Current two-stage object detectors, which mainly consist of a region proposal stage and a proposal recognition stage, may produce unreliable results for objects appearing with little information such as small and occluded objects. This is caused by poor region proposals and inaccurate proposal recognition. To address this problem, we propose a context augmentation algorithm that fully utilizes contextual

With the number of patients with chronic diseases increasing, a hierarchical diagnosis and treatment system is a feature of long-term treatment. During the treatment, if patients with chronic diseases are relieved, they can be referred to community hospitals for rehabilitation treatment, and to promote the rational allocation of hospital medical resources. Therefore, hospitals need referral standards

The uncontrolled charging behaviors of large-scale electric vehicles (EVs) increase the security risk of the power grid and bring a new challenge for the computing ability of the power system. Using vehicle to grid (V2G) technology, most control systems coordinate the power interaction between EVs and power grid by minimizing the load fluctuation and user cost, but their optimization results are often

Self attention mechanism is widely used in relation extraction, emotion classification and other tasks. It can extract a wide range of relevance information in the text. The attention mode of the existing self attention mechanism is soft attention mode, that is, a dense attention matrix is generated by softmax function. However, if the sentence length is long, the weight of important information will

Composite influences coming from signal measurement noises, unknown nonlinear dynamics, external disturbances and input saturation nonlinearity make it challenging to synthesize high-performance closed-loop control algorithms for uncertain robotic manipulators. In the face of these challenges, we employ the nonlinear multilayer neural networks to approach uncertain nonlinear dynamics and exploit the

This paper introduces a new theoretical scheme for the solution of the frequent itemset hiding problem. We propose an algorithmic approach that consists of a novel constraint-based hiding model which encompasses hiding into one pass mining, along with a solution methodology that relies on Linear Programming. The induced patterns by the constraint-based mining algorithm are, in this way, utilized to

A Unique chaotic image encryption on the basis of Enhanced Thorp shuffle and Zig-zag Scan based Convolution (ETS-ZSC) is proposed in this paper. A one-dimensional chaotic map is utilized for both shuffling the plain image and producing the critical grid for the convolution activity. The substitution operation is performed in two ways: forward substitution and reverse substitution with zigzag scan.

Aiming at the formation control of multiple Mecanum-wheeled mobile robots (MWMRs) with physical constraints and model uncertainties, a novel robust control scheme that combines model predictive control (MPC) and extended state observer-based adaptive sliding mode control (ESO-ASMC) is proposed in this paper. First, a linear MPC strategy is proposed to address the motion constraints of MWMRs, which

Recently, live streaming services attract millions of users’ participation and billions of capital investment. In each prevailing live streaming platform, there are thousands of anchors who are broadcasting concurrently, which means it is necessary for the platform to make recommendation to improve user experience. In such platforms, viewers change their watching preference dynamically, and anchors

The multivariate time series often contain complex mixed inputs, with complex correlations between them. Detecting change points in multivariate time series is of great importance, which can find anomalies early and reduce losses, yet very challenging as it is affected by many complex factors, i.e., dynamic correlations and external factors. The performance of traditional methods typically scales poorly

Positive and unlabeled learning (PU learning) has been studied to address the situation in which only positive and unlabeled examples are available. Most of the previous work has been devoted to identifying negative examples from the unlabeled data, so that the supervised learning approaches can be applied to build a classifier. However, for the remaining unlabeled data, they either exclude them from

Coronary artery disease (CAD) is one of the most lethal diseases which is major cause of deaths around the globe. CAD is among such diseases with mortality rate approximately 7 million per annum. Though, early detection, prognostication and timely diagnosis can help in mortality rate reduction. Conventional CAD detection systems are cumbersome and expensive. Moreover, scarcity or uneven distribution

The17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current

Visible-infrared cross-modality person re-identification is an important task in the night video surveillance system, the huge difference between infrared and visible light images makes this work quite challenging. Unlike traditional person re-identification, a cross-modality mission needs to solve intra-class differences and inter-class variations. To solve the problem of huge modality discrepancy

Obesity is a critical public health problem associated with various complications and diseases. Accurate prediction of body fat is crucial for diagnosing obesity. Various measurement methods, including underwater weighing, dual energy X-ray absorptiometry, bioelectrical impedance analysis, magnetic resonance imaging, air displacement plethysmography, and near infrared interactance, have been used to

In recent years, the prevalence of chronic diseases such as type 2 diabetes mellitus (T2DM) has increased, bringing a heavy burden to healthcare systems. While regular monitoring of patients is expensive and impractical, understanding chronic disease progressions and identifying patients at risk of developing comorbidities are crucial. This research used a real-world administrative claim dataset of

Self-learning Antelopes Migration Algorithm (SAMA) is a self-heuristic algorithm that simulates the local exploitation of ordinary antelopes and the global exploration of scout antelopes. Aiming at the imbalance between exploitation and exploration of SAMA, it is easy to fall into local optimum, and the convergence speed and precision will be affected. A pseudo-parallel chaotic self-learning antelope

The kernel subspace clustering algorithm aims to tackle the nonlinear subspace model. The block diagonal representation subspace clustering has a more promising capability in pursuing the k-block diagonal matrix. Therefore, the low-rankness and the adaptivity of the kernel subspace clustering can boost the clustering performance, so an adaptive low-rank kernel block diagonal representation (ALKBDR)

More and more researchers have recently paid attention to video object segmentation because it is an important building block for numerous computer vision applications. Although many algorithms promote its development, there are still some open challenges. Efficient and robust pipelines are needed to address appearance changes and the distraction from similar background objects in the video object

Non-negativity is a critical and explainable property in linear representation-based methods leading to promising performances in the pattern classification field. Based on the non-negativity, a powerful linear representation-based classifier was proposed, namely non-negative representation-based classifier (NRC). With the non-negativity constraint, the NRC enhances the power of the homogeneous samples

Grain segmentation of sandstone that is partitioning the grain from its surrounding matrix/cement in the thin section is the primary step for computer-aided mineral identification and sandstone classification. The photomicrograph of sandstone contain many mineral grains and their surrounding matrix/cement. The distinction between adjacent grains and the matrix is often ambiguous, making grain segmentation

Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer

In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have recently achieved remarkable performance. While the current state-of-the-art methods for skeleton-based action recognition usually assume that completely observed skeletons will be provided, it is problematic to realize this assumption

Deep convolutional networks have been widely applied in super-resolution (SR) tasks and have achieved excellent performance. However, even though the self-attention mechanism is a hot topic, has not been applied in SR tasks. In this paper, we propose a new attention-based network for more flexible and efficient performance than other generative adversarial network(GAN)-based methods. Specifically,

One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked

Realizing the accurate prediction of data flow is an important and challenging problem in industrial automation. However, due to the diversity of data types, it is difficult for traditional time series prediction models to have good prediction effects on different types of data. To improve the versatility and accuracy of the model, this paper proposes a novel hybrid time-series prediction model based

With the more and more in-depth research on intelligent transportation, many scholars have proposed their models for accurate prediction of traffic. In this paper, we analyze the advantages and disadvantages of the existing models and propose our own model. In our model, the temporal and spatial factors are taken into account. Gate Recurrent Unit (GRU) and Gated Linear Units (GLU) are used to learn

The projection twin support vector machine (PTSVM) is a potential tool for classification problem. However the loss function of PTSVM is hinge loss function which is a unbounded loss and not robust enough to outliers. In this work, a robust PTSVM (termed RSHPTSVM) is proposed based on rescaled square hinge loss (RSH-loss) to handle classification problem. A close relationship between RSH-loss and correntropy

Task allocation is an important problem in multi-robot system which can be defined with different setup for different application, i.e. coverage, surveillance and mining mission in static or dynamic scenarios. Our focus in this paper is exploring environment to accomplish tasks distributed over the environment by minimizing overall cost of the system. This problem is defined as a NP-Hard problem, thus

The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research

In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. In this paper, we explore different strategies for detecting body pose from highly ambiguous pressure data, with the aid of pre-existing pose estimators. We examine the performance of pre-trained pose estimators by using them either directly or by re-training them on two pressure datasets

During automatic driving, the complex background and mutual occlusion between multiple targets hinder the correct judgment of the detector and miss detection. When a close-range target is captured again, the vehicle may not be able to respond in time and cause a fatal accident. Therefore, in the application of auxiliary systems, a model that can accurately identify partially occluded targets in complex

Model-based Diagnosis (MBD) with multiple observations is a currently complicated problem with many applications and solving this problem is attracting more and more attention. This paper propose an improved algorithm, called Improved implicit Hitting Set Dualization (IHSD), which is the integration of gate domination in recent works for computing cardinality-minimal aggregated diagnoses in MBD problems

Learning from imbalanced datasets is a challenging problem in machine learning research since the performance of the traditional classifiers suffer from biased classification towards the Majority class resulting in a low Minority class prediction rate. The inherent assumptions of equal class distribution and accuracy-driven evaluation are the identified reasons behind this degraded performance. Further

In the recommendation systems (RSs), it is imperative to incorporate the hidden contextual meaning of users’ provided ratings in the similarity computation. To draw such contextual meanings, existing models use the fixed categorization of accessible ratings. However, due to the excessive variation in similarly co-rated item pairs, they produce ambiguous contextual meanings that yield inconsistent results

Surface defect inspection can greatly improve the efficiency of industrial production by replacing manual operations. However, in actual industrial scenarios, it is difficult to collect and manually label enough defect images. In addition, the complex backgrounds, diverse shapes and sizes, and broad random location distribution of defects in images make defect inspection more challenging. To address

For the sake of removing the rain attached to rain images to restore the clarity of the images and due to the fact that existing rain removal methods cannot effectively remove rain from real-world images, a rain removal method for real-world images fusing deep learning and image enhancement is proposed. Firstly, a deep convolutional neural network based on supervised learning idea, multi-recursive

COVID-19 is one of the largest spreading pandemic diseases faced in the documented history of mankind. Human to human interaction is the most prolific method of transmission of this virus. Nations all across the globe started to issue stay at home orders and mandating to wear masks or a form of face-covering in public to minimize the transmission by reducing contact between majority of the populace

Accurate and rapid pattern recognition of epilepsy from intracranial electroencephalogram (iEEG) recordings is important for medical diagnostics. In this paper, three algorithms based on discrete wavelet transform (DWT) analysis and parallel probabilistic neural network, SA-PNN, SA-PPNN, and LSA-PPNN, are presented to identify iEEG recordings and detect epileptic seizures. Simulated annealing (SA)

Web Services being the predominant aspect of the web, plays an inevitable role in everyday digital life.With an upsurge in web services, the process of combining them to solve a user query has become complicated. Investigators have proposed the usage of various techniques like Artificial Intelligence, Machine Learning and others to solve the problem of service composition which till date has serious

This paper studies robust gait features against pedestrian carrying and clothing condition changes. Inspired by the fact that humans pay more attention to pose details based on part movements when completing a gait recognition task, we introduce pose information into the convolutional network without complex computation of human modeling. We construct a multimodal set-based deep convolutional network

This paper employs Convolutional Neural Networks with pooling module to extract view descriptor of 3D model, and proposes the Group-Pair Deep Feature Learning method for multi-view 3D model retrieval. In the method, view descriptor is learned by the supervised autoencoder and multi-label discriminator to further mine the latent feature and category feature of 3D model. To enhance the discriminative

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user’s behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user’s preferences. Further, although heterogeneous graphs have been applied in

Recently, with the development of deep-learning, the performance of multi-object tracking algorithms based on deep neural networks has been greatly improved. However, most methods separate different functional modules into multiple networks and train them independently on specific tasks. When these network modules are used directly, they are not compatible with each other effectively, nor can they

This paper presents techniques to improve the prediction accuracy of approximation methods used in Gaussian process regression models. Conventional methods such as Nyström and subset of data methods rely on low-rank approximations to the kernel matrix derived from a set of representative data points. Prediction accuracy suffers when the number of representative points is small or when the length scale

Dividing a network into communities has great benefits in understanding the characteristics of the network. The label propagation algorithm (LPA) is a fast and convenient community detection algorithm. However, the community initialization of LPA does not take advantage of topological information of networks, and its robustness is poor. In this paper, we propose a stable community detection algorithm

Based on the dual-inheritance framework of cultural evolution, an improved multiobjective cultural algorithm (IMOCA) with a multistrategy knowledge base is presented in this paper. Inspired by the original versions of the cultural algorithm (CA), four basic types of knowledge sources, i.e., normative, situational, topographical and historical knowledge, are effectively utilized in the proposed IMOCA

Compared to global community detection, local community detection aims to find communities that contain a given node. Therefore, it can be regarded as a specific and personalized community detection task. Local community detection algorithms based on modularity are widely studied and applied because of their concise strategies and prominent effects. However, they also face challenges, such as sensitivity

Uncertainty measure in Dempster-Shafer (D-S) evidence theory is crucial to assess the quality of information conveyed by belief structures. Most of the previous studies consider this issue from the perspective of viewing the D-S evidence theory as a generalization of probability theory. However, the inconsistency between D-S evidence theory and probability theory may lead to some limitations to existing

Obtaining an objective measurement of the pain level of a patient has always been challenging for health care providers. The most common method of pain assessment in the hospital setting is asking the patients’ verbal ratings, which is considered to be a subjective approach. In order to get an objective pain level of a patient, we propose measuring pain level objectively using the pupillary response

Small sample time-series data with insufficient information are ubiquitous. It is challenging to improve the classification reliability of small sample time-series data. At present, the dynamic classifications for small sample time-series data still lack a tailored method. To address this, we first setup the architecture of dynamic Bayesian derivative classifiers, and then establish a dynamic full

Detection of abnormal behavior in surveillance videos is essential for public safety and monitoring. However, it needs constant human focus and attention for human-based surveillance systems, which is a challenging process. Therefore, automatic detection of such events is of great significance. Abnormal event detection is a challenging problem due to the scarceness of labelled data and the low probability

Several studies have shown the excellent performance of deep learning in image segmentation. Usually, this benefits from a large amount of annotated data. Medical image segmentation is challenging, however, since there is always a scarcity of annotated data. This study constructs a novel deep network for medical image segmentation, referred to as asymmetric U-Net generative adversarial networks with

Good prognostic health management (PHM) plays a crucial role in industrial production and other fields. The accurate prediction of remaining useful life (RUL) can ensure good working condition of machines, and the selection of health indicators (HI) is the key to prediction. Due to the length and noise of the data, the selection of features requires a lot of prior knowledge. Therefore, a novel convolution-based

The rise of high-quality cloud services has made service recommendation a crucial research question. Quality of Service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only

Attribute network anomaly detection has attracted more and more research attention due to its wide application in social media, financial transactions, and network security. However, most of the existing methods only consider the network structure or attribute information to detect anomalies, ignoring the combined information of the node structure and attributes in the network. A novel anomaly detection

Causality is getting more and more attention, and its core idea is counterfactual and intervention. However, the current intervention model requires some prior knowledge, and lacks universality. The paper presents a novel solution called Search the Intervention Sample in Sparse Operation Space (SISSOS). SISSOS introduces variational inference and realizes intervention, that’s feature manipulation at

Saliency detection methods which center on RGB images are sensitive to surrounding environments. Fusing complementary RGB and thermal infrared (RGB-T) images is an effective way to promote the final saliency performance. However, there are relatively few datasets and algorithms for RGB-T saliency detection, which is the prominent problem in this research field. Therefore, an unsupervised method that

Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to

Object detection based on convolutional neural network (CNN) should be trained effectively with much data. Data augmentation techniques devote to generate more data, which can enhance the generalization ability and robustness of detection network. For object detection in thermal infrared (TIR) images, objects are difficult to label because of the heavy noise and low resolution. So, it is highly recommended