KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network.

Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields. Knowledge-graph-embedding methods can learn entity information and complex relationships between the entities in knowledge graphs.

Stability and Hopf Bifurcation Analysis of an Epidemic Model with Time Delay.

Epidemic models are normally used to describe the spread of infectious diseases. In this paper, we will discuss an epidemic model with time delay. Firstly, the existence of the positive fixed point is proven; and then, the stability and Hopf bifurcation are investigated by analyzing the distribution of the roots of the associated characteristic equations.

The Quantitative Relationship among the Number of the Pacing Cells Required, the Dimension, and the Diffusion Coefficient.

The purpose of the paper is to derive a formula to describe the quantitative relationship among the number of the pacing cells required (NPR), the dimension , and the diffusion coefficient (electrical coupling or gap junction ). The relationship between NPR and has been investigated in different dimensions, respectively. That is, for each fixed , there is a formula to describe the relationship between NPR and ; and three formulas are required for the three dimensions.

Wide-field and full-focus optical microscopic imaging system.

As a precision instrument, the microscope is typically used by researchers in criminal investigation, information forensics, biology, metallography, etc. However, the traditional microscope has a dilemma in that if it uses higher magnification, its field of view is smaller and its depth of field is more limited. Hence, it seriously challenges the endurance and brain of the observer to observe an object thoroughly.

Data Access Based on a Guide Map of the Underwater Wireless Sensor Network.

Underwater wireless sensor networks (UWSNs) represent an area of increasing research interest, as data storage, discovery, and query of UWSNs are always challenging issues. In this paper, a data access based on a guide map (DAGM) method is proposed for UWSNs. In DAGM, the metadata describes the abstracts of data content and the storage location.

Cross Deployment Networking and Systematic Performance Analysis of Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) have become a new hot research area. However, due to the work dynamics and harsh ocean environment, how to obtain an UWSN with the best systematic performance while deploying as few sensor nodes as possible and setting up self-adaptive networking is an urgent problem that needs to be solved. Consequently, sensor deployment, networking, and performance calculation of UWSNs are challenging issues, hence the study in this paper centers on this topic and three relevant methods and models are put forward.

RiPPAS: A Ring-Based Privacy-Preserving Aggregation Scheme in Wireless Sensor Networks.

Recently, data privacy in wireless sensor networks (WSNs) has been paid increased attention. The characteristics of WSNs determine that users' queries are mainly aggregation queries. In this paper, the problem of processing aggregation queries in WSNs with data privacy preservation is investigated.

Collaborative Filtering Recommendation on Users' Interest Sequences.

As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests.

A game theory-based trust measurement model for social networks.

Background: In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent.

A Cross Structured Light Sensor and Stripe Segmentation Method for Visual Tracking of a Wall Climbing Robot.

In non-destructive testing (NDT) of metal welds, weld line tracking is usually performed outdoors, where the structured light sources are always disturbed by various noises, such as sunlight, shadows, and reflections from the weld line surface. In this paper, we design a cross structured light (CSL) to detect the weld line and propose a robust laser stripe segmentation algorithm to overcome the noises in structured light images. An adaptive monochromatic space is applied to preprocess the image with ambient noises.

Use of customizing kernel sparse representation for hyperspectral image classification.

Sparse representation-based classification (SRC) has attracted increasing attention in remote-sensed hyperspectral communities for its competitive performance with available classification algorithms. Kernel sparse representation-based classification (KSRC) is a nonlinear extension of SRC, which makes pixels from different classes linearly separable. However, KSRC only considers projecting data from original space into feature space with a predefined parameter, without integrating a priori domain knowledge, such as the contribution from different spectral features.

A reliability-based track fusion algorithm.

The common track fusion algorithms in multi-sensor systems have some defects, such as serious imbalances between accuracy and computational cost, the same treatment of all the sensor information regardless of their quality, high fusion errors at inflection points. To address these defects, a track fusion algorithm based on the reliability (TFR) is presented in multi-sensor and multi-target environments. To improve the information quality, outliers in the local tracks are eliminated at first.

Feature weighting algorithms for classification of hyperspectral images using a support vector machine.

The support vector machine (SVM) is a widely used approach for high-dimensional data classification. Traditionally, SVMs use features from the spectral bands of hyperspectral images with each feature contributing equally to the classification. In practical applications, although affected by noise, slight contributions can also be obtained from deteriorated bands.