Influence maximization is a classic and extensively studied problem with important applications in viral marketing. Existing algorithms for influence maximization, however, mostly focus on offline processing, the sense that they do not provide any output to user until final answer derived, allowed terminate algorithm early trade quality of solution efficiency. Such lack interactiveness flexibility leads poor experience, especially when incurs long running time.

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R retrieval methods have been reported. However, there still exist some problems due the complexity modeling process, as well extreme uncertainty experimental environment equipment. To investigate behavior bistatic two ground-truth measurements with different conditions were carried out performance input variables was analyzed...

Information can be disseminated widely and rapidly through Online Social Networks (OSNs) with "word-of-mouth" effects. Viral marketing is such a typical application in which new products or commercial activities are advertised by some seed users OSNs to other cascading manner. The selection of initial yields tradeoff between the expense reward viral marketing. In this paper, we define general profit metric that naturally combines benefit influence spread cost We carry out comprehensive study...

Given a social network G , the influence maximization (IM) problem seeks set S of k seed nodes in to maximize expected number activated via an cascade starting from S. Although lot algorithms have been proposed for IM, most them only work under non-adaptive setting, i.e., when all are selected before we observe how they other users. In this paper, study adaptive IM problem, where select batches equal size b such that choice i -th batch can be made after results first - 1 observed. We propose...

Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionaryalgorithm and provides an efficient way to evolve individuals for solving multiple tasks concurrently.Its efficiency is derived by implicitly transferring genetic information among tasks.However, MFEA doesn?t distinguish quality in transfer compromising algorithmperformance. We propose a group-based that groups similar types selectivelytransfers only within groups. also develop new selection...

Information can be disseminated widely and rapidly through Online Social Networks (OSNs) with “word-of-mouth” effects. Viral marketing is such a typical application in which new products or commercial activities are advertised by some seed users OSNs to other cascading manner. The budget allocation for selection reflects tradeoff between the expense reward of viral marketing. In this paper, we define general profit metric that naturally combines benefit influence spread cost eliminate need...

Learning on 3D scene-based point cloud has received extensive attention as its promising application in many fields, and well-annotated multisource datasets can catalyze the development of those data-driven approaches. To facilitate research this area, we present a richly-annotated dataset for multiple outdoor scene understanding tasks also an effective learning framework hierarchical segmentation task. The was generated via photogrammetric processing unmanned aerial vehicle (UAV) images...

Given a social network G with n nodes and m edges, positive integer k , cascade model C the influence maximization (IM) problem asks for in such that expected number of influenced by under is maximized. The state-of-the-art approximate solutions run O(k(n+m) log n/ ε 2 ) time while returning (1 - 1/ e ε) solution at least 1 probability. A key phase these IM algorithms random reverse reachable (RR) set generation, this significantly affects efficiency scalability algorithms. In article, we...

Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread influence as widely possible. However, it remains open challenge design fast and accurate algorithms find solutions large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow not scalable, while other heuristic do have any theoretical guarantee they been shown produce poor for quite some cases. In this paper, we propose...

The morphology of glands is the main basis colon cancer diagnosis and accurate segmentation from histology images a prerequisite for correct clinical diagnosis. A gland method based on Segnet proposed in this paper. First, Warwick-QU dataset used augmented training Segnet. Second, network parameters are optimized according to results. Finally, trained perform test both Parts B Warwick-QU. results show that presented achieves accuracy 0.882 Part 0.8636 B, shape similarity 106.6471 102.5729...

Proof-of-Work (PoW) is the most widely adopted incentive model in current blockchain systems, which unfortunately energy inefficient. Proof-of-Stake (PoS) then proposed to tackle issue. The rich-get-richer concern of PoS has been heavily debated community. debate centered around argument that whether rich miners possessing more stakes will obtain higher staking rewards and further increase their potential income future. In this paper, we define two types fairness, i.e., expectational...

Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way promote the sales products. An OSN provider is often hired by an advertiser conduct campaigns. The generates revenue from commission paid which determined spread its product information. Meanwhile, propagate influence, activities performed such viewing video ads normally induce diffusion cost provider. In this paper, we aim find seed set optimize profit...

As a dual problem of influence maximization, the seed minimization asks for minimum number nodes to required $\eta$ users in given social network $G$. Existing algorithms mostly consider non-adaptive setting, where all are selected one batch without observing how they may other users. In this paper, we study adaptive several batches, such that choice exploit information about actual previous batches. We propose novel algorithm, ASTI, which addresses $O\Big(\frac{\eta \cdot...

Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on services and pages, analysis online social networks, recommendation in e-commerce. The state the art derives representations all nodes graphs following same diffusion (message passing) model without discriminating their uniqueness. However, (i) labeled involved training usually account a small portion setting, (ii) different locate at graph local...

A surface defect detection method for hot-rolled steel strips was proposed to address the challenges of detecting small target defects, significant differences in morphology, and unclear characteristics. This is based on multiscale feature perception adaptive fusion. First, spatial distribution characteristics strip image, redundant background interference removed using automatic gamma correction Otsu thresholding. Second, defects strips, this paper proposes TDB-YOLO (YOLO with a layer),...

We consider the edge uncertainty in an undirected graph and study k -median (resp. -center) problems, where goal is to partition nodes into clusters such that average minimum) connection probability between each node its cluster's center maximized. analyze hardness of these propose algorithms provide considerably improved approximation guarantees than existing studies do. Specifically, our offer (1 -- 1/e)-approximations for problem (OPTck)-approximations -center problem, OPTck optimal...

Influential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential influence spread that can be generated by these influential is hidden behind structures OSNs, which often held OSN providers and unavailable advertisers for privacy concerns. A advertising model known as influencer have offer price candidate purchase seeding In this setting, a reasonable profile should effectively reflect expected gain they...