Anelectrocardiogram (ECG) consists of complex P-QRS-T waves. Detecting long-term ECG recordings is time-consuming and error-prone for cardiologists. Deep neural networks (DNNs) can learn deep representations and empower automatic arrhythmia detection. However, when applying DNNs in practice, they usually suffer from domain shift that exits between the training data and testing data. Such shift can

Neural network architecture search automatically configures a set of network architectures according to the targeted rules. Thus, it relieves the human-dependent effort and repetitive resources consumption for designing neural network architectures and makes the task of finding the optimum network architecture with better performance much more accessible. Network architecture search methods based on

Density-based clustering is a traditional research topic with the capability of determining clusters of arbitrary shapes. Besides, through the Density Estimator (DE), density-based methods such as MeanShift, and QuickShift can find the local density maximums as Modes that are excellent representatives of the clusters. However, concentrating on the modes only may suffer from the over-segmentation problem

This paper is concerned with the design of false data injection attacks (FDIAs) against fault detectors (FD) for linear cyber-physical systems (CPSs). The purpose of the attacker is to design an stealthy attack scheme such that the defenders cannot detect the faults in time or fail to detect the faults, and at the same time the robustness of the CPS is deteriorated. Unlike the existing optimal attack

Current securities settlement systems employ various strategies to manage risks in settlement effectively. However, exchanging and verifying information between the market participants and intermediaries is time consuming.In addition, the back office and coordination costs are very high. To address such challenges, blockchain technology has attracted the attention of researchers due to its potential

Real-time control is of great significance for practical plants such as intelligent manufacturing systems and transportation systems, and timing information should be integrated in their discrete models. The existing specifications expressed by generalized mutual exclusion constraints (GMECs) do not consider timing specifications in the control design of these models, which would be restrictive for

Unbiased cross-validation (UCV) is a commonly-used method to calculate the optimal bandwidth for the kernel density estimator (KDE), which estimates the underlying probability density function (PDF) for a given data set. Since the UCV method was proposed, there have been few studies that have pointed out its instability when determining the KDE bandwidth. Following the principle of stability improvement

Both problem characteristics in multimodality and multi-objective are involved in multimodal multi-objective optimization problems (MMOPs). How to locate diverse Pareto sets and approximate Pareto front simultaneously is a challenging research topic. To address this issue, a cluster-based immune-inspired algorithm using manifold learning is proposed in this paper for solving MMOPs. First of all, the

This paper investigates the fault-tolerant load frequency control (LFC) design of interconnected wind power systems. For this, a doubly-fed induction generator (DFIG)-based wind farm is integrated into the interconnected power systems. Distinct from the existing works, the communication time-delay is taken into the area control error (ACE) signals of interconnected wind power systems. Also, compared

Ontology verification refers to the activity where an ontology is tested against its ontology requirements to ensure that it is built correctly in compliance with its ontology requirements specification. Therefore, it is an important activity that should be performed in any ontology development process. Since manual verification can be a time-consuming and repetitive task, testing processes to automatically

Recommender system has recently received a lot of attention in the information service community. In many application scenarios, such as Internet of Things (IoTs) environments, item multimodal auxiliary information (such as text and image) can be obtained to expand their feature representation and to increase user satisfaction with recommendations. Motivated by this fact, this paper introduces a novel

Feature selection plays a crucial role in machine learning and data mining, and improves the performance of learning models by selecting a distinguishing feature subset and eliminating irrelevant features. Existing feature selection methods are mainly used for single-label learning and multi-label learning; however, there are only a few feature selection methods for label distribution learning. Label

In research, we found that the purpose of most trackers is to obtain an accurate and robust score map, neglecting how to further examine the confidence of the results. Inspired by the Siamese trackers, which merely use the template from the first frame to locate the target, we propose a novel object-uncertainty policy. Firstly, we propose a dynamic design of the target template set for the tracked

The increased amount of high-dimensional imbalanced data in online social networks challenges existing feature selection methods. Although feature selection methods such as principal component analysis (PCA) are effective for solving high-dimensional imbalanced data problems, they can be computationally expensive. Hence, an effortless approach for identifying meaningful features that are indicative

Named entity recognition and relation extraction are crucial tasks in natural language processing. As the traditional pipelined manners may suffer from the error propagation issue and ignore underlying interactions, joint extraction of entities and relations has become the dominant trend. However, the performance of existing joint extraction models needs improvement. This paper presents a two-stage

The ultimate goal of the optimal alternative selection decision-making is to achieve higher returns with as few risks as possible. However, the existing multi-attribute decision-making (MADM) methods rarely measure their returns and risks simultaneously. In addition, these methods have no clear explanation for how to improve the non-optimal alternatives. Considering this, this paper constructs the

Increasingly more user data are transferred to the cloud for storage and analysis. Due to the dishonesty of the cloud, user data are at risk of leakage. CP-ABE is considered one of the most promising technologies for protecting outsourced data. However, in most existing schemes, the attacker may learn user privacy information from the policy plaintext. Furthermore, schemes supporting policy hiding

Multi-modal brain imaging data reflect brain structural and functional information from different aspects, which have been widely used in brain disease diagnosis, including epilepsy and Alzheimer's disease. In practice, it is difficult to obtain all the modalities of each subject due to high cost or equipment limitation. Therefore, it is highly essential to fuse incomplete multi-modality data to improve

Online social networks (OSNs), as multilingual communication platforms, provide great convenience for rumor spreading. However, a great deal of negative impacts on individuals, society and economy will be caused during the spread of rumor. In order to explore the rules of rumor propagation on online social networks and formulate the corresponding control strategies, in this paper, we propose new 2I2SR

Due to small-quantity and often imbalance of labeled samples, it is challenging to establish a robust and accurate prediction model through data-driven methods. To deal with the small dataset problem, new virtual samples may be generated via virtual sample generation (VSG) methods based on the trend of the original small raw dataset, thereby improving modeling performance. Effective VSG is desirable

As an effective tool to deal with uncertain decision-making, three-way decisions (TWD) have gained wide attention in many applications. Decision-theoretic rough sets (DTRSs) as a classic model of TWD contain two key elements, i.e., conditional probability and loss functions. In this paper, we study the determination of these two elements in depth via the information interaction and modification strategy

Elastic net support vector machine (ENSVM) is an effective and popular classification technique. It has been widely used in many practical applications. However, solving large-scale problems still remains challenging. Inspired by its sparsity, a safe double screening rule (DSR) is proposed for accelerating ENSVM. Its main idea is to reduce the scale of the model by discarding the inactive features

Zero-Shot Learning (ZSL) has gained growing attention over the past few years mostly because it provides a significant scalability to recognition models for classifying instances from new unobserved classes. This scalability is achieved by providing semantic information about new classes, which could be obtained remarkably easier with lower cost, compared to collecting a new training set. Because seen

Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase in amount of data puts forward higher requirements for the capability of real-time signal analysis, it is difficult for existing EMD and its variants to trade off the growth of data dimension

Cross-modal hashing has been intensively studied to efficiently retrieve multi-modal data across modalities. Supervised cross-modal hashing methods leverage the labels of training data to improve the retrieval performance. However, most of these methods still assume that the semantic labels of training data are ideally complete and noise-free. This assumption is too optimistic for real multi-modal

Deep metric learning aims to learn a non-linear function that maps raw-data to a discriminative lower-dimensional embedding space, where semantically similar samples have larger similarity than dissimilar ones. Most existing approaches process each raw-data in two steps, by mapping the raw-data to a higher-dimensional feature space via a fixed backbone, followed by mapping the higher-dimensional feature

RNA-binding proteins (RBPs) play a significant part in several biological processes in the living cell, such as gene regulation and mRNA localization. The research indicates that the mutation of RBPs will lead to some serious diseases. Several deep learning methods, especially the model based on convolutional neural network (CNN), have been used to predict the binding sites. However, these methods

Feature engineering is one of the major challenges of machine learning. While multiple automation solutions have been proposed in recent years, the vast majority focuses on extracting features from the analyzed dataset itself and not from other (external) sources. In this study we present FGSES, a general framework for automatic feature engineering and its application to DBpedia. Our framework automatically

This paper considers switching stochastic delay neural networks (SSDNNs) with all unstable subsystems. By using discretized Lyapunov-Krasovskii functions (DLKFs) combined with the dwell time method, exponential stability of SSDNNs with all unstable subsystems are analyzed, and several novel stability criteria in mean square are obtained. Comparing with the existing works, our results focus on all unstable

This paper addresses the problem of comparing different measures of graph symmetry. Two measures, each based on the number and respective sizes of the vertex orbits of the automorphism group or a graph, are compared. A real valued distance measure is used to compare the symmetry measures by establishing the limiting value of the distances for several well known classes of graphs.

Three-dimensional (3D) face application has attracted significant attention the field of multimedia and image processing. However, the end-to-end 3D face reconstruction method is still immature, and there are some problems, such as overfitting caused by too few training sets and unacceptable 3D face texture alignment performance. Therefore, we design a novel approach to construct a 3D face, named multi-objective

This paper is concerned with the problem of developing more efficient stabilization conditions for discrete-time Takagi–Sugeno fuzzy systems, i.e., not only reducing the conservatism but also (at the same time) alleviating the complexity of fuzzy control synthesis. Firstly, under the framework of traditional fuzzy stabilization, the recent multiple-sums-based method reported in the literature is improved

As an emerging research paradigm in the field of evolutionary computation, evolutionary multi-task optimization (EMTO) has received an increasing amount of attention due to its capability in concurrently solving multiple related optimization tasks. In EMTO, without any prior knowledge about the complementarity between different tasks, the task relatedness is mainly captured dynamically through the

Quality function deployment (QFD) is a team-based product development technique for determining critical engineering characteristics based on customer requirements to realize higher customer satisfaction. Given its simplicity and visibility, QFD has been broadly utilized in a variety of industries for product or service planning. However, the traditional QFD method assumes that the behavior of domain

Real-world information is characterized by uncertainty and partial reliability. In order to model this information, Zadeh introduced the concept of Z-numbers. It has been a challenge to construct a mathematical model to handle Z-number-based information similar to probability theory. One of the basic concepts in probability theory is relative entropy, also known as Kullback–Leibler divergence and information

This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not require any training. We represent the gallery image sets as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image in the test image set. Images of the test set

Performance evaluation is significant in data classification. The existing evaluation methods ignore the characteristics (such as classification difficulty) of each instance. In practice, it is necessary to measure classification performance from the perspective of instances. In this paper, an instance-oriented classification performance metric is proposed based on the classification difficulty of

The pattern-based anomaly detection method has obtained more attention since it was proposed. This is due to its ability to fully identify anomalies by considering two key features, including appearing rarely and deviating from most data elements. Although most pattern-based anomaly detection methods identify the anomalies from full data space, the process involved in performing this functionality

As a complicated cognitive process, multi-attribute decision-making usually focuses on the decision-making issue of seeking the optimal alternative or ranking alternatives under the framework of multiple attributes. The three-way decision approach with the delayed decision can more effectively reduce decision risks than traditional two-way counterparts for multi-attribute decision-making. In this article

Point clouds from scanners or stereo vision inevitably contain outliers which have negative effects on subsequent procedures. Previous works classify outliers according to their characteristics, which guide the design of targeted outlier removal methods. Thus, outlier classification is critical to the corresponding method and outlier removal effect. The proposed type-based outlier removal framework

Personalized top-N recommender algorithms have been investigated widely in decades. The core task of different recommender algorithms is to estimate user-item preference scores and then to suggest, for each user, top-N items that have high preference scores. However, little attention was paid to the recommendation of items with low preference scores. In this work, we use the bayesian estimation theory

With the development of location-based services (LBS), many location-based social sites like Foursquare and Plancast have emerged. People can organize and participate in group activities on those sites. Therefore, recommending venues for group activities is of practical value. However, the group decision making process is complicated, requiring trade-offs among group members. And the data sparsity

Knowledge Tracing (KT) aims to trace the student’s state of evolutionary mastery for a particular knowledge or concept based on the student’s historical learning interactions with the corresponding exercises. Taking the “exercise-to-concept” relationships as input, several existing methods have been developed to trace and model students’ mastery states. However, these studies face two major shortcomings

Training effective simulation scenarios presents numerous challenges from a pedagogical point of view. Through application of the Conceptual Framework for e-Learning and Training (COFELET) as a pattern for designing serious games, we propose the use of the Simulated Critical Infrastructure Protection Scenarios (SCIPS) platform as a prospective tool for supporting the process of providing effective

Categorical data is a significant kind of data in machine learning.Generally, rough set theory (RS-theory) deals with categorical data in the following way.First, an equivalence relation based on the equality of attribute values of categorical data is established.Then, information granules (I-granules) based on equivalence classes are obtained.Finally, information structures (I-structures) consisting

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been proposed to solve multi-objective multi-task optimization problems. However, EMT algorithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission theory for solving multi-objective multitask optimization problems

Influence maximization (IM) is a challenge in social networks, which depends on the spreader selection. We propose a quadratic programming model to identify a fixed number of initial spreaders to affect the maximum nodes within the minimum diffusion time. We solve this model using a new Distance Aware Spreader Finding (DASF) algorithm independent of the community detection problem. On large-scale social

This paper develops a novel insight and procedure that includes a variety of algorithms for finding the best solution in a structured multi-agent system with internal communications and a global purpose. In other words, it finds the optimal communication structure among agents and the optimal policy in this structure. First, a unique reinforcement learning algorithm is proposed to find the optimal

Many embedding approaches of drugs have been proposed for the downstream task of drug-drug interactions (DDIs) prediction in a DDI-derived network where drugs are considered as nodes, and interactions are represented as edges. Among them, one of the most popular approaches is the representation of a drug learned by aggregating its neighboring drugs’ information. However, these kinds of method do not

In recent years, the issue of fairness has become important in the field of machine learning. In clustering problems, fairness is defined in terms of consistency in that the balance ratio of data with different sensitive attribute values remains constant for each cluster. Fairness problems are important in real-world applications, for example, when the recommendation system provides targeted advertisements

Rumors affect a variety of human affairs, which may cause a serious social disorder. In order to accurately simulate the propagation dynamics of rumors in real social networks and effectively ameliorate the harm of rumors to society, a new rumor propagation model is proposed in this paper. The model considers the influence of discussants, and divides the total population into four groups: ignorances

The Takagi-Sugeno-Kang fuzzy system has wide applications across different areas, e.g., regression, classification and decision making, attributed to its high precision and interpretability. However, the existing Takagi-Sugeno-Kang fuzzy system is not an ideal solution to some special scenarios, particularly for those that are constrained monotonically. To this end, a monotonic relation-constrained

The interval type-2 Takagi-Sugeno (T-S) fuzzy dynamic output feedback and H-infinity stability analysis is studied for a class of networked control systems with multiple time-varying additive uncertainties, time-varying signal communication delay and external disturbance. Firstly, the interval type-2 T-S fuzzy is employed to denote the system plant. Secondly, the multiple time-varying additive uncertainties

This paper presents a new approach for identifying interval fuzzy models, which enables estimating fuzzy model structures, parameters, and upper and lower bounds simultaneously and online. It is based on a filtered recursive least squares method combined with an incrementally evolving Gaussian clustering. The proposed method generates interval fuzzy models online, on the fly, and in an evolving manner

With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes

Context information, such as product category, plays a vital role in sequential recommendations. Recently, there has been a growing interest in context-aware sequential recommender systems. However, in previous studies, contexts have often been treated as auxiliary information without the consideration of the inter-sequence dependency between the item sequence and the context sequence. Such a dependency

This article aims to design proportional-derivative (PD) controllers for interval positive linear systems in the continuous-time domain, which still remains a widely-discussed open problem in positive systems theory. The specific objective is to design a PD controller for the system with interval uncertain parameters and time-varying delay, which simultaneously ensures closed-loop system stability

Deep models have achieved prominent results in pattern recognition tasks, especially computer vision and natural language processing. However, the dataset bias caused by the distribution discrepancy between the training and testing data hinders the generalization ability of deep models. Though many domain adaptation approaches have been proposed to mitigate such negative effect, most of them improve