In this paper, a hybrid model with both clustering mechanism and regression mechanism is built with aid of an allocation of information granularity. Some numeric prototypes constructed by clustering methods are expanded into granular prototypes. The regression module accepts granular prototypes and outcomes information granules. Some advantages of this idea can be concluded: 1). The constructed granular

Dimensionality reduction aims at reducing redundant information in big data and hence making data analysis more efficient. Resource-constrained enterprises or individuals often outsource this time-consuming job to the cloud for saving storage and computing resources. However, due to inadequate supervision, the privacy and security of outsourced data have been a serious concern to data owners. In this

The stochastic multi-armed bandit problem is a standard model to solve the exploration–exploitation trade-off in sequential decision problems. In clinical trials, which are sensitive to outlier data, the goal is to learn a risk-averse policy to provide a trade-off between exploration, exploitation, and safety. In this paper, we present a risk-averse multi-armed bandit algorithm to solve a decision-making

Fundamental information extraction tasks, such as relation extraction and event detection, suffer from a data imbalance problem. To alleviate this problem, existing methods rely mostly on well-designed loss functions to reduce the negative influence of imbalanced data. However, this approach requires additional hyper-parameters and limits scalability. Furthermore, these methods can only benefit specific

This paper focuses on the non-fragile robust stabilization problem for uncertain systems with structural uncertainties via observer-based state-feedback aperiodically intermittent control. First, based on the characteristics of intermittent control, a general representation of the observer coupled with the feedback intermittent controller with variable control periods and control widths is presented

In the field of machine learning, collected data always have additional features which are always referred as privileged information. Privileged information learning is mainly used to help train the classifier in the training process, and predict the unseen example by the learned classifier. In this paper, we propose a new method named twin support vector machines with privileged information (TWSVM-PI)

Recently, graph convolutional networks (GCN) have made substantial progress in semi-supervised learning (SSL). However, established GCN-based methods have two major limitations. First, GCN-based methods are restricted by the oversmoothing issue that limits their ability to extract knowledge from distant but informative nodes. Second, most available GCN-based methods exploit only the feature information

Semi-supervised learning is a new challenge that exploits the information of unlabeled instances. In this paper, we propose a novel Laplacian pair-weight vector projection (LapPVP) algorithm for semi-supervised classification. LapPVP consists of two stages: projection and classification. In the projection stage, LapPVP integrates a within-class scatter, a between-class scatter and a Laplacian regularization

Conventional image retrieval techniques for Structure-from-Motion (SfM) are limited in their ability to effectively distinguish symmetric or repetitive textured patterns and cannot guarantee an accurate generation of pairwise matches without costly redundancy. In this paper, we formulate the image retrieval task as a node binary classification problem with graph data: if a candidate node is marked

For the classification of fine-grained images, the subtle differences among the subclasses of the main category must be distinguished. Intuitively, the key to realizing the fine-grained image categorization lies in locating and identifying the detailed differences in the local regions and capturing their feature representations. In this paper, we propose utilizing an attention module combined with

The tree ensemble model has been widely employed as a loan evaluation method in credit risk assessment due to its high accuracy and robustness. However, the tree ensemble model is complex and incomprehensible, which restricts its adoption for decision-making in loan evaluation. In this paper, we propose a novel rule extraction method for improving the ensemble model by balancing predictive performance

Subgraph searching is an essential problem in graph databases, but it is also challenging due to the involved subgraph isomorphism NP-Complete sub-problem. Filter-Then-Verify (FTV) methods mitigate performance overheads by using an index to prune out graphs that do not fit the query in a filtering stage, reducing the number of subgraph isomorphism evaluations in a subsequent verification stage. Subgraph

In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) system designed for Mobile Edge Computing (MEC) and multimodal biometrics. We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to

In this work, we propose a robust cluster analysis methodology based on cellwise trimming as an extension to a robust version of Principal Component Analysis. This new approach is more reasonable than traditional casewise trimming when the dimension is not small. This type of trimming avoids an unnecessary loss of information when only a few cells of the entirely trimmed observations are atypical.

Matrix completion from very limited information is an important machine learning topic, and has received extensive attention in various scientific applications. Matrix completion aims at finding a low-rank matrix to approximate the incomplete data matrix. However, noise in the data matrix may degrade the performance of the existing matrix completion algorithms, especially if there are different types

We introduce unprecedented tools to infer approximate evolution features of the COVID19 outbreak when these features are altered by containment measures. In this framework we present: 1) a basic tool to deal with samples that are both truncated and non independently drawn, and 2) a two-phase random variable to capture a game changer along a process evolution. To overcome these challenges we lie in

Atrial fibrillation (AF) and atrial flutter (AFL) are the most common arrhythmias. Due to the similar clinical symptoms, both are one of the main causes of misdiagnosis for physicians. The visual inspection of electrocardiogram (ECG) signals is the most traditional detection strategy, however, it is often laborious and time-consuming. Thus, we specially propose a modified bidirectional long short-term

This paper presents an efficient graph semisupervised learning (GSSL) method that meets the criterion of optimization without iterations. Most existing GSSL methods require iterative optimization to achieve a preeer relationships. Additionally, existing GSSL methods must learn from scratch for unseen data because graph structures are specifically built for a given dataset. By leveraging the partial

Compared with single-layer networks, multiplex networks can describe real-world scenarios in more detail while suffering from requiring considerable computing and storage resources at the same time. Network feature learning, which aims to embed networks into a low dimensional space, is an effective method for solving these problems. Currently, research on multiplex network embedding faces two major

This paper proposes security techniques for detecting and counteracting attacks in Networked Control Systems (NCSs). A partially homomorphic based encryption technique, namely the Paillier scheme, is used to protect the confidentiality of the transmitted data over the network against eavesdropping attacks. An integrated secure and private observer-based controller with guaranteed stability together

Broad Learning System (BLS) is an emerging network paradigm that has received considerable attention in the regression and classification fields. However, there are two deficiencies which seriously hinder its deployment in real applications. The first one is the internal correlations among samples are not fully considered in the modeling process. Second, the strict binary label matrix utilized in BLS

Mapping nodes in multi-layer and heterogeneous networks to low-dimensional vectors has wide applications in community detection, node classification and link prediction, etc. In this paper, a generalized graph representation learning framework is proposed for information aggregation in various multi-layer and heterogeneous networks. Specifical, an aggregation network is firstly obtained by graph transformation

Most work on musical score models (a.k.a. musical language models) for music transcription has focused on describing the local sequential dependence of notes in musical scores and failed to capture their global repetitive structure, which can be a useful guide for transcribing music. Focusing on rhythm, we formulate several classes of Bayesian Markov models of musical scores that describe repetitions

In this paper, sufficient conditions to ensure the existence of a switching law that makes the solutions of switched Takagi-Sugeno (TS) fuzzy systems ultimately bounded are developed by means of linear matrix inequalities (LMIs). These LMIs are based on the existence of a scalar function, which plays a role similar to Lyapunov energy functions for an auxiliary system formed by a convex combination

The increasing usage of machine-learning models in critical domains has recently stressed the necessity of interpretable machine-learning models. In areas like healthcare, finary – the model consumer must understand the rationale behind the model output in order to use it when making a decision. For this reason, it is impossible to use black-box models in these scenarios, regardless of their high predictive

In multi-label learning, high-dimensionality is the most prominent characteristic of the data. An efficient pre-processing step, named feature selection, is required to reduce “the curse of dimensionality” caused by irrelevant and redundant features in the high-dimensional feature space. However, the difference in significance of the related labels of an instance is ubiquitous in most practical applications

In the new paradigm, learning using privileged information (LUPI) creates a more informative strategy for tasks to achieve better prediction. SVM based methods including SVM+ and TSVM+, have achieved considerable success in LUPI on the clean data. However, these methods typically suffer from the noise and outliers contained in the data, which leads to larger fluctuations in performance. To handle this

In recent years, many methods have been developed for discovering causal relationships from observed data. However, as an important kind of causes existing in many causal systems, combined causes (e.g. multi-factor causes consisting of two or more component variables that individually might not be a cause) have not received enough attention. The existing approach includes both individual and combined

Point clouds and models with semantic information facilitate various indoor automation, ranging from indoor robotics to emergency responses. Studies are currently being conducted on semantic labeling and modeling based on offline mapped point clouds, in which, the performance is strongly limited by the mapping process. To address this issue, we propose a framework to cooperatively perform the three

Recent multi-criteria group decision making methods focus their analysis on the experts preferences. They do not take into account the reasons why each expert has provided a specific set of preferences. In this paper, a method that introduces novel measures capable of explaining the reasons behind experts decisions is presented. A novel concept, the arguments are presented. They represent thhe experts

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which

For many-objective problems, how to maintain the diversity and convergence of the distribution of the solution set over Pareto front (PF) has always been the research emphasis. In the iteration process, the state of population is critical to improve the level of evolution. Therefore, this paper will use two convergence and diversity indicators to further strengthen the usage of evolutionary state information

In this paper, we study the propagation characteristics of rumors in an institution and develop the rumor dissemination model inside the institution. Different from the traditional model, a person in the proposed model can make a judgement about the message and decide to propagate it or refute it, and most people inside the institution can refute the rumor spreaders and propagate the genuine messages

The rising number and size of remote sensing (RS) image archives makes content-based RS image retrieval (CBRSIR) more important. Convolutional neural networks (CNNs) offer good CBRSIR performance, but the features they extract are not rotation-invariant. This is problematic as objects in RS images appear in arbitrary rotation angles. We develop and investigate two new rotation-aware CNN-based CBRSIR

In this study, a self-organizing interval type-3 fuzzy logic system (SO-IT3FLS) with a new learning algorithm is presented. An adaptive kernel size using fuzzy systems is introduced to improve the robustness of conventional correntropy based Kalman filters against non-Gaussian noise. The maximum correntropy Kalman filter (MCKF) and maximum correntropy unscented Kalman filter (MCUKF) with the proposed

Hotel selection method based on online evaluations has become a hot research topic. The existing models based on online ratings or reviews from one website have a disadvantage of information being definite and information amount being small. Therefore, this paper proposes a hotel selection model based on Probabilistic linguistic Term Set (PLTS) which integrates online ratings and reviews from multiple

Nowadays, crowdsourcing is a widespread and effective method to gather the crowd wisdom. At the same time, label aggregation is used to aggregate the noisy and biased data generated by the crowd. In the real-world crowdsourcing tasks, most workers only answer a small fraction of questions, which makes the collected answer sparse. However, the existing label aggregation approaches often build upon some

This paper proposes a two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization problems which have more than one Pareto-optimal solution set corresponding to the same objective vector. The general framework of the proposed method uses two archives, the convergence archive (CA) and the diversity archive (DA), which focus on the convergence and diversity

This research aims to design and prototype a tool to perform intelligence on open sources (OSINT), specifically on official medical bulletins for the detection of false news. MedOSINT is a modular tool that can be adapted to process information from different medical official bulletins. From the processed information, intelligence is generated for decision making, validating the veracity of the COVID-19

In this note, we show a generalized version of convergence in measure theorem of Sugeno integrals in the framework concerning ordered pair of monotone measures. The condition of a kind of absolute continuity we employed is not only sufficient, but also necessary for the generalized version. The previous two versions (standard-form and pseudo-form) of the convergence in measure theorem are recovered

Counterpoising convergence and distribution becomes more intractable in many-objective optimization where the number of objectives exceeds three, evolutionary algorithms (EAs) via decomposition are prominent in convergence promotion yet suffer from diversity loss. The setting of direction vectors (DVs), scalarizing function (SF) and selection strategy can significantly affect the performance of this

In this paper, we characterize the class of solutions for two open problems: one is Problem 1 which is posed by Ouyang et al.  citeY.Ouyang.Chebyshev1, other is Problem 2 which is proposed by Borzová-Molnárová et al.  citeJanaBorzova.SS. Many results being wider than the previous ones have been stated. All are summarized in Theorems: 2,3,4,5,7,9,11 and 12.

Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradients. To achieve robustness to users dropping out, existing practical privacy-preserving federated learning schemes are based on (t, N)-threshold secret sharing. Such schemes rely on a strong assumption to guarantee security: the threshold (t) mut be greater than half

Recently, robust-norm distance related principal component analysis (PCA) for feature extraction has been shown to be very effective for image analysis, which considers either minimization of reconstruction error or maximization of data variance in low-dimensional subspace. However, both of them are important for feature extraction. Furthermore, most of existing methods cannot obtain satisfactory results

This paper is devoted to the construction and analysis of a novel hybrid optimization model of the ℓ0minimization and the ℓ1minimization with a given support estimate. With the help of the Moreau envelop of the ℓ1 norm, we provide reasonable explanation for the claim that the capped-ℓ1 penalty is one of the continuous relaxation to the ℓ0-norm penalty and thus develop the scale iteratively reweighed

Multi-party machine learning is a paradigm in which multiple participants collaboratively train a machine learning model to achieve a common learning objective without sharing their privately owned data. The paradigm has recently received a lot of attention from the research community aimed at addressing its associated privacy concerns. In this work, we focus on addressing the concerns of data privacy

Inner product functional encryption (IPFE), as a novel cryptographic primitive, can be classified into public-key IPFE (PK-IPFE) and secret-key IPFE (SK-IPFE). PK-IPFE has the following functions: an encryptor generates the ciphertext by encrypting a vector x→, and a decryptor decrypts the ciphertext and obtains the inner product 〈x→,y→〉 if it holds the secret key related to vector y→. The general

Synchronizing sequences for large-scale automata have gained popularity recently due to their practical use cases especially to have a faster and better testing process. In many applications, shorter sequences imply less overhead and faster processing time but the problem of finding the shortest synchronizing sequence is NP–hard and requires heuristic approaches to be solved. State-of-the-art heuristics

The success of deep neural networks has contributed to many fields, such as finance, medic and speech recognition. Machine learning models adopted in these fields are always trained with a massive amount of distributed and highly personalized data harvested directly from users. Concerns for data privacy and the demand for better data exploitation have prompted the design of several secure schemes that

Multiple kernel learning (MKL), which combines a set of prespecified basic kernels to improve the clustering performance, has become an important research topic. Unfortunately, the current methods have the following defects in noisy circumstances. 1) Their clustering performance may be significantly reduced due to the noise in the kernel, which is caused by the lack of a reliable discriminant guideline

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices

Recommendation system is facing the “data sparseness” issue. Additional information, including images, texts, and videos, contributes to alleviating this issue. We propose a new multi-modal visual adversarial Bayesian personalized ranking (MVABPR) model to address the issue. The proposed model takes new features, cross-modal semantics, adversarial learning, and visual interface into account. Two multi-modal

To examine the structural learning of the additive noise model in causal discovery, a new algorithm, i.e., RCB (Rank-Correlation-Based), is proposed in combination with the method of Rank Correlation. This algorithm can effectively process multivariate linear Gaussian, non-Gaussian and multivariate nonlinear non-Gaussian data. In this article we have made three contributions. First, it is proven that

Three-way decision and its underlying philosophy of thinking in threes have been investigated in a variety of topics. By observing that many three-way topics are related to data analytics, this work presents three-way data analytics, examine a number of its existing topics, and propose a few potential new topics. Based on the two stages of data analytics (i.e., data preparation and data analysis) and

The clustering is an unsupervised learning technique for grouping the unlabeled data based on the proximity between the data points. Therefore, the performance of clustering techniques mainly depends on the proximity measures. The computation of dissimilarity in high dimensional and noisy datasets as well as datasets with imbalanced feature scale, which appear in various applications, is a challenging

The fuzzy integral is a well-known class of aggregation operators, which includes the Sugeno integral and Shilkret integral. When performing fuzzy integration over vectors of interval values, recent literature showed that using a simplistic method to independently deal with the lower and upper bounds of interval-valued inputs is sometimes not reasonable in practice. This motivate us to conduct a necessary

Network science is a widely studied subject and graph theory has a major role in it. This paper studies connectivity status of vertices in a fuzzy graph. This paper adopt connectivity status to build up the status sequence related to a fuzzy graph. Results on connectivity status and status sequence of different structures are obtained. On the basis of connectivity status, vertices of a fuzzy graph

This paper proposes a new group decision making (GDM) method based on the consistency and the consensus analysis of dual multiplicative linguistic preference relations (DMLPRs). A new type of linguistic variables, called dual multiplicative linguistic variables (DMLVs), is presented, which is defined on the multiplicative linguistic scale. DMLVs are used to represent asymmetrical qualitative hesitancy

We propose a novel cohesive subgraph model called τ-strengthened (α,β)-core (denoted as (α,β)τ-core), which is the first to consider both tie strength and vertex engagement on bipartite graphs. An edge is a strong tie if contained in at least τ butterflies (2×2-bicliques). (α,β)τ-core requires each vertex on the upper or lower level to have at least α or β strong ties, given strength level τ. To retrieve