequence is necessary for the improvement of the accuracy and reliability of the prediction model. This paper presents data denoising techniques for the removal of outliers from data sequences, including complete ensemble empirical mode decomposition with adaptive noise, garrote threshold functions, and sample entropy. The ensemble learning algorithm was then employed to complete the deformation prediction of concrete dams. Through example analysis and comparison with a benchmark model, the proposed model's performance was validated, highlighting its effectiveness and superiority. Additionally, this paper enhances the model's interpretability by visualizing the optimization process. The degree of influence of the input factors on the deformation prediction results is also analyzed using the feature importance metric. The proposed model is expected to be applied to deformation monitoring of other civil and hydraulic structures, as it can effectively reduce the impact of noise on prediction. [ABSTRACT FROM AUTHOR]

An active sonar system consists of a source emitting a sound pulse (ping) and a receiver listening to the reflection of the wave on a target, known as the echo. Such a system is further divided into two distinct configurations. The first one, named monostatic, is made up of a collocated source and receiver, while the second one, referred to as bistatic, is based on a non-collocated source and receiver. To this extent, a Multistatic Sonar Network (MSN) is thus comprised of a set of sources and receivers deployed across a given Area of Interest (AoI), which, taken pairwise, form sonar systems in monostatic and/or bistatic configuration. In this paper, we therefore propose an efficient two-phase greedy heuristic to solve the Area Coverage (AC) problem in the scope of MSNs, a special case of Wireless Sensor Networks (WSNs), while taking into account existing coastlines. For this problem, the objective is to determine the optimal spatial layout of the MSN, i.e. the one that maximize the overall coverage of the AoI with regard to a limited number of sensors and a given probabilistic detection model. Furthermore, we use a Mixed Integer Linear Program (MILP) from the literature as a reference for the numerical experiments conducted on a dataset of diversified instances. The latter were specifically derived from Digital Elevation Models (DEMs) of AoIs selected throughout the globe and in such a way as to encompass a wide spectrum of peculiar geometric situations. • Proposal of a first heuristic dealing with coastlines in multistatic sonar placement. • Taking into account binary and probabilistic models as well as the direct blast. • Creation of a benchmark of 27 instances derived from pre-processed elevation data. • Comparison with the best model in the literature re-implemented in this paper. • Construction of an original visualization tool for the solution output. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AV)s have become a trending topic nowadays since they have the potential to solve traffic problems, such as accidents and congestion. Although AV systems have greatly evolved, it still have their limitations. For example, Google reported that their AVs have been involved in several collisions and near misses. While most of these collisions and near misses were caused by third parties, the AVs should be able to predict and avoid them. Events like this show that there is still room for improvement in the AV system. This paper aims to present a review of the state-of-the-art algorithms proposed to enable AV behaviour prediction systems to predict trajectories and intentions for pedestrians and vehicles. This will be achieved by using information from previous literature review papers, recent works, and results obtained using well-known datasets. • An AV behaviour prediction system must have a long (> 3s) prediction horizon. • It is a challenging task to model interaction between agents. • Datasets from onboard sensors should be used to predict road agents' behaviour. • Deep learning techniques have shown better results to predict behaviours. • Algorithms should be evaluated using the same datasets and evaluation metrics. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are currently the major causes of death globally. Among the strategies to prevent cardiovascular issues, the automated classification of heart sound abnormalities is an efficient way to detect early signs of cardiac conditions leading to heart failure or other, even asymptomatic, complications, quite effective for timely interventions. Despite the significant improvements in this field, there are still limitations due to the lack of solutions, available data-sets and poor (mainly binary — normal vs abnormal) classification models and algorithms. This paper presents a Medical Cyber–Physical System (MCPS) for the automatic classification of heart valve diseases onsite, in a timely manner. The proposed MCPS, indeed, can be deployed into personal and mobile devices, addressing the limitations of existing solutions for patients, healthcare practitioners, and researchers, through an efficient and easy accessible tool. It combines different neural network models trained on a new Italian dataset of 132 adult patients covering 9 heart sound categories (1 normal and 8 abnormal), also validated against two main open-access (Physionet/CinC Challenge 2016 and Korean) datasets. The overall MCPS performance (time, processing and energy resource utilization) and the high accuracy of the models (up to 98%) demonstrated the feasibility of the proposed solution, even with few data. The dataset supporting the findings of this paper is available upon request to the authors. • DB of 132 heart sounds of Italian patients in 9 categories, available upon request. • MCPS for automated HVD classification via hierarchical ML model structures. • Compared six ML classifiers (RF, SVM, NB, LR, DNN, LSTM) for HVD and majority voting. • MCPS validated on two open access datasets (Physionet/CinC 2016 and Korean). • Time, processing and energy efficiency of MCPS validated, with usage guidelines. [ABSTRACT FROM AUTHOR]

Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage. • Comparison of Machine Learning and Cointegration in SHM. • Consideration of damages and false alarms. • Usage of a reduced scale structure to reproduce the behavior of a bridge column. • Cointegration is the fastest to detect damage. [ABSTRACT FROM AUTHOR]

A meta-evolutionary framework called Differential Evolution Ensemble Designer (DEED) has been proposed in this paper to automate the design of DE ensemble algorithms. Given the design components of DE ensembles and a set of optimization problems, DEED evolves effective and robust DE ensemble designs. The design components of DE ensemble algorithms include population management, constituent algorithms in the ensemble, information mixing amongst the sub-populations in the ensemble and the numerical parameters associated with various aspects of the ensemble. DEED employs Dynamic Structured Grammatical Evolution (DSGE) as the meta-evolutionary algorithm. A Backus–Naur form (BNF) grammar has been developed in this paper to represent the design space of DE ensembles and used by DSGE to evolve DE ensemble designs. DEED has been employed to evolve DE ensemble designs for solving 30-dimensional CEC'17 benchmark functions. The evolved designs (both the best design as well as all the final evolved designs) have been validated on CEC'14 and CEC'17 functions at 10, 30 and 50 dimensions and on real-world numerical optimization problems in CEC'11 benchmark suite. The DEED evolved designs have also been tested against the state-of-the-art algorithm configurator - irace. The performance of DEED evolved ensemble designs have been observed to be very competitive against that of manually designed and tuned state-of-the-art DE ensemble algorithms in the literature. DEED has also been demonstrated to evolve both co-operative and competitive style DE ensembles. The simulation experiments demonstrate the effectiveness as well as robustness of the evolved ensemble designs and the reliability of DEED framework in consistently evolving effective DE ensemble designs. • An automated ensemble differential evolution (DE) designer named DEED is presented. • A meta-evolutionary approach to automatically choose ensemble DE design components. • Grammatical evolution forms the meta-evolutionary engine in DEED. • DEED can support researchers and practitioners in the ensemble DE design process. [ABSTRACT FROM AUTHOR]

Citation counts is a crucial factor in evaluating the quality of research papers. Therefore, it is vital to accurately predict citation counts and explore the mechanisms underlying citations. In this study, we focus on predicting the citation counts in the field of statistics. We collect 55,024 academic papers published in 43 statistics journals between 2001 and 2018. Furthermore, we collect and clean a high-quality dataset and then construct multi-layer networks from different perspectives, including journal networks, author citation networks, co-citation networks, co-authorship networks, and keyword co-occurrence networks. Additionally, we extract 77 factors for citation counts prediction, including 22 traditional and 55 network-related factors. To address the issues of zero-inflated and over-dispersed citation counts, a neural network model is designed to achieve high prediction accuracy. Furthermore, we adopt a leave-one-feature-out approach to investigate the importance of these factors. The proposed neural network model achieves an MAE value of 7.352, which outperforms other machine learning models in the comparison. Thus, this study provides a useful guide for researchers to predict citation counts and can be easily extended to other research fields. • Extracting factors based on multi-layer academic networks. • Extracting research topics using keyword co-occurrence network. • Using the neural network model to predict the citation counts. • Indicating network-related factors possess strong predictive. performance. • Measuring factor importance using the leave-one-feature-out method. [ABSTRACT FROM AUTHOR]

One of the expression forms of uncertain information are interval-valued fuzzy information systems (IFISs). Whereas, psychological experiments show that psychological behaviors of decision maker (DM) affect the decision results under uncertain information. Consequently, the main research objectives of this paper are two-fold: 1. To study how to portray the influence of psychological behaviors on decision-making under IFISs, so that the decision-making process is more realistic. 2. To investigate how to efficiently solve decision-making problems in the context of IFISs. Three-way decision (3WD) theory and fuzzy rough sets (FRSs) are effective tools for solving uncertain information. For that reason, this paper develops a 3WD model based on prospect theory (PT) under IFISs, which can provide the best decision action for objects from a viewpoint of optimization. First, an attribute-oriented interval fuzzy set is introduced, which establish a bridge between the state sets in 3WD and the reference points in PT. Furthermore, conditional probability of an object is reasonably estimated by interval-valued fuzzy rough sets (IVFRSs). Meanwhile, an attribute-oriented relative value model is proposed, which can get the prospect values of an object under each action. Second, an intelligent 3WD model is developed from a viewpoint of information granularity. Finally, through the search of the existing literature, relevant research data is obtained, and then case research method, qualitative and quantitative combination method, control variable method are adopted in turn to design the case study, comparative and experimental analyses. In short, the developed model not only expands the development of related theories, 3WD, PT, FRSs, but also offers new methods for solving the decision-making problems under IFISs, which has important practical guidance significance. • An intelligent three-way decision model is developed from a viewpoint of information granularity. • An attribute-oriented interval fuzzy set is introduced. • Conditional probability of an object is reasonably estimated with the help of interval-valued fuzzy rough sets. • An attribute-oriented relative value model is proposed. [ABSTRACT FROM AUTHOR]

Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The overall goal of the FTC is to accommodate defects in the system components while they are in use and maintain stability with little to no performance reduction. These systems are crucial for mission-critical and safety-related applications where the safety of people is at stake and service continuity is crucial. In this review paper, a systematic study has been done for the development of FTC with machine learning, deep learning, and transfer learning algorithms. The challenges and limitations faced with their possible solutions through machine learning theories for the IFTC model are lined up. This paper guides researchers on the different possible types of machine learning algorithms and their advanced forms like deep learning and transfer learning. The differences among these are highlighted by the challenges and limitations of each. The paper is significant such that most of the important literature references from the Scopus database particularly related to important electrical and mechanical industrial problems have been discussed to guide the researchers who want to apply IFTC for specific industrial problems, being the research gap. Finally, future research directions for the development of IFTC are highlighted. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new deformable convolutional network with attention mechanism to deal with the Chinese character of various dynasties. These ancient Chinese characters are hieroglyph with special spatial structure and gradually evolving into modern Chinese characters. However, because of the intrinsic limitations of traditional convolutional networks in the model, the geometric structure of the convolutional module is fixed. Therefore, we build a new architecture called attention-based deformable networks for this task. Besides, we construct a new dataset called the Chinese characters from Various Dynasties Dataset (CCDD), which includes the evolution of Chinese characters between major dynasties. Deformable convolution based on attention mechanism can obtain those more important offsets, resulting in better performance than the original deformable convolution. In the experimental stage, extensive experiments validate the performance of our approach. Firstly, the effectiveness of this network was verified on two text datasets, Mnist(99.38%) and notMnist(98.22%) both comparing with several modules. Finally, the best performance was achieved on the CCDD dataset(91.50%) compared to other classical deep learning networks. This is the first paper to apply deep learning methods to recognize and classify the characters of major dynasties in China. And archaeological workers need to be able to recognize Chinese characters from various periods in China, and our algorithm can assist them in completing this task to a certain extent. It can also help cultural relic enthusiasts roughly determine the age of cultural relics. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KGs) model entities or concepts and their relations in a structural manner. The incompleteness has turned out to be the main challenge that hinders the application of KGs. Recently, reinforcement learning (RL) has been recognized as an effective method to deal with such a challenge, which models research tasks into a sequence decision problem without labels. Although an increasing number of studies investigate and analyze KGs using RL, there lacks a systematic literature review that comprehensively and quantitatively analyzes the landscape of RL-based KG research (RL-KG for short). As a result, researchers may have encountered difficulties in appropriately adopting RL techniques in KG research, even reinventing the wheels. In this paper, we follow the Systematic Literature Review (SLR) methodology to survey, screen, and investigate papers of RL-KG. Specifically, we identify 109 highly related papers from 1542, and systematically investigate them with regard to the following five research questions: (1) to what extent RL-KG have been investigated; (2) what application domains have been covered; (3) what RL techniques have been mainly considered; (4) whether there is a connection between the influence and reproducibility of these papers; (5) what specialized datasets, evaluation metrics, and publication venues have been applied. Through an in-depth analysis of the review results, we systematically and comprehensively identify some significant phenomena and analyze the reasons and difficulties of these phenomena. Based on such analysis, we tentatively propose promising future research topics to promote the RL-KG. • Researchers have difficulties in applying reinforcement learning in knowledge graph. • Regarding the design of Markov decision process, existing works need to be improved. • Most researchers ignore the advantages of actor–critic-based algorithms. • Only the monotonous cooperation between agents has been explored by existing works. • Most of the existing work lacks the application of domain knowledge. [ABSTRACT FROM AUTHOR]

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring. • Marine environments and fish habitats are widely surveyed using underwater cameras. • Deep learning (DL) offers an effective solution for processing underwater videos. • We write a tutorial on the key concepts and steps of DL for underwater monitoring. • We provide a survey of key DL-based visual fish monitoring methods/datasets. • We shed light on challenges/opportunities of DL for underwater fish monitoring. [ABSTRACT FROM AUTHOR]