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Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems. Extensive efforts have been devoted to improving the efficiency of SAEAs through the development of proficient model-assisted solution selection methods, which, however, first requires generating high-quality solutions. The fundamental paradigm of evaluating a limited number of solutions in each generation within SAEAs reduces the variance of adjacent populations, thereby affecting the quality of offspring solutions. This is a frequently encountered issue yet receives little attention. To address the issue, this paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs. The surrogate model is employed to identify high-quality solutions for the direct generation of new solutions without evaluation. To ensure dependable selection, we have introduced two tailored relation models to select the optimal solution and the unevaluated population. A comprehensive experimental analysis is performed on two test suites, which showcases the superiority of the relation model over regression and classification models in the solution selection phase. Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to enhance the efficiency of the algorithm significantly. [ABSTRACT FROM AUTHOR]

Reading newly-published papers in time is important for researchers since these papers provide the latest research findings. However, it is challenging to retrieve newly-published papers through common query-based search engines because the papers lacking sufficient citations and links are usually ranked too low in the search list. To this end, we design a time-aware joint model to infer users’ preference for the newly-published papers with the help of subsidiary relations of social and article linkages. The temporal preference of researchers for articles is jointly modeled with social and article relations by a group of matrices sharing common dimensions of researchers and articles. A joint multi-relational factorization algorithm is devised to approximate the latent factor matrices along with a temporal recommendation algorithm to predict the personalized new referential papers based on the factor matrices. The experimental results on real-world datasets show that the proposed model outperforms the state-of-the-art methods.

Expensive multiobjective optimization problems pose great challenges to evolutionary algorithms due to their costly evaluation. Building cheap surrogate models to replace the expensive real models has been proved to be a practical way to reduce the number of costly evaluations. Supervised learning techniques from the community of machine learning have been widely applied to build either regressors, which approximate the fitness values of candidate solutions, or classifiers, which estimate the categories of candidate solutions. Considering the characteristics of the data produced in optimization, this article proposes a new surrogate model, called a relation model, for evolutionary multiobjective optimization. Instead of estimating the qualities of candidate solutions directly, the relation model tries to estimate the relationship between a pair of solutions $\langle \mathbf {x}, \mathbf {y}\rangle $ , i.e., $\mathbf {x}$ dominates $\mathbf {y}$ , $\mathbf {x}$ is dominated by $\mathbf {y}$ , or $\mathbf {x}$ is nondominated with $\mathbf {y}$ in the case of multiobjective optimization. To implement this idea, first a balanced training set is prepared, then a classifier is built based on the training data set to learn the relationship, and finally, the classifier with a voting-scoring strategy is applied to estimate the relationship between the candidate solutions and parent solutions. By this way, the promising candidate solutions are recognized and evaluated by the real models. The new approach is applied to three well-known benchmark suites and two real-world applications, and the experimental results suggest that the proposed method outperforms some state-of-the-art methods based on regression and classification models on the given instances. [ABSTRACT FROM AUTHOR]

The better way to understand marine life and ecosystems is to surveil and analyze the activities of marine organisms. Recently, research on marine video surveillance is becoming increasingly popular. With the rapid development of deep learning (DL), convolutional neural networks (CNNs) have made remarkable progresses in image/video understanding tasks. In this article, we explore a visual attention and relation mechanism for marine organism detection, and propose a new way to apply an improved attention-relation (AR) module on an efficient marine organism detector (EMOD), which can well enhance the discrimination of organisms in complex underwater environments. We design our EMOD via integrating current state-of-the-art (SOTA) detection methods in order to detect organisms and surveil marine environments in a real time and fast fashion for high-resolution marine video surveillance. We implement our EMOD and AR on the annotated video data sets provided by the public data challenges in conjunction with the workshops (CVPR 2018 and 2019), which are supported by National Oceanic and Atmospheric Administration (NOAA) and their research works (NMFS-PIFSC-83). Experimental results and visualizations demonstrate that our application of AR module is effective and efficient, and our EMOD equipped with AR modules can outperform SOTA performance on the experimental data sets. For application requirements, we also provide the application suggestions of EMOD framework. Our code is publicly available at https://github.com/zhenglab/EMOD. [ABSTRACT FROM AUTHOR]

To remedy the deficiency of the existing methods on 3D direction relations, the 3DR46 direction relations model is proposed. The model can represent and distinguish 246 complex 3D direction relations. To process the dynamic of 3D direction relations, the dynamic relations of the 3DR46 model are studied and the dynamic adjacent relations of the dynamic direction blocks are given. What's more, the check algorithm of inspecting the continuity of the dynamic direction relations is put forward. The effective verification of the continuity of direction relationship can improve the accuracy of judgment and prediction. To accomplish the reducing dimension processing for the complex 3D direction relations, the bi-directional mapping relations of the 3DR46 direction relation model are given for some continuous 3D objects with regular shape without separated parts. The method comparison and experimental analysis are further carried out. The theoretical research and experiment show that the research results can process the complex 3D dynamic direction relations and the reasoning problems of complex direction relations. The ability of presenting and analyzing the 3D directions of the spatial database is enhanced.

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Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Following the growth in global trade activities, vessel traffic has increased dramatically in some busy waterways and ports. However, such increments have made it more complex to manage the regional vessel traffic, which can increase the risk of an accident in the area. To model and analyze the relationship between vessel traffic and maritime traffic, this paper proposes a gridded geography information system (GIS)-based relation analysis model using the historical automatic identification system (AIS) data and accident records over a 10-year-span. Firstly, the extent of the hazards posed by a maritime accident in terms of hull loss, fatality, and direct economic loss is quantified using set pair analysis. Consequently, the hazardous degree posed by an accident is obtained. The relative consequence of the regional hazard (RCORH) is then estimated by summing up all the relative hazardous degrees of accidents that have occurred in a certain gridded area. Secondly, the vessel traffic in the gridded areas is analyzed using characteristics such as speed, heading variance, and traffic volume as indicators. Based on the analysis of both the maritime traffic accidents and the vessel traffic, the spatial relationships are analyzed with an overlay between the RCORH and vessel traffic data of each grid, as well as a regression analysis. In a case study of the Western port of Shenzhen City, China, the methodology proves to be effective for vessel traffic management and traffic engineering design.

Software composition for timely and affordable software development and evolution is one of the oldest pursuits of software engineering. In current software composition techniques, Component-Based Software Development (CBSD) and Aspect-Oriented Software Development (AOSD) have attracted academic and industrial attention. Black box composition used in CBSD provides simple and safe modularization for its strong information hiding, which is, however, the main obstacle for a black box composite to evolve later. This implies that an application developed through black box composition cannot take advantage of Aspect-Oriented Programming (AOP) used in AOSD. On the contrary, AOP enhances maintainability and comprehensibility by modularizing concerns crosscutting multiple components but lacks the support for the hierarchical and external composition of aspects themselves and compromises the important software engineering principles such as encapsulation, which is almost perfectly supported in black box composition. The role and role model have been recognized to have many similarities with CBSD and AOP but have significant differences with those composition techniques as well. Although each composition paradigm has its own advantages and disadvantages, there is no substantial support to realize the synergy of these composition paradigms; the black box composition, AOP, and role model. In this paper, a new composition technique based on representational abstraction of the relationship between component instances is introduced. The model supports the simple, elegant, and dynamic composition of components with its declarative form and provides the hooks through which an aspect can evolve and a parallel developed aspect can be merged at the instance level. [ABSTRACT FROM AUTHOR]

Following the growth in global trade activities, vessel traffic has increased dramatically in some busy waterways and ports. However, such increments have made it more complex to manage the regional vessel traffic, which can increase the risk of an accident in the area. To model and analyze the relationship between vessel traffic and maritime traffic, this paper proposes a gridded geography information system (GIS)-based relation analysis model using the historical automatic identification system (AIS) data and accident records over a 10-year-span. Firstly, the extent of the hazards posed by a maritime accident in terms of hull loss, fatality, and direct economic loss is quantified using set pair analysis. Consequently, the hazardous degree posed by an accident is obtained. The relative consequence of the regional hazard (RCORH) is then estimated by summing up all the relative hazardous degrees of accidents that have occurred in a certain gridded area. Secondly, the vessel traffic in the gridded areas is analyzed using characteristics such as speed, heading variance, and traffic volume as indicators. Based on the analysis of both the maritime traffic accidents and the vessel traffic, the spatial relationships are analyzed with an overlay between the RCORH and vessel traffic data of each grid, as well as a regression analysis. In a case study of the Western port of Shenzhen City, China, the methodology proves to be effective for vessel traffic management and traffic engineering design. [ABSTRACT FROM AUTHOR]