增强覆盖度与非相似性的标签选择多样化方法

标签云是社交网站提供在线资源说明与导航功能的一种流行机制.标签选择即从大量标签中选出有代表性的有限标签,是创建标签云的核心任务.标签选择结果的多样性,是影响用户满意度的一个重要因素.信息覆盖度与标签非相似性是在标签选择中引入多样性的两个主要角度.为了进一步提高标签选择结果的信息覆盖度与标签非相似性,提出了3种标签选择方法.在每种方法中,定义了目标函数以同时量化标签集合的信息覆盖度与标签非相似性,并设计了近似算法以求解相应的最大化问题;同时,还分析了近似算法的近似比.利用CiteULike网站与Last.fm网站的标注数据集,将所提出的方法与已有方法进行了比较.实验结果表明,所提出的方法在信息覆盖度与标签非相似性方面都具有较好的效果.     

Multi-focused geospatial analysis using probes

Traditional geospatial information visualizations often present views that restrict the user to a single perspective. When zoomed out, local trends and anomalies become suppressed and lost; when zoomed in for local inspection, spatial awareness and comparison between regions become limited. In our model, coordinated visualizations are integrated within individual probe interfaces, which depict the local data in user-defined regions-of-interest. Our probe concept can be incorporated into a variety of geospatial visualizations to empower users with the ability to observe, coordinate, and compare data across multiple local regions. It is especially useful when dealing with complex simulations or analyses where behavior in various localities differs from other localities and from the system as a whole. We illustrate the effectiveness of our technique over traditional interfaces by incorporating it within three existing geospatial visualization systems: an agent-based social simulation, a census data exploration tool, and an 3D GIS environment for analyzing urban change over time. In each case, the probe-based interaction enhances spatial awareness, improves inspection and comparison capabilities, expands the range of scopes, and facilitates collaboration among multiple users.     

A Review of Ontology‐Based Tag Recommendation Approaches

Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology‐based recommender systems is favorably compared to other systems in the literature.     

A theoretical model for the automatic generation of tag clouds

This paper presents a new approach to information retrieval from non-structured attributes in databases, which involves the processing of text attributes. To make retrieval more effective, frequent text sequences are extracted and mathematically represented as intermediate forms which permit a clearer and more precise definition of operations on texts. These intermediate forms appear to users in the form of tag clouds to facilitate content identification, exploration, and querying. In this sense, tag cloud visualization is a simple, user-friendly visual interface to data. This paper proposes a theoretical model for the representation of frequent text sequences and their operations as well as a general procedure for generating tag clouds from text attributes in databases. The tag clouds thus obtained were compared with conventional tag clouds composed of single terms. Our study showed that automatically generated multi-term tag clouds provide better results than mono-term tag clouds.     

互联网社群图像标签排序研究进展

互联网社群图像标签排序是目前计算机视觉、机器学习等领域最热门的课题之一。图像标签序列的合理性直接影响到图像检索等应用的效果。目前图像标签排序的方法多种多样,根据标签排序方法的不同将其划分为基于语义相关度与基于视觉显著性的标签排序,着重介绍了两类方法的典型标签排序方法,分析其各自的优缺点。最后就图像标签排序的评价方法以及发展趋势做了简单的论述。     

Using aging to visually uncover evolutionary processes on networks

Networks are widely used to describe many natural and technological systems. Understanding how these evolve over time poses a challenge for existing visualization techniques originally developed for fixed network structures. We describe a method of incorporating the concept of aging into evolving networks, where nodes and edges store information related to the amount of local evolutionary change they have experienced. This property is used to generate visualizations that ensure stable substructures maintain relatively fixed spatial positions, allowing them to act as visual markers and providing context for evolutionary change elsewhere. By further supplementing these visualizations with color cues, the resultant animations enable a clearer portrayal of the underlying evolutionary process.     

Tag-Splitting: Adaptive Collision Arbitration Protocols for RFID Tag Identification

Tag identification is an important tool in RFID systems with applications for monitoring and tracking. A RFID reader recognizes tags through communication over a shared wireless channel. When multiple tags transmit their IDs simultaneously, the tag-to-reader signals collide and this collision disturbs a reader's identification process. Therefore, tag collision arbitration for passive tags is a significant issue for fast identification. This paper presents two adaptive tag anticollision protocols: an Adaptive Query Splitting protocol (AQS), which is an improvement on the query tree protocol, and an Adaptive Binary Splitting protocol (ABS), which is based on the binary tree protocol and is a de facto standard for RFID anticollision protocols. To reduce collisions and identify tags efficiently, adaptive tag anticollision protocols use information obtained from the last process of tag identification. Our performance evaluation shows that AQS and ABS outperform other tree-based tag anticollision protocols.     

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Tag identification is an important tool in RFID systems with applications for monitoring and tracking. A RFID reader recognizes tags through communication over a shared wireless channel. When multiple tags transmit their IDs simultaneously, the tag-to-reader signals collide and this collision disturbs a reader's identification process. Therefore, tag collision arbitration for passive tags is a significant issue for fast identification. This paper presents two adaptive tag anticollision protocols: an adaptive query splitting protocol (AQS), which is an improvement on the query tree protocol, and an adaptive binary splitting protocol (ABS), which is based on the binary tree protocol and is a de facto standard for RFID anticollision protocols. To reduce collisions and identify tags efficiently, adaptive tag anticollision protocols use information obtained from the last process of tag identification. Our performance evaluation shows that AQS and ABS outperform other tree-based tag anticollision protocols     

基于模式串的快速标签识别算法

提出一种基于电子标签Tag的ID返回及模式串识别过程的RFID标签识别算法,解决了RFID仲裁过程中存在的零标签响应问题。实验结果表明,对5 000个标签样本进行处理时,该算法所需平均标签响应次数为经典算法Memoryless的48.74%,为Tree Split的33.89%。对标签ID的广播次数平均不超过上述2种算法的76.35%,缩短了标签识别时间,提高了标签识别效率。     

Personalized Tag Recommendation Using Social Influence

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object,which provides a feasible solution for content-based multimedia information retrieval.In this paper,we study personalized tag recommendation in a popular online photo sharing site - Flickr.Social relationship information of users is collected to generate an online social network.From the perspective of network topology,we propose node topological potential to characterize user’s social influence.With this metric,we distinguish different social relations between users and find out those who really have influence on the target users.Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user’s social network.We evaluate our method on large scale real-world data.The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks.We also analyze the further usage of our approach for the cold-start problem of tag recommendation.     

基于标签信息特征相似性的协同过滤个性化推荐

标签推荐系统旨在利用标签数据为用户提供个性化推荐。已有的基于标签的推荐方法往往忽视了用户和资源本身的特征,而且在相似性度量时仅针对项目相似性或用户相似性进行计算,并未充分考虑二者之间的有效融合,推荐结果的准确性较低。为了解决上述问题,将标签信息融入到结合用户相似性和项目相似性的协同过滤中,提出融合标签特征与相似性的协同过滤个性化推荐方法。该方法在充分考虑用户、项目以及标签信息的基础上,利用二维矩阵来定义用户-标签以及标签-项目之间的行为。构建用户和项目的标签特征表示,通过基于标签特征的相似性度量方法计算用户相似性和项目相似性。基于用户标签行为和用户与项目的相似性线性组合来预测用户对项目的偏好值,并根据预测偏好值排序,生成最终的推荐列表。在Last.fm数据集上的实验结果表明,该方法能够提高推荐的准确度,满足用户的个性化需求。     

基于MapReduce的Web标签SOINN聚类算法

Web标签有助于用户根据自己特定的兴趣完成信息资源的分类、组织和检索。然而,正是由于协同标记系统特有的公开性、自由化的特点,采用其对信息资源进行描述、组织、分类和检索,存在着信息描述不精确、标签组织混乱和标签语意模糊等问题。在此背景下提出了3种基于特征向量表示法(FVR)的Web标签SOINN聚类算法:基于资源的特征向量表示法、基于其他共现标签的特征向量表示法和基于全集共现标签的特征向量表示法。同时应用MapReduce框架将SOINN算法进行并行化。实验表明,当类中心数量超过2000时,3种分布式聚类FVR算法的召回率和准确度优于原始算法,可获得很好的加速比。从而证明此分布式聚类算法具有很好的可扩展性,可以用于更为海量的Web日志聚类分析系统。     

Visual Boosting in Pixel‐based Visualizations

Pixel‐based visualizations have become popular, because they are capable of displaying large amounts of data and at the same time provide many details. However, pixel‐based visualizations are only effective if the data set is not sparse and the data distribution not random. Single pixels – no matter if they are in an empty area or in the middle of a large area of differently colored pixels – are perceptually difficult to discern and may therefore easily be missed. Furthermore, trends and interesting passages may be camouflaged in the sea of details. In this paper we compare different approaches for visual boosting in pixel‐based visualizations. Several boosting techniques such as halos, background coloring, distortion, and hatching are discussed and assessed with respect to their effectiveness in boosting single pixels, trends, and interesting passages. Application examples from three different domains (document analysis, genome analysis, and geospatial analysis) show the general applicability of the techniques and the derived guidelines.     

Temporally distributed symptoms in a diagnostic application

For various reasons many diagnostic expert systems do not explicitly represent time. In a number of cases this is a useful abstraction. In other applications — such as MOLTKE's —, however, some symptoms evolve over time in a characteristic manner and require several measurements which have to be carried out in a particular temporal order. In this paper we show how such symptoms can be represented declaratively in the knowledge base and how a general temporal matching algorithm can be smoothly integrated with MOLTKE's otherwise static framework. We emphasize that the extension preserves the general philosophy of the system and does not lead to unacceptable overhead when time-independent symptoms are processed.     

基于朴素贝叶斯Web新闻内容的抽取方法

针对网页信息自动抽取问题,提出一种将网页按标记分块并根据朴素贝叶斯理论从中识别新闻正文的方法。该方法将各分块的标记信息、文本相似度以及字长特征作为机器学习的特征属性。为提高标记属性的表征作用,减少相关标记之间的干扰,算法采用χ2检验法来检验标记属性之间以及标记属性与类别之间的相关性并实现属性约减。新闻正文抽取过程中同时考虑正文与非正文分块的后验概率,以提高抽取准确率。实验结果表明,选取适当的参数值,抽取新闻正文的准确率达到85%。      

A framework for tag-aware recommender systems

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.     

Bridging the Data Analysis Communication Gap Utilizing a Three‐Component Summarized Line Graph

Communication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision‐makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time‐series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.     

无芯射频标签编码方式研究

无芯射频标签编码是射频识别技术中一项非常重要的技术。主要研究了基于蝶形无芯射频标签工作频率在2 GHz-10 GHz的雷达散射波的幅度或者相位,并对其相位或幅度采用PCM-△编码,是无芯射频标签编码的新方式。研究结果表明,该编码方式能够对紧凑的开槽标签结构进行有效编码,且编码方式灵活,扩充方便。     

Exploring Linked Data with contextual tag clouds

In this paper we present the contextual tag cloud system: a novel application that helps users explore a large scale RDF dataset. Unlike folksonomy tags used in most traditional tag clouds, the tags in our system are ontological terms (classes and properties), and a user can construct a context with a set of tags that defines a subset of instances. Then in the contextual tag cloud, the font size of each tag depends on the number of instances that are associated with that tag and all tags in the context. Each contextual tag cloud serves as a summary of the distribution of relevant data, and by changing the context, the user can quickly gain an understanding of patterns in the data. Furthermore, the user can choose to include RDFS taxonomic and/or domain/range entailment in the calculations of tag sizes, thereby understanding the impact of semantics on the data. In this paper, we describe how the system can be used as a query building assistant, a data explorer for casual users, or a diagnosis tool for data providers. To resolve the key challenge of how to scale to Linked Data, we combine a scalable preprocessing approach with a specially-constructed inverted index, use three approaches to prune unnecessary counts for faster online computations, and design a paging and streaming interface. Together, these techniques enable a responsive system that in particular holds a dataset with more than 1.4 billion triples and over 380,000 tags. Via experimentation, we show how much our design choices benefit the responsiveness of our system.     

Proof-of-concept of an information visualization classification approach based on their fine-grained features

The misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine-grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri-variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain.