面向工作者能力评估的众包任务分配方法的研究进展综述

随着互联网技术和共享经济模式的快速发展,作为一种新型的群体计算模式,近年来众包(Crowdsourcing)已经得到了广泛的应用并成为研究热点.针对众包应用的特点,为确保众包任务的完成质量,现有研究从工作者能力评估的角度出发已提出了各种不同的众包任务分配方法.首先介绍了众包的概念和分类,阐述了众包平台的工作流程及其任务...     

空间众包中多类型任务的分配与调度方法

针对空间众包多类型任务完成的质量与数量问题,提出多类型任务的分配与调度方法。首先,在任务分配过程中,结合空间众包中多类型任务和用户的特点,对贪婪分配算法改进,提出基于距离ε值分配（ε-DA）算法;然后,将任务分配给附近的用户,以提高任务完成质量;其次,利用分支定界思想（BBS）,根据专业匹配分数的大小,对任务序列进行调度;最后,找到最佳的任务序列。针对分支定界思想的调度算法运行速度较慢的问题,提出最有前途分支启发式（MPBH）算法。通过MPBH算法,使得在每次任务分配过程中实现局部最优化,与分支定界思想的调度算法相比,在运行速度上提高了30%。实验结果表明,所提方法能够提高任务完成的质量以及数量,有效地提高了运行速度与精确性。     

On semantic annotation of decision models

The growth of service sector in recent years has led to renewed research interests in the design and management of service systems. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as models and data, thus forming the “back stage” of service systems. In this article, we identify the requirements for semantically annotating decision models and propose a model representation scheme, termed Semantically Annotated Structure Modeling Markup Language (SA-SMML) that extends Structure Modeling Markup Language (SMML) by incorporating mechanisms for linking semantic models such as ontologies that represent problem domain knowledge concepts. This model representation format is also amenable to a scalable Service-Oriented Architecture (SOA) for managing models in distributed environments. The proposed model representation technique leverages recent advances in the areas of semantic web, and semantic web services. Along with design considerations, we demonstrate the utility of this representation format with an illustrative usage scenarios with a particular emphasis on model discovery and composition in a distributed environment.     

软件众包任务发布优先级计算方法

针对现有软件众包平台对任务发布次序考虑不足的问题,提出一种基于任务发布者权重与任务权重的软件众包任务发布优先级（TRP）计算方法。首先,基于半正弦曲线的时间权重函数度量任务发布者的活跃度及其任务累积成交额,以此计算任务发布者权重;然后,根据系统架构图和数据流图度量模块复杂度、设计复杂度和数据复杂度,得到任务复杂度,并结合任务报价及任务期限,计算任务效益因子和任务紧急程度因子,计算任务权重;最后,根据任务发布者权重和任务权重计算任务发布优先级。实验结果表明,该算法不仅具有较高的有效性和合理性,而且任务成功分配率最高可达98%。     

Typicality ranking: beyond accuracy for video semantic annotation

In video annotation, the typicalities or relevancy degrees of relevant samples to a certain concept are generally different. Thus we argue that it is more reasonable to rank typical relevant samples higher than non-typical ones. However, generally the labels of the training data only differentiate relevant of irrelevant; that is to say, typical or non-typical training samples have the same contribution to the learning process. Therefore, the learned scores of the unlabeled data cannot well measure the typicality. Accordingly, three pre-processing approaches are proposed to relax the labels of the training data to real-valued typicality scores. Then the typicality scores of the training data are propagated to unlabeled data using manifold ranking. Meanwhile, we propose to use a novel criterion, Average Typicality Precision (ATP), to replace the frequently used one, Average Precision (AP), for evaluating the performance of video typicality ranking algorithms. Though AP cares the number of relevant samples at the top of the annotation rank list, it actually does not care the typicality order of these samples, while which was taken into consideration of the evaluation strategy ATP. Experiments conducted on the TRECVID data set demonstrate that this typicality ranking scheme is more consistent with human perception than normal accuracy based ranking schemes.     

Automatic semantic annotation of real-world web images

As the number of web images is increasing at a rapid rate, searching them semantically presents a significant challenge. Many raw images are constantly uploaded with little meaningful direct annotations of semantic content, limiting their search and discovery. In this paper, we present a semantic annotation technique based on the use of image parametric dimensions and metadata. Using decision trees and rule induction, we develop a rule-based approach to formulate explicit annotations for images fully automatically, so that by the use of our method, semantic query such as " sunset by the sea in autumn in New York" can be answered and indexed purely by machine. Our system is evaluated quantitatively using more than 100,000 web images. Experimental results indicate that this approach is able to deliver highly competent performance, attaining good recall and precision rates of sometimes over 80%. This approach enables a new degree of semantic richness to be automatically associated with images which previously can only be performed manually.     

基于个性化本体的图像语义标注和检索

针对目前图像检索系统较难实现语义检索的问题,提出了一种新的以本体为核心的图像语义标注和检索模型。构建个性化本体描述图像语义,继而提取基于概念集的图像语义特征并利用本体中“Is-A”关系设计相似性度量方法最终实现语义扩展检索。其难点在于顶级本体向个性化本体进化,以及基于概念集和“Is-A”关系实现语义相似度量的方法。通过系统的初步实现与相关实验的验证,该模型的检索准确度可达88.6%,明显高于传统的基于关键字和基于通用本体的图像检索,实现了图像智能检索功能。     

语义相似度计算在语义标注中的应用

提出了一种词汇和本体概念间的语义相似度计算方法。该方法利用编辑距离和维基百科从语法和语义两方面综合考虑词汇和概念间的语义相似度。在领域本体的指导下,将方法应用于语义标注过程,建立词汇与本体概念之间的映射。在标注过程中建立知识库,提高算法性能,实验结果说明该方法是行之有效的。     

Point of interest mining with proper semantic annotation

Mining geo-tagged social photo media has received large amounts of attention from researchers recently. Points of interest (POI) mining from a collection of geo-tagged photos is one of these problems. POI mining refers to the processes of pattern recognition (namely clustering), extraction and semantic annotation. However, based on unsupervised clustering methods, many POIs might not be mined. Additionally, there is a great challenge for the proper semantic annotation to data clusters after clustering. In practice, there are many applications which require the accuracy of semantic annotation and high quality of pattern recognition such as POI recommendation. In this paper, we study POI mining from a collection of geo-tagged photos in combination with proper semantic annotation by using additional POI information from high coverage external POI databases. We propose a novel POI mining framework by using two-level clustering, random walk and constrained clustering. In random walk clustering step, we separate a large-scale collection of geo-tagged photos into many clusters. In the constrained clustering step, we continue to divide the clusters that include many POIs into many sub-clusters, where the geo-tagged photos in a sub-cluster associate with a particular POI. Experimental results on two datasets of geo-tagged Flickr photos of two cities in California, USA have shown that the proposed method substantially outperforms existing approaches that are adapted to handle the problem.     

基于本体知识库的自动语义标注*

为了产生语义Web中的元数据,需要提取Web文档中的语义信息。面对海量的Web文档,自动语义标注相对人工和半自动的语义标注是可行的方法。提出的基于本体知识库的自动语义标注方法,旨在提高标注的质量。为识别出文档中的候选命名实体,设计了语义词典的逻辑结构,论述了以实体之间语义关联路径计算语义距离的方法。语义标注中的复杂问题是语义消歧,提出了基于最短路径的语义消歧方法和基于n-gram的语义消歧方法。采用这种方法对文档进行语义标注,将标注结果持久化为语义索引,为实现语义信息检索提供基础。针对构建的测试数据集,进行的标注实验表明该方法能够依据本体知识库,有效地对Web文档进行自动语义标注。     

An impactful crowdsourcing intermediary design - a case of a service imagery crowdsourcing system

Crowdsourcing is usually implemented using an intermediary organization or directly coordinated by solution seekers. For those using an IT-based crowdsourcing intermediary, the important factors of intermediaries that impact the crowdsourcing outcome are yet unclear. In addition, little research was conducted on how to design a crowdsourcing intermediary that can address the combined challenges of considering different cognitive demand levels of solution providers’ contributions, combining contributions and evaluating contributions. This paper identifies three important factors and provides a novel design of crowdsourcing intermediary to cope with these challenges. This study uses a case that focuses on how to assist small and medium businesses (SMBs) to develop their service imageries in triggering service innovation and designing their service experiences as to fulfill the desired outcomes of customers. Through the case, the benefits of our crowdsourcing intermediary design are demonstrated and justified. The three important factors are the crowdsourcing intermediary knowledge base, generative networks and empowerment of crowd members. This study shows that the crowdsourcing process can facilitate achieving a higher chance of attaining creative solutions for SMBs’ innovation problems when the three factors are well incorporated and managed within the crowdsourcing intermediary design. This study also presents a novel design of crowdsourcing intermediary that can address the combined challenges of coping with different cognitive demand levels of crowd members and combining and evaluating crowd members’ contributions, in order to attain impactful crowdsourcing outcome.     

Content annotation for the semantic web: an automatic web-based approach

Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.     

Supervised learning of semantic classes for image annotation and retrieval

A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning     

Characterization of 3D shape parts for semantic annotation

3D content stored in big databases or shared on the Internet is a precious resource for several applications, but unfortunately it risks being underexploited due to the difficulty of retrieving it efficiently. In this paper we describe a system called the “ShapeAnnotator” through which it is possible to perform non-trivial segmentations of 3D surface meshes and annotate the detected parts through concepts expressed by an ontology. Each part is connected to an instance that can be stored in a knowledge base to ease the retrieval process based on semantics. Through an intuitive interface, users create such instances by simply selecting proper classes in the ontology; attributes and relations with other instances can be computed automatically based on a customizable analysis of the underlying topology and geometry of the parts. We show how our part-based annotation framework can be used in two scenarios, namely for the creation of avatars in emerging Internet-based virtual worlds, and for product design in e-manufacturing.     

一种半监督三维模型语义自动标注方法

三维模型语义自动标注的目标是自动给出最适合描述模型的标注词集合,是基于文本的三维模型检索的重要环节。语义鸿沟的存在使得相似匹配技术得到的标注效果有待提高。为了在用户提供的有限模型数量和对应的标注词信息下,在自动标注过程中利用大量的未标注样本改善三维模型的标注性能,提出了一种半监督测度学习方法完成三维模型语义自动标注。该方法首先使用基于图的半监督学习方法扩展已标注模型集合,并给出扩展集合中语义标签表征模型的语义置信度,使用改进的相关成分分析方法学习马氏距离度量,依据学习到的距离和语义置信度形成多语义标注策略。在PSB（Princeton Shape Benchmark）数据集上的测试表明,该方法利用了大量未标注样本参与标注过程,取得了比较好的标注效果。