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Automatically generating a survey draft is a challenge to text summarization research because it needs to select important sentences from important references in a large set of candidate papers for composing sections that are in line with section titles and different sections discuss the most relevant reference papers of different number, which are beyond the capability of previous text summarization approaches as they assume that all candidate papers should be included into one summary. This paper proposes an approach to generating survey draft according to a pattern consisting of sections with titles given by the user who requests the survey. The problem of generating each section can be divided into the following sub-problems: (1) rank the input scientific documents (in short documents) according to the title of a section, (2) determine the number of documents that are most relevant to the title, and (3) rank and select sentences from the selected documents according to the title. A position closeness distance of key word is proposed to rank a set of documents by measuring how closely two key words within section title are distributed within each document, which is used to rank the documents. The rationale is that the positions of the neighboring key words of a section title should be closer in more relevant documents than other words. As different sections have different number of selected documents, a method is proposed to determine the number of documents to be included into the current section based on the slope shape of the sorted rank curve of documents according to the section title. Based on the duality property of the closeness, ranks of sentences within a document can be directly obtained when the document is ranked according to the title of section, and both the importance and coherence of selected sentences can be reflected without extra calculation for ranking sentences. Experiments and manual evaluation show that the proposed methods achieve significant improvements compared with other approaches. The proposed approach is significant in applications as different surveys can be generated according to different patterns given by different users. [ABSTRACT FROM AUTHOR]

The emergence of artificial intelligence (AI) and its related capabilities has led industries to rethink the existing practices of conventional supply chain management and data analysis. Machine learning (ML), Deep Learning (DL) and their unique ability to predict future data and classify data have led to important research in the supply chain (SC) domain, particularly in identifying and prioritizing supply chain risks. This paper proposes several DL methodologies to exploit the benefit of DL, particularly to identify whether any product will be delivered late due to any unforeseen reason in a complex SC system. Four different DL architectures (Simple-LSTM, Deep-LSTM, 1D-CNN, and TCN-1DSPCNN models) are proposed to extract features, while six variant classifiers: Softmax, random trees (RT), random forest (RF), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM), were used to classify delay or non-delay information. By seamlessly capturing intricate temporal dependencies, these DL models enhance accuracy in robustly identifying supply chain late orders. Leveraging their hierarchical feature learning, these proposed DL models excel in recognizing subtle patterns and correlations, making them ideal for classifying late orders within the supply chain. Their parallel processing prowess facilitates real-time decision support, allowing organizations to address potential delays and allocate resources effectively and proactively. Five-fold cross-validation is presented to avoid over-fitting and to prove the efficiency of the proposed DL models. The total accuracies of the six ML classifiers are 74.03, 75.81, 93.35, 87.72, 93.59, and 95.10, respectively, while the maximum accuracies obtained from four proposed DL methodologies obtained an accuracy of 97.6, 98.63, 100, 100% respectively using the SVM classifier for predicting late orders based on five-fold cross-validation. • This paper investigates a few DL approaches to extract features of SC data. • Both RNN and CNN are applied in the same model. • An improved CNN model has been proposed for feature extraction. • An online dataset is employed to validate the proposed DL architectures. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of multi-institutional regionalization of primary health care units. The problem consists of deciding where to place new facilities, capacity expansions for existing facilities, and demand allocation in a multi-institutional system to minimize the total travel distance from demand points to health care units. It is known that traditional exact methods as branch-and-bound are limited to solving small- to medium-size instances of the problem. Given that real world-instances can be large, in this paper we propose an iterated greedy algorithm with variable neighborhood descent search for handling large-scale instances. Within this solution framework, several methods are developed. A greedy constructive method and two deconstruction strategies are developed. Another interesting component is the exact optimization of a demand allocation subproblem that is obtained when the location of facilities is previously fixed. An empirical assessment using real-world data from the State of Mexico's Public Health Care System is carried out. The results demonstrate the effectiveness of the proposed metaheuristic in handling large-scale instances. [ABSTRACT FROM AUTHOR]

Multiple Instance Learning (MIL) is a learning paradigm, where training instances are arranged in sets, called bags, and only bag-level labels are available during training. This learning paradigm has been successfully applied in various real-world scenarios, including medical image analysis, object detection, image classification, drug activity prediction, and many others. This survey paper presents a comprehensive analysis of MIL, highlighting its significance, recent advancements, methodologies, applications, and evolving trends across diverse domains. The survey begins by explaining the core principles that form the basis of MIL and how it differs from traditional learning approaches. This sets the foundation for comprehending the distinct challenges and techniques of solving MIL problems. Next, we discuss how supervised learning algorithms are tailored to support MIL and combine this discussion with a review of seminal MIL algorithms as well as the latest innovations that incorporate neural networks, deep learning architectures, and attention techniques. This comprehensive analysis helps to understand the strengths, limitations, and adaptability of these methods across diverse data modalities, complexities, and applications. In summary, this survey paper provides an essential resource for researchers, practitioners, and enthusiasts seeking a comprehensive understanding of Multiple Instance Learning. It covers foundational concepts, traditional methods, recent advancements, and future directions. By providing a holistic view of MIL's dynamic landscape, this paper aims to inspire further innovation and exploration in this ever-evolving field. • A survey on the current state of the Multiple Instance Learning. • We provide applications of Multiple instance learning in various domains. • We discuss how existing supervised learning algorithms are modified to support MIL. • Publicly available datasets and open research challenges in MIL are discussed. [ABSTRACT FROM AUTHOR]

The existing feature selection algorithms often utilize local structure of data, but do not fully mine internal structure and ignore potential correlation information between samples. To address the problems and fully utilize manifold information of data, this paper proposes non-convex feature selection based on feature correlation representation and dual manifold optimization (FDNFS). Firstly, FDNFS constructs feature graph of original data, which can obtain feature correlation representation to represent the interconnection information. Based on the obtained interconnection information, FDNFS unifies feature correlation representation learning and feature selection through feature transformation matrix, so that the interconnection information between data guides feature selection. Secondly, to make multivariate frameworks guide feature selection, FDNFS introduces self-representation on the improved sparse regression model. Using self-representation can make basis matrix and reconstruction coefficient matrix reconstruct original data more accurately. Next, in order to preserve local structure information abundantly, FDNFS has two-part manifold regularization on the pseudo-label matrix in sparse regression model and the reconstructed coefficient matrix in self-representation framework. This can fully use the manifold information of data. In addition, FDNFS imposes the non-convex constraints. It can ensure the sparsity of feature selection matrix. In turn, this can select features with lower redundancy, and then select a better feature subset. Finally, this paper adopts an iterative optimization method. FDNFS is compared with nine algorithms on seven datasets. The clustering results reflect better performance of FDNFS. • Feature correlation representation and dual manifold optimization is proposed. • It constructs feature graph to represent the interconnection information. • It unifies feature correlation representation learning and feature selection. • It introduces self-representation on the improved sparse regression model. • It has two-part manifold regularization on the pseudo-label matrix. [ABSTRACT FROM AUTHOR]

The research on bipedal robots with limited foot area is gaining increasing attention. To tackle the challenge of dealing with unknown disturbances in the environment, the adjustment of footstep placement plays a vital role in maintaining stable motion during bipedal walking. This paper introduces an innovative approach based on the relationship between the Divergent Component of Motion (DCM) and footstep. It utilizes a DCM prediction model to optimize the optimal speed for recovering the foothold position. The goal is to enable quick and relevant footstep selection for bipedal robots, thereby facilitating the swift recovery of robot speed. The paper provides insights into the process of designing the desired DCM for achieving an optimal average walking speed without relying on predefined footstep sequences. By establishing a state equation between the DCM and footstep placement, this approach enables the prediction of multiple footstep positions within a fixed walking cycle, thereby facilitating the desired average motion speed. Extensive numerical simulations are conducted to compare the proposed method with various conventional footstep adjustment algorithms. The results emphasize our method's ability to converge more rapidly to the target speed, even with minor step adjustments. To validate the feasibility and robustness of the algorithm, we conduct experiments on the bipedal robot BHR-B2. These experiments further confirm the algorithm's effectiveness. Given its promising performance, this algorithm holds potential for applications in legged robots with point feet. • The adjustment of the footstep is crucial for the speed recovery of bipedal walking. • A multi-step prediction footsteps adjustment algorithm based on DCM is proposed. • The desired speed of motion is mathematically related to the target DCM state. • Three adjustment algorithms were compared through simulation. • Algorithms have been applied and effectively validated on a bipedal platform. [ABSTRACT FROM AUTHOR]

The surveillance or monitoring of places is crucial to ensuring security, protecting people and assets, preventing crimes, and detecting emergencies, to mention some. Unmanned Aerial Vehicles (UAVs) play a vital role in these applications, offering versatility, agility, and aerial vision. A crucial step for such tasks is to protect the UAV path ahead. This paper focuses on a methodology harnessing the unpredictable nature of chaotic systems to generate trajectories around a closed area or contour. However, although a vast quantity of research papers mention the use of chaotic path generation, they have yet to learn about the control system and the dynamics affecting the UAV, where developing the control theory is challenging. In this paper, we design controllers based on predetermined-time stability, ensuring the achievement of the desired trajectory before a specified time. Additionally, adjusting control parameters is a crucial step during the control design, impacting the control performance. Hence, we present a method to optimize and adapt controller parameters through evolutionary optimization, demonstrating precision enhancement. We validate the proposed system's performance and the controllers through numerical simulations, indicating that the UAV effectively and accurately follows some types of chaotic trajectories like a square contour, aiming at the feasibility of this methodology in real UAV surveillance applications. • Design of Predefined-Time Control (PTC) for chaotic trajectory tracking with UAVs. • Generation of complex and unpredictable trajectories based on chaotic systems. • Optimization of controller parameters by Differential Evolution metaheuristic. • Lyapunov analysis for the design and convergence of Predefined-Time Controllers. [ABSTRACT FROM AUTHOR]

This paper proposes physics-informed meta-learning-based surrogate modeling (PI-MLSM), a novel approach that combines meta-learning and physics-informed deep learning to train surrogate models with limited labeled data. PI-MLSM consists of two stages: meta-learning and physics-informed task adaptation. The proposed approach is demonstrated to outperform other methods in four numerical examples while reducing errors in prediction and reliability analysis, exhibiting robustness, and requiring less labeled data during optimization. Moreover, compared to other approaches, the proposed approach exhibits better performance in solving out-of-distribution tasks. Although this paper acknowledges certain limitations and challenges, such as the subjective nature of physical information, it highlights the key contributions of PI-MLSM, including its effectiveness in solving a wide range of tasks and its ability in handling situations wherein physical laws are not explicitly known. Overall, PI-MLSM demonstrates potential as a powerful and versatile approach for surrogate modeling. • A proposed approach uses meta-learning and PIDL to train surrogates with small data. • A model-agnostic meta-learning model is introduced to learn surrogate model weights. • A PIDL approach is introduced to adapt to target tasks with meta-learned weights. • Our approach outperformed other methods in numerical examples given limited data. • Proposed approach showed robustness in solving out-of-distribution tasks. [ABSTRACT FROM AUTHOR]

In the context of early childhood education, students need to acquire fundamental writing skills for their lifelong development. Public schools, especially in low- and middle-income countries, should monitor individual student progress to mitigate the detrimental effects of socioeconomic vulnerabilities in education. Furthermore, the volume of students often overwhelms teachers responsible for assessing handwriting texts and providing feedback. This article conducts a Systematic Literature Review (SLR) focusing on solutions for automatically evaluating students' handwriting, discussing their performance, future research directions, and areas needing further investigation. We used a mixed-methods approach to conduct our SLR, encompassing a search across four databases (ACM Digital Library, IEEE Xplore, ScienceDirect, and SpringerLink) and employed the snowballing technique. We used the initial set of papers from the database search as the foundation for the subsequent snowballing search. Findings revealed that the studies introduced computational techniques, examined or enhanced existing methods, and developed assessment tools. These solutions predominantly employed techniques such as artificial neural networks and used available datasets comprising handwritten images, answers, or student essays. Only some studies provide low-cost solutions for automatically assessing the writing abilities of underserved public school students. • We reviewed 491 papers, extracting data from 22 focusing on handwriting assessment. • We applied a mixed method using database search and the snowballing technique. • Studies frequently adopt deep learning methods to tackle the handwriting recognition problem. • Studies employ various methods to automatically assess text production, such as ANN, latent semantic analysis, content vector analysis, and CNN. • The mean accuracy for handwriting assessment was notably high at 93.64%, suggesting that the models in this group exhibited good performance. [ABSTRACT FROM AUTHOR]

Adversarial attacks can mislead deep learning models to make false predictions by implanting small perturbations to the original input that are imperceptible to the human eye, which poses a huge security threat to computer vision systems based on deep learning. Physical adversarial attacks, which is more realistic, as the perturbation is introduced to the input before it is captured and converted to a image inside the vision system, when compared to digital adversarial attacks. In this paper, we focus on physical adversarial attacks and further classify them into invasive and non-invasive. Optical-based physical adversarial attack techniques (e.g. using light irradiation) belong to the non-invasive category. The perturbations can be easily ignored by humans as the perturbations are very similar to the effects generated by a natural environment in the real world. With high invisibility and executability, optical-based physical adversarial attacks can pose a significant or even lethal threat to real systems. This paper focuses on optical-based physical adversarial attack techniques for computer vision systems, with emphasis on the introduction and discussion of optical-based physical adversarial attack techniques. [ABSTRACT FROM AUTHOR]

• Comprehensive CHP unit scheduling formulation. • Quality assurance through optimal gap evaluation. • Two-stage stochastic approach with MINLP. • Uncertainty management in unit commitment. • Modeling of electrical and thermal ramp constraints. Renowned for their remarkable overall efficiencies ranging from 70% to 90%, combined heat and power systems stand as a pivotal strategy for optimizing energy consumption by capitalizing on the synergistic relationship between electricity and thermal energy production. However, achieving optimal performance in combined heat and power systems remains a formidable challenge due to the intricate interplay of numerous variables. This paper presents a novel approach to daily scheduling optimization for combined heat and power units, focusing on the integration of electrical and thermal storage systems and meticulous consideration of security constraints. The optimization of combined heat and power unit scheduling introduces a mixed-integer nonlinear programming challenge, replete with deterministic and random variables. Addressing this complexity requires resilient solutions. In this study, we employ a multilevel optimization technique, transforming the problem into a bilevel structure. The initial step involves mapping operating parameters and minimizing costs through a water cycle optimization algorithm, laying a robust foundation for combined heat and power unit operation. Subsequently, we immerse ourselves in the realm of stochastic contingency scenarios, acknowledging the myriad uncertainties inherent in real-world systems. To empirically validate the efficacy of our proposed algorithm, extensive simulations are conducted on IEEE 18-bus and 24-bus test systems that emulate practical power networks. The results unequivocally showcase the power of our approach in navigating the complexities of optimal CHP unit planning. This paper's contributions lie in its innovative multilevel optimization technique, adeptly addressing both deterministic and stochastic aspects, ultimately paving the way for increased energy efficiency and enhanced system reliability in practical applications. [ABSTRACT FROM AUTHOR]

With the wide application of vision in the fields of autonomous driving and medical imaging, the demand for overexposure image correction algorithms is becoming increasingly urgent. However, existing overexposure image correction algorithms can lead to problems such as blurring, color bias, and over-enhancement of the generated images. Optimizing overexposure image quality has a significant impact on improving system performance, accuracy, and safety. In this paper, we propose an overexposure image correction network. First, we built the Detail Enhancement Module (DEM). It adopts global average pooling on each channel of the input feature map. After pooling, an activation function is used for nonlinear mapping to generate a channel attention weight vector. And it is multiplied with the original input feature map to achieve the purpose of enhancing the details of the overexposed image. Second, we construct a context-aware backbone (CAB) to extract features such as color and texture. The linear attention gating mechanism replaces the multi-head attention module in Transformer, and reduces the computational complexity in high-resolution images while maintaining performance by learning linear transformation and attention gating. Finally, we design an attention-guided feature fusion (AGFF) to fuse shallow and deep features. It computes weight vectors for shallow features through an attention module. The calculated result is converted to the same dimension as the input feature by bilinear interpolation, so as to enrich the semantic information and detailed information of the generated image. In addition to designing the network structure, we design a hybrid loss function to improve the quality of the generated image from the spatial and structural aspects, and the exposure function can correct the exposure degree of the generated image. Experiments are conducted on two public datasets and the dataset in this paper. Specifically, the PSNR and SSIM of images generated on the dataset MSEC increased by 1.3813% and 5.56%. The PSNR and SSIM of images generated on the dataset SICE increased by 1.545% and 4.64%. The proposed method can effectively generate clear and high-fidelity images. • An end-to-end overexposure correction algorithm. • The context-aware networks is constructed to extract exposed feature information. • The exposure loss is employed to prevent over-enhancement. • The proposed model aims to generator normally exposed images. [ABSTRACT FROM AUTHOR]

In 2019, a chaotic image encryption scheme based on a variant of the Hill cipher (VHC-CIES) was proposed by the Moroccan scholars. VHC-CIES introduces a Hill cipher variant and three improved one-dimensional chaotic maps to enhance the security. In this paper, we conduct a comprehensive cryptanalysis, and find that VHC-CIES can resist neither chosen-plaintext attack nor chosen-ciphertext attack due to its inherent flaws. When it comes to chosen-plaintext attack, firstly, we select a plaintext with the pixel values are all 0 and its corresponding ciphertext, and then use algebraic analysis to obtain the equivalent key stream for cracking VHC-CIES. Secondly, we select a plaintext which the pixel values are invariably 1 and obtain its corresponding ciphertext to obtain some Hill cipher variant parameters of VHC-CIES. Finally, we use the resulting steps of the first two to recover the original plain image from a given target cipher image. Similarly, a chosen-ciphertext attack method can also break VHC-CIES. Theoretical analysis and experimental results show that both chosen-plaintext attack and chosen-ciphertext attack can effectively crack VHC-CIES with data complexity of only O (2). For color images of size 256 × 256 × 3 , when our simulation encryption time is 0.3150 s, the time for complete breaking by chosen-plaintext attack and chosen-ciphertext attack is about 0.6020 s and 0.9643 s, respectively. To improve its security, some suggestions for further improvement are also given. The cryptanalysis work in this paper may provide some reference for the security enhancement of chaos-based image cryptosystem design. [ABSTRACT FROM AUTHOR]

The human-centric philosophy is considered a crucial development direction for smart Product-Service Systems (smart PSS). Smart PSS has effectively advanced the circular economy by enhancing user experience, incorporating servitization and digital servitization, and extending product lifecycle, among other key aspects of sustainable development. However, there is currently a lack of a reasonable, accurate, and actionable framework and methodology to express the human element, namely user profiling, within this philosophy. To address this gap, this paper utilizes statistical methods such as factor analysis, cluster analysis, and regression analysis, building upon the concept of traditional user profiling. The aim is to integrate the three prominent approaches of goal-oriented, scenario-based, and data-driven user profiling, with the goal of complementing each other and designing a top-level user profiling framework for smart PSS. Furthermore, Industry 4.0 technologies and text mining techniques are employed to collect data on users' product and service usage. As these data contain real-time information about user needs, behaviors and goals at different time periods, they can be used to construct dynamic features of user profiling, and ultimately achieve the construction of dynamic user profiling for smart PSS. To validate the proposed user profiling framework and dynamic user features of smart PSS, this paper presents a case study focusing on the user group of maternal women. This case study promotes the in-depth exploration of smart PSS research in expressing the human element. [ABSTRACT FROM AUTHOR]

Hot-rolled strip products have diverse applications, and enhancing the detection, diagnostics, and prognostics of product quality during hot rolling is essential. Nevertheless, the multivariable, strong coupling, nonlinear, and time-varying nature of the production process poses a rigorous challenge for accurate hot-rolled prognostics. This paper implements a progressive layered extraction (PLE) multi-task learning (MTL) framework to simultaneously estimate multiple quality indicators, such as strip crown, center line deviation, exit temperature, wedge, width, and symmetry flatness. Additionally, the paper proposes the implements of Hybrid Bayesian Neural Network (HBNN) experts and a gating network with attention mechanism to integrate private and shared task features. It also puts forth an auxiliary task involving a Variational Autoencoder with Generative Adversarial Networks (VAE-GAN) to extract latent states from the original sequence. Moreover, an adaptive joint loss optimization is employed to update the weight of individual task losses for MTL training problems, and three sets of field hot-rolled datasets are used for model evaluation. In the experimental validation, considering the noisy field data and limited conditions in the real hot rolled production, comparative experiments are conducted to demonstrate the improved generalization and robustness of the proposed MTL approach. These experiments involve different percentages of the total data, ranging from 5% to 20%, and various prediction horizons ranging from 1 to 50 steps for model establishment. In addition, the paper discusses the interpretation of the model and strategies for further enhancing model performance. [ABSTRACT FROM AUTHOR]

Keyphrase Extraction is an automatic task that involves identifying the key words or phrases that capture the main content of an article. It is useful for various downstream tasks, including text search, text clustering, and text classification. Embedding-based methods for keyphrase extraction have shown promising results by utilizing pre-trained language models to represent candidate phrases and documents separately. These methods then rank the candidate phrases based on the cosine similarity between the document and the candidate phrases embeddings. However, there are mainly two shortcomings in such methods: I) Redundancy errors, when there are partial repetitions of candidate keyphrases, the methods tend to use redundant long phrases as keyphrases; II) Low keyphrase coverage, such as some keyphrases used to describe locally important information are ignored. In this paper, we propose an unsupervised keyphrase extraction method called "MICRank", which evaluates the importance of candidate keyphrases from three perspectives: global information, local information, and attribute information, and solved the aforementioned issues. The experimental results on six benchmarks demonstrate that the proposed MICRank method outperforms the state-of-the-art unsupervised keyphrase extraction methods. In addition, this paper improves the judgment criterion of correct keyphrase extraction and introduces a new evaluation metric called S1@M (M ∈ {5,10,15}) to address the issue of synonyms being considered incorrect predictions. [ABSTRACT FROM AUTHOR]

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to aid game developers in finding the most suitable pathfinding algorithm for their games. Despite recent advancements in this field, there are few available studies that can be compared due to the absence of a standard benchmark set for weighted environments. This paper presents a new dataset for pathfinding in weighted environments. Furthermore, an investigation was conducted into the impact of node weights on pathfinding speed, and a correlation between them was identified. The complexity added to the maps due to node weights was defined as weight complexity, and two metrics were introduced to estimate it. The weight correlation factor has been identified as the most effective metric for estimating the weight complexity of the map. Another contribution of this paper pertains to the development of a model for estimating the pathfinding speed of algorithms based on weight complexity. This was accomplished through the utilization of the non-linear least squares method, which was applied to create a model for each algorithm, considering both its average search time and weight correlation factor values associated with the map. Finally an overall score metric was developed by using the integral of the models, enabling the evaluation of different algorithms in various scenarios. [Display omitted] • Testing JPSW (weighted jump point search) algorithm search speed. • Introducing the first standardized benchmark for weighted gridmaps. • Introducing a new metric for analyzing gridmap complexity. • Using edge detection techniques for weighted gridmap analysis. • Introducing a scoring system for pathfinding algorithms in weighted environments. [ABSTRACT FROM AUTHOR]

• The interval data effectively aggregated by the induced continuous aggregation operator. • The Shanghai composite index can be accurately predicted by the combination prediction model. • Thorough study of effectiveness theory of the combination prediction model has been completed. This paper proposes an interval combination prediction model for Shanghai composite index, utilizing the Theil coefficient and the induced continuous generalized ordered weighted logarithmic harmonic averaging (ICGOWLHA) operator. The effectiveness of the proposed model under specific weight conditions and the existence of its analytical solution are demonstrated. Shanghai composite index's case analysis demonstrates that, in terms of interval root mean squared error (IRMSE), interval mean absolute error (IMAE), interval mean absolute percentage error (IMAPE), and interval mean squared percentage error (IMSPE), the proposed model's predictive performance improvements over the best-performing single prediction model are 29.33%, 25.72%, 26.10%, and 28.86%, respectively. At the same time, the theoretical properties of the model are verified in the results of the case analysis, and the model's convergence is reflected in sensitivity analysis. Through extensive model comparisons, it is observed that the model proposed in this paper exhibits strong generalization, without specific limitations on data size or feature count. It demonstrates good aggregation prediction performance for interval data. Moreover, it is applicable to various fields, including finance, environment, and others. [ABSTRACT FROM AUTHOR]

Automatic humor detection has compelling use cases in modern technologies, such as humanoid robots, chatbots, and virtual assistants. In this paper, we propose a novel approach for detecting and rating humor in short texts based on a popular linguistic theory of humor. The proposed technical method initiates by separating sentences of the given text and utilizing the BERT model to generate embeddings for each one. The embeddings are fed to a neural network as parallel lines of hidden layers in order to determine the congruity and other latent relationships between the sentences, and eventually, predict humor in the text. We accompany the paper with a novel dataset consisting of 200,000 short texts, labeled for the binary task of humor detection. In addition to evaluating our work on the novel dataset, we participated in a live machine-learning competition to rate humor in Spanish tweets. The proposed model obtained F1 scores of 0.982 and 0.869 in the performed experiments which outperform general and state-of-the-art models. The evaluation results confirm the model's strength and robustness and suggest two important factors in achieving high accuracy in the current task: (1) usage of sentence embeddings and (2) utilizing the linguistic structure of humor in designing the proposed model. • A novel method for humor detection and rating based on a general linguistic theory of humor. • Introduced a very large novel dataset with 200k short texts for humor detection. • Achieved 98% accuracy and outperformed five strong baselines on the new dataset. • Demonstrated accuracy and robustness in a data science competition for Spanish texts. • The novel approach and dataset contributed to tens of research projects. [ABSTRACT FROM AUTHOR]

Disruptions occurring at an urban rail transit (URT) system can severely affect its normal operations, and an effective bus bridging service (BBS) is able to help to reduce the negative effects. Transit operators usually arrange BBS to depart from the disrupted stations to evacuate the stranded passengers. However, the overload of passengers at the disrupted stations, especially at turnover and transfer stations, may incur the secondary operation disruptions such as stampede accidents. To mitigate the negative effects of disruptions and reduce the number of passengers stranded at the disrupted stations, the strategy based on the passenger guidance and extended BBS (E-BBS) is introduced in this paper. Different from the widely applied standard BBS running within the disrupted links, E-BBS runs among the normal operating stations or between the normal operating stations and the disrupted stations. A mixed integer linear programming model is developed in this paper to guide the stranded passengers and design an optimal E-BBS solution to transport them. Considering the bi-directional running trains along the disrupted links, a dynamic decision framework is developed to manage the disruptions. Given the impacts of the high uncertainty on bus running time which could affect the performance of E-BBS, a robust model is proposed to obtain more reliable travel guidance and E-BBS schemes. Numerical experiments based on Chengdu subway system in China are conducted. The results indicate that the proposed model can obtain optimal travel guidance and E-BBS solutions in a timely manner. When the uncertainty on bus running time is ignored, the total travel cost for affected passengers is reduced 14.20 % on average with the aid of the optimal travel guidance and E-BBS solutions. Moreover, the number of passengers gathering at the disrupted stations decreases by 36.80 %. The robust model can obtain more reliable travel guidance and E-BBS schemes in consideration of uncertain bus running time. The proposed model shows great potential to effectively mitigate the negative effects of disruptions and help to enhance the capability of a URT system to respond to disruptions. [ABSTRACT FROM AUTHOR]

• The 2S attack matrix on the actual time sequence with the two loopings. • The precise forensics' timeline on the real case attack. • The in-depth technical details of the attacks within the energy sector. • The insight into the attacker's behaviors for network admins. • The protection begins with a clear understanding of the ransomware attack pattern. Due to the Coronavirus pandemic (COVID-19) throughout 2020–22, remote working has played an important part in organizations, businesses, and agencies worldwide. This situation makes the various cybersecurity threats the Internet poses, especially ransomware. Ransomware will remain the top cybersecurity threat, and the energy sector is the prime target. Previously, research papers only focused on the analytical and protection frameworks. These papers rarely provide real evidence and detailed digital forensics. Interestingly, the ransomware gangs developed new methods but still used similar attack patterns. The authors envisage that a precise understanding of the ransomware attack characteristics is a starting point for the correct detection process. This paper presents a true case study demonstrating the actual occurrence of digital forensics and in-depth technical details of the attacks within the energy sector. The significant attack patterns, which have never been emphasized in research papers, can be proposed for the two loops. The results led to a novel ransomware attack matrix with two loop patterns (dwell time factor) applicable to other ransomware gangs for the detection stage of the NIST. [ABSTRACT FROM AUTHOR]

A dual stator winding induction motor (DSWIM) has two isolated three-phase windings in its stator, and its rotor is a usual squirrel cage type. In a sensor-less DSWIM drive, the standard and optimal operating mode in all operating speed ranges is the synchronous mode. Until now, researchers use the asynchronous mode to control it in the low speed range that is not the standard mode. But, this paper proposes a new intelligent model reference adaptive system (IMRAS) to control a DSWIM drive without the speed sensor based on a ten-switch converter in the low speed range. In the proposed intelligent sensor-less DSWIM drive, the rotor speed is estimated via an intelligent model as single and bi-objective functions. In the proposed IMRAS, the second objective increases the speed estimation accuracy at very low speeds. In this idea, the rotor speed error is used as data in the rotor speed estimation process. As a result, the convergence between estimated speed and reference speed in the proposed IMRAS is increased at zero and very low speeds. Also, in this paper, the power losses, the capital cost, and the number of switching elements in the converter are reduced via the concept of flux compensation and using a ten-switch converter. The intelligent proposed sensor-less DSWIM drive, unlike conventional methods, works in its standard operating mode (synchronous mode) and has a suitable performance in the low speed range. Hence, the converter power losses are significantly decreased. The suggested techniques are simulated in MATLAB software. The simulation of the proposed DSWIM drive system is performed under different scenarios of operating conditions and the obtained results approve the assumptions. [ABSTRACT FROM AUTHOR]

The transformer architecture with its attention mechanism is the state-of-the-art deep learning method for sequence learning tasks and has achieved superior results in many areas such as NLP. Utilizing the transformer architecture for the prediction of sequential time series such as financial time series has hardly been investigated in previous studies. In this research paper, the transformer architecture with time embeddings is used in foreign exchange (FX) trading, the world's largest financial market, and tests its suitability. A systematic comparison is made between transformer and benchmark models. It also examined which influence multivariate, cross-sectional input data have on the forecasting performance of the various models. The goal of the paper is to contribute to the empirical literature on FX forecasting by introducing a transformer with time embeddings to the forecasting community and assessing the accuracy of corresponding models by forecasting exchange rate movements. Empirical results indicate the suitability of transformer models for FX-Spot forecasting in general but also evidence the need for transformer models for multivariate, cross-sectional input data to outperform other state-of-the-art neural networks such as LSTM. [ABSTRACT FROM AUTHOR]

Largely inspired by the practice that both the brand name supplier and the platform motivate to encroach on the online retailing market, this paper investigates the optimal encroaching decisions in an online retailing marketplace that is currently owned by multiple online retailers. Depending on whether and who will encroach on, this paper conducts three different encroaching scenarios including the benchmark without encroaching, the brand name supplier encroaching, and the platform encroaching. A Stackelberg game model is then developed to capture competitions among three parties and thus address equilibria in this paper. With analytical studies and numerical experiments, several key findings are derived. For example, but not limited to, the optimal commission set by the platform is contingent largely upon the encroaching scenarios and the number of online retailers. Interestingly, the optimal commission tends to be consistent when there are as many online retailers as possible, regardless of whether and who will encroach. This paper also analytically reveals that the encroaching cost, the product inherent (the fraction of product cost over its market potential), and the optimal commission, induce key impacts on the individuals' retailing encroaching decisions. A free rider effect incurred by the retailing encroaching is found and examined in this platform supply chain. The results derived in our paper shed lights on operational and encroaching decisions for the platform supply chain. • Develop a game theoretical model in an E-commerce platform supply chain with multiple online retailers. • Explore an endogenous commission set by the platform across different encroaching scenarios. • Investigate the optimal retailing encroaching decisions for two potential entrants. • Reveal impacts of retailing encroaching on individual profits and find a free ride effect incurred by the encroaching. [ABSTRACT FROM AUTHOR]

The users' click prediction holds significant commercial value in online purchase platforms. Currently, some session-based interest extraction models overlook the impact of temporal interval factors on the distribution pattern of behaviors within a session. To address the above issue, this paper designs a module named session-based time-aware gated recurrent units (ST-GRU) to integrate the temporal interval factors into session sequence. The ST-GRU module utilizes a memory decay function, enabling the simulation process to identify the users' memory changes within each session. Subsequently, to account for the interdependence between different sessions, this paper introduces bi-directional gated recurrent units (Bi-GRU) with an attention mechanism to learn interaction relations of user interests. Additionally, for high-dimensional potential features, this paper integrates above modules and proposes a method called time interval aware deep session interest network (TIAE-DSIN) model. Correspondingly, comparative experiments are carried out on three public datasets, and the obtained results indicate that the AUC of TIAE-DSIN model is superior to other session interest extraction models. [ABSTRACT FROM AUTHOR]

The Baltic Dry Index (BDI) is an essential index to measure international dry bulk shipping freight, which can reflect global economic changes to a certain extent. Accurate forecasting of BDI supports shipping market participants in grasping risks and making scientific decisions. Based on the Prophet model, this paper considers the impact of multi-dimensional significant events related to the shipping industry and conducts BDI forecasting research. Firstly, we simulate the most momentous events in the world in recent years, namely the 2008 Global Financial Crisis and the 2019 New Crown Epidemic, and utilize the Prophet model to decompose the BDI sequence into three parts: trend, seasonality, and momentous event shocks. Secondly, we extensively collect other multi-dimensional significant event uncertainty indexes to establish a "significant event database". The Maximal Information Coefficient and Boruta methods were employed to extract the uncertainty index particularly correlated with BDI as an exogenous variable for forecasting. Then, we employ the K-Shape method to cluster exogenous variables and explore the combined sense of clustering. Finally, we utilize the Prophet model to forecast BDI in stages. It discusses the influence of exogenous variables and their cluster combinations on the forecasting effect individually and sequentially. Empirical results show that considering the two momentous events of the Financial Crisis and COVID-19 can remarkably improve the accuracy of BDI forecasting. In addition, the study of exogenous variable significance found that during the Financial Crisis, economic policy uncertainty in Europe and the Americas greatly impacted BDI forecasting. During COVID-19, global and developed economic policy uncertainty was noteworthy in the BDI forecasting. The comparative experimental results show the Prophet model has an exemplary forecasting result and strong robustness. It also performs excellently in model generalization and interpretability. This paper proposes a reliable and advanced algorithm for shipping freight rate index forecasting, which has noteworthy reference value for shipping market participants to make investment decisions and risk avoidance. [ABSTRACT FROM AUTHOR]

Selecting weighting factors for H-infinity controller requires tones of knowledge and experience without guaranteeing obtaining the best results. This paper proposes novel idea for H-infinity controller synthesis using metaheuristics (MHs). The optimisation problem for the H-infinity controller synthesis is posed to find the weighting factors to optimise the control performance subject to stability and control handling constraints. A number of established MHs are used to solve the problem while their performance are investigated. The results indicate that the Artificial Hummingbird Algorithm and Grey Wolf Optimizer is the most efficient algorithm for the optimisation problem of H-infinity flight control design. Based on this study, the performance of several MHs on solving the H-infinity flight control design are examined while the optimum weighting factors for H-infinity flight controller are obtained. This paper is said to be a based line of studying in easy and efficient way for H-infinity controller synthesis using MHs. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm(SCA) is a population-based optimization algorithm, to find the optimal solution. However, SCA has problems such as premature convergence, insufficient solution precision for high-dimensional functions, and slow convergence speed. To solve the problems above, this paper proposes a multi-strategy-based Adaptive Sine Cosine Algorithm (ASCA). Firstly, a more uniform initial population is generated by the Halton sequence so that the initial population covers the entire search space to maintain the diversity of the initial population. Secondly, the adaptive grading strategy is adopted to sort according to the fitness value, and the population dynamics are divided into 4 grades: excellent, good, medium and poor. For the purpose of improving the convergence accuracy of the algorithm and enhancing the ability to jump out of the local optimum, hybrid mutation and elite guidance methods are applied to different levels of populations for perturbing mutations. Finally, in order to improve the convergence speed of the algorithm, a dynamic opposition-based learning global search strategy is proposed. The ASCA is tested on a set of 20 functions in low- dimensional and high-dimensional, and the improved algorithm is compared with Particle Swarm Optimization (PSO), Backtracking Search Algorithm(BSA), Genetic Algorithm(GA)and other improved Sine Cosine Algorithms. The results show the improved convergence accuracy and speed of the ASCA. Moreover, the ASCA proposed in this paper is applied to engineering optimization design. The solution results show that the ASCA is better than other algorithms in superiority-seeking ability, and can effectively solve the optimization problems in engineering. [ABSTRACT FROM AUTHOR]

• This paper studies the impact of oil shocks on systemic risk of G7 stock markets. • We construct a risk spillover network among G7 stock markets by TVP-VAR. • The results indicate that the impact of oil supply shock is insignificant. • Demand shock contributes the most to systematic risk in the short term. • G7 stock markets play a role as receiver and all oil structural shocks as emitter. In this paper, we delve into the critical role of oil shocks in influencing the systematic risk of G7 stock markets—a concern of paramount importance given the interconnectedness of global economies and the pivotal role of energy markets. We employ Delta Conditional Value at Risk (ΔCoVaR) and Marginal Expected Shortfall (MES) to measure the systematic risk of G7 stock markets due to volatility in the oil market. By decomposing oil price change into oil supply shocks, aggregate demand shocks, and oil-specific demand shocks using a structural vector autoregression model, we offer a more granular perspective to investigate the time-varying effects of oil price fluctuations on the systematic risk of G7 stock markets. The results indicate that the impact of oil supply shock is insignificant compared with aggregate demand shock and speculative demand shock. Speculative demand shock contributes the most to systematic risk in the short term, but their impact declines as the number of periods increases. In the long term, the biggest impact on systematic risk is aggregate demand shock. Additionally, we construct a network among G7 stock markets and oil structural shocks based on time-varying spillover and the empirical results show that G7 stock markets play a role as receiver and all oil structural shocks as emitter. Besides, Japan is the country in G7 countries most affected by oil shocks. [ABSTRACT FROM AUTHOR]

In recommender system, the proportion of interacted items with users is extremely sparse compared to the total number of items. This data sparsity problem particularly affects collaborative filtering methods. Even in modern collaborative filtering recommendation models based on deep learning, effectively acquiring more neighbor information and exploring how to utilize this information efficiently remains a promising research area. This paper not only explores heuristic algorithms for neighbor selection but also extends the application of neighbor selection to the GAN framework and negative sampling. To capture user neighbors that contain more potential feature information, this paper proposes a neighbor strategy that integrates explicit and implicit user information. It aggregates reciprocal neighbors of users based on the fusion of their explicit and implicit information, aiming to capture user preference information through neighborhood information. Furthermore, by designing an MRNGAN model based on a twin-tower generator, this paper successfully embeds user neighborhood information as feature embedding into the generator of GAN. In addition, this paper also proposes a heuristic negative sampling mechanism in the recall phase. It uses explicit and implicit information to fuse the nearest neighbors of items with the farthest neighbors of users. Negative samples that contain user neighborhood information are embedded into the discriminator of MRNGAN. The deep relationships between nearest and farthest neighbors are explored through the adversarial training of GAN, and their impact on recommendation performance is validated. The experimental results on three real-world datasets show that the MRNGAN model significantly improves the accuracy of Top-N recommendation. [ABSTRACT FROM AUTHOR]

As a novel and economical transportation way, ride-sharing has attracted more and more passengers and drivers to participate. How to match passengers with drivers efficiently has become a key issue. Specifically, drivers are usually heterogeneous with different costs, and they may behave strategically (e.g. reveal their private cost information untruthfully) in order to make more profits. Drivers' strategic behavior may lead to inefficient matching, which results in the loss of social welfare of ride-sharing platforms and drivers. In this paper, an incentive-compatible and efficient mechanism is proposed to solve this issue, which can match passengers with drivers and determine the payments to drivers in order to maximize social welfare while ensuring drivers reveal their cost information truthfully. Specifically, an order matching algorithm with branch and bound based route planning is designed to accelerate the matching process. Meanwhile, the payments to drivers are computed based on the second-pricing algorithm. In so doing, a second-pricing based ride-sharing mechanism (SPRM) is proposed, which satisfies incentive compatibility, individual rationality, budget balance and computational efficiency. Based on the real Manhattan taxi order data and vehicle fuel consumption data, extensive experiments are conducted to evaluate the proposed mechanism. The experimental results show that SPRM can guarantee drivers' profits and improve the ratio of drivers' participation and the ratio of served orders, and eventually achieve greater social welfare than four typical benchmark approaches, GPri, ND, mT-share and mdp. The research of this paper can provide useful insights for designing efficient ride-sharing system to maximize the social welfare of all participants in the real life. • An efficient mechanism to match orders with drivers is proposed. • A branch and bound based route planning method is used to accelerate the matching. • A second pricing based mechanism is proposed to compute the payments to drivers. • The proposed mechanism can outperform benchmark approaches in the social welfare. [ABSTRACT FROM AUTHOR]

Hierarchical text classification (HTC) aims to classify a text into multiple categories organized in a hierarchical structure. The state-of-the-art HTC methods usually employ graph networks, where label graphs are constructed and label representation is learned to interact with text representations for classification. In general, label graphs are built on the intrinsic label hierarchy, label semantic similarity, or label co-occurrence. Such graphs have been proven to be effective, but they only exploit knowledge from training data or simple label descriptions, without considering the vast external knowledge in the open sources. Actually, external knowledge from open sources could bring in complementary information to enhance the label graph's representation power. Motivated by the above considerations, we explore the use of external knowledge for improving HTC in this paper. We categorize knowledge into micro-knowledge and macro-knowledge, which are defined as the fundamental concepts related to a single class label and the correlations among class labels, respectively. For tailor-made incorporation of the two types of knowledge into representation learning and classification, we propose Adaptive Micro- and Macro-Knowledge Incorporation for Hierarchical Text Classification (AMKI-HTC) model in this paper. The micro-knowledge incorporation helps capture class-relevant keywords in the text and hence produce discriminative representations, while the macro-knowledge incorporation improves the accuracy of label graphs. Finally, a confidence maximization fusion strategy is developed for adaptive aggregation of multi-view features. Extensive experiments on three benchmark HTC datasets demonstrate that AMKI-HTC consistently outperforms state-of-the-art models. • Conceptualize extensive external knowledge as micro-knowledge and macro-knowledge. • Aggregate micro- and macro-knowledge into original text features adaptively. • Construct text-knowledge graphs to emphasize discriminant information. • Simplify hierarchy-GCN as SH-GCN with improved performance. • Adaptively fuse features from multiple label graphs by confidence maximization. [ABSTRACT FROM AUTHOR]

Decision-making problem is a fundamental activity in human life every day. Regarding group decision-making, individuals have insufficient comprehension of the decision objects owing to the limited experience and knowledge and drawback the evaluation process. Therefore, the evaluation information is missing and the group cannot get the precise information for decision objects resulting in the generation of incomplete information systems. It is meaningful for individuals finding an approach to Leverage and tap into existing information. On the basis, the work presented in this paper focuses on group decision-making problems (GDM) with incomplete information. Under the framework of group decision-making, a GDM approach with an incomplete information system is presented combining the formal concept analysis and rough set theory. Firstly, we propose a concept called formal decision context and discuss the properties of the formal decision context, which could divide the incomplete information system into many cognitive units, and the definitions of cognitive units will be given. Then, the property and the relations of various cognitive units will be discussed. Additionally, the upper and lower approximation space under the formal decision context is founded considering the risk attitudes integrating fuzzy rough set and formal concept context. Considering the order relation of various cognitive units and decision objects, we introduce the dominance relation and the importance degree of each cognitive unit under the framework of formal decision context. Furthermore, a theoretical framework of the multi-criteria decision-making problem is presented and two models for the multi-criteria group decision-making problem are discussed combined with fuzzy rough set and formal decision context from two perspectives, including qualitative analysis and quantitative analysis. Finally, an illustrated example is applied to verify the validity and effectiveness of the proposed models. The contributions of this paper include two aspects. On the one hand, this paper provides a new perspective and paradigm considering MCGDM problems with the incomplete information system from the perspective of group consensus. On the other hand, this paper gives two approaches for group decision-making problems from sorting and set theory by the perspective of multi-granular rough set and group consensus. • A model of formal decision context with incomplete information to describe the cognitive situation. • An approach to MCDM problem combined rough set with formal decision context with incomplete information. • Two approaches to MCGDM problem from qualitative analysis and quantitative analysis. [ABSTRACT FROM AUTHOR]

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]

• 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]

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]

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]