Mining sequential rules from a sequence database usually returns a set of rules with great cardinality. However, in real world applications, the end-users are often interested in a subset of sequential rules. Particularly, they may consider only rules that contain a specific set of items. The naïve strategy is to apply such itemset constraints into the post-processing step. However, such approaches

The matrix game and bi-matrix game are typical non-cooperative games. Nowadays, interactive game decisions on social management are mainly concerned, especially when players are faced with uncertain payoffs. The aim of this paper is to develop simple and effective parameterized linear programming methods for solving two types of matrix games with payoffs expressed by intuitionistic fuzzy (IF) information

Designed as remedies for uncertain parameters and tiny optimal weights in the portfolio selection problem, we consider a class of distributionally robust portfolio optimization models with cardinality constraints. For considering the statistical significance and tractability, we construct two kinds of ambiguity sets based on L1-norm and moment information, respectively. The nominal distribution, as

Chinese selection is one of the most important subtasks in Chinese medical question-answer system. To obtain the representations of question and answer, an attractive method is to use the attentive pooling based deep neural network. However, this method suffers from the over-pooling problem. It generates attentive information by only using the related medical keywords, and neglects the local semantic

A large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as hierarchical classification problems, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for hierarchical classification, introducing a stack

Recently, online learning has been gradually accepted and approbated by the public. In this context, an effective prediction of students’ engagement can help teachers obtain timely feedback and make adaptive adjustments to meet learners’ needs. In this paper, we present a novel model called the Deep Facial Spatiotemporal Network (DFSTN) for engagement prediction. The model contains two modules: the

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been

We present a structural attention network (SAN) for graph modeling, which is a novel approach to learn node representations based on graph attention networks (GATs), with the introduction of two improvements specially designed for graph-structured data. The transition matrix was used to differentiate the structures between the nodes. The output features of nodes in the graph are represented as the

The filled function methods are a kind of effective method to find the global minimum for optimization problems. However, there exist four limitations to this kind of method: 1) A large number of local minima may take a lot of iterations (time and computation cost) for the methods to find the global minimum. 2) The parameters (if any) in the constructed filled function are difficult to adjust and control

Symbolic knowledge representation and reasoning and deep learning are fundamentally different approaches to artificial intelligence with complementary capabilities. The former are transparent and data-efficient, but they are sensitive to noise and cannot be applied to non-symbolic domains where the data is ambiguous. The latter can learn complex tasks from examples, are robust to noise, but are black

The quality of the proposed solutions by Case-Based Reasoning (CBR) systems is highly dependent on recorded experiences and their describing attributes. Hence, to keep them offering accurate and efficient responses for a long time frame, the maintenance of Case Bases (CB) and Vocabulary knowledge is required. However, maintenance operations are usually unable to exploit provided domain-experts knowledge

Yager has proposed the decision making under measure-based granular uncertainty, which can make decision with the aid of Choquet integral, measure and representative payoffs. The decision making under measure-based granular uncertainty is an effective tool to deal with uncertain issues. The intuitionistic fuzzy environment is the more real environment. Since the decision making under measure-based

Processing the 3D objects is a research field of computer graphics that can be applied in 3D simulation, 3D image processing, game industry, etc. This is one of the mathematics-based research fields where geometrical knowledge is the background of existing methods. Geometric modeling is a branch of this research field based on the applied mathematics and computational geometry. It studies methods and

In machine learning, feature selection is a kind of important dimension reduction techniques, which aims to choose features with the best discriminant ability to avoid the issue of curse of dimensionality for subsequent processing. As a supervised feature selection method, Fisher score (FS) provides a feature evaluation criterion and has been widely used. However, FS ignores the association between

Deep learning-based financial approaches have received attention from both investors and researchers. This study demonstrates how to optimize portfolios, asset allocation, and trading systems based on deep reinforcement learning using three frameworks. In the proposed deep learning structure, the input data are first decomposed through wavelet transformation (WT) to remove noise from stock price time-series

For person re-identification (re-ID), a core problem is how to learn discriminative feature representations of pedestrians. In this paper, we propose a novel enhanced siamese angular softmax network (ES-ASnet) to integrate identification, verification and metric learning into a unified network. First, a dual joint-attention (DJA) based identification model is proposed that can focus on both key local

Aspect-level sentiment classification aims to predict sentiment polarities for different aspect terms within the same sentence or document. However, existing methods rely heavily on modeling the semantic relevance of an aspect term and its context words, and ignore the importance of syntax analysis to a certain extent. Consequently, this may cause the model to pay attention to the context word which

Discovering periodic patterns consists of identifying all sets of items (values) that periodically co-occur in a discrete sequence. Although traditional periodic pattern mining algorithms have multiple applications, they have two key limitations. First, they consider that a pattern is not periodic if the time difference between two of its successive occurrences is greater than a maxPer threshold. But

Multi-target regression (MTR) refers to learning multiple relevant regression tasks simultaneously. Although much progress has been made in multi-target regression, there are still two challenging issues, that is, how to model the underlying relationships between input features and output targets, and how to explore inter-target dependencies. In this study, an effective algorithm named LLIC is proposed;

The coronavirus COVID-19 pandemic is today’s major public health crisis, we have faced since the Second World War. The pandemic is spreading around the globe like a wave, and according to the World Health Organization’s recent report, the number of confirmed cases and deaths are rising rapidly. COVID-19 pandemic has created severe social, economic, and political crises, which in turn will leave long-lasting

The global epidemic of COVID-19 makes people realize that wearing a mask is one of the most effective ways to protect ourselves from virus infections, which poses serious challenges for the existing face recognition system. To tackle the difficulties, a new method for masked face recognition is proposed by integrating a cropping-based approach with the Convolutional Block Attention Module (CBAM). The

Structural damage detection is of very importance to improve reliability and safety of civil structures. A novel sensor data-driven structural damage detection method is proposed in this paper by combining continuous wavelet transform (CWT) with deep convolutional neural network (DCNN). In this method, time-frequency images are obtained by CWT from original one-dimensional sensor signals. And, DCNN

Multi-view subspace clustering has been widely applied in practical applications. It fuses complementary information across multiple views and treats all samples of a view as a set of bases of a generalized subspace. Meanwhile, it assumes that an instance has all features corresponding to all views. However, each view may lose some features due to the malfunction, which results in an incomplete multi-view

In the past decade, facial emotion recognition (FER) research saw tremendous progress, which led to the development of novel convolutional neural network (CNN) architectures for automatic recognition of facial emotions in static images. These networks, though, have achieved good recognition accuracy, they incur high computational costs and memory utilization. These issues restrict their deployment

Transductive learning is a generalization of semi-supervised learning which attempts to learn a distinctive classifier from large amounts of unlabeled data. In addition, Universum data can bring prior knowledge to the classifier. The Universum data mean the data which do not belong to the positive or negative classes, which can improve the performance of the learning task. In this paper, we address

Online learning is an important technical means for sketching massive real-time and high-speed data. Although this direction has attracted intensive attention, most of the literature in this area ignore the following three issues: (1) they think little of the underlying abstract hierarchical latent information existing in examples, even if extracting these abstract hierarchical latent representations

A border surveillance operation requires sophisticated sensor planning, as sensors are usually scarce and cannot cover an entire region simultaneously. Border patrol agents act as moving sensors in a border region, and the border patrol agents’ coverage moves around the region dynamically, increasing the chance of approaching a trespassing agent. Typically, the locations of trespassing agents cannot

Maintaining a good balance between convergence and diversity is crucial in many-objective optimization, while most existing dominance relations can not achieve a good balance between them. In this paper, we propose a new dominance relation to better balance the convergence and diversity. In the proposed dominance relation, a convergence indicator and a niching technique based adaptive parameter are

The twin support vector machine improves the classification performance of the support vector machine by solving two small quadratic programming problems. However, this method has the following defects: (1) For the twin support vector machine and some of its variants, the constructed models use a hinge loss function, which is sensitive to noise and unstable in resampling. (2) The models need to be

One of the key factors that affect the efficiency and scope of work of wall-climbing robots is how the climbing robot can achieve autonomous transition between adjacent vertical planes. This paper studies the problem of ground-wall transition of a self-developed suctorial wall-climbing hexapod robot (WelCH). In view of the feasibility of the robot performing transition, this paper makes a detailed

Machine reading comprehension(MRC), which employs computers to answer questions from given passages, is a popular research field. In natural language, a natural hierarchical representation can be seen: characters, words, phrases, sentences, paragraphs, and documents. Current studies have demonstrated that hierarchical information can help machines understand natural language. However, prior works focused

Clustering ensemble is one of the popular methods in the field of data mining for discovering hidden patterns in unlabeled datasets. Researches have shown that selecting base clustering results with certain differences and high quality to participate in the fusion process can improve the quality of the final result. However, the existing inherent characteristics of uncertainty, ambiguity, and overlap

A theory of three-way decision concerns the art, science, and practice of thinking, problem solving, and information processing in threes. It explores the effective uses of triads of three things, for example, three elements, three parts, three perspectives, and so on. In this paper, I examine geometric structures, graphical representations, and semantical interpretations of triads in terms of basic

Remote photoplethysmography (rPPG) is a noncontact heart rate (HR) measurement technique. The current heart rate measurement methods based on rPPG all require ideal lighting conditions, but the lighting in real scenes is complicated, Therefore, this article proposes a robust heart rate measurement method when unstable light (time-varying light and uneven spatial illumination) exists. First, the method

Truncated loss functions are robust to class noise and outliers. A robust twin bounded support vector machine is proposed in this paper that truncates the growth of its loss functions at a pre-specified point, thus, flattens the function that pre-specified score afterwards. Moreover, to make the proposed method capable of handling datasets with different imbalance ratio, cost-sensitive learning is

Recent years have witnessed rapid development of deep learning technology and its successful application in the saliency prediction of traditional 2D images. However, when using deep neural network (DNN) models to perform saliency prediction on omnidirectional images (ODIs), there are two critical issues: (1) The datasets for ODIs are small-scale that cannot support the training DNN-based models. (2)

Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are

Automating fault detection in communication networks and distributed systems is a challenging process that usually requires the involvement of supporting tools and the expertise of system operators. Automated event monitoring and correlating systems produce event data that is forwarded to system operators for analyzing error events and creating fault reports. Machine learning methods help not only

Clustering method is a widely used and effective technique in color image segmentation. In general, traditional clustering-based image segmentation algorithms consider only one objective function and the segmentation performance is easily influenced by the noise in the image. Therefore, utilizing many clustering criteria and the neighborhood statistic information of pixels are more effective to improve

The objective of area exploration is to traverse the area effectively and random walk methods are the most commonly used searching strategy for swarm robots. Existing research mainly compares the effectiveness of various random walk methods through experimental verification, which has relatively large limitations. In order to make the application of the random walk methods more convenient, this paper

Multi-label feature selection is a critical dimension reduction technique in multi-label learning. In conventional multi-label feature selection methods based on information theory, feature relevance is evaluated by mutual information between candidate features and each label. However, previous methods ignore two issues: the influence of the already-selected features on the feature relevance and the

Probabilistic matrix factorization (PMF) is the most popular method among low-rank matrix approximation approaches that address the sparsity problem in collaborative filtering for recommender systems. PMF depends on the classical maximum a posteriori estimator for estimating model parameters; however, these approaches are vulnerable to overfitting because of the nature of a single point estimation

Retinal vessel segmentation plays an important role in the automatic assessment of eye health. Deep learning technology has been extensively employed in medical image segmentation. Specifically, U-net based methods achieve great success in medical image segmentation. However, due to its continuous pooling layer and convolution operation, the spatial information and texture information of the image

Object tracking in a real-life scenario is very challenging due to occlusion. State-space models like Kalman and particle filters are well known to handle such a particular problem. The particle filter’s performance for solving such a problem depends on two issues - motion model and observation (i.e., likelihood) model. The question remains to exist due to the lack of useful observation and efficient

Unlike a simple network with just nodes and edges in between them, the real-world networks can contain much more, such as a set of attributes associated with every node in the network. These networks opened up a new avenue in community detection called attributed graph clustering (AGC). Furthermore, the clusters in real-world are not usually disjoint, as compared to most of the work that has been carried

Community detection in dynamic networks is one of the most challenging tasks in the field of network analysis. In general, networks often evolve smoothly between successive snapshots. Therefore, the community structure detected in each snapshot should not only be of high quality but also reflect the smoothness of the variations compared with the previous snapshot. In this paper, we propose a novel

In multi-label learning, each instance is associated with a subset of predefined labels. One common approach for multi-label classification has been proposed in Godbole and Sarawagi (2004) based on stacking which is called as Meta Binary Relevance (MBR). It uses two layers of binary models and feeds the outputs of the first layer to all binary models of the second layer. Hence, initial predicted class

The super-resolution reconstruction of X-ray images is one of the hot issues in the field of medical imaging. Due to the limitations of X-ray machines, the acquired images often have some problems, such as blurred details, unclear edges and low contrast, which seriously affect doctors’ interpretations of X-ray images. In view of the above problems, an X-ray image super-resolution reconstruction method

Person re-identification (re-ID) aims to match person images under different cameras with disjoint views. Although supervised re-ID has achieved great progress, unsupervised cross-domain re-ID remains a challenging work due to domain bias. In this work, we divide cross-domain re-ID task into two phases: domain-invariant features learning and domain-specific features learning. Our contributions are

Due to the complexity of the financial market, security returns are sometimes expressed by expert estimates rather than historical data. In this paper, we deal with a multiobjective multiperiod portfolio selection problem based on uncertainty theory. We propose a new uncertain multiobjective multiperiod mean-semisentropy-skewness portfolio optimization model, in which uncertain semi-entropy is used

Images captured by cameras in low-light conditions have low quality and appear dark due to insufficient light exposure, which critically affects the view. Most of the traditional enhancement methods are based on the entire image for exposure enhancement, so overexposed areas in the image have the risk of secondary enhancement. In order to fully consider the exposure in low-light images, we propose

This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis of global Concern by the World Health Organization (WHO). Various outbreak models for COVID-19 are being utilized by researchers throughout the world to get well-versed

Since the time series data have the characteristics of a large amount of data and non-stationarity, we usually cannot obtain a satisfactory result by a single-model-based method to detect anomalies in time series data. To overcome this problem, in this paper, a combination-model-based approach is proposed by combining a similarity-measurement-based method and a model-based method for anomaly detection

G raph C onvolutional N etwork (GCN) has been commonly applied for semi-supervised learning tasks. However, the established GCN frequently only considers the given labels in the topology optimization, which may not deliver the best performance for semi-supervised learning tasks. In this paper, we propose a novel G raph C onvolutional N etwork with E stimated labels (E-GCN) for semi-supervised learning

Feeding system modelling is the foundation for control strategy optimization, contour error compensation, etc., to improve the productivity and quality of a part. This paper proposes a novel residual learning approach for fitting the simulation error of the dynamics model of a machine tool feeding system. Then, the feeding system model consisting of the dynamics model and the residual model is constructed

Automatic fabric defect detection systems improve the quality of textile production across the industry. To make these automatic systems accessible to smaller businesses, one potential solution is to use limited memory capacity chips that can be used with hardware platforms with limited resources. That is to say, the fabric defect detection algorithm must ensure high detection accuracy while maintaining

Population structure has an impact on the performance of metaheuristic algorithms. To better improve the performance of differential evolution (DE), an elite-guided hierarchical differential evolution algorithm (EHDE) is proposed. First, an elite-guided hierarchical mutation mechanism is presented, which integrates elite elements into the hierarchical population structure. During each generation, the

Context-aware processing is a research hotspot in the recommendation area, which achieves better recommendation accuracy by considering more context information such as time, location and etc. besides the information of the users, items and ratings. Tensor factorization is an effective algorithm in context-aware recommendation and current approaches show that adding bias to the tensor factorization

Traditional fault diagnosis methods of DC (direct current) motors require high expertise and human labor. However, the other disadvantages of these methods are low efficiency and poor accuracy. To address these problems, a new adaptive and intelligent mechanical fault diagnosis method for DC motors based on variational mode decomposition (VMD), singular value decomposition (SVD), and residual deep

Ensemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and accuracy of the predicted results of ensemble models. However, the problem of multi-class data is still encountered. In the proposed approach, cost-sensitive