A fuzzy approach for the reviewer assignment problem

Assignment of experts to project proposals is a significant task for funding agencies which have to assess the potential value of the research and development (R&D) projects through peer review. The problem is known as reviewer assignment problem and has real-world applications in funding agencies, conferences and research journals. Given a set of experts and a set of proposals; the problem can be defined as assigning the most suitable experts to the proposals under some constraints, which are generally encountered by funding agencies. In this study, a fuzzy model is offered to solve the reviewer assignment problem. The objective of the model is to maximize the total matching degree of assigned experts under some constraints such as cost of forming a panel and the size of a panel. The matching degrees are defined using linguistic variables to denote the expertise of each expert with respect to each proposal. The fuzzy mathematical model, which also takes into account different constraints related to the problem, is solved via the selected fuzzy ranking methods namely; the signed distance method and the method of ranking fuzzy numbers with integral value. The solution of an example problem – inspired from a real-life situation – with both of the mentioned methods revealed the effectiveness of the solution approach. It is believed that the use of the offered fuzzy approach could improve the accuracy of the decisions made by funding agencies.     

New method for solving reviewer assignment problem using type-2 fuzzy sets and fuzzy functions

Reviewer Assignment Problem (RAP) is one of the cardinal problems in Government Funding agencies where the expertise level of the referee reviewing a proposal needs to be optimised to guarantee the selection of good R&D projects. Although many solutions have been proposed for RAP in the past, none of them deals with the inherent imprecision associated with the problem. For instance, it is not possible to determine the “exact expertise level” of a particular reviewer in a particular domain. In this paper, we propose a novel approach for assigning reviewers to proposals. To calculate the expertise of a reviewer in a particular domain, we create a type-2 fuzzy set by assigning relevant weights to the various factors that affect the expertise of the reviewer in that domain. We also create a fuzzy set of the proposal by selecting three keywords that best represent the proposal. We then use a fuzzy functions based equality operator to compute the equality of the type-2 fuzzy set of experts and the fuzzy set of proposal keywords, which is then subjected to a set of relevant constraints to optimize the solution. We consider the four important aspects: workload balancing of reviewers, avoiding Conflicts of Interest, considering individual preferences by incorporating bidding and mapping multiple keywords of a proposal. As an extension to this approach, we further consider the relative importance of each keyword with respect to the submitted proposal by using representative percentage weights to create the FUZZY sets which represent the keywords. Hence, we propose an integrated solution based on the strong mathematical foundation of fuzzy logic, comprised of all the different aspects of expertise modeling and reviewer assignment. An Expert System has also been developed for the same.     

Consensus fingerprint matching with genetically optimised approach

Fingerprint matching has been approached using various criteria based on different extracted features. However, robust and accurate fingerprint matching is still a challenging problem. In this paper, we propose an improved integrated method which operates by first suggesting a consensus matching function, which combines different matching criteria based on heterogeneous features. We then devise a genetically guided approach to optimise the consensus matching function for simultaneous fingerprint alignment and verification. Since different features usually offer complementary information about the matching task, the consensus function is expected to improve the reliability of fingerprint matching. A related motivation for proposing such a function is to build a robust criterion that can perform well over a variety of different fingerprint matching instances. Additionally, by employing the global search functionality of a genetic algorithm along with a local matching operation for population initialisation, we aim to identify the optimal or near optimal global alignment between two fingerprints. The proposed algorithm is evaluated by means of a series of experiments conducted on public domain collections of fingerprint images and compared with previous work. Experimental results show that the consensus function can lead to a substantial improvement in performance while the local matching operation helps to identify promising initial alignment configurations, thereby speeding up the verification process. The resulting algorithm is more accurate than several other proposed methods which have been implemented for comparison.     

Fuzzy Cognitive Maps for stereovision matching

This paper outlines a method for solving the stereovision matching problem using edge segments as the primitives. In stereovision matching the following constraints are commonly used: epipolar, similarity, smoothness, ordering and uniqueness. We propose a new matching strategy under a fuzzy context in which such constraints are mapped. The fuzzy context integrates both Fuzzy Clustering and Fuzzy Cognitive Maps. With such purpose a network of concepts (nodes) is designed, each concept represents a pair of primitives to be matched. Each concept has associated a fuzzy value which determines the degree of the correspondence. The goal is to achieve high performance in terms of correct matches. The main findings of this paper are reflected in the use of the fuzzy context that allows building the network of concepts where the matching constraints are mapped. Initially, each concept value is loaded via the Fuzzy Clustering and then updated by the Fuzzy Cognitive Maps framework. This updating is achieved through the influence of the remainder neighboring concepts until a good global matching solution is achieved. Under this fuzzy approach we gain quantitative and qualitative matching correspondences. This method works as a relaxation matching approach and its performance is illustrated by comparative analysis against some existing global matching methods.     

移动边缘计算中的动态用户分配方法

在边缘计算环境中,为用户匹配合适的服务器是一个关键问题,可以有效提升服务质量。文中将边缘用户分配问题转换为一个受距离和服务器资源约束的二分图匹配问题,并将其建模为一个0-1整数规划问题进行优化。在离线状态下,基于精确式算法的优化模型可以求得最优分配策略,但其求解时间过长,无法处理规模较大的数据,不适用于现实服务环境。因此,提出了基于启发式策略的在线分配方法,以在时间有限的情况下优化用户-服务器的分配。实验结果显示,基于近邻启发式的在线方法的竞争比能够接近100%,可以在可接受的时间范围内求得较优的分配解。同时,近邻启发式方法比其他基础启发式方法的表现更优秀。     

Genetic algorithm for the personnel assignment problem with multiple objectives

The assignment problem is a well-known graph optimization problem defined on weighted-bipartite graphs. The objective of the standard assignment problem is to maximize the summation of the weights of the matched edges of the bipartite graph. In the standard assignment problem, any node in one partition can be matched with any node in the other partition without any restriction. In this paper, variations of the standard assignment problem are defined with matching constraints by introducing structures in the partitions of the bipartite graph, and by defining constraints on these structures. According to the first constraint, the matching between the two partitions should respect the hierarchical-ordering constraints defined by forest and level graph structures produced by using the nodes of the two partitions respectively. In order to define the second constraint, the nodes of the partitions of the bipartite graph are distributed into mutually exclusive sets. The set-restriction constraint enforces the rule that in one of the partitions all the elements of each set should be matched with the elements of a set in the other partition. Even with one of these constraints the assignment problem becomes an NP-hard problem. Therefore, the extended assignment problem with both the hierarchical-ordering and set-restriction constraints becomes an NP-hard multi-objective optimization problem with three conflicting objectives; namely, minimizing the numbers of hierarchical-ordering and set-restriction violations, and maximizing the summation of the weights of the edges of the matching. Genetic algorithms are proven to be very successful for NP-hard multi-objective optimization problems. In this paper, we also propose genetic algorithm solutions for different versions of the assignment problem with multiple objectives based on hierarchical and set constraints, and we empirically show the performance of these solutions.     

An intelligent decision support approach for reviewer assignment in R&D project selection

In the process of Research and Development (R&D) project selection, experts play an important role because their opinions are the foundation on which to judge the potential value of a project. How to assign the most appropriate experts to review project proposals might greatly affect the quality of project selection, which in turn could affect the return on investment of the funding organization. However, in many funding organizations, current approaches to assigning reviewers are still based on simply matching the discipline area of the reviewers with that of the proposal, which could result in poor quality of project selection and poor future financial return. Additionally, these approaches might make it difficult to balance resources and resolve conflicts of interests between reviewers and applicants. Therefore, to overcome these problems, there is an urgent need for a systematic approach to support and automate the reviewer assignment process. This research aims at proposing an intelligent decision support approach for reviewer assignment and developing an Assignment Decision Support System (ADSS). In this approach, heuristic knowledge of expert assignment and techniques of operations research are integrated. The approach uses decision models to determine the best solution of reviewer assignment that maximizes the total expertise level of the reviewers assigned to proposals. It also balances the distribution of proposals at different grades and solves conflicts of interests between reviewers and applicants. Its application in the National Natural Science Foundation of China (NSFC) and the computational results of its effectiveness and efficiency are also described.     

Optimal business process deployment cost in cloud resources

Cloud computing is the fastest emerging technology that proposes several resources under various pricing strategies that are specified based on temporal constraints. The main aim of cloud computing is to enhance the performance level and minimize operating costs. Thus, organizations looking towards optimizing their spending on IT infrastructure find such pricing strategies very attractive, especially, to deploy their business process models. However, discovering the optimal deployment cost of a business process in cloud resources proposed under various pricing strategies becomes a highly challenging problem. So, the objective of the present paper is to present an approach that assists business process designers in finding an optimal assignment or scheduling based on the variety of pricing strategies. We use linear programming models with an objective function under a set of constraints. Besides, we propose an extension of the famous cloud simulator provided in the market, CloudSim, to simulate the cloud resources consumed to deploy a business process model. The experimental results show the feasibility, effectiveness, and performance of our approach.

     

Using hybrid scheduling for the semi-autonomous formation of expert teams

Selecting and scheduling human experts to cooperatively solve a problem can be a highly complex task, given various constraints (such as what expertise is needed and when) and preferences (such as which expertise an expert most prefers to exercise). Computational agents can thus greatly help users form and schedule expert teams. This paper introduces a new formulation of the team formation and scheduling problem as a Hybrid Scheduling Problem (HSP) and compares the performance of an agent using the HSP formulation to a prior agent-based approach. We empirically demonstrate the promise of the HSP formulation and highlight how the application of HSP techniques to this problem has led us to identify important modifications to mechanisms that improve HSP solving. Finally, we summarize how the HSP formulation can support human-agent collaboration during the process of forming and scheduling expert teams.     

Assignment of Movies to Heterogeneous Video Servers

A video-on-demand (VOD) system provides an electronic video rental service to geographically distributed users. It can adopt multiple servers to serve many users concurrently. As a VOD system is being used and evolved, its servers probably become heterogeneous. For example, if a new server is added to expand the VOD system or replace a failed server, the new server may be faster with a larger storage size. This paper investigates how to assign movies to heterogeneous servers in order to minimize the blocking probability. It is proven that this assignment problem is NP-hard, and a lower bound is derived on the minimal blocking probability. The following approach is proposed for assignment: 1) problem relaxation—a relaxed assignment problem is formulated and solved to determine the ideal load that each server should handle, and 2) goal programming—an assignment and reassignment are performed iteratively while fulfilling all the constraints so that the load handled by each server is close to the ideal one. This approach is generic and applicable to many assignment problems. This approach is adopted to design two specific algorithms for movie assignment with and without replication. It is demonstrated that these algorithms can find optimal or close-to-optimal assignments.     

Team formation in social networks based on collective intelligence – an evolutionary approach

The tremendous growth of the social web has inspired research communities to discover social intelligence, which encompasses a wide spectrum of knowledge characterized by human interaction, communication and collaboration, thereby exploiting collective intelligence (CI) to support the successful existence of social communities on the Web. In this work, we address the team formation problem for generalized tasks where a set of experts is to be discovered from an expertise social network that can collaborate effectively to accomplish a given task. The concept of CI that emerges from these collaborations attempts to maximize the potential of the team of experts, rather than only aggregating individual potentials. Because the team formation problem is NP-hard, a genetic algorithm-based approach is applied to optimize computational collective intelligence in web-based social networks. To capture the essence of CI, a novel quantitative measure Collective Intelligence Index (CII) is proposed that takes two factors into account –the “enhanced expertise score” and the “trust-based collaboration score”. This measure relates to the social interactions among experts, reflecting various affiliations that form a network of experts that help to drive creativity by deepening engagements through collaboration and the exchange of ideas and expertise, thereby enriching and enhancing the knowledge base of experts. The presented model also captures the teams’ dynamics by considering trust, which is essential to effective interactions between the experts. The computational experiments are performed on a synthetic dataset that bears close resemblance to real-world expertise networks, and the results clearly establish the effectiveness of our proposed model.     

Combining global and local matching of multiple features for precise item image retrieval

With the fast-growing of online shopping services, there are millions even billions of commercial item images available on the Internet. How to effectively leverage visual search method to find the items of users’ interests is an important yet challenging task. Besides global appearances (e.g., color, shape or pattern), users may often pay more attention to the local styles of certain products, thus an ideal visual item search engine should support detailed and precise search of similar images, which is beyond the capabilities of current search systems. In this paper, we propose a novel system named iSearch and global/local matching of local features are combined to do precise retrieval of item images in an interactive manner. We extract multiple local features including scale-invariant feature transform (SIFT), regional color moments and object contour fragments to sufficiently represent the visual appearances of items; while global and local matching of large-scale image dataset are allowed. To do this, an effective contour fragments encoding and indexing method is developed. Meanwhile, to improve the matching robustness of local features, we encode the spatial context with grid representations and a simple but effective verification approach using triangle relations constraints is proposed for spatial consistency filtering. The experimental evaluations show the promising results of our approach and system.     

LS(Graph): a constraint-based local search for constraint optimization on trees and paths

Constrained optimum tree (COT) and constrained optimum path (COP) problems arise in many real-life applications and are ubiquitous in communication networks. They have been traditionally approached by dedicated algorithms, which are often hard to extend with side constraints and to apply widely. This paper proposes a constraint-based local search framework for COT/COP applications, bringing the compositionality, reuse, and extensibility at the core of constraint-based local search and constraint programming systems. The modeling contribution is the ability to express compositional models for various COT/COP applications at a high level of abstraction, while cleanly separating the model and the search procedure. The main technical contribution is a connected neighborhood based on rooted spanning trees to find high-quality solutions to COP problems. This framework is applied to some COT/COP problems, e.g., the quorumcast routing problem, the edge-disjoint paths problem, and the routing and wavelength assignment with delay side constraints problem. Computational results show the potential importance of the approach.     

Domain problem-solving expert identification in community question answering

Question-Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high-quality content continuously contributed by users with high-level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining well-designed expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian-Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real-world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state-of-the-art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian–Gamma mixture clustering model is capable of detecting excellent domain problem-solving experts who exhibit both domain interest and expertise.     

RiMOM: A Dynamic Multistrategy Ontology Alignment Framework

Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely. We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on two estimated factors. In the approach, we consider both textual and structural characteristics of ontologies. With RiMOM, we participated in the 2006 and 2007 campaigns of the Ontology Alignment Evaluation Initiative (OAEI). Our system is among the top three performers in benchmark data sets.     

具有域极点配置的混合H2 /H∞ 滤波

解决了具有域极点配置的连续时不变系统的混合H₂ /H_∞ 滤波问题. 通过采用线性矩阵不等式 (LMI)方法描述域稳定性限制、H₂ 和H_∞ 优化, 以建立求解这个问题的总框架. 这个问题的可解性的充分必要条件由一组LMI给出. 最后用一个数字例子来说明所给出的设计方法.     

Efficient task assignment for spatial crowdsourcing: A combinatorial fractional optimization approach with semi-bandit learning

Spatial crowdsourcing has emerged as a new paradigm for solving problems in the physical world with the help of human workers. A major challenge in spatial crowdsourcing is to assign reliable workers to nearby tasks. The goal of such task assignment process is to maximize the task completion in the face of uncertainty. This process is further complicated when tasks arrivals are dynamic and worker reliability is unknown. Recent research proposals have tried to address the challenge of dynamic task assignment. Yet the majority of the proposals do not consider the dynamism of tasks and workers. They also make the unrealistic assumptions of known deterministic or probabilistic workers’ reliabilities. In this paper, we propose a novel approach for dynamic task assignment in spatial crowdsourcing. The proposed approach combines bi-objective optimization with combinatorial multi-armed bandits. We formulate an online optimization problem to maximize task reliability and minimize travel costs in spatial crowdsourcing. We propose the distance-reliability ratio (DRR) algorithm based on a combinatorial fractional programming approach. The DRR algorithm reduces travel costs by 80% while maximizing reliability when compared to existing algorithms. We extend the DRR algorithm for the scenario when worker reliabilities are unknown. We propose a novel algorithm (DRR-UCB) that uses an interval estimation heuristic to approximate worker reliabilities. Experimental results demonstrate that the DRR-UCB achieves high reliability in the face of uncertainty. The proposed approach is particularly suited for real-life dynamic spatial crowdsourcing scenarios. This approach is generalizable to the similar problems in other areas in expert systems. First, it encompasses online assignment problems when the objective function is a ratio of two linear functions. Second, it considers situations when intelligent and repeated assignment decisions are needed under uncertainty.     

科研项目同行评议专家学术专长匹配方法

现有的评审专家推荐过程通常依赖于人工匹配,在进行专家推荐时不能充分捕捉评审项目所属学科与专家研究兴趣之间的语义关联,导致专家推荐的精确性较低。为解决这个问题,提出了一种科研项目同行评议专家学术专长匹配方法。该方法构建学术网络以建立学术实体联系,并设计元路径捕捉学术网络中不同节点间的语义关联;使用随机游走策略获得项目所属学科与专家研究兴趣共现关联的节点序列,并通过网络表示学习模型训练得到具有语义关联的项目所属学科与专家研究兴趣的向量表示;在此基础上,按照项目学科树层次结构逐层计算语义相似度,以实现多粒度的同行评议学术专长匹配。在爬取的知网和万方论文数据集、某专家评审数据集、以及百度百科词向量数据集上得到的实验结果表明,所提方法能提升项目所属学科与专家研究兴趣间的语义关联,并能有效应用于项目评审专家的学术专长匹配。     

A constraint-based topic modeling approach for name disambiguation

Name ambiguity refers to a problem that different people might be referenced with an identical name. This problem has become critical in many applications, particularly in online bibliography systems, such as DBLP and CiterSeer. Although much work has been conducted to address this problem, there still exist many challenges. In this paper, a general framework of constraint-based topic modeling is proposed, which can make use of user-defined constraints to enhance the performance of name disambiguation. A Gibbs sampling algorithm that integrates the constraints has been proposed to do the inference of the topic model. Experimental results on a real-world dataset show that significant improvements can be obtained by taking the proposed approach.     

An Efficient Scan-to-Map Matching Approach Based on Multi-channel Lidar

Accurately localizing the vehicle against a pre-built high precision map is an essential step for the Autonomous Land Vehicle (ALV). This paper proposes an efficient scan-to-map matching approach based on multi-channel lidar. We firstly advocate the usage of both the lidar reflectance map and the height map, as these two maps contain complementary information. Then, borrowing ideas from the Lucas-Kanade optical flow approach, we formulate the scan-to-map matching problem in a similar form, and propose an efficient gradient descent approach to solve it. Finally, the proposed approach is integrated into a filtering framework for real-time online localization. Experiments on real-world dataset have demonstrated the validity of our approach.