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The application of blockchain technology is growing rapidly, which has aroused great attention in the academic and industrial fields. Based on blockchain 2.0, Ethereum is a mainstream smart contract development and operation platform. The trading process of Ethereum users is facing a serious threat of financial fraud. In particular, the Ponzi scheme is a classic form of fraud. Relevant works have investigated the issue of Ponzi schemes smart contract detection on Ethereum based on machine learning approaches. Nevertheless, the detection approaches still fall short in dealing with the big data-space Ponzi scheme smart contract detection application based on the class-imbalanced training data. We propose PSD-OL, a Ponzi schemes detection approach based on oversampling-based Long Short-Term Memory (LSTM) for smart contracts in this paper. PSD-OL takes the contract account features and the contract code features together into consideration. Oversampling technique is utilized to fill the class-imbalanced Ponzi scheme smart contracts' sample feature data. An LSTM model is trained by learning from the feature data for future Ponzi scheme detection. Experimental results conducted on the well-known XBlock dataset demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article introduces a biologically-inspired model capable of detecting both an object's motion direction and speed, based on retinal neural mechanisms verified through biological experiments. It aims to address the interpretability issues present in current deep learning models. The proposed Motion Detection Neuron (MDN) model, inspired by early research on the retina's direction and speed sensitivity, replicates the motion detection functions of the retina and primary visual cortex. The design of the MDN, inspired by the layered structure of the retina and incorporating various cell types and functions, has been validated through biological experimentation, providing it with robust biological interpretability. Extensive experiments have been conducted to assess the MDN's detection accuracy and robustness against various types of noise. Additionally, to verify that the MDN not only offers enhanced biological interpretability but also maintains detection accuracy comparable to leading deep learning algorithms, we compared its performance with that of LeNet ,EfficientNet and RegNet under identical conditions. The results show that the MDN not only provides better biological interpretability and lower hardware demands but also excels in accuracy under specific conditions, comparable to advanced deep learning algorithms. [Display omitted] • This paper employs a biologically inspired model MDN, which diverges from conventional deep learning approaches by simulating the architecture of the biological retina and the primary visual cortex, thereby providing superior biological interpretability. • The subject of this paper is the detection of object motion, an intrinsic ability of biological organisms, not acquired through learning. Consequently, by employing a direct modeling approach inspired by the biological visual system, this methodology not only circumvents the opaqueness associated with the learning 'black box', but also substantially diminishes both temporal and equipment-related expenditures. • Within the framework of the biologically inspired model, innovatively conducts detection of both an object's motion direction and speed, further advancing the simulation of functionalities inherent to the biological visual system. Furthermore, the experimental scope of this paper extends beyond mere binary images to include grayscale images and color images, and an array of comparative analyses, thereby reinforcing the model's credibility. [ABSTRACT FROM AUTHOR]

In this paper, we explore the possibility of exploiting retrospective clinical data from Electronic Health Records (EHR) for classification tasks in chronic patients. The different intervals, short length and high class imbalance make it unfeasible to use traditional time series techniques. The first contribution of the paper is a preprocessing method to construct a multivariate time series dataset using EHR data, which infers missing data and regularizes the data frequency. The second contribution addresses class imbalance by using domain knowledge and existing short EHR series. We synthetically extrapolate patients' data by using similar long time series and a fuzzy-based approach. The paper addresses the problem of detection of Diabetic Retinopathy (DR). Expert domain knowledge from ophthalmologists has been used in the proposed techniques to guide the processing of time series. The novelty in that case study consists in not using eye-fundus image analysis. Instead, the proposed methods are based solely on EHR data. Several multivariate multiclass time series classifiers are used to detect the four levels of DR severity from the pre-processed data sequences. Experiments prove the quality of the sequence preprocessing techniques proposed for EHR data. Results indicate that the TapNet classifier is the best one for DR grading. Despite being tested for DR detection, the proposed data preparation methods are applicable to other diseases with similar characteristics. [Display omitted] • Historical data from diabetic patients can be used to assess their retinopathy risk. • Long-term diabetic patients are harder to classify than new diabetic patients. • The original sequences have more similarities to the proposed double interpolation. • Data imbalance can be solved by boosting short time series using a fuzzy-based method. • TapNet has the best results among the tested multi-variate time series classifiers. [ABSTRACT FROM AUTHOR]

One of the pivotal challenges in multi-label image classification lies in the fact that each image is often tagged with multiple semantic labels, without the aggregate prediction probabilities being bound to unity. This aspect complicates the straightforward application of conventional single-label image classification algorithms to multi-label contexts. To tackle this challenge, this paper introduces a variational estimation-based multi-label category enhancement fusion distillation technique. The devised loss function focuses on maximizing the biochemical mutual information, thereby enhancing category recognition capabilities. The goal is to adeptly extract and capitalize on the pivotal features of multi-label image scores and structural information, thus elevating the accuracy and efficiency of classification endeavors. This paper not only furnishes a thorough exposition of the issues tackled and the comprehensive architecture of the proposed algorithm but also delineates its operational principles and design rationale via an exhaustive analysis of each critical step within the algorithm. Through an array of experiments across diverse network architectures and datasets, coupled with comparative analyses against extant models and empirical validations, this paper unequivocally validates the efficacy of the suggested algorithm and markedly augments the performance of multi-label classification tasks. [ABSTRACT FROM AUTHOR]

In this paper, bipartite synchronization for memristive neural networks with multi-delay couplings is investigated. The evaluation index of unbounded coupling delays on synchronization could be quantitatively analyzed by considering proportional delay, which will undoubtedly and strongly impede synchronous behavior. By simultaneously taking synchronization and anti-synchronization patterns into account, a novel impulsive controller with a signed form is elaborately designed. For the purpose of selecting suitable impulsive instants, a dynamic self-triggered mechanism is introduced. Additionally, to mitigate the possible risk of the dynamic mechanism transitioning into a static mechanism in exceptional scenarios, a memory-based adaptive updating law is therefore proposed in this paper. It should be noted that the adaptive control related dynamic parameters considered in this paper are in a non-monotonic form. By utilizing Lyapunov stability theorem, parameter variation approach and contradiction analysis method, sufficient conditions for ensuring the synchronization are successfully derived. Finally, two experiments are presented to demonstrate the practicability of the derived results. • Synchronization patterns and anti-synchronization patterns in some previous works could be thought as one of the special cases in our paper. • A non-monotonic dynamic updating law is devised by incorporating memory information, specifically utilizing the sparse historical states of nodes. • Lower bound of dynamic parameter is proved to be greater than a specific positive scalar in this work, that is, the importance of the role played by the dynamic parameter in determining impulsive intervals is enhanced. [ABSTRACT FROM AUTHOR]

Drug recommendation is one of the most crucial research topics in smart healthcare. Its goal is to provide a set of safe drug combination based on the patient's electronic health records (EHRs). Drug recommendation is challenging because it is difficult to obtain an appropriate representation of patient's health state from these personalized historical records. Meanwhile, drug recommendation must prioritize the safety of drug combination because drug–drug interactions (DDIs) could result in side effects. To address these issues, this paper proposes a novel recurrent aggregation neural network for safe drug recommendation, called RASNet. RASNet introduces a straightforward but efficient recurrent aggregation neural network to capture historical records related to the patient's health state of the current visit, which could improve the performance of EHR-based personalized modeling, particularly in cases where the patient's condition changes periodically. Furthermore, this paper presents a novel exponential controller for DDI loss to enhance the safety of drug combination. The proposed controller not only balances the DDI rate between the safety and accuracy of the drug recommendation but also ensures the performance even when the DDI rate is low. Extensive experiments on the MIMIC-III dataset demonstrate that RASNet achieves state-of-the-art performance. Moreover, RASNet exhibits excellent efficiency and safety in drug recommendation. [Display omitted] • Drug recommendation aims to suggest effective and safe drugs based on the patients' medical history records. • RASNet could address the noisy data problem caused by periodic changes due to chronic diseases. • The exponential controller of drug–drug interaction loss could ensure the safety and accuracy of drug recommendation. • RASNet demonstrates outstanding accuracy and efficiency in drug recommendation. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problems (CMOPs) are widespread in practical applications such as engineering design, resource allocation, and scheduling optimization. It is high challenging for CMOPs to balance the convergence and diversity due to conflicting objectives and complex constraints. Researchers have developed a variety of constrained multi-objective optimization algorithms (CMOAs) to find a set of optimal solutions, including evolutionary algorithms and machine learning-based methods. These algorithms exhibit distinct advantages in solving different categories of CMOPs. Recently, constrained multi-objective evolutionary algorithms (CMOEAs) have emerged as a popular approach, with several literature reviews available. However, there is a lack of comprehensive-view survey on the methods of CMOAs, limiting researchers to track the cutting-edge investigations in this research direction. Therefore, this paper reviews the latest algorithms for handling CMOPs. A new classification method is proposed to divide literature, containing classical mathematical methods, evolutionary algorithms and machine learning methods. Subsequently, it reviews the modeling and algorithms of CMOPs in the context of practical applications. Lastly, the paper gives potential research directions with respect to CMOPs. This paper is able to provide guidance and inspiration for scholars studying CMOPs. [ABSTRACT FROM AUTHOR]

Medical image diagnosis has developed rapidly under the impetus of the deep network. Previous works mainly focus on improving the diagnostic accuracy of models, i.e., first use a backbone network to extract image global features and then feed it into the classifier for diagnosis. However, these methods do not fully explore the transparent and reasonable decision-making process of the final classification results, which is crucial for medical diagnosis. In this paper, we propose a framework called Ca usal I ntervention-based M ulti-head A ttention network (CaIMA) to enhance the explainability of medical diagnosis from a causal inference perspective, by exploring the inherent causal relationship between multi-region attention and diagnosis results. Specifically, it consists of three key components: (1) The multi-region attention module enables the network to focus on the distinct discriminative lesion regions that hold causal relationships with the predicted outcome. (2) The attention-driven data augmentation module provides accurate localization of discriminative regions and enhances model explainability. (3) The causal intervention module aims to explore the intrinsic causal relationship between the attention map and the predicted outcome, encouraging the network to learn more useful attention maps for medical image diagnosis. Besides, to address the learning difficulty of this network, we further introduce a non-overlapping multiple attentional guidance loss that encourages the learned multiple attention maps to focus on specific lesion regions without overlapping. We compare the proposed CaIMA with state-of-the-art methods on multimedia medical datasets, including three public medical image datasets (Kvasir, ISIC2018, COVID-19) and one private dataset (CLC), and the experimental results substantiate the effectiveness of CaIMA in terms of diagnosis accuracy and explainability. • This paper provides a fresh perspective on medical image diagnosis from a causal standpoint. • The proposed CaIMA incorporates causal interventions with the multi-region attention framework. • A composite loss is proposed to provide reliable causal visual explanations and enhance model performance. • The effectiveness and superiority of the proposed models are verified. [ABSTRACT FROM AUTHOR]

In the realm of object detection, traditional domain adaptive object detection (DAOD) methods assume that source and target data completely share one identical class space, which is often difficult to satisfy in many real-world applications. To address this limitation, this paper introduces universal domain adaptive object detection (UniDAOD), a learning paradigm that relaxes identical class space assumption to be a different but overlapped class space. Intuitively, the main challenge of UniDAOD is to reduce the negative transfer of private classes (i.e., classes only existed in one domain) and reinforce the positive transfer of the common classes (i.e., classes shared across domains). In this paper, we provide a rigorous theoretical analysis and induce a new generalization bound of the expected target error under the UniDAOD setting. On the basis of theoretical insight, we then propose weighted adaptation (W-adapt) to suppress the interference of private classes and reinforce the positive effects of common classes. In particular, we propose a pseudo category margin (PCM) to quantify class importance based on dynamic pseudotarget label prediction to recognize common classes. Furthermore, to alleviate the impact of inaccurate pseudotarget labels, we propose a temporary memory-based filter (TMF) to dynamically store and update the PCM during progressive training. On the basis of the learned TMF, we design a weighted classwise domain alignment loss to adapt two domains across common classes. Experiments on four universal scenarios (i.e., partial-set, open-partial-set, open-set, and closed-set) show that W-adapt outperforms several domain adaptation methods. [ABSTRACT FROM AUTHOR]

Contrastive learning predicts whether two images belong to the same category by training a model to make their feature representations as close (positive samples) or as far away as possible (negative samples). Selecting appropriate samples is critical to effectively train a model, however, existing methods suffer from false or uninformative sample problems. This paper rethinks how to mine samples in contrastive learning and the proposed method is more comprehensive. It takes into account both positive and negative samples, and mines potential samples from two aspects. First, for positive samples, this paper incorporates both the augmented sample views and the mined sample views. A weighted combination of these positive samples is achieved by using both hard and soft weighting strategies simultaneously. Second, considering the existence of false and uninformative negative samples, this paper analyzes the negative samples from the perspective of gradient and mines negative samples that are neither too difficult nor too easy as potential negative samples, i.e., those negative samples that are close to positive samples. Compared with previous state-of-the-art self-supervised methods, experiments show the obvious advantages of the proposed method, and the corresponding top-1 accuracies of linear classification are improved by 0.77%, 2.39%, and 1.01% on CIFAR10, CIFAR100, and TinyImageNet, respectively. Source code and pretrained models are available at https://github.com/dhkdhk/PSM. [ABSTRACT FROM AUTHOR]

Liver diseases represent a significant healthcare challenge, impacting millions globally and posing complexities in diagnosis. To address this global health concern, this paper introduces a groundbreaking enhancement to the Kepler Optimization Algorithm, termed I-KOA, designed specifically for feature selection in high-dimensional datasets. By harnessing the synergies of Opposition-Based Learning and a Local Escaping Operator grounded in the k-nearest Neighbor (kNN) classifier, I-KOA asserts itself as a potent tool for local exploitation, balanced exploration, and evasion of local optima. To our knowledge, this is the first work to exploit KOA as a feature selection method. Pioneering the utilization of KOA as a feature selection method, the paper rigorously tests I-KOA in two extensive experiments, tackling the complex CEC'22 benchmark suite functions and the intricate landscape of five liver disease datasets. Results underscore I-KOA's unparalleled performance, validated through the Friedman test, where it surpasses seven rival optimization algorithms. Achieving an outstanding overall classification accuracy of 93.46%, Feature selection size of 0.1042, sensitivity of 97.46%, precision of 94.37%, and F1-score of 90.35% across the liver disease datasets, I-KOA's randomized algorithm ensures robust feature selection, striking a compelling balance between subset size and classification efficacy. Acknowledging computational demands and generalization nuances, I-KOA is a formidable tool ready to revolutionize medical diagnosis and decision support systems. The open source codes of the proposed I-KOA are available at https://www.mathworks.com/matlabcentral/fileexchange/161376-improved-kepler-optimization-algorithm. • This paper encompasses a proficient I-KOA algorithm based on OBL and LEO methods. • A new optimized feature selection model for liver disease classification using five datasets. • We employ comprehensive analysis metrics to thoroughly assess the I-KOA algorithm's efficacy. • I-KOA stands out by surpassing its competitors, attesting to its remarkable performance. [ABSTRACT FROM AUTHOR]

In graph few-shot learning, few-shot node classification (FSNC) at the node-level is a popular downstream task. Previous FSNC methods primarily rely on meta-learning or metric learning techniques, aiming to mine prior knowledge from the base classes. However, these methods still have some limitations that need to be addressed, namely: (1) conducting multiple tasks for parameter initialization leads to expensive time costs. (2) ignoring the rich information present in novel classes leads to model over-fitting. To address these issues, this paper proposes a novel graph augmentation method for FSNC on graph data, which includes both parameter initialization and parameter fine-tuning. Specifically, the parameter initialization conducts only one multi-classification task on the base classes, improving generalization ability and reducing time costs. The parameter fine-tuning is designed to include two data augmentation modules (i.e. , support augmentation and shot augmentation) on the novel classes to mine the rich information, thus alleviating model over-fitting. As a result, this paper introduces the first graph augmentation method for FSNC. Experimental results showed that our method achieves supreme performance, compared with state-of-the-art FSNC methods. • We use data augmentation and GCN with optimized initialization for efficiency. • The parameter initialization module aims to improve generalization and efficiency. • The proposed support and shot augmentation modules aim to mine rich information. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a knowledge-based system for automatically diagnosing issues in Vehicular ad hoc networks (VANETs). VANETs enable communication between vehicles and infrastructure, enhancing road safety and efficiency through timely information exchange. The proposed system aims to efficiently maintain and ensure the continuity of network service by leveraging innovative pattern recognition methods tailored to VANETs. The automatic diagnosis problem in VANETs involves estimating the operating class of network components based on sensor observations. This entails associating sensor measurements with specific operating modes. By implementing condition-based preventive maintenance procedures, potential component failures can be detected early, mitigating network disruptions. Various approaches, such as expert systems, fault trees, network state models, and statistical learning through pattern recognition, can be employed to address this problem. This paper primarily focuses on the statistical learning approach, where a classification or regression function is learned from a set of examples to assign operation modes to new measurements. It discusses relevant metrics and preprocessing techniques to simplify the decision-making process. The diagnostic system's results are determined based on the formulation of the classification or regression problem. The learning base is constructed, and an appropriate classification method is selected to develop and validate the automatic diagnosis system. While non-parametric models like support vector machines are commonly used, this article emphasizes the significance of considering assumptions and leveraging additional information to enhance performance. It proposes a more specific formalization of the problem, integrating the unique characteristics of VANETs. The contributions of this article revolve around the theory of belief functions, a generative approach, and the utilization of parametric models defined using graphical models. Experimental studies conducted on artificial datasets have demonstrated the benefits of the semi-supervised approach within the context of VANET networks. [ABSTRACT FROM AUTHOR]

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents. [ABSTRACT FROM AUTHOR]

When a multi-fingered dexterous hand interacts with the external environment, it encounters various challenges, including the utilization of complex control techniques and the intricate coordination of finger motion sequences. Previous studies have primarily concentrated on investigating the interaction between multi-fingered dexterous hands and external objects, usually using model-based control or model-free reinforcement learning techniques. However, during practical implementation, various constraining factors are encountered, such as intricate modeling and limited interaction capabilities. In practical scenarios, the utilization of multi-fingered dexterous hands is imperative for the swift and efficient execution of a wide range of interactive tasks, including but not limited to throwing a ball and playing rock-paper-scissors. These tasks require skilled manual dexterity to demonstrate both precise control and quick responsiveness. To tackle this issue, we propose a hierarchical control approach for multi-fingered dexterous hands with interactive functionalities, utilizing model-free deep reinforcement learning. The complex interaction task is decomposed into simple sub-tasks using hierarchical strategy and action primitive decomposition, which effectively reduces the complexity of the action space, and achieves the motion planning and end finger trajectory control of dexterous hand. In a simulated environment, the aforementioned method has successfully executed interactive tasks, including ball throwing and playing rock-paper-scissors. It achieved a maximum normalized reward of 0.83 and an 84 % success rate. These results are noteworthy in terms of both control accuracy and response speed. This study offers novel insights into the effective resolution of the intricate challenges associated with interactions involving multi-fingered dexterous hands and human-computer interaction. [ABSTRACT FROM AUTHOR]

Many real-world datasets encounter the issue of label noise (LN), which significantly degrades the learning performances of classification models. While ensemble learning (EL) has been widely employed to tackle this problem, the Dynamic Selection (DS) of classifiers, as a promising EL branch, is particularly sensitive to LN. To address this issue, a meta-learning-based sample discrimination (MSD) framework is proposed in this paper. Initially, this paper analyzes how LN affects the performance of DS methods through a visual example. Subsequently, under the premise that DS methods are only applicable to samples whose neighborhood is minimally affected or unaffected by LN, a meta-learning dataset is generated in the framework, where the meta-features and meta-labels are derived from the characteristics and the real class distribution of local regions of the samples, respectively. With this dataset, a meta-learner is constructed to determine the feasibility of using DS methods directly to classify a given sample in the presence of LN. For samples that DS methods cannot handle, a novel DS process based on the Genetic Algorithm is designed to mitigate the negative impact of LN. The effectiveness of the MSD framework is validated through extensive experiments conducted on thirty real datasets. These experiments demonstrate the capability of the MSD framework to improve the performances of DS methods across different levels of LN. Furthermore, the efficacy of the proposed MSD framework in handling LN is also highlighted by comparing it with a state-of-the-art method and four mainstream EL methods. [ABSTRACT FROM AUTHOR]

Fuzzy Cognitive Mapping (FCM) and the extensive family of models derived from it have firmly established their strong position in the landscape of machine learning algorithms. Specifically designed for pattern classification and multi-output regression, the recently introduced Recurrence-aware Long-term Cognitive Network (r-LTCN) model is one of these FCM-inspired extensions. On the one hand, this recurrent neural network connects all temporal states generated during the reasoning process with the decision-making layer. On the other hand, it uses a quasi-nonlinear reasoning rule devoted to avoiding convergence issues caused by unique fixed points, which typically emerge in other FCM models. In the original paper, the authors employed a combination of unsupervised and supervised learning to compute the r-LTCNs' learnable parameters. Despite r-LTCNs' astounding performance for a wide variety of pattern classification problems, the literature reports no attempt to train these recurrent neural systems in a fully supervised manner nor provide insights into their performance in other machine learning settings. This paper brings forward a modified Backpropagation Through Time learning (BPTT) algorithm devoted to training r-LTCN models used for multi-output regressions tasks rather than pattern classification. The proposed BPPT includes a simple yet effective mechanism to deal with the vanishing gradient within the recurrent layer that operates as a closed system while being tailored to the quasi-nonlinear reasoning mechanism. Empirical evaluation of the proposed BPTT algorithm using 20 multi-output regression problems reveals that it produces lower prediction errors compared with other state-of-the-art learning approaches. [ABSTRACT FROM AUTHOR]

Zero-shot retrieval aims to transfer knowledge from seen classes to unseen classes by embedding semantic information on class attributes, thus solving the unseen class retrieval problem. However, existing works have focused mainly on unimodal zero-shot retrieval tasks. In this paper, we introduce an efficient method, termed zero-shot discrete hashing with adaptive class correlation (ZSDH-ACC), to speed up cross-modal retrieval. Specifically, this proposed method combines label information with class attribute information to construct a semantic enhancement embedding, in which the problem of class attribute correspondence of multilabel instances can be solved. Furthermore, we learn semantic enhancement embedding to merge more semantic information for feature representation, and its goal is to learn more discriminative hash codes and hash functions. In addition, our proposed method adaptively learns the correlation between class attributes and then embeds more class attribute information into hash codes. Finally, pairwise similarity is used to constrain the learning of hash codes, and thus more discriminative hash codes can be generated. Extensive experimental results on four benchmark multimodal datasets demonstrate that the proposed ZSDH-ACC method can achieve encouraging performance in cross-modal retrieval tasks. The source code of this paper can be obtained from https://github.com/szq0816/ZSDH_ACC. [ABSTRACT FROM AUTHOR]

The integration of Machine Learning (ML) and Constrained Optimization (CO) techniques has recently gained significant interest. While pure CO methods struggle with scalability and robustness, and ML methods like constrained Reinforcement Learning (RL) face difficulties with combinatorial decision spaces and hard constraints, a hybrid approach shows promise. However, multi-stage decision-making under uncertainty remains challenging for current methods, which often rely on restrictive assumptions or specialized algorithms. This paper introduces unify , a versatile framework for tackling a wide range of problems, including multi-stage decision-making under uncertainty, using standard ML and CO components. unify integrates a CO problem with an unconstrained ML model through parameters controlled by the ML model, guiding the decision process. This ensures feasible decisions, minimal costs over time, and robustness to uncertainty. In the empirical evaluation, unify demonstrates its capability to address problems typically handled by Decision Focused Learning, Constrained RL, and Stochastic Optimization. While not always outperforming specialized methods, unify 's flexibility offers broader applicability and maintainability. The paper includes the method's formalization and empirical evaluation through case studies in energy management and production scheduling, concluding with future research directions. [ABSTRACT FROM AUTHOR]

Recent advances in generative models have skyrocketed the popularity of conversational chatbots and have revolutionized the way we interact with artificial intelligence. At the same time, research has shown that machine learning models can unconsciously reflect and amplify human biases. This is particularly dangerous for generative models given the huge popularity of such technologies. Specifically, a fundamental source of bias of such technologies is the resources on which the models are trained. To address this issue, this paper proposes a methodology to analyze intrinsic gender bias in Natural Language Generation (NLG). Some works already propose metrics and approaches to measure bias in the Natural Language processing field. However, there is a lack of standard methodology to measure gender bias in NLG. Therefore, adapting the Bias Score approach, our proposal involves three sequential stages applied to individual texts to detect intrinsic gender bias on NLG effectively. Those steps are as follows: (i) word scoring; (ii) word filtering; and (iii) generative-word analysis. This methodology is applied to recent datasets and pre-trained models widely used for the generation of text with common sense. In particular, this paper analyzes the potential gender bias in CommonGen and C 2 Gen datasets and the SimpleNLG and T5 models. The results show the ability of the proposed methodology to detect gender bias in word distributions, presenting a strong correlation with the words typically associated with a specific gender. Results indicate that both tested datasets are intrinsically gender-biased, and therefore, tested models fine-tuned with those datasets also are. [ABSTRACT FROM AUTHOR]

Transfer learning of multi-source domain adaptation seems a promising way for fault diagnosis of roller element bearings under variable working conditions. Data imbalance affects the performance of multi-source domain adaptation greatly and is expected to be solved by GAN. However, GAN-based transfer learning diagnosis models suffer pattern collapse and training instability, leading to unsatisfying diagnosis results in practical engineering. This paper proposes a denoising diffusion multi-source domain adaptation model (DDMDA). The proposed model uses diffusion denoising, which has better performance and is simpler to train than GAN, to generate shifted source domains for solving the data imbalance problem. A new noise prediction structure in diffusion denoising named Utrans-net, is constructed to restore the data distribution in the shifted source domain. Also, a multiple-domain discriminator structure is designed to extract features from multiple source domains to solve the issue of variable working conditions. Advanced models are used in this paper to compare with the proposed model for validation. Experimental demonstrations show that the proposed model is superior to the comparison models with satisfying performance. [ABSTRACT FROM AUTHOR]

Currently, generative models are showing exceptional abilities to identify and generate triplets expressed within sentences within the field of Aspect Sentiment Triplet Extraction (ASTE). Although these models are capable of recognizing terms and sentiment representations, they are not fully capable of generating multi-word aspects and opinion terms. In response to these challenges, this paper presents a dual-enhanced generative model with graph attention network and contrastive learning for aspect sentiment triplet extraction (GAC). In the GAC model, we construct a graph triplet loss module, which integrates dependency syntactic information to deepen the understanding of complex sentence structures, and utilizes graph attention network to explicitly define the dependencies between words, which makes the model better at recognizing aspects and opinions within complex structures. Furthermore, we designed the triplet representation contrastive learning module, which significantly enhances the model's ability to identify complex sentiment types and differentiate aspect and opinion terms composed of single words and sentences by capturing the internal connections between sentiment types and term lengths. In the experimental section, the paper tests two public datasets. According to the results, the GAC model outperforms existing methods in generating triplets, confirming the efficiency and advancement of our approach in tackling the ASTE challenges. Specifically, on different subsets (14lap, 14res, 15res, 16res) of the ASTE-Data-v2 and ASTE-Data-v1 datasets, the F 1 scores of our method were 66.47%, 76.01%, 69.04%, 76.25% and 64.14%, 76.44%, 68.94%, 76.37%, respectively. [ABSTRACT FROM AUTHOR]

The crack is an important index to evaluate the damage degree of concrete structure. While, the traditional algorithms of crack detection have complex operations and weak generalization. The performance of crack detection algorithms based on deep learning has been improved, but it also increases the complexity of network structure. Thus, this paper proposes a simplified real-time network for automatic detection of concrete cracks. Taking advantage of a novel transformer-based detector (DETR) architecture integrating receptive fields attention blocks and a feature assignment mechanism, the proposed network can achieve a model with more accurate detection of cracks. Analyzing the association between crack targets and receptive fields on feature layers, the receptive fields attention block is introduced into the backbone, focusing on the target receptive field features. In addition, a neck block is added to the encoder to fuse multi-scale features, with a new feature assignment mechanism assigning features to shallow layers, in order to detect cracks in the shallow features. Finally, given the effect of the inconsistent cracks size on the precision of the loss function, a novel adaptive loss function is applied to replace the loss in the original model. In this paper, the improved model is applied on the homemade crack dataset, and ablation study are done on the added modules to verify their effectiveness. Also, our model is compared with advanced models in cracks detection by using a TIDE toolbox. It is proven that the proposed model has a good effect on crack detection, and has better performance compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

With the increasing number of smartphone users, the potential of the shared power bank industry is increasing. The shared power bank supplier selection can be regarded as a multi-criteria group decision making (MCGDM) problem with linguistic hesitant fuzzy (LHF) sets (LHFSs). This paper proposes a linguistic hesitant fuzzy MCGDM method based on the MULITMOORA (Multi-Objective Optimization on basis of a Ratio Analysis plus the Full Multiplicative form), the BWM (best and worst method), and the prospect theory (PT). Firstly, a new comparison approach of LHFSs is proposed. We propose the LHF Bonferroni mean (BM), the LHF weighted BM and the LHF generalized weighted BM (LHFGWBM) operators. The decision makers' weights are acquired via a tri-objective optimization model. This paper extends the BWM to obtain the subjective criteria weights and employs the entropy to acquire the objective criteria weights. The comprehensive criteria weights are generated by the Jenson-Shannon divergence. The LHFGWBM operator is used to generate the collective decision matrix. The comprehensive prospect value decision matrix is obtained. Then, we propose the PT-based MULTIMOORA method for LHF MCGDM. An example of shared power bank supplier selection is used to show the superiorities of the proposed LHF MCGDM method. [ABSTRACT FROM AUTHOR]

The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability. [ABSTRACT FROM AUTHOR]

The mining result set of spatial prevalent co-location patterns(SPCPs) is often large and redundant, especially when the prevalence threshold is set to low or long SPCPs are present. Meanwhile, the distribution of SPCPs in continuous space and complex spatial relationships with each other of spatial data make the compression and reorganization of SPCPs a challenging problem. To solve this problem, in this paper, a representative co-location pattern mining framework based on the maximal row instance(MRI) representation model is proposed. First, the MRI representation model is proposed to effectively preserve the pattern distribution information of prevalent co-location patterns. To establish the MRI representation model, the basic algorithm and geometric algorithm are proposed in this paper. Two materialization methods based on the MRI representation model, 0-1 vector and key–value vector, are presented. Secondly, the similarity measure of SPCPs under the context of the MRI representation model is proposed, which calculates the similarity between any two co-location patterns without adding additional information such as domain background. Furthermore, the mining framework based on the k nearest neighbors density peak clustering algorithm is presented to extract representative co-location patterns. Finally, the efficiency and scalability of the proposed method are verified on the synthetic data and real data. Compared with the existing methods, the representative co-location patterns of the proposed method have well compression performances. • Visible relationships depict inter-pattern relations based on pattern distribution. • Proposes unified representation method for expressing patterns. • The representation method supports downstream machine learning applications. • Defines visible representative patterns to summarize prevalent co-location patterns. [ABSTRACT FROM AUTHOR]

Evolutionary Algorithms (EAs) are a well-known domain within Artificial Intelligence. EAs have demonstrated their ability to tackle intricate optimization problems using evolutionary theory principles. However, balancing the dual exploration and exploitation processes remains a crucial concern. This paper introduces the Balanced Hill Climbing Weight Algorithm with Diversity (BHWEAD), an innovative approach that combines elements from classic Genetic Algorithm and Differential Evolution. BHWEAD uniquely employs the Hill Climbing local search to guide the influence of its operators, ensuring an optimal interplay between exploration and exploitation. Additionally, it incorporates a diversity control mechanism, resetting specific solutions to prevent premature convergence to suboptimal solutions. The main contribution of the BHWEAD is the mechanism that permits the balance of the exploration and exploitation stages; also, the incorporation of Hill Climbing permits a proper balance of the influence of the operators. Notice that the proposal can escape from suboptimal solutions using a diversity-based strategy. Tested against the CEC2017 benchmark functions in both 50 and 100 dimensions, BHWEAD outperformed 12 notable EAs, underscoring its potential for high-dimensional optimization problems. Besides, the proposed BHWEAD has also been tested over seven engineering problems, and the comparisons include some memetic algorithms., The paper provides additional insights into the algorithm's design, conducts a comparative analysis, and identifies potential areas for improvement. • Introduce a new algorithm that balances exploration and exploitation. • Use easy operators and a GA structure to create a new optimization algorithm. • Use the Hill Climbing to handle the influence of operators in an EA. • Implement a diversity control strategy to avoid falling into suboptimal solutions. [ABSTRACT FROM AUTHOR]

Multi-view clustering has attracted substantial attention thanks to its ability to integrate information from diverse views. However, the existing methods can only generate hard or fuzzy partitions, which cannot effectively represent the uncertainty and imprecision when facing objects in overlapping clusters, thus increasing the risk of error. To solve the above problems, in this paper, we propose an adaptive weighted multi-view evidential clustering (WMVEC) method based on the theory of belief functions to characterize the uncertainty and imprecision in cluster assignment. Technically, we integrate view weight assignments and credal partition between objects and cluster prototypes into a joint learning framework. The credal partition offers a more comprehensive insight into the data by enabling objects to be associated with not only singleton clusters but also subsets of these clusters (termed meta-clusters) and the empty set, which represents a noise cluster. To avoid the interference of irrelevant and redundant features, we further present a weighted multi-view evidential clustering with feature preference (WMVEC-FP) to learn the importance of each feature under different views. We suggest the objective functions of WMVEC and WMVEC-FP and design alternating optimization schemes to obtain the optimal solutions, respectively. Through an extensive array of experiments, it has been demonstrated that our proposed clustering methods outperform other related and state-of-the-art methods in terms of their advantages and overall effectiveness. • The paper presents a multi-view version of evidential clustering with view-weight learning. • The paper further proposes a multi-view evidential clustering with view-weight and feature-weight learning. • The methods create credal partition to represent uncertainty and imprecision in cluster assignment in multi-view data. • Extensive experiments show the better performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Accurate pedestrian trajectory prediction is essential for the advancement of intelligent robot or autonomous vehicle, which is a challenging and interesting task. In this paper, a Scene-Interaction fusion Transformer (SIF-TF) for trajectory prediction is proposed, which takes into account three fundamental factors, i.e. social interaction, past trajectory, and semantic scene. A scene-social modeling method is added to the model to integrate social interaction and semantic scene. The proposed SIF-TF contains two critical components: the scene-social transformer and the temporal transformer. The scene-social transformer is tasked with capturing social interaction and semantic scene information, while the temporal transformer focuses on extracting temporal correlation information. Furthermore, the SIF-TF employs a two-stage trajectory prediction approach to jointly generate future trajectories. To evaluate the effectiveness, the comparative experiments were conducted on five widely-used public datasets. The experiments results, with an average evaluation metric of ADE/FDE of 0.23/0.47, significantly outperforms other state-of-the-art methods. These findings demonstrate that the proposed SIF-TF is capable of delivering more precise pedestrian trajectory predictions across diverse scene backgrounds. [Display omitted] • A Scene-Interaction fusion Transformer (SIF-TF) for trajectory prediction is proposed. • Social interaction information and scene semantic information are effectively fused in the paper. • Temporal correlation, scene semantics and social interaction of a trajectory are taken into account. • As for a specific scenario, a highly robust trajectory prediction model can be obtained via training with only a small amount of data through SIF-TF. • An average performance of 0.23/0.47 on ADE/FDE metrics is achieved by the proposed SIF-TF in the experiments on the five widely-used public datasets. [ABSTRACT FROM AUTHOR]

• This paper extends two statistics which were used in the validation of binary-attribute Q-matrix, for validating the polytomous-attribute Q-matrix. • Based on the two statistics, the paper proposes two algorithms applicable for real-world scenarios with intensive studies to evaluate the performance of the statistics. • Plug in the proposed algorithms, the statistics were compared under various conditions. Guidance on how to validate polytomous-attribute Q-matrix in different scenarios were provided. Compared with typical binary attributes, polytomous attributes can take three or more values (corresponding to different levels of mastery of a respondent or measurement of an item). They can indicate whether a respondent possesses the attributes of interest and mastery levels. Therefore, the test with polytomous-attribute Q -matrix can become more informative and provide respondents with richer diagnostic information than the test based on the dichotomous-attribute Q -matrix. This paper extends the S -statistic and the residual method applicable for the Q -matrix of binary attributes to validate the polytomous-attribute Q -matrix. Under two common scenarios in real-world applications, two associated validation algorithms: the joint validation (JV) algorithm and the online validation (OV) algorithm, are proposed. Both simulation studies and an empirical data example were employed to assess the robustness and usefulness of these two methods under various conditions. Results indicate that the JV algorithm is suitable for validating a Q -matrix predefined by subject matter experts. Especially when the Q -matrix contains fewer misspecifications, while the OV algorithm can be applied to define the attribute vector of "new items". Based on a certain number of "operational items", the OV algorithm can achieve a promising performance for obtaining the specification of the new items. [ABSTRACT FROM AUTHOR]

This paper presents a method for providing explainability in the integration of artificial intelligence (AI) and data mining techniques when dealing with meteorological prediction. Explainable artificial intelligence (XAI) refers to the transparency of AI systems in providing explanations for their predictions and decision-making processes, and contribute to improve prediction accuracy and enhance trust in AI systems. The focus of this paper relies on the interpretability challenges in ordinal classification problems within weather forecasting. Ordinal classification involves predicting weather phenomena with ordered classes, such as temperature ranges, wind speed, precipitation levels, and others. To address this challenge, a novel and general explicable forecasting framework, that combines inductive rules and fuzzy logic, is proposed in this work. Inductive rules, derived from historical weather data, provide a logical and interpretable basis for forecasting; while fuzzy logic handles the uncertainty and imprecision in the weather data. The system predicts a set of probabilities that the incoming sample belongs to each considered class. Moreover, it allows the expert decision-making process to be strengthened by relying on the transparency and physical explainability of the model, and not only on the output of a black-box algorithm. The proposed framework is evaluated using two real-world weather databases related to wind speed and low-visibility events due to fog. The results are compared to both ML classifiers and specific methods for ordinal classification problems, achieving very competitive results in terms of ordinal performance metrics while offering a higher level of explainability and transparency compared to existing approaches. [ABSTRACT FROM AUTHOR]

In recent years, federated learning has made significant progress in preserving data privacy. In this paradigm, clients train local models without sharing their raw data, thereby substantially mitigating the vulnerability to private data exposure. However, it is still possible to infer clients' raw data by leveraging the gradient parameters exchanged between the clients and the server. To address this problem, this paper proposes a novel algorithm that introduces deep compressed sensing into federated learning to support one time encryption, called FL-OTCSEnc, to secure the communication data exchanged between the clients and the server. The process starts by creating a dataset of deep learning model parameters and training a system for both encryption and decryption using deep compressed sensing. This system is then used to secure the communication between clients and the server in federated learning, by encrypting and decrypting the data exchanged. To enhance the security of the proposed algorithm, we introduce an assessment method for evaluating the security level of the clients, facilitating the selection of suitable candidates for deployment within distributed training encryption and decryption models that are updated in real time. To enhance the accuracy of the decrypted deep network model, we introduce a tandem loss function in the training process. Moreover, this paper proves that the proposed end-to-end encryption method satisfies additive homomorphic encryption properties. Extensive experiments demonstrate that the deep compressed sensing encryption in federated learning achieves promising results without increasing the computational complexity. [ABSTRACT FROM AUTHOR]

The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in the presented V2I paradigms regarding security, cyber-attacks, and application processing among heterogeneous nodes. These cyber-attacks, Sybil attacks, and their attempts cause a lack of security and degrade the V2I performance in the presented paradigms. This paper presents a new secure blockchain framework that handles cyber-attacks, as mentioned earlier. This paper formulates this complex problem as a combinatorial problem, encompassing concave and convex problems. The convex function minimizes the given constraints, such as time and security risk, and the concave function improves performance and accuracy. Therefore, numerous constraints, such as time, energy, malware detection accuracy, and application deadlines, require optimization for the considered problem. Combining the jointly non-dominated sorting genetic algorithm (NSGA-II) and long short-term memory (LSTM) schemes is the best way to meet the problem's limitations. In this study, the paper designed a malware dataset with known and unknown malware. The different kinds of malware lists (e.g., cyber-attacks) are considered in the form of known and unknown malware lists with the characteristics, size of code, where malware comes from, attack on which data, and current status of the workload after being attacked by the malware. Our main idea is to present blockchain, NSGA-II, and LSTM schemes that handle phishing, routing, Sybil, and 51% of cyber-attacks without compromising application performance. Simulation results show that the study reduces delay and energy, improves accuracy, and minimizes security risks for vehicular applications. [ABSTRACT FROM AUTHOR]

Symbolic data analysis data has provided several advances in regression models concerning the type of symbolic variable. Due to the advantages of using symbolic polygonal data, this paper introduces a linear regression approach for polygonal data based on the generalize linear model theory that provides a unified method to broad range of modeling problems for different types of response as asymmetric continuous and discrete. Ordinary polygonal residuals and a way for finding model inadequacies are presented. Moreover, a quality measure of fit for polygons is also proposed in this paper. Experimental evaluation results illustrate the usefulness of the proposed approach regarding synthetic and real polygonal data. • An approach based on Generalized Linear Models for symbolic polygonal data is proposed. • Polygonal residuals are defined for evaluating the adequacy of the fitted model. • The prediction quality is measured by a metric based on Euclidean distance and polygon vertices. • Synthetic and real polygonal data sets are considered in the experimental evaluation. [ABSTRACT FROM AUTHOR]